Land cover mapping provides spatial information on the physical properties of the Earth’s surface, for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is crucial to developing solutions to a variety of environmental problems such as destruction of important wetlands/forests, and loss of fish and wildlife habitats. This has made land cover mapping one of the most widespread application areas in remote sensing computational imaging. However, due to the differences between modalities in terms of resolutions, content, and sensors, integrating complementary information that multi-modal remote sensing imagery exhibits into a robust and accurate system still remains challenging, and classical segmentation approaches generally do not give satisfactory results for land cover mapping. In this paper, we propose a novel dynamic deep network architecture, AMM-FuseNet, that promotes the use of multi-modal remote sensing images for the purpose of land cover mapping. The proposed network exploits the hybrid approach of the Channel Attention mechanism and Densely Connected Atrous Spatial Pyramid Pooling (DenseASPP). In the experimental analysis, in order to to verify the validity of the proposed method, we test AMM-FuseNet applied to four datasets whilst comparing it to the 6 state-of-the-art models of DeepLabV3+, PSPNet, UNet, SegNet, DenseASPP, and DANet. In addition, we also demonstrate the capability of AMM-FuseNet under minimal training supervision (reduced number of training samples) compared to the state-of-the-art, achieving less accuracy loss even for the case with 1/20 of the training samples.

The desert locust remains a major threat to global food security. Control operations are a crucial tool to manage crisis; this research investigated the nature of control operations conducted between 2019-2021. Historical data on desert locust and control operations were obtained from the survey reports at the FAO Locust Hub and analysed with respect to survey reports, land cover types, cropland/rangeland extent and crop productivity data. We found that 16.1% of the grid cells with locust presence and 14.9% of the grid cells with control operations had a proportion of rangeland higher than 0.75; while 13.3% of the grid cells with locust presence and 13.2% of the grid cells with control operations had a proportion of croplands higher than 0.75, highlighting that locust presence and control operations were reported in both rangeland and cropland. Control operations continue to be used both to reduce overall locust numbers and to protect crops. Furthermore, through identifying which crops were most at risk, our analyses indicate that wheat production was under the highest strain during periods of increased locust infestations.

While the scientific community has focused on documenting environmental degradation and developing scenarios that help identify the operational margins for system Earth, less attention has been given to the mental models of decision-makers that underpin environmental policies. We suggest that global efforts to stop deforestation and biodiversity loss are failing in part due to a critical blind spot in the analysis—human agency. To address this weakness, we propose to formulate mental models and translate them into strategy games. This will increase the representation of agency in scenario development and create spaces for deliberation between different worldviews. We claim that personal transformation can be achieved through transparent democratic dialogues that identify, challenge, and respond to the human and social limitations inherent to decision-making and we present empirical examples that validate that claim. Their transformation through gaming gives decision-makers access to the experience of consciousness: “what is it like being a stakeholder?”. Such experience will help to break free of established norms in science and political processes.

The post-2007 crisis-induced an agricultural expansion across Africa, but local level production and marketing experiences remain understudied. This study assesses the dynamics of agricultural expansion and small-scale farmers’ experiences in rural Zambia. Using a mixed research design, data were drawn from surveys, multi-level interviews, group discussions policy reviews and observations. Results show an agriculture expansion among small-scale farmers is underway due to favourable climatic conditions, land, and water availability, enabled by state subsidies, and an emerging market in commercial and supermarket outlets. However, farmers encounter production and marketing challenges related to poor tenure security, late delivery of inputs, and low financing. They face low and fluctuating prices, poor infrastructure, including low levels of mechanisation necessary to expand the production. Overall, despite an agricultural and land-use expansion taking shape, actual benefits for real transformation are largely missing – and currently over their heads. An argument is made that whilst policy actors continue to impress farmers to organise themselves to maximise benefits of an agricultural expansion, actual processes on how farmers can achieve this are missing in policy and practice. This necessitates a focus on multi-level processes aimed at addressing production, storage and marketing dynamics within a progressive coordination arrangement that centralizes small producers. Until that is addressed, the prospects for local development and poverty reduction for small-scale farmers under an agricultural expansion will be slender but continue to centrally reside in the state efforts to create enabling local and community environment that addresses real challenges. Overall, this study helps to extend the debate on diverse processes shaping rural transformation in Zambia and across sub-Saharan Africa, including the role and importance of agricultural expansion.

The major global pressures of rapid urbanization and urban growth are compounded by climate impacts resulting in increased vulnerability for urban dwellers, with these vulnerabilities exacerbated during COVID-19. Much of this is concentrated in urban and peri-urban areas where urban development spreads into hazard-prone areas. Often this development is dominated by poor quality homes in informal settlements or slums with poor tenure security. Lessons from a current resilience-building project shows that the fit-for-purpose (FFP) approach to land administration can provide a solution to increase the number of households with security of tenure and improve resilience outcomes as informal settlements grow. This paper discusses the influence of FFP land administration on vulnerabilities to multiple shocks related to climate change and COVID-19. This paper proposes ways the growth of human settlements can be better managed through responsible governance of land tenure rights, and effective land-use planning to improve resilience to different shocks and stresses and provide adequate access to safe land and shelter. Land administration systems can support improved resilience to the multiple stressors of climate and pandemics through improving tenure security and enhancing land use planning controls. Climate change adaptation and risk management need to be better mainstreamed into two major elements of land governance: (i) securing and safeguarding of land rights, and (ii) planning and control of land-use.

Changes in agricultural policy may have rapid impact even on landscapes which have taken millennia to form. Here we explore the potential of UK leaving the EU as a catalyst for profound changes in pastoral landscapes in Wales. Impending change of the trading regime governing agricultural produce, concurrent to public pressure to use agricultural subsidies for environmental goals, may lead to unforeseen consequences for Welsh natural environment. We employ a combination of change demand modelling and ‘story and simulation approach’ to predict the effect of five hypothetical scenarios on land use and land use change in Wales by 2030. We show that the most extreme trade scenario would result in a near-uniform distribution of broadleaf woodland across most of Wales. Abandonment of marginal and low productivity grazing would likely give way to afforestation, initiating a return to forested landscapes not seen in Wales for several thousands of years.

Leclère et al.¹ have outlined the possibility of a biodiversity transition for the 21^st century, a line of thinking equivalent to the Forest Transition theory and what it says about forest cover globally². The authors use a suite of global models to explore the impacts on global biodiversity of interventions on land-use, consumption and production patterns. They outline six strategies that have the potential to stop the downfall of global terrestrial biodiversity by 2050 and redress it to a pre-1970 level by 2100. Although robust, sophisticated and well-illustrated, the conclusions of this paper cannot alone be used to frame a post-2020 biodiversity strategy.

Numerous cities in our modern world are unfortunately encountering the negative effects of urban sprawl: this includes unrestricted settlement, degradation in the quality of their environment, traffic congestion, sub-standard buildings, and air pollution as well as flooding, swampy areas, landslides, and settlement zones with dilapidated utilities and infrastructures that are not safe for living. The Ulaanbaatar City land management master plan defined the settlement zone area suitable for living as 33,698 hectares. However, due to unrestricted urban sprawl caused by exponential growth of the city’s population, the settlement zone area reached 39,235 hectares, which exceeds the limit by 5,537 hectares. In order to tackle this issue, several urban planning concepts were developed to be implemented within the Ulaanbaatar City urban planning framework. It is, in any case, problematic to choose a single planning concept due to the fact that neither measurements nor analyses are being made of the respective spatial quantitative indicators in urban planning assumptions that are taking the current situation into consideration. One of the prerequisites for identifying an optimal concept in urban planning is an assessment of the current situation, and measuring the impacts against its quantitative data. In the current research, when defining Ulaanbaatar city sprawl, the base year was selected as 1990, the time when the city started to sprawl. Research analyses were made using geographic information systems based on the satellite data 1990, 1995, 2000, 2005 Landsat ТМ-5, 2010 Quickbird, 2015 World View and 2020 Sentinel2, respectively. Based on the results of determining the city sprawl using spatial indicators, the urban planning concepts applied thus far have been analyzed in relation to land use efficiency and land use structural changes. This research paper addresses the issue of reducing unrestricted urban sprawl by increasing the internal density of the city. The research results show that, by applying the concept of a compact city in urban redevelopment planning for 4,604 hectares, and by allocating the settlements in 12,479 hectares, it is possible to reduce the urban expansion threefold and increase land use efficiency accordingly.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

European strategic policy directions towards a sustainable blue economy have strengthened interest in maritime investments, thus increasing sectoral competition for marine space. Emerging repercussions out of such a rising interest need to be handled by Marine Spatial Planning (MSP) as a means for: properly allocating marine space to diverse uses; managing conflicts and promoting synergies among them; and pursuing a multi-use perspective of this space. A critical stage of each MSP exercise is the exploration of land-based spatial/developmental directions and their current or potential repercussions in the marine environment in order for constraints but also perspectives in policy choices/maritime uses in the MSP context to be featured; and attainment of a successful coherence or coordination between land and marine spatial/developmental policies to be ensured, by analyzing Land-Sea-Interaction (LSI) and embedding it in the MSP. The latter is the focus of this paper aiming to illuminate critical issues emerging from land-based (terrestrial) policy frameworks for feeding MSP endeavors; and is accomplished in the Northern Aegean Sea, Greece. Towards this end, a multilevel approach is adopted, delving into diverse policy contexts at the local, regional and national/European level and aiming to attain more informed MSP decisions in the specific study region.

Questions：SAP in mountainous cities of China serve as crucial carriers for species and wild food, constituting potential green infrastructure. These patches should be recognized as locally specific resources. This study investigates the transformation characteristics of the landscape patterns of SAP, along with their elements, across urban-rural gradients, providing foundational support for the planning of conservation and utilization strategies. Location: Yongchuan area of Chongqing in southwest China. Methods: The study investigated urban-rural gradient-based transformation of SAP. A four-direction transect space was selected and divided into uniform sample squares. Fragstats 4.3 calculated the landscape index of SAP, and Spearman correlation coefficients in SPSS were used to evaluate the influence of landform and artificial factors on agroforestry patch transformation. Results: 1) Patch area (PA), Plaque density (PD), and CLUMPY of SAP systematically transform, showing increased SAP areas and higher connectivity with greater distance from the city, resulting in reduced patch fragmentation. However, Edge density (ED) and Largest plaque index (LPI) exhibit irregular transformation patterns. 2) With increased distance from the city, woodland patch area and connectivity increase, leading to decreased fragmentation, dispersal, and lower fractal dimensions. Conversely, Cultivated land follows the opposite trend. garden land patch area, fractal dimension, fragmentation, and dispersal all increase, while connectivity decreases. 3) Landscape pattern transformations of SPA are significantly influenced by artificial factors. Woodland patches are influenced similarly by landform and artificial factors. In contrast, cultivated land and garden land patches are more significantly impacted by artificial factors. Conclusions: SAP exhibit differentiated conservation and utilization measures across urban-rural gradients. Small patches located far from urban centers may benefit from systematic conservation measures to prevent further fragmentation and erosion in peri-urban areas.

Wang et al. (Nature 623, 340—350; 2023)¹ used satellite observations and phenological signatures to map and estimate the extent of planted rubber (Hevea brasiliensis Müll.Arg.), and associated deforestation, for eight Southeast Asian countries. Their assessment implies that planted rubber may have caused over 4 Mha of deforestation since 1993 — a value greater than assumed in setting deforestation policies. Unfortunately, their estimates rely on incorrect methods. In addition, their definition of “deforestation” is misleading because it includes all tree cover loss, not just natural forests, but also from established rubber gardens, and other plantations. Our assessment indicates that since 2001 the actual extent of deforestation associated with planted rubber is approximately one quarter that reported by Wang et al.

Urban Heat Islands (UHI) are a growing concern in rapidly urbanizing regions, leading to significant ecological and public health challenges. This study investigates the spatiotemporal dynamics of UHI intensity in Mbombela City, Mpumalanga Province, South Africa, from 2008 to 2023. Using Landsat satellite imagery and geospatial analysis techniques, the research quantifies changes in land cover and their impact on Land Surface Temperature (LST) and UHI intensity. The results show that built-up areas increased by 86.67% over the study period, while vegetation cover decreased by 22.22%. Mean LST rose from 28.3°C in 2008 to 30.1°C in 2023, indicating a significant intensification of the UHI effect. Statistical analyses reveal strong correlations between urbanization metrics and UHI intensity, highlighting the role of human activities in exacerbating this phenomenon. This research contributes valuable insights for urban planning and climate adaptation strategies, particularly in secondary cities like Mbombela, where rapid development is creating new environmental challenges. The findings underscore the need for green infrastructure and nature-based solutions to mitigate the negative impacts of urbanization.

Mobility hubs (MHs), where various transport modes converge, are increasingly being implemented as a key policy strategy to promote sustainable travel behavior. The existing literature is rich with proposals for various types of MHs and their suitable siting locations. However, studies comparing the role of land use patterns on the performance of different types of MHs are scarce. This study aims to fill this gap by analyzing transit patronage and active mode share as performance indicators of MHs. It compares the effects of land use patterns on the performance of different types of MHs classified by the nature of transport integration (e.g., train-tram-bus, train-tram, and train-bus) in different contexts (e.g., city center and suburb) in the Greater Melbourne Area, Australia. Results show that MHs, irrespective of their type, significantly enhance the use of both transit and active transport services compared to a unimodal train station, with maximum usage observed in a train-tram-bus hub, followed by train-tram and train-bus hubs. However, the underlying land use patterns significantly affect their performance. While the size of commercial land use consistently and positively influences transit patronage across MH types, certain land use patterns (e.g., intersection density, cultural centers, care facilities) exert their influence in specific types of MHs. These findings suggest that MH typologies should be designed in tandem with supportive land uses to maximize sustainable travel behavior.

Evapotranspiration is a critical component of the hydrological cycle, and it has a decisive impact on the ecosystem balance in arid and semi-arid regions. The Yinchuan Plain, located in the Gobi of Northwest China, has a strong surface ET. In order to assess the ET pattern in this region, we obtained the ETa of Yinchuan Plain between 1987 and 2020 using the GEE platform. We employed the Surface Energy Balance Model (SEBAL), specifically geeSEBAL, to analyze the change pattern of ET over different seasons, years, and land types. The results show that: ① In spring, the daily ETa of the Yinchuan Plain is highest in the central lake wetland area, with a maximum value of 4.32 mm day-1. In summer, it is concentrated around the croplands and water bodies, with a maximum value of 6.90 mm day-1; In Autumn and winter, it is mainly concentrated around the water bodies and impervious areas, with maximum values of 3.93 and 1.56 mm day-1, respectively. ② From 1987 to 2020, the ET of the Yinchuan Plain showed an obvious upward and downward trend in some areas with significant land use changes, but the overall ET of the region remained relatively stable without dramatic fluctuations. ③ The evapotranspiration of different underlying land use types in the Yinchuan Plain region is as follows: water body>cultivated land>impervious>grassland>bare land. Our results showed that geeSEBAL is highly applicable in the Yinchuan Plain area. It allows for the accurate and detailed inversion of ET in the study area, which facilitates the study of regional hydrological cycle and water governance.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. However, capturing the shrub biomass accurately by satellite observations is challenging due to the short and sparse features of shrubs. In this study, we presented a framework to estimate shrub biomass at a 10m spatial resolution by integrating ground in-situ data, unmanned aerial vehicle (UAV), Sentinel-2, and Sentinel-1 observations. The pilot study was conducted in the Helan mountains of Ningxia province, China. First, the spatial distribution map of shrubland and other land cover types was generated in 2023. Then, a prediction model of shrub biomass was developed in a Random Forest Regression (RFR) approach driven by different predicted variable datasets based on in-situ measurements, UAV, and satellite images. Finally, the developed model was used to produce the biomass map of shrubland in 2023. The uncertainty was characterized by creating a standard deviation (SD) map using the Leave-One-Out Cross-Validation (LOOCV) method in the shrub biomass estimates. The resultant shrubland distribution map in 2023 has an F1 score of 0.92. The shrub biomass model driven by spectral bands and vegetation indices (R2= 0.62) demonstrated superior performance than that only driven by spectral bands (R2=0.33) or vegetation indices (R2=0.55). The uncertainty of the estimated biomass was lower than 4%, with the lowest values (<2%) happening in the high shrub coverage (>30%) and biomass production (>30kg/m2) regions. Furthermore, the findings show that the magnitude of shrub biomass was affected by the aridity and precipitation significantly in the study region. This study provides a workflow to accurately monitor the biomass of shrublands in a complicated environment based on remote sensing images from multi-platform observations.

Our study reviews the significance of archaeological parks in the tourism sector, with a specific focus on how digital presence and visitor feedback impact their cultural and touristic appeal. Utilizing statistical analyses such as regression and cluster analysis on data sourced from both Hungarian and Western European parks, the research scrutinizes TripAdvisor and Google ratings, alongside the volume of photographs and reviews. The findings highlight a modest correlation between digital visibility and overall visitor ratings, underscoring the complex relationship between online engagement and the quality of visitor experience. Notably, the cluster analysis identified two distinct groups, signifying diverse levels of visitor engagement and digital visibility across the parks. The conclusions drawn advocate for a comprehensive strategy to enhance visitor experiences, emphasizing the integration of digital engagement with the promotion of the parks cultural and historical values. By exploring the influence of digital presence on the appeal of archaeological parks, our study sheds light on the evolving interface between urban studies and digital heritage, highlighting how digital tools and online platforms are becoming increasingly integral in shaping the perceptions and experiences of urban cultural sites.

Wetland ecosystems provide valuable services to human beings, but they are also among the most threatened ecosystems in the world (1). Sixteen ecosystem services are identified through the South African Water Research Commission's Wet-Health Tool (2). This study sought to establish which of these ecosystem services are highly ranked/rated by the community members living in the Soutpansberg area in South Africa. This area is a strategic water source/watershed. The area is rich in biodiversity and is also experiencing competing administrative jurisdictions: the local traditional leadership and the municipal council. This study also sought to determine the influence of age, education status, employment status, and family income, among other demographic factors, on how people view the importance of services derived from wetlands. It also sought to establish whether people in urban settings view the ecosys-tem services obtained from wetlands similarly to people in rural settings. A literature review was conducted to understand the wetland ecosystem services and how communities benefit from these services. A close-ended questionnaire was used to collect data for the current study, which was circulated among the villagers around the wetlands, particularly those living close to the wetlands. One hundred and sixteen responses from the study were recorded. Food for livestock was ranked the greatest benefit or service derived from the wetlands rated by 82% of the participants, followed by the provision of cultivated foods at 60%, provision of water for human use at 58%, provision of harvestable resources at 42% and erosion control at 38% completes the top 5 ranked ecosystem services in the area. The study concluded that the participants living in rural areas had greater awareness of wetlands' ecosystem services than their urban counterparts.

Flood disasters have become an annual occurrence along the East Coast of North Sumatra (ECNS) and Medan City. Numerous studies have examined the causal factors flood disasters in this area, such as changes in land use, drainage issues, and population growth, but the intensity of flood disasters continues to rise. In this research, we focused on evaluating the unexplored factors of land subsidence and rising sea levels in this region. Our investigation uses multi-sensor satellite data and field surveys to conduct an assessment of the rate of land subsidence and sea level rise and assess their implications on flood disasters. This study highlights the potential of multi-sensor satellite data as a primary resource for assessing flood disasters in coastal areas and densely populated large cities. Furthermore, we propose a solution to reduce the negative impact of flood disasters by by engaging local communities and environmental enthusiasts in mangrove reforestation along the coastline.

Image classification in Remote Sensing and Geographic Information Systems (GIS) containing various land-cover classes is essential for efficient and sustainable land-use estimation, and other tasks like object detection, localization and segmentation. Deep Learning (DL) techniques have shown a tremendous potential in the GIS domain. While Convolutional Neural Networks (CNN) have dominated most of the image analysis domain, a new architecture called transformers have proved to be a unifying solution for several AI-based processing pipelines. Vision Transformers (ViT), a variant of transformers can have comparable and in some cases better accuracy than a CNN. However, they suffer from a significant drawback associated with an excessive use of training parameters. In this research we explore several modifications in the vision transformer architectures, especially MobileViT that can be optimized while boosting accuracy. To verify our proposed approach these new architectures are trained on four land-cover datasets AID, EuroSAT, UC-Merced, and WHU-RS19. Experiments reveal that combination of lightweight convolutional layers including ShuffleNet along with depthwise separable convolutions and average pooling can reduce the trainable parameters by 17.85% and yet achieve higher accuracy than the base MobileViT. It is also observed that utilizing a combination of convolution layers along with multi-headed self attention layers in MobileViT variants provide better performance in capturing local and global features unlike the standalone ViT architecture that utilizes almost 95% more parameters than the proposed MobileViT variant.

In Ethiopia, land degradation in the form of gully development and soil nutrient depletion is a very serious issue. The goal of this study is to determine how different types of land management practices and slope positions affect particular soil physico-chemical characteristics in the Agamssa watershed. Using systematic sampling techniques, soil samples were taken using a sampling quadrat (15 * 15 m) that was placed 50 m apart from one another. A total of 54 quadrats were planted at three slope positions: upper (>15%), middle (10–15%), and foot (3–10%) slope respectively. 18 composite soil samples were then. A two-way ANOVA was used to analyze the data, and the Least Significant Difference (LSD) test was used to separate the means at p ≤ 0.05. According to the study's findings, the value of most soil physical and chemical characteristics was higher in closed area than on open grazing land, as well as at foot slope position compared to middle and upper slope positions. The higher mean value (7.3 ppm) was recorded at the foot slope, while the lower mean value (4.9 ppm) was found at the upper slope position. The exchangeable cations such as Mg+2, K+, and Na+ were significantly (p≤ 0.01) affected by the main effect of land-use types. The higher mean values of Mg+2 (9.70 mq/100g), K+ (0.38 mq/100 g), and Na+ (0.035 mq/100 g) were found at area closure, whereas the lower mean values of Mg+2 (7.33 mq/100g), K+ (0.30 mq/100 g), and Na+ (0.027 mq/100g) were recorded at open grazing land. It was able to conclude this study that area closure enhances the physical and chemical qualities of soil and recovers woody plant species. Therefore, converting open grazing land into a closure area is advised.

In response to the problem of increasing climate change and energy security, investment in renewable energy sources has increased significantly both in Europe and globally. Wind and solar power plants are expected to be the largest contributors to global decarbonization, ranking first and second in projected capacity by 2050. Since all power plants have some impact on the environment, this also applies to photovoltaic power plants. Because of the large capacities projected for PV installed capacity, their environmental impacts should be evaluated. The environmental impacts of PV power plants change over time. Improving the manufacturing technology of PV system components, increasing the efficiency of solar cells, and using materials that are less harmful to the environment will reduce these impacts. Manufacturing PV system components is a highly energy-intensive process that involves greenhouse gas emissions. As new renewable energy capacity is built, the amount of "green" electricity on the grid increases, reducing CO2 emissions per kWh consumed. The objective of this paper is to analyze the current status of the environmental impact of PV power plants under these changing conditions in terms of CO2 emissions, land use, pollutant and noise emissions, and water consumption. The capacity installed to date will reach the end of its lifetime by 2050, which means that the amount of waste associated with it will increase over time. This can have a significant impact on the environment, which is why part of the work is dedicated to this problem. In addition to the available information from the literature, in the part related to land use, the authors also made their own estimates based on data on newly installed PV power plants and PV modules available on the market. The results of the analysis show that there is enough land both in Europe and worldwide to install the planned capacities of rooftop and ground-mounted PV power plants. CO2 emissions are at the same level as for concentrated solar power, with a decreasing trend. Pollutant emissions, noise and water consumption are not a major problem compared to other types of power plants. Overall, it can be concluded that the expansion of PV capacity has a very positive impact on the environment.

Large-scale land cover plays a crucial role in global resource monitoring and management, as well as research on sustainable development. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, creating challenges for dynamic monitoring of large-scale land cover. Therefore, improving the efficiency of each stage in large-scale land cover mapping and automating the mapping process is currently an urgent and critical issue that needs to be addressed. We propose a high-performance automated large-scale land cover mapping framework(HALF) that introduces high-performance computing technology to the field of land cover production. HALF optimizes key processes, such as automated sample point extraction, sample-remote sensing image matching, and large-scale classification result mosaic and update. We selected several 10°×10° regions globally and the research makes several significant contributions:(1)We design HALF for land cover mapping based on docker and CWL-Airflow, which solves the heterogeneity of models between complex processes in land cover mapping and simplifies the model deployment process. By introducing workflow organization, this method achieves a high degree of decoupling between the production models of each stage and the overall process, enhancing the scalability of the framework. (2)HALF propose an automatic sample points method that generates a large number of samples by overlaying and analyzing multiple prior products, thus saving the cost of manual sample selection. Using high-performance computing technology improved the computational efficiency of sample-image matching and feature extraction phase, with 10 times faster than traditional matching methods.(3)HALF propose a high-performance classification result mosaic method based on the idea of grid division. By quickly establishing the spatial relationship between the image and the product and performing parallel computing, the efficiency of the mosaicking in large areas is significantly improved. The average processing time for a single image is around 6.5 seconds.

Substantial reductions of the human and economic activities like road traffic for several months in 2020 were one of the consequences of the Coronavirus disease. This unprecedented change in urban metabolism also affected temperature and air pollutants. This study investigates the effects of the first COVID-19 lockdown over 43 cities in Europe. It determines the influence of anthropogenic activities on nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5) as well as on land surface temperature (LST), and the surface urban heat island intensity (SUHII), using satellite, modelled, and mobility data. Our findings show that there are great temporal and spatial differences and distinct patterns between the cities regarding the magnitude of change of the variables under study. In general, the results indicate a significant decrease in NO2 concentrations in most of the studied cities compared to the reference period 2015-2019. However, reductions could not be attributed to mobility changes like less traffic at transit stations, contrary to the results of previous studies. O3 levels increased during the first lockdown mainly influenced by the decreasing NO2 concentrations. The PM pattern was inconsistent over time and space. Similar as the NO2 results, no relation to the altered mobility behavior was found. No clear signal could be detected for LST and the SUHII, likely due to dominating meteorological influences. Therefore, single city case studies may be misleading.

The application of machine learning techniques to satellite imagery has been the subject of interest in recent years. The increase in quality and quantity of images, made available by Earth observation programs, such as the Copernicus program, led to the generation of large amounts of data. Among the various applications of this data is the creation of land cover maps. The present work aimed to create machine learning models capable of accurately segment and classify satellite images, to automatically generate a land cover map of the Portuguese territory. Several experiments were carried out with the spectral bands of the Sentinel-2 satellite, with vegetation indices, and with several sets of land cover classes. Three machine learning architectures were evaluated, which adopt two different techniques for image classification. One of the classification techniques follows an object-oriented approach, and in this case the architecture adopted in our models was a U-Net artificial neural network. The other classification technique is pixel-oriented, and the machine learning models tested were random forest and support vector machine. The overall accuracy of the results obtained ranged from 68.6% to 94.75%, depending strongly on the number of classes into which the land cover is classified. The result of 94.75% was obtained when classifying the land cover only into 5 classes. However, a very interesting accuracy of 92.37% was achieved by the model when trained to classify 8 classes. These results are superior to those reported in the related bibliography.

Abstract: In the last five years, the urban development of Limassol City has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development introduces several concerns about the aggravation of the land subsidence in the Limassol coastal front. Fifty Copernicus Sentinel-1 data from 2017-2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatters (StaMPS). A case study for the identification and analysis of the elements (PS) in pixels in a series of interferograms, and then, the quantity of the land displacements in the Line of Sight, in the Limassol coastal front, is presented in this research, with the subsidence rates up to about (-5 to 4 mm / year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, taking into account that there are a significant number of skyscrapers planned to be built, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.

According to this study, approximately half of Africa's forests are utilized primarily or partially for the production of wood and non-wood commodities. Aims to evaluate Africa's forestry and forest products, namely Wood Forest Products (WFPs) and Non-wood Forest Products (NWFPs) in the sixteen (16) West African countries. While adhering to the following guidelines: wood extraction and preparation, analyzing wood primarily used as an energy source in Africa, identifying non-wood forest products in Africa, the state of export, trade, and customs procedures in West Africa, and examining the role of forests and forest stakeholders in Africa's low-carbon economy transition. An exploratory literature review of selected wood forest products and non-wood forest products (plants and animals) in West Africa identifying the country, the natural land area with the natural habitat issues of the forest, the species most harvested and traded in the West African sub-region. The study reemphasized some government legislation, policies, and market trade failures and limitations while also stating that trees may help in the low-carbon revolution through interventions aimed at maintaining, improving, and restoring natural capital have demonstrated that high environmental requirements of sustainable forest management (SFM) may be met in both natural and planted forests. The study identified a systematic assessment of the most common forest products (wood and non-wood forest products) considering the available data on the national forest reserves of the selected countries in West Africa. The study also revealed the need for biodiversity conservation of the available forest reserves to help mitigate the impact of global warming targeting the United Nation’s Sustainable Development Goal 13- Climate Action. Which is focused on integrating climate change mitigation, adaptation, impact reduction, and early warning signs into the national policies, improving forest planning and management education, awareness-raising, and institutional capacity within the sub-region.

The 2018 summer drought in Europe was particularly extreme in terms of intensity and impact due to the combination of low rainfall and high temperatures. However, it remains unclear how this drought developed in time and space in such an extreme way. In this study we aimed to get a better understanding of the role of land-atmosphere interactions. More specifically, we investigated whether there was a change in water vapor originating from land, if that caused a reduction in rainfall, and by this mechanism possibly the propagation and intensification of the drought in Europe. Our first step was to use remote sensing products for soil moisture content (SMC) and the normalized difference vegetation index (NDVI) to see where the 2018 drought started and how it developed in time and space. Our SMC and NDVI analysis showed that the 2018 drought started to impact the soil and vegetation state in June in Scandinavia and the British Isles. After that it moved towards the West of Europe where it intensified in July and August. In September, it started to decay. In October, drought was observed in southeast Europe as well. Based on the observed patterns we divided Europe into six regions of similar spatiotemporal characteristics of SMC and NDVI. Then, we used a global gridded dataset of the fate of land evaporation (i.e., where it ends up as precipitation) to investigate whether the drought intensification and propagation was impacted by the reduction in water vapor transported from the regions that first experienced the drought. This impact was investigated by identifying the anomalies in the water vapor originating from land recycling, imports and exports within Europe during the spring, summer, and autumn seasons. From these regions we identified four drought regions and investigated the changes in water vapor originating from source regions on the development of drought in those regions. It was found that during the onset phase of the 2018 drought in Europe that the water vapor originating from land played an important role in mitigating the precipitation anomalies as, for example, the share of land evaporation contributing to precipitation increased from 27% (normal years) to 38% (2018) during July in West of Europe. Land evaporation played a minor role in amplifying it during the intensification phase of the drought as the share of land evaporation contribution to precipitation decreased from 23% (normal years) to 21% (2018) during August in West of Europe. These findings are somewhat in contrast to similar studies in other continents that found the land surface to play a strong amplifying role for drought development. Subsequently, we found that the relative increase in the amount of land water vapor originating from eastern half of Europe played a role in delaying the onset and accelerating the decay of the 2018 drought in West of Europe.

Abstract: Green walls and green dams are increasingly being considered as part of many nation-al and international desertification initiatives. This paper studies the spatiotemporal evolution of the green dam in the Moudjbara region (Djelfa Province, Algeria) from 1972 to 2019 by using Landsat imagery, Land Change Modeler and Open Land package. The future evolution of pine plantations for the year 2029 was also forecasted, based on an anthropogenic scenario (i.e., an-thropogenic pressure is the main driver of the green dam destruction). Our findings revealed that the green dam project was successful for a few years, but after that, pine plantations deteri-orated significantly due to forest harvesting, livestock overgrazing, and the proliferation of the pine caterpillar processionary, which destroyed most of the reforestation. Land Change Modeler predicted a huge degradation of pine plantations for the year 2029, and if the deforestation con-tinues at the same rate, the green dam will disappear in the Moudjbara region during the next few decades. Aware of this threat, the Algerian authorities are now planning to reforest more than 1.2 million ha under the latest rural renewal policy by introducing new principles related to sustainable development, fighting desertification, and climate change adaptation

In response to the complex social, environmental and economic situation caused by the "Terra dei Fuochi" phenomenon, the IZSM collaborated closely with the Terra dei Fuochi working group, carrying out additional monitoring surveys both on food (QR Code Campania project[30]), and on the environment (Campania Trasparente project[31]). This model was developed in the context of the experience accumulated in the field of environmental and food monitoring, and represents an innovative tool aimed at increasing knowledge of the environmental context of the Campania Region through an objective, integrated and organic synthesis of complex environmental phe-nomena and territorial dynamics. The model proposed here is useful for the global and synthetic assessment of environmental pressure on a municipal basis. As shown, it can also be applied to aggregations of municipalities. Furthermore, it can be used in the context of institutional actions for the planning and monitoring of improvements on a local or regional scale. Finally, the pro-posed municipality-based environmental pressure index represents the basis for geo-stratification of the sample in the context of population biomonitoring studies on a regional scale, as in the described biomonitoring study design applicable to the Campania Region.

Analysis of the correlation between indices (Normalized Difference Vegetation Index, Normalized Difference Barren Index and Modified Normalized Difference Water Index) and land surface temperature is used to natural resources and environmental studies. This research aimed to analysis of Land Surface Temperature due to dynamics of Different Indices (NDVI, NDBaI and MNDWI) Using Remote Sensing Data in three selected districts (Gida Kiremu, Limu and Amuru), western Ethiopia. From thermal and multispectral bands of landsat imageries (Landsat TM of 1990, landsat ETM+ of 2003 and landsat OLI/TIRS of 2020) Land surface temperature and NDVI, NDBaI and MNDWI were calculated. Correlation analysis was used to indicate relationships between LST with NDVI, NDBaI and MNDWI. The study found that Land Surface Temperature was increased by 5⁰C from 1990 to 2020. Vegetation areas (NDVI) and Water bodies (MNDWI) have strong negative relationship with Land Surface Temperature (R²= 0.99, 0.95) whereas Barren land (NDBaI) has positive relationship with Land Surface Temperature (R²= 0.96). Finally, we recommend the decision makers and environmental analyst to emphasis the importance of vegetation cover and water body to minimize the potential impacts of land surface temperature.

Taking a typical forest underlying surface as the research area, this study employed the unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including tree height and canopy radius, which were used to improve the Noah-MP land surface model conducted in Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of CN-Din forest followed a Weibull distribution. The replacement of the parameters by these observed UAV would result in the Noah-MP model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux which could decrease up to -11% in the midday while increase up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately deliver the heterogeneity for the underlying surface.

The stormwater drainage problem is one of the major challenges facing in Shire Endaslasse town, Ethiopia. In a town, Street flooding and overtopping drainage system problems are occurring during the rainy season. This causes ponding which poses difficulties in ease of transportation and it hinders the day-to-day activity of the people. So, the study focuses on the performance of the stormwater drainage system in Shire Endaslasse town using Arc GIS and SWMM5.1. For this study, the primary data were collected by field surveys and interviews with the council body. Simulation results for storm events show that in some of the drainage systems in different regions of Shire Endaslasse town have flooded. During the field observation, the drainage structures are filled with solid wastes, inadequate inlet and outlet structures and some of the top element of the manhole have been broken this may cause a problem of aesthetic and healthy at large it may increase flood risk. The flooding risk in the drainage systems is very high due to the drainage system is undersized to cope with the current rainfall rates, but also is very limited to face the upcoming predicted rainfall.

Agricultural land fires have been linked to various and adverse impacts on ecosystems, food security and the agriculture sector. Understanding the patterns and drivers of agricultural land fires is essential for effective agricultural land fire management. The key objectives of this study were to (1) analyze the temporal and spatial patterns of agricultural land fires using satellite remote sensed data, (2) assess a range of environmental conditions that could drive the occurrence of agricultural land fires, (3) determine the best model for predicting agricultural land fires and (4) determine the relative contribution of each environmental condition variable on the best predictive model. We used both univariate and multivariate regressions for the fire prediction capability of four independent environmental conditions (fuel, weather, topographic and anthropogenic). Analysis of historical satellite data revealed that agricultural land fires were more frequent than forested land fires. Our analyses also revealed that fuel condition was the most important variable for predicting agricultural land fires followed by weather, topographic and anthropogenic conditions. This study provides a novel multivariate model for predicting agricultural land fires that harbors the potential to improve agricultural land fire management and reduce fire risk within the agricultural sector.

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961-2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into 1961-1995 (normal lake level) and 1996-2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.

Demographic and socio-economic developments couple with other requirements to satisfy human needs result in rapid urban expansion sometimes with increasing rate of surface extent greater than the rate of growth of population, that result in continuous sealing of ground surface thereby affecting ecosystem services. This study applied remote sensing toward achieving the progress of SDGs and stage to determine the ratio of the rate of land consumption to the rate of population growth of Gombe metropolis. QGIS 2.18 was used for the image processing and classification analysis for the key Landsat ETM+ (Enhanced Thematic Mapper), Impervious Surface Indices and population data to inform on the urban trend and LCR/PGR for the periods 2000-2005, 2005-2010, and 2010-2015. The result appears that the LCR/PGR for the periods of study show split trends. During 2000-2005 the result shows that the study area expanded outward with LCR/PGR of 1.2. The result also indicate that during 2005-2010, the study area densified with little expansion with the LCR/PGR of 0.8. The result further reveals that during 2010-2015 the LCR/PGR reached 1.8. Pointing that the study area expanded outward with the rate of ground sealing getting high.

Abstract: Many countries around the world suffer from the lack of a sea port directly linked to the rest of the world. Such countries are called "landlocked countries". This leads to Weak competitiveness of their products in the global market, as well as to the high cost of the imports. Africa has the largest share of these countries, with 16 of the 43 landlocked countries around the world. The aim of this paper is to propose a general framework for criteria that can be used to choose between ports in transit countries that can be used for import or export. These criteria are related to the assessment of the sea ports in terms of infrastructure and tariffs. It is also related to transport infrastructure from the transit country to the landlocked country and the level of safety. The study identified nine criteria that could be used to compare between ports in transit countries. Using Full Consistency Method (FUCOM) to evaluate those criteria showed that the number of navigation lines is the most important criteria followed by the port service level.

High frequency knowledge of the spatio-temporal distribution of the Downwelling Surface Shortwave Flux (DSSF) and its diffuse fraction (fd) at the surface is nowadays essential for understanding climate processes at the surface-atmosphere interface, plant photosynthesis and carbon cycle, and for the solar energy sector. The EUMETSAT Satellite Application Facility for Land Surface Analysis operationally delivers estimation of the MDSSFTD (Downwelling Surface Short-wave radiation Fluxes – Total and Diffuse fraction) product with an operational status since the year 2019. The method for the retrieval was presented in the companion paper [40]. The part 2 now focuses on the evaluation of the MDSSFTD algorithm and presents the comparison of the corresponding outputs, i.e. total DSSF and diffuse fraction (fd) components, against in-situ measurements acquired at four BSRN stations over a seven-month period. The validation is performed on an instantaneous basis. We show that the satellite estimates of DSSF and fd meet the target requirements defined by the user community for all-sky (clear and cloudy) conditions. For DSSF, the requirements are 20Wm^-2 for DSSF<200Wm^-2, and 10% for DSSF>=200Wm^-2. The MBE and rMBE compared to the ground measurements are 3.618Wm^-2 and 0.252%, respectively. For fd, the requirements are 0.1 for fd<0.5, and 20% for fd>=0.5. The MBE and rMBE compared to the ground measurements are -0.044 and -17.699%, respectively. The study also provides a separate analysis of the product performances for clear sky and cloudy sky conditions. The importance of representing the cloud-aerosol radiative coupling in the MDSSFTD method is discussed. Finally, it is concluded that the quality of the Aerosol Optical Depth (AOD) forecasts currently available is enough accurate to obtain reliable diffuse solar flux estimates. This quality of AOD forecasts was still a limitation a few years ago.

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error-covariance in the EnOI is sampled in two ways: i) by using the stochastic spread from an ensemble open-loop run, and ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in-situ observations at a 95% significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in-situ data at the same significance level; however this improvement is dependent on which in-situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95% significance level. The study also suggest that the EnOI can be used for bias-correction of the atmospheric fields where post-processed data are not available. Finally, we assimilate three different Level-2 satellite derived soil moisture products from ESA Climate Change Initiative (CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in-situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in-situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.

Earth’s land surface area is raised from conventionally flat 15 to 64 Gha to account for hilly undulation and soil relief detail. Three main aspects are: topography, rugosity/tortuosity and micro-relief/porosity of vegetation-free ground. Recalibration is arrived at from four approaches: first, direct empirical estimates from the few compiled satellite or LiDAR data with means of +2.5–26% progressively overlain by +94% at cm² scale for soil ruggedness then +108% for mm² micro-relief; second, from digital elevation models with 1.6–2.0 times flat areas; third, by ‘reverse engineering’ global soil bulk densities and carbon reserves requiring x 2–6 land. Finally, a Fermi estimation conveniently sets the World’s new surface area – that exposed to Sun, air and rain – at 100 Gha (with 36 Gha flat ocean). Soil organic carbon (SOC) is thence raised to 8,580 Gt mainly in SOM/humus with its biotic complexity plus roots, VAM-fungi and leaf-litter, that itself = 17,800 Gt. Although four to six times IPCC’s or NASA/NOAA’s calculations of just 1,500–2,300 Gt SOC, this is likely an underestimation. Global biomass and biodiversity are at least doubled (x 2–3.5) and net primary productivity (NPP) similarly increased on land to >270 Gt C yr^-1 due to terrain.

Accurate, detailed and recent Information about land cover/use is important and much more needed for different aspects of sustainable development and environmental management. As remote sensing datasets are becomes one of the most important and effective tools to generate such information, this study aimed to generating land cover map for sub area in Al-Ahasaa Oasis, Saudi Arabia, by using and classifying a subset of Landsat-ETM+ image of the selected study area, as bases and required input for future studies and researches. Different image preprocessing techniques in addition to a will-known and widely used classification method (i.e., Maximum Likelihood classifier) were applied. To be reliable with the final product, accuracy assessment was carried out with 89% agreement and accepted according to the applied method. Different land cover classes were found in the study area, which includes (Sand dunes, Water bodies, Sabakha, Bare soil, Urban, and Agricultural lands). The study also revealed that the dominant land cover class is sand dunes with approximately ± 70% in area. The study strongly indicated that the area has long been affected by sand movement. Finally, the study suggested that, further researches with more advanced methods rather than traditional methods are needed in the future to support the findings of this study, with high degree of accuracy.

Regional heat capacity change is calculated from the ratio between the addition or subtraction of heat (ΔQ) with the increase or decrease in temperature (ΔT) region. The purpose of this study is to calculate the regional heat capacity change due to the changes of land cover composition with forest, shrubs, oil palm plantation and bare soil using Landsat-5 TM satellite data on 1994, 2000 and 2010. Total area that used on ​​this study is 12971 ha. In 1994-2000, 4 % of forest area and 2% shrubs were increased, followed by additional of biomass forest 4.01 tons/ha and 2.83 tons/ha for shrubs. The increased of forest area and biomass (tons/ha) caused by forest and shrubs growth processing towards climax that added the canopy volume. So that, the regional heat capacity in 1994 amounted 19384 MJC^o-1 increased to 19929 MJC^o-1 in 2000. Data observation for 2000-2010 showed that forest area decreased by 66% due to forest’s clearing into oil palm plantations (47%), shrubs (8%) and bare soil (11%). But, plant’s biomass continue to increased, i.e 1.48 ton/ha for forest, 2.73 tons/ha for shrubs and 4.63 tons/ha for bare soil. Before 2000, there was no land cover by oil palm plantations, so the increasing rate from this land was the biggest than the three other lands, amounting to 122.29 tons/ha. Decreasing in the percentage of forest area does not cause a decrease in the heat capacity of the region. Intensive maintenance on oil plam plantation such as water management, fertilizer and planting space made it biomass productivity and ability to save the heat is greater than the forest. As the result, in 2010 regional heat capacity increased to 22508 MJC^o-1.

Woody vegetation in farmland acts as a carbon sink and provides ecosystem services for local people, but no macro-scale assessments of the impact of management and climate on woody cover exists for drylands. Here we make use of very high spatial resolution satellite imagery to derive wall-to-wall woody cover patterns in tropical West African drylands. In arid and semi-arid Sahel, areas of more people are associated with more trees: mean woody cover is greater in farmlands (12%) than in savannas (6%), and likewise it is higher close to villages than further away. In sub-humid savannas of West Africa, woody cover is generally above 20% and decreases with increasing population density, but remains around 15% in farmlands, independent of rainfall. In the region as a whole, rainfall, terrain and soil are the most important (80%) determinants of woody cover, while management factors play a smaller (20%) role. We conclude that agricultural expansion cannot generally be claimed to cause woody cover losses, and that observations in Sahel contradict simplistic ideas of a high negative correlation between population density and woody cover.

The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies focused on the temporal variations of urbanization effects on LSP. In this study, we used the MODIS EVI, MODIS LST data and China’s Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity and urbanization effects on LSP in Northeast China during 2001–2015. Land surface temperature (LST) and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. Mann-kendall nonparametric test and Sen's slope were used to evaluating the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average land surface phenology (LSP) during 2001–2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban area had become earlier and earlier than rural area and the differences of SOS between urbanized area and the rural area changed greatly during 2001–2015 (−0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural area especially in urbanized area (0.92 days/year, p < 0.01), but the differences of the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China.

Climate change-induced Sea level rise, shoreline changes, and coastal erosion are projected to drive massive population displacement and mobility in Africa. This study was conducted to examine the pattern of shoreline changes, coastal erosion, land use/Land cover dynamics their projections, and its implications on internal migration in Senegal, Kenya and Tanzania representing West and East Africa. The digitized shoreline was mapped into both erosion, accretion and trend analysis which further explains the vulnerability and physical processes that could trigger human displacement within the context of environmental/climate migration. Analysis of landuse and land cover dynamics was obtained from Landsat 5 TM of 1986, Landsat 7 ET of 2006, Landsat 8 OLI/TIRS of 2016 and Landsat 9 OLI/TIRS 2022 and computed using ArcGIS 10.7 for Land-use change and percentage change in square kilometers was conducted to examine landuse/landcover dynamics and their contributions to the risk of coastal erosion in the study regions. The outcome of the shoreline analysis reveals that 972.03sqkm of land has been lost to coastal erosion in Senegal from 1986-2022 with 2016-2022 described as the period with the highest in terms of land loss. In Kenya, -463.30sqkm of land has also been lost to coastal erosion and agents of wave processes with 1986-2006 recording the highest share of -87.74% loss of valuable land while In Tanzania, -1033.35sqkm of valuable land has been lost from 1986-2022 to coastal erosion with 2006-2016 alone recording -10.4634% of land loss. The result of the land use/land cover percentage change analysis indicates a massive loss of vegetation cover with a significant increase in settlement representing urbanization. The scenario modeling of the shoreline on 10,20 and 30ms indicates that 567 persons per sq, 25904.5 persons per sqm and 25904.5 persons per sq km for both 20 and 30ms will be displaced in Senegal while in Kenya 577.5 persons per sq km will be displaced at 10m followed by 1210.3 persons per sq km at 20m and 7737.3 sq km at 30m. In, Tanzania, 702001.9 persons per sq km are projected to be displaced at 10m shift in the shoreline. Structured Questionnaires were administered to elicit responses from coastal dwellers on their perception of coastal erosion and environmental migration as part of ground truthing and the result of the survey affirms that coastal erosion and its exposure are the major drivers of environmental migration in the study area.

Although floods, as well as other natural disasters, can be considered relevant causes of intra-generational inequalities, the frequent catastrophes and the resulting damages to territory reflect the generalized indifference about inter-generational justice. Societal concerns, such as land protection, typically involve the administrative system performing proactive policies in the perspective of inter-generational solidarity, but subsidiarity has made more and more independent the local communities. As a consequence, the attention toward the long run effects – typically concerning the territorial system, as a whole, at the geographical scale – has been dispersed, and the proactive policies coming from the central government has became more ineffective. Regarding the case of the flood happened in 2009 in the Fiumedinisi-Capo Peloro hydraulic basin, in the northeastern part of Sicily, Italy, we propose an economic valuation – carried out by performing the method of the imputed preferences – in order to compare the expenses incurred by the public authorities responsible for protecting the territory to the costs of the rehabilitation of the damaged areas. Some considerations about the economic significance of the proactive policies for the arrangement of territory are addressed according to the role played by the social discount rate in the inter-temporal economic calculation.

Ziziphus tree species offer valuable socio-economic and ecological benefits but experience significant damage from insect pests. In Ethiopia, there is limited knowledge on the insects attacking Ziziphus fruits and a study aimed to identify these pests, assess their impact and understand how different land use types (LUTs), affect them were conducted. Sampling involved collecting fifty fruits and ten leaves from ten randomly chosen Ziziphus trees per LUT within each agroecological zone from August to December in 2022 and 2023. Samples were visually assessed for incidence and infestation levels, and the five morphotypes were identified using molecular techniques through phylogenetic analysis. Fruit pest incidence varied during the season yet a positive correlation (r = 0.84) was observed among the assessment months and years. Most fruits showed low to medium infestation levels (5–50%), while severe infestations (&gt;75%) were predominant in the lowland agroecological zone. The insects attributed to the damage were identified as Carpomya incompleta (Becker), Drosophila hydei Sturtevant, D. simulans Sturtevant and Zaprionus indianus Gupta. Fruits showed higher incidence and infestation levels than leaves, indicating significant yield and income losses. Thus, implementing effective management strategies is vital to minimize these losses and achieve sustainable production in Ethiopia.

The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region's ecological restoration and protection. Most existing studies focus on the trends of vegetation cover change, while fewer studies investigate the driving factors or only conduct quantitative analyses. This study uses the Geodetector (Geographic Detector Model) to assess the impact of natural and human factors such as temperature, precipitation, soil type, and land use on vegetation growth, revealing the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau from 1982 to 2022. It also quantitatively calculates the influence of different factors. The findings indicate that vegetation cover on the Loess Plateau has generally risen from 1982 to 2022, but there was a noticeable difference around 2000. The annual mean slope of vegetation cover from 1982 to 2000 was 0.00129, which increased to 0.01075 from 2000 to 2022. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use, and the interaction between these factors has a greater effect on vegetation cover than any single factor alone. This study identifies the ideal ranges or types for vegetation growth on the Loess Plateau over four time periods, offering a scientific basis for soil restoration and soil and water conservation efforts in the area.

The Olympic Games are a sporting event and a catalyst for urban development in the host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data, cloud computing, and Google Earth Engine (GEE) methods, this study examines the effects of various Olympic Games facility urban planning in different historical moments and location typologies: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games from Paris’24 to Munich’72. However, this paper focuses specifically on the representative cases of Paris’24, Tokyo’20, Rio’16, Beijing’08, Sydney’00, Barcelona’92, Seoul’88, and Montreal’76. The study is not only concerned with obtaining absolute Land Surface Temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, locally Normalized Land Surface Temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it is identified that Olympic planning has resulted in the development of green spaces, creating "green spots" that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become “hotspots”. Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context.

Urbanization-led economic growth has created cities to drive infrastructure investments and population accumulation and exploit natural resources at an extreme phase. While coastal cities become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, effective measures to improve the resilience of cities are pivotal to developing sustainable living environments. This study proposes a framework to assess urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that studies focus on either the vulnerability or coping capacity of urban communities to assess resilience, where effective strategies to manage disaster risk demand adaptation and mitigation measures at a spatial scale. Quantified resilience at small spatial units assists urban planning and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential growth zones while minimizing the natural disaster risks in urban growth management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions.

Relationships of the three bryophyte lineages (liverworts, mosses, and hornworts) to vascular plants is a pivotal question in the study of origin and evolution of land plants. In recent decades, this question has been subject to intense phylogenetic analyses using morphological characters, multigene DNA sequences, and genome structural characters. A tentative consensus reached ten years ago suggested that bryophytes are a paraphyletic group, with liverworts being sister to all other land plants and hornworts being sister to vascular plants. However, several more recent nuclear phylogenomic studies have concluded that bryophytes represent a monophyletic group that is sister to vascular plants. A discussion is presented here on strengths and weaknesses of different types of characters (morphological traits, nucleotide sequences, and genome structural arrangements), and their suitability for resolving deep phylogenetic relationships. Moreover, several criteria for credible phylogenetic reconstruction are proposed. Strong statistical support for reconstructed relationships should be derived from high quality, independent characters selected for suitability to the particular question being addressed. The relationships inferred in a study should be congruent with those from as many other lines of phylogenetic evidence as possible. Any incongruities should be explicable by well-understood biological mechanisms. It is concluded that the relationships of the three bryophyte lineages to vascular plants should currently be viewed as unresolved. This is a difficult phylogenetic problem; the land plants underwent a rapid radiation a long time ago. Yet, further exploration of analytical methods and careful choice of characters should lead to eventual elucidation of diversification patterns among early land plants.

This article presents the analysis of the allocation of important plant areas (IPA), which urgently identifies the most valuable sites for protecting plant objects. The study is based on a comprehensive inventory of the species composition of plants in the flora of the Western Karatau Ridge and the Mangyshlak Peninsula (arid regions)- key sites Akmysh, Samal, and Kogez, considered standard and the best preserved with natural vegetation cover. The flora of these critical sites was analysed using a combination of field surveys, herbarium studies, and a literature review. The results revealed a rich biodiversity, with 104 species from 82 genera and 33 families. Most species belong to 9 families, making up 60.6% of the total taxa. By the nature of life forms, perennial herbaceous plants dominate in all key sites (30 species, 50.0%). The eco-phylogenetic spectrum showed that a significant share of the flora of the studied IPAs is made up of xerophytes (21 species, 35%) and xeromesophytes (9 species, 15%); desert and mountain-steppe species prevail (53.0%). Rare, endangered and Red Book species account for 10-21.3% of those growing in the region. Regarding economic value, fodder, ornamental, honey, medicinal and food species dominate. The Shannon, Simpson, and Jaccard indices assessed alpha-beta and gamma diversity. The index showed that biodiversity in the Samal and Kogez sites is higher and more evenly distributed compared to the Akmysh site. The Whittaker, Harrison and Cody indices confirm significant differences in the species composition between the sites. In light of these compelling results, a clear and urgent call to action is proposed to allocate these areas to the IPA category, a crucial step towards ensuring the long-term existence of natural biocenoses.

In Ethiopia, farmers have changed cropland to Eucalyptus globulus plantations for construction, fuelwood and economic benefits. However, it has adverse effects on soil properties. The investigation was, therefore, to evaluate the impact of Eucalyptus globulus plantations on soil properties in northwestern Ethiopia. About 30 soil samples were collected from two soil depths (0-20 and 20-40 cm) and three land uses (Eucalyptus globulus plantations, cropland and grazing land) with five replications. Soil properties were analyzed using a two-way analysis of variance (ANOVA). Based on the result, soil properties such as silt particles, moisture content (MC), organic matter (OM), total nitrogen (TN) and cation exchange capacity (CEC) were significantly higher under the soil of Eucalyptus globulus plantations than in cropland and grazing land. Whereas the lower contents of bulk density (BD), soil pH, available phosphorus (AP), electrical conductivity (EC) and exchangeable bases (Ca, Mg, K, and Na) were recorded in Eucalyptus globulus plantations. Besides, clay, BD, MC, pH, EC, CEC, percent base saturation (PBS) and basic cations increased with soil depth while decreasing sand, silt, TP, OM and AP. The basic cations ( Ca, Mg, K), PBS and AP decreased in the soil of the 25-year old Eucalyptus globulus plantation compared to cropland by 38%, 36%, 43 %, 45% and 47% and grazing land by 56%, 60%, 76 %, 60% and 56%, respectively. Therefore, appropriate and effective land use management principles should be implemented in the study area.

Our study clarified what is the same or not between Tier 1(Tokyo) and Tier 2(Kansai) metropolitan areas on shrinking cities problems. Both of metropolitan areas will dramatically decrease the Housing Asset Value (HAV) where areas of bedroom communities are located. On the other hand, compared to Tokyo, Kansai has been hit by decline at a faster pace. This raises the question of whether Kansai will be more or less impoverished by the deflation of the HAV. According to our results, Kansai municipalities should expect the HAV losses of 38% by 2045, but the HAV losses would be higher in the northern suburbs of Osaka. Kansai has to face the effects of the HAV deflation faster than Tokyo. But Tokyo has a neck of higher residential land price, the income multiplier of the HAV per household is about 4. The HAV deflation hits more severe problems for elderly households in Tokyo. Considering our study, Tier 2 metropolitan area is much faster effects of the HAV deflation, but Tier 1 metropolitan area has more serious social problem than Tier 2. Hence, there are different problems of different natures rather than which one is more severely affected by the deflation of the HAV.

The purpose of this study is to assess the solid waste management status and practice in Sendafa town, Oromia regional state residents. In order to achieve this goal, the researcher intends to employ both primary and secondary data from the Sendafa town municipality. With the aid of multi-cluster sampling and a purposive sampling method, the researchers chose a sample size of 398 people and 10 interview responders. To utilize a systematic sampling technique at intervals of residences, individual samples were chosen. Quantitative data was cleaned and analysed using STATA software. A multiple linear regression model and logistic regression other descriptive statistics methods were then used to change the results. The qualitative data were imported, coded, and thematised using ATLAS.ti.9 in order to prepare respondents' interviews for analysis. The findings indicate that residents' poor solid waste management practices are a result of a lack of awareness, low family income transportation issues, issues with waste collectors, a lack of a sense of responsibility and belonging, and a failure to enforce current solid waste management laws. Solid waste accumulates near roads, bridges, and residential areas that are close to landfills, contaminating the area and leading to diseases like TB and Asthma (and other related health issues in children and adults who live there). Thus, researcher recommends that to have sustainable solid waste management the town administrators should have plan to develop awareness creation symposium or panel of discussion, resolve transportation issues, monitor and guide the routine activities of waste collectors, develop sense of belongingness for themselves and for the community, develop integrated solid waste land fill projects and working with all stakeholders like NGO and health institution will minimize the adverse effect of improper solid waste management. Keywords: Solid Waste, Status of Solid Waste Management, Land fill site suitability

In addition to changes in the amount of rain, changes in land use upstream are considered a factor that directly affects the maximum runoff flow in a basin, especially in areas that have experienced floods and flash floods. This research article presents the application of the SWAT model to assess runoff in areas that have experienced flash floods, in order to analyze the proportion of land use change to the maximum runoff. Study areas that had experienced flash floods were in 3 basins in Thailand (Lam Saphung Basin, Phrom River Basin, and Chern River Basin Part 1 which is a subbasin of the Nam Chi River Basin). This study analyzed two main factors that influence runoff in the river basin: Changes in rainfall and changes in land use by considering 2 simulation situations.: (1) Changes in land use affecting runoff assessed by considering land use maps in 2006, 2008, 2010, 2015, 2017, 2019, and 2021 when using rainfall data only in the year 2021 for all cases, and (2) Changes in the amount of rainfall influencing runoff by considering the rainfall record in 2006, 2008, 2010, 2015, 2017, 2019, and 2021 when using the land use data only in the year 2021 for all cases. The results of the study found that the SWAT model can be effectively applied to estimate annual runoff in areas that have experienced flash floods under 8 parameters with R2 values of 0.74, 0.82 and 0.74 for the Lam Saphung River Basin and the Phrom River Basin and the Chern River Basin Part 1 respectively. In addition, it was found that the proportion of land use changes that involved changes from forest areas to residential areas was the greatest from 2008 to 2010 in the Phrom River Basin and Chern River Basin Part 1. This resulted in an increase in the maximum runoff amount of 77.78% and 46.87%, respectively. When land use was constant, it was found that the rainfall in 2010, which was the highest, also had the greatest impact on the runoff in all 3 areas.

The role of land surface temperature (LST) is of utmost importance in multiple academic disciplines such as climatology, hydrology, ecology, and meteorology. Until to now, many methods have been proposed to estimate LST from satellite thermal infrared data. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12μm, affected strongly by both atmospheric water vapor and ozone. Three study areas, namely Baotou, Heihe River Basin, and Yantai-sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted by cross-comparison between the retrieved LST and MODIS LST product. The results revealed that the WVS-based method exhibited an average bias of 0.63K and an RMSE of 1.62K compared to the in-situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77K and RMSE of 2.0K. These findings provide that the WVS-based method is effective to estimate LST from ZY1-02E IRS data.

Nowadays, the demand of agricultural production increase, Moreover, the rapid increase of the population in Egypt led to an increase in the demand for food, prompting the government to reclaim more desert lands. Such a study contributes to providing information that contributes to the promising optimization of land resources. This study carried out in an area south Egypt covering 386,171.04 hectares (ha). Remote sensed data were manipulated for defining the land cover/use features. Setting up DEM configures the network of natural drainage flows via the descending slopes. It was a guide for allocating micro dams as water harvesting sites as well as minimizing the runoff hazards and managing the priority of land use suitability versus flood hazards. Land cover/use units were defined as terrestrial irrigated areas of sequentially herbacous crops and permanent trees covering 1387.83 ha in levees; 1608.82 ha in point bar; 718.89 ha in bow bar and 23938.29 ha in alluvial plain. Terrestrial natural vegetation is dominated by xerophytes herbaceous in wadis (123533.56 ha). Terrestrial non-vegetated areas includes bare areas of rock land (98102.59 ha) and 67201.57 in bajada. Artificial non linear surfaces include buildings (5403.59 ha) and linear features include roads and railways (875.78 ha). Aquatic areas include artificial irrigation and drainage canals (1062.15 ha) and natural water body of River Nile course (2785.92 ha). Current land suitability for specific Land Utilization Types (LUTs) was assessed by matching soil attributes with the growth requirement of each LUT. Current land suitability can be improved by decreasing of salinity and framing practices. Accordingly, the land units can be potentially more profitable for increasing the ability of extra crops to be more productive. Old cultivated areas are the most profitable land cover/use units highly suitable (S1) for all LUTs comparing with other land cover categories.

The watershed hydrologic conditions in the Madre de Dios (MDD) basin in the Peruvian Amazon have been irreversibly impacted by deforestation and changes in land use cover. These changes have also had detrimental effects on the geomorphology, water quality, and aquatic habitat within the basin. However, there is a scarcity of hydrological modeling studies in this area primarily due to the limited availability of hydrometeorological data. The primary objective of this study was to examine how deforestation impacts the hydrological conditions in the MDD basin. By implementing the Soil and Water Assessment Tool (SWAT) model, this study determined that replacing 12% of the evergreen broadleaf forest area with bare land resulted in an significant increase in surface runoff by 38% monthly, a reduction of evapotranspiration by 1% annually, and an average monthly streamflow increase of 12%. Changes in spatial patterns reveal that the primary impacted watershed is the Inambari River subbasin, a significant tributary of the Madre de Dios River. This area experiences an annual average surge of 187% in surface runoff generation while witnessing an annual average reduction of 8% in evapotranspiration.These findings have important implications, as they can contribute to instances of flooding and extreme inundation events, which have already occurred in the MDD region.

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product, while remaining immune to pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of ERA5 LST with minimal missing pixels. The pixel-wise average R-squared and mean absolute error for MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions at a 1000 m scale. Accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only clear sky LST as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.

Accurate seasonal weather forecasts are vital across a broad spectrum of applications, bearing significant environmental and socioeconomic implications. This importance renders the subject a matter of primary interest to a wide range of stakeholders, including the general public, agricultural sector, emergency responders, financial institutions, and policy strategists. The need for precision in long-term predictive models necessitates the development of innovative methodologies. These methodologies should be capable of deciphering atmospheric patterns and mechanisms with detail, especially at a local level, without the resource constraints that dynamical downscaling imposes. In response to this expanding demand, this study presents a novel solution, combining a stochastic deep learning methodology, specifically Generative Adversarial Networks (GANs), with a Digital Elevation Model (DEM). The cornerstone of the proposed model is the enhancement of gridded temperature fields from seasonal forecasts. The area of focus was the Hellenic region, wherein the spatial resolution is amplified from a coarse 1° x 1° grid to an impressively detailed 0.1° x 0.1° grid. This offers a transformative perspective for interpreting and employing this crucial meteorological data. The results suggest that the downscaled fields adequately approximate the actual spatial distribution, although the predicted values tend to slightly overestimate and in some cases underestimate the original ones. This study underlines the potential of this approach to significantly enhance the resolution and utility of weather forecasts, thereby contributing to a variety of sectors dependent on reliable meteorological data.

Land use is an embodiment of human socioeconomic activities and represents a bridge between these activities and natural systems. Through social activities, humans transform land use to promote social and economic development and improve production, living conditions, and eco-logical functions of land. Rural residential land represents a space for rural residents to reside in and exhibits spatial characteristics that evolve over time, which is proof of rural socioeconomic development. This paper observes rural residential land in 21 cities on the Grand Canal and analyses its spatial differentiation. Then, it explores the driving factors of this land using spatial grid analysis and the geographic detector model. Lastly, it proposes three different forms of rural residential land based on the results. The study found that: (1) the change of rural residential land in the northern part of the Grand Canal was more volatile than that in the southern part. The change of rural residential land from 1990 to 2020 conformed to the pattern of cultivated land - rural residential land - urban construction land; (2) Based on the driving factors of rural residen-tial land, the land is divided into one-dimensional cities, two-dimensional cities and three-dimensional cities. Circular, linear, and scattered cities of different sizes were affected by socioeconomic factors, transportation accessibility, and the natural environment, respectively; and (3) Finally, according to the spatial differentiation characteristics and the driving factors of rural residential land, the study proposes the construction of three types of villages through the strate-gies of constructing large-scale villages, relocating and reconstructing new villages, and con-structing high-quality villages, respectively. Enhancing the scientific planning of rural residential land and its efficiency can offer the protection of agricultural land and the integration of urban and rural areas in the new era.

During few last years, climate change including global warming which is attributed to human activities and also its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics which have arisen many concerns and efforts to find the possible solutions. Since the warmth arising from Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in the Land Surface Temperature (LST) patterns in different seasons through non-linear methods. Here, we particularly want to estimate the non-integer dimension and fractal structure of the land surface temperature. For this study, the (LST) data has been obtained during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on what time of the year data has been collected, temperatures change in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the U.S. state of Kansas, which does not belong to any of this type locations and we are interested to observe the seasonal changes in temperature in this county. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal process with non-integer dimension.

In its Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change, Brazil committed to reducing greenhouse gas emissions and restoring its forests. This study examines the challenges of fulfilling these commitments in Brazilian Amazonia. We carry out a detailed assessment of the current status of land tenure in the region and its relation to deforestation. After dealing with conflicts and overlaps between data from various sources, we produce a new map of public and private land tenure in Amazonia. Combining this map with Brazil's official data on deforestation, we find out how much natural vegetation has been preserved in each public or private area. The result is used to estimate how much deforestation is illegal. We also establish how much deforestation is associated with each land tenure type. Our results show that most deforestation inside rural properties is done by a few landowners, a finding that has important consequences for law enforcement. We then assess the challenges for reforestation in detail. To do so, we consider how much forest needs to be rehabilitated according to Brazil's Forest Code. Our analysis provides a comprehensive appraisal of the potential opportunity costs for forest restoration in the biome, considering farm size and land use. This analysis provides insights into targeted land use policies that can meet Brazil’s forest restoration goals.

Land resources have been under tremendous anthropogenic pressure with the consequence of their degradation. It is therefore necessary that the land resources must be managed effectively for sustainable development. Different from the developed countries, carbon inventories and data bank to monitor carbon sequestration potential of different ecosystems are unavailable in India. Micro-level studies are essential for sustainable land use management for a land scarce nation like India. To achieve the desirable goal of the present study, a total of 33 tree-based land uses were identified from forested and agricultural landscapes. Of these total land uses, five were in forest landscapes and rest in agricultural landscapes categorized into forest tree plantations (8 land uses), agroforestry (nine land uses), commercial crop plantations (six land uses) and fruit orchards (five land uses). A stratified random nested quadrate sampling method was adopted for vegetation analysis of the different land uses. The SOC, biomass and carbon accumulation in the tree-based land uses were significantly different from each other. Mixed forest soil had the highest amount of SOC, primary nutrients, standing biomass carbon, and ecosystem carbon. Positive correlations were observed between SOC, total standing biomass, litter production, and ecosystem carbon. The sequence of best tree based land uses in terms of total SOC (up to 60 cm depth), total plant biomass, total plant biomass carbon and ecosystem carbon was mixed species forest (126.67, 781.21, 390.61 and 517.27) > sole tree species stands in forest landscape (109.71, 192.56, 96.28 and 205.98) > tea plantations (103.19, 77.07, 38.54 and 141.74) > homegardens (90.34, 97.38, 48.69 and 139.02) > mixed plantation of Anthocephalus cadamba + Swietenia macrophylla (60.07, 111.86, 55.93 and 116.02) > Swietenia macrophylla based agroforestry (62.49, 83.82, 41.91 and 104.40) > mixed plantation of Tectona grandis + Milvus migrans (60.0, 85.97, 42.99 and 102.90). Similarly, the order of the major land uses was forest > commercial crop plantation > forest tree plantations > agroforestry > fruit orchards. The overall average ecosystem carbon accumulation in forests was 3.24 times more than the land uses in agricultural landscapes. The ecosystem carbon accumulation in the tree-based land uses in both forest and agricultural landscape was highly variable and was significantly different from each other. Land use conversion from forest to agriculture can reduce more than half of the carbon stock, but converting into homegardens, tree plantations or agroforestry enhanced carbon storage of the land use systems. The present findings can be used as baseline information for developing prediction models for probable effects of different land use, future intervention and sustainable management of land use systems.

Solar energy (SE) is essential for decarbonization of our economy and for energetic transition. Solar energy can be a sustainable economic activity as long as a balance is struck between the benefits it brings to climate change mitigation and the damage it can cause to biodiversity and ecosystems. Here we study this balance in an area with high biodiversity under pressure for installation of numerous photovoltaic plants (PPs). Our results show that developers give priority to the cheapest land close to connection points, while other values (e.g., environmental, landscape) are secondary. The regulatory process carried out by the Administration does not ensure the preservation of natural values, as several PPs with a high impact on important conservation areas have been approved. Experts' allegations provide quality information to the Administration to evaluate and demand changes to the projects presented. Such demands show that companies are willing to relocate plants to land occupied by olive groves. In this way, greater efficiency is achieved in land occupation, as well as shorter evacuation lines, water savings and less environmental impact. Prior strategic territorial planning could have avoided the impact of PPs already built, made the deployment of new PPs compatible with biodiversity conservation, and contributed to improving the management of key resources, such as subway aquifers. The proposed regulatory changes to the environmental assessment procedure (exclusion of renewables and public participation from the procedure) are detrimental, as they will make SE unable to meet the requirements of the Taxonomy Regulation.

A large number of studies have concluded that since housing pressure will affect the mobility of highly skilled young migrants (HSYM) in Chinese cities and regions, it is necessary to apply corresponding housing policies to adjust housing unaffordability for HYSM. This study uses a survey data conducted in China's Zhejiang Province of China, where specific policies have been implemented to attract talent and found that housing does crowds out the HSYM from a city, but the HSYM who have a master's degree or above or who work in government organizations or state-owned enterprises are more tolerant of housing unaffordability. The unmarried or those staying in the city for a long period are less tolerant of housing unaffordability. Meanwhile, there are the heterogeneous impacts of factors on the HSYM's tolerance for housing unaffordability across cities of different levels. Therefore, housing policies should highlight urban differences and intra-group differences.

Intercropping as a practice is very crucial in livelihood sustainability among smallholder farm-ing communities in many growing countries. However, for most cropping systems, the benefits of intercropping have yet to be optimized due to a lack of knowledge regarding spatial ar-rangements and planting densities. The objective of the study was to find out the profitability of maize-vegetable intercropping and the yields obtained from various spatial arrangements and planting densities of intercrops. From May to October 2019 and 2020 respectively, Experimental trials were set up in the rainy season in Northern, Upper West and Upper East regions of Ghana comprising of eight treatments each. A randomized complete block experimental design was used for the field layout with three to four replications. Data was collected on grain and fruit yield and land equivalent ratios was estimated (LER). At the end of the trial, important spatial arrangements and planting densities were identified that can be adopted by smallholder famers for system intensification. For okra, the optimal intercropping system under sufficient rainfalls was 2 okra rows at higher density and 2 maize rows at lower density for Upper West Region. In Upper East Region, the optimal spatial arrangement to recommend is 1 row of maize at recom-mended density: 2 rows of okra at lower density under well distributed rainfalls. For roselle, intercropping with spatial arrangement of 2 rows of maize at higher density: 1 row of okra at recommended density was recommended in Northern Region.

Reaching the land-related UN-Sustainable Development Goals (SDGs) and similar goals articulated by the EU-Green Deal (GD) by 2030 presents a major challenge and requires a pragmatic approach to be focused on joint learning by land users (mostly farmers), researchers and other stakeholders in “Living Labs” and system experiments at experimental farms of research organizations. Defining specific indicators and thresholds for ecosystem services in line with land-related SDGs, are crucial to establish: “Lighthouses” that can act as inspiring examples if they meet the various thresholds. This exploratory paper discusses indicators and thresholds for an arable farm operating on marine, calcareous light clay soils in the Netherlands. Studies of a system experiment are used to discuss and test operational methodology to be widely applied when characterizing many “Living Labs” in future as planned by the European Union. The important role of soils, contributing to ecosystem services, is discussed in terms of soil health. Recommendations are made for innovative methodology to be associated with all land-related SDGs. Satisfying thresholds of ecosystem services, that will vary by soil type, region and farm-type, can be the basis for farm subsidies such as the Common Agricultural Policy (CAP). Research on Living Labs and in system experiments has to be judged by different criteria than those associated with traditional linear research. Important contributions by soils to achieve ecosystem services are framed in terms of soil health and are the most effective way to promote soil science in a by now widely desired inter- and transdisciplinary context.

Farm fragmentation is the occurrence of numerous and often discontinuous land parcels associated with a single farm. Farm fragmentation is considered to be a defining feature of Northern Ireland’s (NI) agricultural landscape, influencing agricultural efficiency, productivity, and the spread of livestock diseases. Despite this, the full extent of farm fragmentation in cattle farms is not well understood, and little is known of how farm fragmentation either influences, or is influenced by, different animal production types. This study describes and quantifies farm fragmentation metrics for cattle enterprises in NI, presented separately for dairy and non-dairy production types. We find that 35% of farms consist of five or more fragments, with larger farms associated with greater levels of farm fragmentation, fragment dispersal and contact with contiguous farms. Moreover, this was particularly evident in dairy farms, which were over twice the size of farms associated with non-dairy production types, with twice as many individual land parcels and twice as many fragments. We hypothesise that the difference in farm fragmentation and farm size between dairy and non-dairy production types is associated with the recent expansion of dairy farms after the abolition of the milk quota system in 2015, which may have driven the expansion of dairy farms via the acquisition of land. The high levels of land fragmentation, fragment dispersal and contiguous contact observed in NI cattle farms may also have important implications for agricultural productivity and epidemiology alike. Whilst highly connected pastures could facilitate the dissemination of disease, highly fragmented and parcellised land could also hamper productivity via diseconomies of scale, such as preventing the increase of herd sizes or additionally, adding to farm costs by increasing the complexity of herd management.

In Ethiopia, bamboo thickets and woodlands play an important role in soil-water conservation and climate change mitigation in arid and semi-arid regions. However, bamboo mass flower-ing, rapid demographic changes and expansion of agricultural investments to bamboo domi-nated areas have led to deforestation and land degradation. In this study, we determined the effects of deforestation and subsequent cultivation on soil physical and chemical properties along a chronosequence of closely located agricultural lands with different ages (1, 3, 5 and 7 years) since converted from natural lowland bamboo forest. Hence, soil samples (n = 90) have been taken from both natural bamboo forests and adjacent agricultural lands at two soil depths (0-20 cm and 20-40 cm). Our result showed that CEC, K+, Ca+, Mg+ and available P were varied significantly with respect to cultivation periods and soil depth, while soil pH and Na+ varied with soil depth (P < 0.001). Soil C and total N contents (g/kg) in 0-20 cm soil layer declined significantly and exponentially with increasing years under cultivation. Conversion of natural bamboo forest to cropland during the past seven-year period significantly increased soil pH with soil depths, while CEC was declined throughout the cultivation period and soil depth. In general, the result revealed that conversion of natural lowland bamboo and subsequent cultivation of soil had negative effects on measured soil physicochemical properties.

The reconstruction of land use practices in hyper arid Saharan Africa is often hampered by the accuracy of the available tools and by unconscious biases that see these areas as marginal and inhospitable. Considered for a long time the living space of pastoral mobile communities, new research is showing of agriculture might have been more important in these areas than previously thought. In this paper, after a review of present-day land use strategies in Saharan Africa, we show how ethnographic and ethnoarchaeological data can offer us a different point of view and help in better defining land use and food production strategies in this area. Ultimately, these insights can be integrated into the ongoing effort of reconstructing past land use globally.

Long-term, high-accuracy mapping of built-up land dynamics is essential for understanding urbanization and its consequences for the environment. Despite advances in remote sensing and classification algorithms, built-up land mapping using early satellite imagery (e.g., from the 2000s and earlier) remains prone to uncertainty. We mapped the extent of built-up land in the North China Plain, one of China’s most important agricultural regions, from 1990 to 2019 at three-year intervals. Using dense time-stack Landsat data, we applied discrete Fourier transformation to create temporal predictors and reduce mapping uncertainties for early years. We improved overall accuracy by 8% compared to using spectral and indices predictors alone. We implemented a temporal correction algorithm to remove inconsistent pixel classifications, further improving accuracy to a consistently high level (>94%) across years. A cross-product comparison showed that our study achieved the highest levels of accuracy across years. Total built-up land in the North China Plain increased from 37,941 km² in 1990–1992 to 131,578 km² in 2017–2019. Consistent, high-accuracy built-up land mapping provides a reliable basis for policy planning in one of the most rapidly urbanizing regions of the planet.

Land degradation is a critical issue globally putting our future generations at risk. The decrease in farm productivity over the years is evidence of land degradation severity in Nepal. Among the many strategies in place, agroforestry, which is an integrated tree-based farming, is widely recommended to address this productivity issue. This paper thoroughly examines what influences the choice of agroforestry adoption by farmers and what discourages the adoption. For this, a total of 288 households were surveyed using a structured questionnaire. Two agroforestry practices were compared with conventional agriculture with the help of the Multinomial Logistic Regression (MNL) model. The likelihood of adoption was found to be influenced by gender; the male-headed households were more likely to adopt the tree-based farming practice. Having a source of off-farm income was positively associated with the adoption decision of farmers. Area of farmland was found being the major constraint to agroforestry adoption for smallholder farmers. Some other variables that affected positively included livestock herd size, provision of extension service, home-to- forest distance, farmers’ group membership and awareness of farmers about environmental benefits of agroforestry. Irrigation was another adoption constraint that the study area farmers were faced with. The households with means of transport and with larger family (household) size were found to be reluctant towards agroforestry adoption. A collective farming practice could be a strategy to engage the smallholder farmers in agroforestry.

An all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. The product is based on clear-sky LST retrieved from MSG/SEVIRI infrared (IR) measurements, complemented by LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a Microwave (MW) based LST product derived from AMSR-E measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

Droughts occurring more and more frequently in recent years are usually associated with progressive climate changes. Modern monitoring methods is mainly associated with agricultural use due to the high market demand for services protecting against agricultural losses. In natural wetland ecosystem this problem is neglected which is probably due to fact that this are ecosystems rich in water by definition. Nevertheless this droughts might become a problem of wetlands, whose good condition depends on adequate hydration. Therefore this study focused on determination Crop Water Stress Index (CWSI) for natural heterogeneous wetland habitats. CWSI based on remotely sensed land surface temperature and associated basic meteorological (air temperature and humidity) measurements. CWSI was calculated based on high resolution LST measurements from UAV platform and compare with on ground measurements of soil moisture, CCI, fAPAR and meteorological conditions. Results shows that CWSI properly shows water condition of plants in measured habitats and revels high importance of metrological conditions during data acquisition.

Land consolidation engineering inevitably interferes with terrestrial ecosystems, leading to natural capital loss. Therefore, conducting an ecological sensitivity evaluation of a project area before consolidation engineering is very important for reducing unnecessary human interference. Conservation of terrestrial ecosystems and the biodiversity therein to the greatest possible extent is urgently needed. This research analyzes the interference by human activities caused by land consolidation engineering in terrestrial ecosystems. GIS technology, ecological values, landscape pattern indexes, and an ecological risk evaluation were used to construct an ecological sensitivity evaluation index. The coefficient of variation method and a comprehensive sensitivity rating evaluation were used to calculate the weights and results. The project area was divided into sensitivity zones according to the results, and the results and suggestions are as follows: In the highly eco-sensitive zone, where bare rocks, gravel, and grass-covered areas compose the main landscape type, vegetation should be restored, and forests should be planted. In the medium eco-sensitivity zone, where irrigated paddy fields and arid land compose the main landscape type, land parcels should be merged, and agricultural infrastructure should be constructed or improved. In the low eco-sensitivity zone, where forests compose the main landscape type, roads should be closed, natural habitat should be restored, and buffer zones should be created. This study provides suggestions for future land management decisions.

Abstract: The air pollution dispersion modelling via spatial analyses (Land Use Regression – LUR) is an alternative approach to the air quality assessment to the standard air pollution dispersion modelling techniques. Its advantages are mainly much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate other factors affecting pollutant’s concentration. The goal of the study was to model the PM10 particles dispersion modelling via spatial analyses v in Czech-Polish border area of Upper Silesian industrial agglomeration and compare results with results of the standard Gaussian dispersion model SYMOS’97. Results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover and were included into the LUR model, the LUR model results were significantly improved (65% determination coefficient) to the level comparable with Gaussian model. The hybrid approach combining the Gaussian model with the LUR gives superior quality of results (65% determination coefficient).

Assessment of the trends of land cover and vegetation dynamics (VD) using remote sensing (RS) and indicators such as anthropogenic activities and the socio-demographic information is essential in order to make proper planning for sustainable management. This paper attempts to evaluate land cover change (LCC) and VD in Kebbi State, Nigeria using historical Landsat data from 1986-2016 by means of remote sensing. The Driver-Pressure-State-Impact-Response (DPSIR) framework was later employed using both primary and secondary data for a better understanding of the drivers, the state of the environmental condition, the causes as well as the impact of the change. The images were classified into five thematic land cover classes as Dense Vegetation, shrubs/built area, farmland, bare/grassland and water body by means of Maximum likelihood supervised classification technique in accordance with Anderson classification scheme level 1, with acceptable accuracy. Pre-classification and post-classification change detection (CD) methodologies were executed using Normalized difference vegetation index (NDVI) and Image differencing respectively. The study illustrates a steady decline in dense vegetation and shrubs/build areas while farmland and bare/grassland increases, however, water bodies remain unchanged. The DPSIR pin-point that the major drivers of change in the study area have been the pressing need for farming land as the population grows and socioeconomic demands including fuelwood consumption and endemic poverty. Expansion of Farming land, fuelwood consumption and the need for construction materials are identified as the main key elements exerting pressure for the change. The state of the condition indicates a steady decline in dense vegetation and shrubs areas while farmland and bare/grassland are increasing significantly. The impacts include land degradation, the decline in the provision ecosystem goods and services, biodiversity loss through loss of habitats. The study, however, noted that many international and national policies in response to land degradation are channelled toward land restoration and remediating of the environment, through afforestation programs and improving the livelihood of the rural people through providing alternative income sources since they depend heavily on land for sustenance. However, the state governments, communities and individual commonly organized annual tree planting campaign with the main purpose of environmental protection.

In this paper, a land utilization efficiency evaluation model, which takes environmental loss into consideration, has been structured via taking advantage of the slack-based measure (SBM) model. Meanwhile, based on the panel data from 280 prefecture-level cities in China from 2003 to 2013, the paper thoroughly probed into, and discussed, the effect imposed by industry clustering and specialization on the utilization efficiency of urban land. Research results indicate several conclusions, as follows: (1) Taking environmental loss into account, the land utilization efficiency of prefecture-level cities in China is generally low. During the research period, the average value of the land utilization efficiency of prefecture-level cities in China is only 0.349, with, first, a declining trend, and then a rise. Geographically speaking, the land utilization efficiency presents a “depression in the center” phenomenon which means the land utilization efficiency of prefecture-level cities in the central China are relatively lower than in the east and west. Now, the difference among the urban land utilization efficiency in China significantly reflects the distinctions among Eastern, Western, and Central China. Moreover, the contribution degree of the difference of the land utilization efficiency among cities of central China to the aggregation difference shows an ascending momentum. Additionally, the relation between the population scale and land utilization efficiency in cities manifests as a U shape; (2) theoretically speaking, the relation between industry clustering and urban land utilization efficiency presents an inverted-U shape. However, this kind of relation is not significant in Western and Central China and medium-sized cities. Moreover, most of cities are still relatively far away from the inflection point or the critical value; and (3) the industry professional level has imposed a positive influence on urban land utilization efficiency. However, that influence is not significant in Eastern China and large cities. Consequently, strengthening the industry professional development of Western and Central China and small and medium-sized cities, facilitating diversified development of industries in Eastern China and large cities, and accelerating industrial clustering, all of these measures above will be conducive to improving urban land utilization efficiency in China.

In Brazil, during the four last administrations of Worker’s Party (PT) 2003-2015, the support to the agrarian reform seems to have stagnated, even with the influence of landless workers' movements. Thus in 2016, the impeachment President Dilma Roussef have marked a brutal stop in the agrarian reform process. How to explain that which seems at first to be a contradiction and has become a decadence of an important federal public policy?. Furthermore, how can we evaluate the debates within Brazilian society and the federal government on this theme? The article analyzes the tensions, debates, advances and impasses of the past fifteen years of agrarian reform policy in Brazil looking at the interaction between social movements and public policies. The method associates bibliography, official statistic synthesis and research results in Northeast, Amazônia and Cerrado regions among several projects. The first part results put on evidence the crescent reduction of agrarian reform settlements and beneficiary families since 2006. The second part presents the main reasons offering an analysis of government and society debates in Brazil about land reform. The analysis conclude to the less of power and representation in the society of the pro agrarian reform large and popular coalition.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

We evaluated the potential of using fish species and functional traits as indicators of land use impacts to fish assemblages. We used environmental data collected at multiple spatial scales (local, reach, and upstream catchment) for 19 tributary and main stem sites in the Nolichucky River watershed in Tennessee. Canonical correspondence analyses showed that temperature, elevation, specific conductivity, sediment yield, impervious surfaces, and row crop cover at the catchment scale were strongly associated with fish assemblage structure, as well as forest cover from all three spatial scales. Blocked indicator species analysis, with stream size as the block, showed that significantly strong indicators of the least-impacted riparian land use condition (≥60% forest cover) were Saffron Shiner (Notropis rubricroceus), Rainbow Trout (Oncorhyncus mykiss), Longnose Dace (Rhynichthys cataractae), Creek Chub (Semotilus atromaculatus), and Mottled Sculpin (Cottus bairdi). Traits indicative of the least-impacted sites were the herbivorous trophic guild, mean female age-at-maturity, longevity, rock-gravel spawners, montane geology and pelagic swimmers. Specific conductivity was strongly related to multiple catchment-scale land use variables, and was a strong local-scale influence on fish assemblage structure. Our results show promise for using a relatively common but endemic southern Appalachian fish species, the Saffron Shiner, as an indicator for land-use related impacts to these streams.

Footprints are powerful indicators for evaluating the impact of the bioeconomy of a country on environmental goods, domestically and abroad. In this study, we apply a hybrid approach combining a Multi-Regional Input-Output model and land use modelling to compute the agricultural land footprint (aLF). Furthermore, we added information on land-use change to the analysis and allocated land conversion to specific commodities. The German case study shows that the aLF abroad is larger by a factor of 2.5 to 3 than the aLF in Germany. In 2005 and 2010, conversion of natural and semi-natural land-cover types abroad allocated to Germany due to import increases was 2.5 times higher than the global average. Import increases to Germany slowed down in 2015 and 2020, reducing land conversion attributed to the German bioeconomy to the global average. The case study shows that the applied land footprint provides clear and meaningful information for policymakers and other stakeholders. The presented methodological approach can be applied to other countries and regions covered in the underlying database EXIOBASE. It can be adapted, also for an assessment of other ecosystem functions, such as water or soil fertility.

Land cover refers to the combination of various material types and their natural characteristics on the Earth. Accurately mapping the spatial distribution and temporal changes of the earth's land cover is of great significance for studying the energy balance and carbon cycle of the earth system. However, there is still a high degree of human participation in the production of multi-temporal land cover products. Developing an automated method for multi-epoch land cover mapping has become a key research focus. To this end, an automatic training sample extraction method was first employed using multi-source prior land cover products. Then, based on the generated training dataset and the Random Forest classifier, local adaptive land cover classification models of the reference year were developed. Finally, by migrating the classification model to the target epoch, the multi-epoch land cover products were generated. Yuli County in Xinjiang and Linxi County in Inner Mongolia were used as test cases. The classification models were first generated in 2020 and then transferred to 2010 to enable automatic classification of multi-temporal land cover. The mapping results showed high accuracy in both regions, with Yuli County achieving 92.52% in 2020 and 88.33% in 2010, and Linxi County achieving 90.28% in 2020 and 85.28% in 2010. Additionally, uncertainty analysis of the model migration method revealed that land types such as water bodies, wetlands, and impervious surfaces, which exhibit significant spectral changes over time, are less suitable for the model migration. Our research can offer valuable insights for fine-resolution land cover mapping. Furthermore, the approach provides a scalable solution for multi-period land cover monitoring, which could facilitate more efficient and accurate environmental assessments.

The distribution of impervious surface is one of the important assessment indicators for evaluating the quality of urban ecological environment. Taking Xuzhou city as an example, we study the influence of the spatial distribution of impervious surfaces on urban surface temperature, using Landsat 8 remote sensing imagery and nighttime lighting data to extract the impervious surface information from 2013 to 2022 and carry out surface temperature inversion. The study area was divided into built-up and non-built-up areas using nighttime lighting data and the relevant indices were used to establish interest zones and finally the extraction of impervious surfaces was completed using supervised classification methods. The experimental results show that the kappa coefficient is not less than 0.8562, which makes the result more reliable. By extracting the impervious surface of Xuzhou City, it was found that the impervious surface of Xuzhou City continued to increase from 2013 to 2022, in which the growth rate was faster in 2014-2016 and 2019-2021, and slowed down in 2017-2018 and 2021-2022. The expansion of impervious surface was found to be one of the reasons for the increase of urban surface temperature through stacked analysis and linear regression analysis.

South Africa (SA) is grappling with a severe food insecurity crisis exacerbated by socioeconomic challenges. This resulted in the Department of Basic Education (DBE) advocating for the establishment of vegetable gardens at schools. However, information on the suitability of the soils in SA for sustainably growing Beetroot is unavailable. This study aimed to assess the suitability of soils for sustainable Beetroot production at various schools in the Kgakotlou circuit. Four Secondary Schools were selected: Mothimako, Madipoane, Klaas Mothapo and Phomolong. A detailed soil survey was carried out to study soils in vegetable gardens. Soil samples were collected for laboratory analysis of physicochemical properties. The soil suitability was evaluated using the physicochemical properties of the soils and optimal soil requirements for Beetroot production. We found various soils in the schools, including Cambisols (Hutton and Nkonkoni), Leptosol (Glenrosa), Plinthosol (Westleigh), and Acrisols (Tshiombo and Tubatse). Portions of the gardens on Hutton and Westleigh soil were moderately suitable (S2), limited by electrical conductivity (EC). Meanwhile, portions of Tshiombo, Tubatse, and Nkonkoni were marginally suitable (S3), limited by pH and EC. Portions on Glenrosa soil at Klaas Mothapo and Phomolong were not suitable (NS) and were limited by shallow depth, soil pH and EC. The results reveal the importance of considering soil suitability before establishing vegetable gardens in school settings. Indexing soil suitability using the physicochemical properties of soil resources for sustainable beetroot production is proposed for schools in the Kgakatlou circuit and other parts of SA.

This study constructs an evaluation system of high-quality utilization of urban land (HUUL) and comprehensively adopts the entropy method, the kernel density estimation, and the geodetector model to analyze the spatio-temporal evolution and driving factors of the HUUL levels of 284 Chinese cities from 2006 to 2020. The measurement results show that during 2006-2020, the HUUL level showed an obvious upward trend, and the eastern region > the central region > the overall region > the western region > the northeastern region. According to the kernel density map, except for the region of the west, there is a trend of different degrees of enlargement of the difference in the main peak position of the HUUL level among cities in the other three regions, and there are individual cities in the eastern and western regions with much higher HUUL levels than the others; According to the results of factor detection, it can be seen that innovative utilization and open utilization are the endogenous dominant factors that promote the improvement of HUUL level, and the level of economic development is the exogenous dominant factor that supports the im-provement of high-quality utilization of urban land.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

The growing population with changing consumption patterns in developing countries is causing significant challenges with regards to solid waste management. Plastic wastes are particularly problematic, with single-use plastics leaking into the environment, including the marine environment, at an unprecedented rate. Approximately 80 percent of ocean plastics come from land-based sources or about 8 million tons are dumped into the ocean every year. Thailand was identified as one of the top 10 countries ranked by mass of mismanaged plastic wastes, with more than 60,000 tons per year entering the ocean through multiple outlets, including rivers. In the environment, plastic wastes can be degraded into small sizes (less than 5 mm), called microplastics (MPs), which could contaminate the ecosystems and the food chain, including foodstuffs and water supply. Tap water samples collected from a water supply treatment plant and Academic Institutions 1 and 2 in central Thailand were found to contain MPs about 304 ±90, 270 ±109 and 386 ±102 particles/L, respectively. In addition, MPs concentrations of 211 ±70 and 122 ±60 particles/L were also found in drinking water samples collected from commercial bottled water and membrane filtration water, respectively. The MPs sizes of 0-50 µm were most abundant in both the tap and drinking water samples whose shapes were mainly fragments and fiber. These data indicated potential health risks to the people who consume these water sources, and recommendations for health impacts minimization were proposed.

In order to alleviate the conflict between populations and land-resource, Tianjin has adopted multi-phase reclamation projects to formed large-scale artificial reclamation land. However, the reclamation areas are susceptible to subsidence, which demonstrate a serious threat to infrastructure and people’s lives and property. The SBAS-InSAR was used to acquired surface deformation of Tianjin Binhai New Area from January 2017 year to December 2022 year, analyzed in depth the response relationship between land subsidence and reclamation projects time as well as the land use type. The results show that the Lingang Industrial Zone was the earliest to be reclaimed, with extensive reclamation completed by 2016 year, while Nangang Industrial Zone and Hangu Port started reclamation projects in 2009 year, some areas are still currently under construction. There is a strong correlation between surface deformation and reclamation time, the severe land subsidence occurred over newly reclaimed areas. Surface deformation gradually intensifies from west to east, the maximum surface settlement in Nangang Industrial Zone, Lingang Industrial Zone from the west to the east has changed from -50 mm to -890 mm,45 mm to -580 mm, respectively, reclamation area of Hangu Port with maximum surface deformation is -250 mm. Significant differences deformation among different land use types, which reclamation projects completed in the same time. Subsidence is positively correlated with surface load, in areas with higher surface loads, the surface settlement is also severer,the average surface settlement for the heavy shipyard, 67 grain storage tanks, 27 grain storage tanks, road, and bare land are -201 mm, -166 mm, -107 mm, -64 mm, and -43 mm, respectively. This study reveals significant differences of surface deformation in the reclamation completed at different times and the load is the main driving factor of settlement difference in the reclamation land completed at the same time. Which has important guiding significance for preventing and controlling geological disasters in the reclamation area and later development planning.

The notion that ‘Stock-G’ humpback whales off western South America are primarily deep oceanic migrators is poorly supported. We documented 66 land-based sightings from Pucusana, Lima Sur: 18 incidental (2002–2023) and 48 dedicated sightings during the northbound winter migration (June-July 2023), covering respectively 38 and 105 individual whales, despite a mean daily observer effort of only 58.7 min. Estimated distances from shore ranged 20–7,000 m (mean= 2,729 m) at depths 25–99 m, while 50% migrated at < 2,500 m (median) from shore. Several groups skirted the coastline from headland to headland. Of five southbound migrating groups, four included an adult with neonate; no northbound groups enclosed a neonate. Two cases of single juvenile/subadult whales moving multidirectionally and closely associated with large seabird feeding frenzies, were considered foraging also. In 2023, 94% of all northbound nearshore sightings occurred from 14 June-11 July, signalling the maximum threat period for interactions with coastal fisheries off metropolitan Lima. If migration is temporally and spatially predictable, effective mitigating measures should be feasible.

A land cover map of two arctic catchments, nearby the Abisko Scientific Research Station, was obtained from a classification of a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. The random forests algorithm correctly identified 88% of polygon pixels reserved for testing. The developed workflow relied solely on open source software and acquired ground observations. Space organization was directed by the altitude as demonstrated by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 shows some trends of vegetation cover evolution in response to climate change in the considered area.

One of the issues facing modern society, whatever the socio-economic level of the communities involved, is the development of sustainable strategies in the management of sludge/biosolids. Today, it is imperative to replace solutions aimed at simply “disposing of” with those oriented towards “maximizing recovery benefits”. It is desirable that agricultural use remains the main option in sludge/biosolids management, but to ensure effective and safe agronomic benefits, correctly fulfill the legal requirements, and build stakeholder and public confidence, rigorous and sustainable procedures need to be estab-lished. The development of realistic and enforceable regulation is crucial as it represents the right bal-ance between the different aspects of a coordinated and effective management. Furthermore, it is to recognize that regulation needs to be supported by standardized character-ization procedures and guidelines of good practices, because well-defined procedures allow le-gal requirements to be correctly and uniformly met, thus ensuring reliable comparison of re-sults obtained under different conditions. In this article, main aspects to consider for a sustainable application of this management practice are discussed, together with the parameters that need to be evaluated for the characterization of sludge/biosolids, according to the various aspects related to the agricultural use.

Keywords: Kenaf; liquid cultivation; solid cultivation; in vitro propagation, life on land

Knowledge about ecological conditions and processes in centers of endemism (CoEs) is still limited with respect to various systematic groups of organisms, ecosystem types, ecological conditions and ecosystem functions. Endemics and CoEs play a prominent role in nature conservation monitoring and management, and in the organization of zoos, aquaria and botanical gardens. We review the meaning of different groups of organisms and indicators for the characterization of endemic-rich regions, the importance of heterogeneity in space, continuity in time, isolation and ex-situ management for the survival of endemic and threatened species. Currently, conversion of land cover and land use change are the most important causes of biodiversity decline and extinction risk of endemic and endangered species concentrated in CoEs. These are followed by climate change, including severe weather, and then natural processes such as volcanism, landslides or tsunamis. For conservation purposes the management of zoos, aquaria, botanical gardens, of regional land use, and social aspects of the diversity of endemics and CoEs have to be taken into account as well. We need better answers to questions about the relationship between ecoregions, CoEs and regional awareness of biodiversity, which is linked with human culture including the management of zoos, aquaria and botanical gardens, aesthetics, wellbeing, health and trade.

Over the last several decades, large wildfires are increasingly common across the United States causing disproportionate impact on forest health and function, human well-being, and economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011-2020) using a wide array of meteorological, vegetational, and topographical features in the Deep Neural Network model. A total of 4,538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43 % of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the climatological forcings, land cover, location, and elevation of the ecosystem. Overall, results may serve useful guide in managing landscapes under changing climate and disturbance regimes.

A land cover map of two arctic catchments, nearby the Abisko Scientific Research Station, was obtained from a classification of a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. The Random Forest algorithm correctly identified 83% of polygon pixels reserved for testing. The developed workflow relied solely on open source software and acquired ground observations. Space organization was directed by the altitude as shown by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 demonstrates some trends of vegetation cover evolution in response to climate change in the considered area. The potential applications in terms of permafrost modeling (hiperborea.omp.eu) are finally discussed.