Many ecosystems are being severely degraded, leading the United Nations to deem 2021-2030 as the Decade on Ecosystem Restoration. To be successful, this effort requires robust monitoring tools to assess land reclamation practices. Our study aimed to evaluate the quality of recovery efforts in mined areas by developing a Recovery Quality Index (RQI) based on soil and vegetation indicators. Using the heavily mined Iron Quadrangle region of Brazil as an example, we selected four local, undisturbed reference areas as restoration goals: Atlantic Forest (AF); ferruginous rupestrian grassland with dense vegetation (FRGD); ferruginous rupestrian grassland with sparse vegetation (FRGS); and quartzite rupestrian grassland (QRG). We also selected four areas that were directly or indirectly affected by mining, including an environmental compensation area set aside 5 years prior to the study (COMP-5), two sterile piles that had undergone recovery for 15 and 20 years (SP-20 and SP-15), and a cave area with 15 years of recovery (CAVE-15). The four recovery areas were grouped together with each individual reference area (making four combinations of sites), and measurements of 2 vegetation parameters and 34 soil attributes were used in a Principal Component Analysis (PCA) for each grouping. We determined the RQI for each group by summing weighted PCA scores for responsive indicators. Vegetative parameters had the lowest RQI weights in all four groups. Soil physical indicators tended to be the most important, except in AF, where chemical indicators were most relevant. RQI values were also lowest when AF was used as the reference, showing that the forest was a unique ecosystem, and the CAVE-15 site had lower RQI scores than the other restored sites, indicating the high degree of disturbance that occurred in that low-lying area. The SP-20 site tended to have higher RQI values than the SP-15, and similar values to the less disturbed COMP-5 areas, potentially indicating greater recovery of native soil properties during the longer recovery period. This RQI-based approach has excellent potential for robust assessment of the recovery of areas degraded by mining and can support decision-making during monitoring.

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine learning based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions namely; Iran, Mozambique, Sri-Lanka, and Sudan where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction , and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 to 0.98 across the four test regions at hand.

Fire is a common disturbance in the Brazilian savanna (Cerrado), wherein high-frequency fires drive the vegetation structure, composition, function, and dynamics of savanna ecosystems. Under climate change pressure, further understanding of fire-vegetation relationships and interactions can provide new approaches for establishing strategies for integrated fire management and promote savanna vegetation recovery post-fire. To understand how 15 years of yearly manipulated burning affects the vertical and horizontal structure of the vegetation, the species composition and diversity metrics (species richness, Shannon's diversity, and Pielou's evenness), and the aboveground carbon stocks, we surveyed all woody plant species with a diameter greater than three centimeters in 15 plots of a typical Brazilian savanna (cerrado stricto sensu) at an experimental research station in central Brazil (Cerrado biome). Fifteen plots (five per treatment) were differently affected by fire events over a decade, comprising three treatments: (i) annual fire, (ii) legacy fire (> 11 years since the last fire event), and control (not burned in the past 30 years). Non-metric multidimensional scaling (NMDS) analysis indicated a significant effect of fire on the species composition among treatments and that some species benefited from fire, such as Erythroxylum suberosum, whereas others propagated better without fire, such as Camisala montana and Dalbergia miscolobium. Over a decade of annual fire events led to decreases in Shannon's diversity, species richness, and stem density, which were significantly lower in the annual fire treatment than in the control and legacy fire treatments. Stem density by diameter and height size class (except for the 1-2 m class and above 8 m) was higher in the control than in the annual fire treatment, but the number of dead trees did not differ between the control and annual fire treatments. Our results also showed that fire was a factor in changes in the evaluated parameters, such as annual fire treatment, which reduced the amount of biomass and, therefore, the carbon stock. This study suggests that, if burned yearly, typical Brazilian savannas can become less biodiverse in terms of woody plant species and negatively affect their fire resilience. Therefore, fire management practices should focus on determining the frequency with which these ecosystems may benefit the most from fire disturbances.

This study analyzed the CO2 capture potential for Parque Natural Regional Serrania de las Quinchas buffer area in Colombia. For this purpose, multitemporal analysis for land covering for years 1989, 2000, 2006, 2011, 2017 and 2021 was made using the Normalized Difference Vegetation Index (NDVI), and Corine Land Cover (CLC) methodology. In the same way, Aboveground Biomass (AGB) was measured for representative parcels by measuring tree’s diameters and heights and applying the adequate allometric models; carbon content in soils was measured too. Results show that dense forest has grown since 1989 and the carbon stock in all the vegetation covers increased by 14,3% from 1989 until 2021, using literature data for tropical humid forests. If measured AGB values obtained in this study are used, the carbon stock average is 3 times this value. Measured carbon average content in AGB was 374,3 Ton C/Ha, well above the average for the content reported for humid tropical forests in Colombia; carbon content in soils was 897,19 Ton/Ha, 2,4 times that found in the AGB. In total, an average of 1271,49 Ton C/Ha (AGB+ soil) was found and a pattern of dense forest recovery was found, but in a more dispersed way than in the past. A very interesting potential for existing forests recovery was found for this area. Strategies for this include the development of sustainable practices, land use management, everything pursuing biodiversity preservation and the participation and leading of the local communities.

Due to the intensification of climate change in the world, the incidence of natural disasters is increasing year by year, and monitoring, forecasting, and detecting evolution using satellite imaging technology is an important guide for remote sensing. This study aims to monitor the occurrence of fire disasters using Sentinel-2 satellite imaging technology, to determine the burned severity area with its classification and the recovery process for determining the extraordinary natural phenomena. The study area was sampled in the southeastern part of Mongolia, where have most wildfires in each year, near the Shiliin Bogd mountain in the natural steppe zone and in Bayan-Uul soum in the forest-steppe natural zone. For the methods, the NBR was used to map the area of ​​the fire site and the classification of the burned area into 5 categories: unburned, low, moderate-low, moderate-high, and high, which are process-defined works. NDVI index was used to determine the recovery process in a timely series in the summer from April to October. In result, the burned areas were mapped from the satellite images, and the total burned area of steppe natural zone was 1164.27 km2, of which 757.34 km2 (65.00 percent) was low, 404.57 km2 (34.70 percent) was moderate-low, and remaining 2.36 km2 (0.30 percent) was moderate-high, and the total burned area of forest-steppe natural zone was 588,35 km2, of which 158.75 km2 (26.90 percent) was low, 297.75 km2 (50.61 percent) was moderate-low, 131.25 km2 (22.31 percent) was moderate-high and the remaining 0.60 km2 (0.10 percent) was high-medium. Finally, we believe that this research is most important to helpful for emergency workers, researchers, and environmental specialists.

Exploring the relationship between cereal yield and remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression method is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS NDVI data.

The assessment of ecosystem quality and the maintenance of optimal ecosystem function requires understanding vegetation area dynamics and its relationship with climate variables. This study aims to detect vegetation area changes over a downstream dam and to understand the influence of the dam as well as climatic variables on the region’s vegetation areas. The case study is located in an arid area with an average rainfall amount of 50 to 100 mm/year. An analysis of seasonal changes in vegetation areas was conducted using the Normalized Difference Vegetation Index (NDVI), and supervised image classification was used to evaluate changes in vegetation areas using Landsat imagery. Pearson correlation and multivariate linear regression were used to assess the response of local vegetation areas to both hydrologic changes due to dam construction and climate variability. The NDVI analysis reveals a considerable vegetation decline after the dam construction in the dry season. This is primarily associated with the impoundment of the seasonal water by the dam and the increase in cropland areas due to dam irrigation. A significantly stronger correlation between vegetation changes and precipitation and temperature variations is observed before the dam construction. Furthermore, multivariant linear regression was used to evaluate the variations of equivalent water thickness (EWT), climate data, and NDVI before and after the dam construction. The results suggest that 85 percent of the variability in the mean NDVI was driven by climate variables and EWT before the dam construction. On the other hand, it was found that only 42 percent of the variations in NDVI were driven by climate variables and EWT after the dam construction for both dry and wet seasons.

Land Use /Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key among the past changes is the loss of natural vegetation related to changes in different Land Use/Land Cover (LULC) categories, e.g., water bodies, wetland and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in the Africa continent. Here we applied an open and synergistic framework to update and improve the existing LULC maps at every five years intervals from 1990 to 2020 in West Africa at a finer spatial resolution of 30m. Next, we detected spatial temporal pattern in past and present LULC changes with the intensity analysis framework in the periods of 1990–2000, 2000–2010, and 2010–2020. A faster annual rate of overall transition was detected in 1990–2000 (first period) and 2010–2020 (recent period) than in 2000–2010 (middle). We observed consistent gains in rangeland (shrubland and grassland) in all the periods, which confirms the observed re-greening of rangeland in the West Africa continent. By contrast, forestland areas experienced consistent losses for the entire periods, which indicate deforestation and degradation. As regards cropland, a net loss was observed in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all the periods. We observed net gains in water bodies in 1990–2000 and net losses in the last period. We highlighted the active forestland losses as systematic and hence a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concern and must send an early warning signal to policy makers that the food security of marginal geographic locations is under threat despite the massive expansion of cropland observed in this study area. The intensity analysis framework was vital to identify the settlement category as relevant for LULC change management intervention across time and space in West Africa instead of focusing on dynamics of all the LULC categories that may be irrelevant and hence a cost effective LULC change management approach. Policies to control the expansion of settlements will indirectly regulate the expansion of cropland field because we observed that as settlement encroached on cropland, simultaneously cropland encroached on natural vegetation to meet the food security requirements of the inhabitants.

In the presence of aquatic plants in streams, a variety of flow structures have been reported. Re-sults of earlier investigations indicated that the hydrodynamic features of flows in rivers, and streams are influenced by a wide range of submerged and non-submerged vegetation. In the pre-sent study, eight experiment runs were conducted in two artificial pools built in a laboratory flume to assess the flow structures across vegetation cover. The slopes for entrance and exit sec-tions of these two pools are of 5 and 10 degrees, respectively. A patch of artificial grass covered the entire pool area over the gravel bed. The effect of stream flow velocity was investigated by considering two aspect ratios of 2 and 2.7. Also, distributions of flow velocity, Reynolds normal and shear stresses, turbulence intensities, turbulent kinetic energy (TKE), and higher-order mo-ments of velocity fluctuations have been investigated. By means of quadrant analysis, it is found that the skewness coefficients of velocity profiles are significant parameters to evaluate. Based on skewness values for the bursting processes, along the pool entrance section with a smaller slope, the contribution of sweep motion occurs in a more restricted zone above the canopy, and this im-plies that the ejection motion occurs in a pool entrance section with a higher slope. Immediately above the vegetation canopy, the dominant process is the sweep motion of bursting events. Whereas, at the upstream boundary of the vegetation patch, an outward motion with slightly pos-itive value occurs; and around the downstream boundary of the vegetation patch, the isotropic turbulence appears at the boundary of the vegetation and gravel bed. Over the vegetation canopy in the pool, less mixing eddies result in lower Reynolds shear stress values, and thus the turbulent flow becomes weak with lower turbulent kinetic energy (TKE). Because of the shallow flow in an artificial channel with an aspect ratio less than five and the availability of vertical non-zero ve-locity components, secondary currents become stronger in the channel. Consequently, there is a divergence from a linear dispersion of Reynolds shear stress.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with > 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by >1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

The flora and vegetation of oceanic islands have been deeply affected by human settlement and further landscape modifications during prehistoric and historical times. The study of these transformations is of interest not only for understanding how current island biotas and ecological communities have been shaped but also for informing biodiversity and ecosystem conservation. This paper compares two oceanic insular entities of disparate geographical, environmental, biological, historical and cultural characteristics – Rapa Nui (Pacific Ocean) and the Azores Islands (Atlantic Ocean) – in terms of human settlement and further landscape anthropization. The similarities and differences between these islands/archipelagos are discussed considering their permanent colonization, the possibility of earlier settlements, the removal of the original forests and the further landscape transformations leading to either full floristic/vegetational degradation (Rapa Nui) or major replacement (Azores). This comparison uses evidence from varied disciplines, notably paleoecology, archaeology, anthropology and history, to obtain a holistic view of the development of the respective socioeconomic systems from a human ecodynamic perspective. The most relevant issues still to be resolved are identified and some prospects for future research are suggested. The cases of Rapa Nui and Azores Islands may help set a conceptual basis for ocean-wide global comparisons among oceanic islands/archipelagos.

Throughout the world, salinity is a major environmental issue that limits agricultural productivity, particularly in arid and semi-arid regions. In addition, climate change is the most important reason for the salinization of agricultural soils in the world, so it is now essential to find solutions to increase salinity tolerance in plants. This study investigated the potential of arbuscular mycorrhizal fungi (AMF) inoculation to enhance the growth and yield performances of flax under different salinity levels by conducting a pot experiment. The experiment was laid out in a two-factor completely randomized design including AMF inoculation (AMF+: with and AMF-: without inoculation) and irrigation water salinity (0, 50, 100, and 150 mM NaCl). According to the results, it is evident that salt stress caused negative physiological effects, including limited growth, reduced photosynthesis, and decreased nitrogen (N) and phosphorus (P) content in shoots and roots of flax plants. Moreover, mycorrhizal association improved the salt tolerance of the plants by increasing chlorophyll content, enhancing N and P shoot and root contents, and consequently yield parameters, such as seed, and stem fiber yield, particularly at moderate salt concentrations (50 and 100 mM NaCl). As a result of using AMF, flax plants grown under salt stress exhibited tolerance, suggesting that AMF could be applied in saline environments to maintain ecological stability.

Drought poses a significant environmental risk and can deeply affect the growth of grasslands. However, there is still uncertainty regarding the precise impact of varying levels of drought on grassland growth. To address this gap, we utilized several key indicators, including the Nor-malized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), the Global Or-biting Carbon Observatory-2 based Solar-induced Chlorophyll Fluorescence (GOSIF), and the Gross Primary Productivity (GPP), in conjunction with drought indices (the Standardized Precip-itation Evapotranspiration Index (SPEI) and the soil moisture (SM). Our study aimed to compre-hensively assess the consistency of spatio-temporal patterns in grassland vegetation and its re-sponsiveness to different drought levels in the Inner Mongolia region from 2002 to 2020. Results indicated that NDVI, EVI, GOSIF, and GPP in grassland vegetation across Inner Mongolia exhibited significant increasing trends from 2002 to 2020. Specifically, NDVI, EVI, GOSIF, and GPP all dis-played consistent spatial patterns, with 25.83%, 21.18%, 22.65% and 48.13% of the grassland area showing significant increases, respectively. Drought events, as described by SPEI and SM, in June 2007 to September 2007 and June 2017 to July 2017, were selected to evaluated the response of grassland vegetation to drought. The drought events of 2007 and 2017 resulted in reductions in NDVI, EVI, GOSIF, and GPP relative to multi-year average (2002-2020). GOSIF exhibited a more intense response to drought, suggesting that GOSIF may reflect the inhibition of water stress on grassland photosynthesis better than NDVI and EVI during drought in 2007 and 2017. The reduc-tions in NDVI, EVI, GOSIF and GPP in grassland increased significantly across different drought-levels, with the sharpest reductions observed during extreme drought. Under the severe and extreme drought events, the most substantial reductions in NDVI, EVI, GOSIF, and GPP were observed in the temperate steppe (TS). Moreover, the effects of different drought severity levels within the same grassland type varied, with the most significant reductions in NDVI, EVI, GOSIF and GPP observed during extreme drought. Our results provide new perspectives for developing and implementing effective strategies to address grassland carbon cycling management and climate change in Inner Mongolia.

Remotely sensed data acquired by multispectral sensors can be used to monitor soil moisture (SM) across a larger land area than in situ monitoring alone. Although there have been wide-ranging applications of remote sensing tools in SM estimation on many ecosystems, there is a limited understanding of their accuracy in restored wetlands. The objective of this study was to examine the potential of remotely sensed data from Landsat-9, Sentinel-1A SAR, and Blackswift E2 Uncrewed Aircraft Systems (UAS) in predicting SM in a restored wetland complex in the Gunnison Basin of Colorado. We tested two response variables, gravimetric SM and volumetric SM, to determine which indicator of SM was better predicted with remotely sensed data. We also tested the accuracy of remotely sensed data in predicting SM at different soil depths. Overall, satellite and UAS indices predicted gravimetric SM better than volumetric SM. The Normalized Difference Red Edge (NDRE) index from Blackswift E2 UAS had the best prediction of GSM at the depth of 0 to 5 cm (R2 = 0.86, RMSE = 7.41). Results from the study provide information on the accuracy of remotely sensed data for SM monitoring on restored wetlands.

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional sources of information, e.g. remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forests state. In this work, we combine the new generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results. For the majority of the forest stands, we generated good reflectances with all levels forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R²=0.62). This work provides a forward modelling tool for relating forest reflectance to forest characteristics. With this tool it is possible to generate a large set of forest stands with corresponding reflectances. This opens the possibility to understand how reflectance is related to succession and different forest conditions.

This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.

Vegetation identification using remote sensing is essential for understanding terrestrial ecosystems and monitoring climate change. This technology by providing high-resolution images of the Earth's surface from satellites, aircraft, and drones has revolutionized the way scientists study and un-derstand vegetation patterns, providing information on the distribution, structure, and health of plant communities in different regions and biomes. However, identifying vegetation in multi and hyperspectral images remains a difficult, time-consuming, and costly process. Traditional methods for vegetation mapping involve the in-terpretation of spectral indices such as the Normalized Difference Vegetation Index (NDVI), alt-hough they often suffer from limitations such as manual interpretation and limited spatial resolution of satellite images. Furthermore, machine learning techniques, artificial neural networks, decision trees, support vector machines, and random forests have shown promising results in identifying and classifying vegetation cover from hyperspectral images. However, due to the need for per-son-nel with specialized expertise, their application on a large scale is still challenging. On the other hand, correlation methods between curves have emerged as powerful and simplified tool for comparing spectral signatures of hyperspectral image pixels and efficiently classifying vegetation in a study area. Pearson's correlation coefficient and spectral angle mapper are among the most common correlation methods used in vegetation mapping. These correlation methods, among many others available, offer an easily implemented and computationally efficient approach, making them particularly useful for applications in developing countries or regions with limited resources. In this article, a comparative study of correlation/distance metrics was conducted for the detection of vegetation pixels in hyperspectral images. The study allowed for the comparison of five distance and/or correlation metrics: direct correlation, cosine similarity, normalized Euclidean distance, Bray-Curtis’s distance, and Pearson correlation. The two metrics that yielded the best accuracy results in vegetation pixel detection were direct correlation and Pearson correlation. Based on the selected methods, a vegetation detection algorithm was implemented and validated on a hyper-spectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library was utilized for image processing, while the numpy and scipy libraries in the Python programming language were used for the mathematical calculation of correlations. Both the study's approach and the implemented algorithm aim to serve as a reference for conducting detection studies of various material types in hyperspectral images using open-access programming platforms.

Enterococcus faecalis is a bacterial pathogen that can cause opportunistic infections. Studies indicate that initial biofilm formation plays a crucial regulatory role in these infections, as well as in colonising and maintaining the gastrointestinal tract as a commensal member of the microbiome of most land animals. It has long been thought that vegetation of endocarditis resulting from bacterial attachment to the endocardial endothelium requires some pre-existing tissue damage, and in animal models of experimental endocarditis, mechanical valve damage is typically induced by cardiac catheterisation preceding infection. This section reviews historical and contemporary animal model studies that demonstrate E. faecalis ability to colonise the undamaged endovascular endothelial surface directly and produce robust microcolony biofilms encapsulated within a bacterially derived extracellular matrix. This report reviews both previous and current animal model studies demonstrating the resilient capacity of E. faecalis to colonise the undamaged endovascular endothelial surface directly and produce robust microcolony biofilms encapsulated in a bacterially derived extracellular matrix. The article also considers the morphological similarities when these biofilms develop on different host sites, for example when E. faecalis colonises the gastrointestinal epithelium as a commensal member of the common vertebrate microbiome, lurking in plain sight and transmitting systemic infection. These phenotypes may enable the organism to survive as an unrecognised infection in asymptomatic subjects, providing an infectious resource for subsequent clinical process of endocarditis.

Synanthropic flora (weeds, spontaneously developed plants) are plants that accompany humans, appearing as a result of anthropopressure as well as after its cessation. The potential of synanthropic flora in shaping green areas usually brings many measurable benefits that depend on land management in cities. The study aimed to determine the preferences and attitudes of adults towards synanthropic flora in settlement units. The article presents survey results from 447 city residents. The obtained results were statistically analyzed using the k-means clustering method to identify segments of respondents with similar attitudes and preferences toward synanthropic flora. Mann-Whitney U tests were employed to determine statistical differences in the demographic variables among the analyzed clusters of respondents. Additionally, multiple regression analysis was performed to identify the opinions and attitudes towards synanthropic flora, that may influence their greater acceptance in the vicinity of residential areas. The results obtained in multiple regression allow for the statement that the respondent’s evaluation of the level of support for the introduction of more synanthropic vegetation into urban greenery (Yw) grows with the increase of acceptance of synanthropic plants occurrence in surroundings. Respondents’ opinions on the subject of the presence of synanthropic plants were studied using such variables concerning: synanthropic vegetation found in turfgrasses has a positive impact on the visual attractiveness of a site (X1), synanthropic plant communities present themselves better than others (X2), presence of synanthropic vegetation in an estate improves the quality of life in that location (X3), introduction of synanthropic flora into estate arrangements is a positive (X4), support for the use of synanthropic flora for educational purposes - as elements of playgrounds or in the form of environmental workshops (X5). Residents’ acceptance of synanthropic flora in green areas is at a relatively high level.

Accurate and robust methods are needed to monitor crop growth and predict grain yield and quality in breeding programs under variable agrometeorological conditions. Field experiments were conducted during two successive cropping seasons (2021, 2022) at four trial locations (Estonia, Latvia, Lithuania, Norway). The focus was on assessment of grain yield (GY), grain protein content (GPC), and UAV-derived NDVI measured at different plant growth stages. Performance and stability of 16 selected spring wheat genotypes were assessed under two N application rates (75, 150 kg N ha−1) and over contrasted agrometeorological conditions were assessed, and the quantitative relationships between agronomic traits and UAV-derived variables were figured out. None of the traits were subject to a significant (p<0.05) genotype by nitrogen interaction. High-yielding and high-protein genotypes were detected with high WAASB stability, specifically under high and low N rates. This study highlights the significant effect of NDVI analysis on growth stages GS55 and GS75 as key linear predictors, especially in the context of spring wheat GY. However, the effect of these indices depends on the specific growing conditions in the respective locations, thus limiting their universal utility.

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.

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

Indonesia is a mega biodiversity country that has a very abundant diversity of plants. Plant diversity is inseparable from the role of insects that help to pollinate them. One such insect is the stingless bee (Tetragonula laeviceps). Tetragonula laeviceps is a bee that has a relatively small-to-medium body size and does not have a sting. Sumedang Regency is one of the regencies in the province of West Java; most of the Sumedang area consists of mountains and has protected forest areas with high biodiversity. The purpose of this study is to determine the biodiversity index of the vegetation around the Tetragonula laeviceps wild hives based on the composition and structure of the vegetation around the Tetragonula laeviceps bee hives. Data collection is carried out using the method of inventory and systematic checkered lines, with a total plot of 320. The results of the study show that the vegetation is composed of 229 plant species from 75 families. The most dominant vegetation type with the highest value of KR, FR, DR, and INP in all plant categories is the Aren tree species (Arenga pinnata). The Shannon–Wiener diversity index in this tree category is 3,186, the pole category is 3,107, the sapling category is 3,418, the seedling category is 3,657, and the understory plants category is 3,409, with all of the plant categories included in the high-diversity category.

The response of boreal vegetation to global warming has shown a weakening trend over the last three decades. However, in previous studies, models of vegetation activity responses to temperature change have often only considered changes in mean daily temperature (Tmean), with the diurnal temperature range (DTR) being neglected. The goal of this study is to evaluate the temporal trends of the relationships between two temperature factors (Tmean and DTR) and vegetation activity across the boreal regions on both annual and seasonal timescales, by simultaneously employing satellite and climate datasets. We found that the interannual partial correlation between growing season (GS) NDVI and Tmean (RNDVI−Tmean) showed a significant decreasing trend over the last 34 years. At the seasonal scale, RNDVI−Tmean shows a significant upward trend in the spring, while in the summer and autumn, RNDVI−Tmean exhibits a significant downward trend. The temporal trend characteristics of the partial correlation between NDVI and DTR (RNDVI−DTR), at both the GS and seasonal scales, are fully consistent with RNDVI−Tmean. The area with a significant decrease in GS RNDVI−Tmean and RNDVI−DTR accounted for approximately 44.40% and 41.21% of the boreal region with the 17-year moving window, respectively. In stark contrast, the area exhibiting a significant increasing trend in GS RNDVI−Tmean and RNDVI−DTR accounted for only approximately 14.65% and 13.59% of the boreal region with the 17-year moving window, respectively. With respect to the seasonal patterns of RNDVI−Tmean and RNDVI−DTR, the area with a significant upward trend in spring was greater than that with a significant downward trend. Nevertheless, more areas had a significant downward trend of RNDVI−Tmean and RNDVI−DTR in summer and autumn than had a significant upward trend. Overall, our research reveals a weakening trend in the impact of temperature on vegetation activity in the boreal regions, and contributes to a deeper understanding of the vegetation response to global warming.

The coastal wetlands of the Mediterranean area are among the most vulnerable and threatened environments due to human pressure and activities. Fragmentation is generally considered as one of the most driving factors in habitats loss. In this paper, we investigated the floristic composition, structure, conservation status, and trends of the habitat types sensu 92/43EEC Directive of a coastal wetland area in south-eastern Sicily (“Saline di Priolo” SAC). Based on 128 phytosociological surveys and several plant collections, a total of 304 taxa, 28 plant communities and 16 habitats have been identified. Furthermore, three new plant associations were described, including two in the wetland and one on the rocky coasts. For the classification of plant communities and habitats, a hierarchical clustering was performed by using Euclidean coefficient and beta-flexible algorithm. The life form spectrum of the current flora highlights the dominance of therophytes and hemicryptophytes. The Mediterranean species are largely prevailing with 123 taxa. The cartographic analysis performed with ArcGis 10.3 shows a radical reduction of the wetland habitats in the last 70 years, and a strong alteration of the ecological succession of the psammophilous-hygrophilous vegetation. Moreover, landscape configuration of the coastal vegetation and habitat types was well highlighted by a set of specific landscape metrics. In particular, our outcomes identify three habitats (2110, 2210, and 5220* EU code) with bad conservation status, among which one of priority conservation (Zyziphus arborescent matorral), that requires urgent restoration measures.

The goods and services produced by forest vegetation ecosystem are the driving force for the rapid, high-quality and sustainable development of human society. In history, with the increase of population and economic development, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services in a country or a region. To meet the demands, most of the original forest has become farmland, plantation and grass and crops land. As a result, the plant water relationship changed from equilibrium to non-equilibrium, which easily led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the question and realize the sustainable utilization of soil water resources and the high-quality sustainable development of forest, it is necessary to apply the limit theory of plant utilization of soil water resources and the vegetation carrying capacity theory in the critical period of plant-water relationship regulation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit and realize high-quality and sustainable development.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in recent 20 years.Evaluating climate and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trend in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2\% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1\% of the urban built-up area, while vegetation greening occurred in the rest part. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing mountain areas through correlation analysis. Human activities have significantly promoted the vegetation growing in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing.

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

Many papers in recent years have been devoted to estimating the per pixel proportions of three broad classes of materials (e.g. photosynthetic vegetation, non-photosynthetic vegetation and bare soil) using data from multispectral sensors. Many of these papers use estimation methods based on the linear mixture model. Very few of these papers assess the accuracy of their estimators. I show how to produce confidence intervals (CIs) and joint confidence regions (JCRs) for the proportions associated with various linear mixture models. There are two main models, both of which assume that the coefficients in the model are non-negative. The first model assumes that the coefficients sum to 1. The second does not, but uses rescaling of the estimated coefficients to produce estimated proportions. Three variants of these two models are also analysed. JCRs are shown to be particularly informative, because they are typically better at localising the information than CIs are. The methodology is illustrated using examples from Landsat Thematic Mapper data at 1169 locations across Australia, each of which has associated field observations. There is also discussion about the extent to which the methodology can be extended to hyperspectral data.

UAV-captured multispectral imagery was used to characterize and associate Moriche’s palm 1 canopy features with the maturity stage of the corresponding fruits. Deep learning models based on 2 convolutional neural networks (CNN) were trained in order to determine correlations between the 3 photosynthetic radiation of the palms with the fruit. Here, we compare several approaches for feature 4 extraction based on vegetation indices and graph-based models. Also, a comprehensive dataset has 5 been collected and labeled, containing plant data for an entire phenological cycle of the Moriche 6 palms. Experimental results reported an average estimation accuracy of 72%, by using the proposed 7 method in dense forests of the Amazonian region.

Coastal lagoons are dynamic transitional water ecosystems hosting valuable biological communities, including rich and diverse macrophyte assemblages. Aquatic macrophytes must cope with large fluctuations of environmental conditions on a spatial and seasonal scale. Salinity is one of the most variable parameters, changing from nearly freshwater to hypersalinity, and it is known to have a strong influence on the composition and structure of macrophyte assemblages. This study is focused on the effect of salinity on macrophyte communities of the eight most important coastal lagoons of Apulia (south-eastern Mediterranean Sea). A set of eleven transitional water body types (sensu Water Framework Directive) were allocated in a range of meso- to hyperhaline lagoons. Macrophyte sampling was carried out between 2011 and 2019 and a total of 324 samples (18 sampling stations x 2 seasons x 9 years) was analyzed. Then, macrophyte occurrence in each transitional water body (T-WB) was expressed as frequency values (%) and assemblages were compared to assess any similarity in relation to four salinity classes (mesohaline, polyhaline, euhaline, hyperhaline). Species richness varied according to the salinity class, being much higher in polyhaline and euhaline T-WBs and strongly decreasing at the extremes of salinity range (mesohaline and hyperhaline T-WBs). Moreover, statistical analysis showed a high resemblance of macrophyte assemblages of T-WBs within the same salinity class, which shared a great number of species. Four distinct macrophyte communities were distinguished, reflecting the salinity conditions of different T-WB types and confirming the effectiveness of a lagoon typology based on this descriptor.

Evaluating soil quality across various altitudinal gradients, both with and without vegetative cover, is crucial for the proper functioning of soil and optimal crop growth, thereby contributing to the sustainability of agroecosystems. Although the altitudinal gradient significantly influences soil quality, the ability to predict this quality, expressed through an index, in soils with and without vegetative cover, is still insufficiently explored. This study aimed to assess soil quality in the Jun Jun micro-watershed, located in the Tungurahua region, Ecuador. To achieve this objective, three categories of altitudinal gradients were selected: less than 2800 meters above sea level (masl), between 2800 and 2900 masl, and greater than 2900 masl. A total of 24 soil samples were collected in the mentioned gradients, considering both areas with and without vegetative cover. The indicators used included chemical and biological parameters such as soil organic carbon (OC), earthworm density (WD), earthworm biomass (WB), organic matter (OM), pH, total nitrogen (TN), and organic carbon (OC). The results revealed that in areas with altitudinal gradients below 2800 masl, soil quality index values were higher compared to other altitudes. In vegetated areas, a decrease in index values was observed as the altitudinal gradient increased, indicating a deterioration in soil quality with altitude. These findings are significant in providing a quantitative assessment of the effects of altitudinal gradient and vegetative cover influence on soil quality.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

The concept of ecosystem services (ESs) provides a valuable approach to communicate societies' dependence on natural ecosystems, assisting in the evaluation of measures to protect vegetation in watersheds that promote water-related ESs. Vegetation cover and land use are important factors related to the capacity of ecosystems to provide soil loss regulation and sediment retention services, which are highly relevant for sediment management in watersheds draining into reservoirs with multiple water uses. This study aims to assess the relationship between potential changes in land use and land cover and the sediment retention service provided by riparian vegetation in a watershed draining into a hydroelectric reservoir in the Brazilian Cerrado. Sediment load was selected as a physical variable that affects different water uses in reservoirs, with these uses being dependent on the quality or quantity of stored water. Scenarios were analyzed to represent landscape reconfigurations from a baseline condition in order to account for the recovery of riparian vegetation by the agricultural sector, as foreseen in the Brazilian Forest Code. To map and quantify variations in annual sediment retention, the InVEST model was used, which allows estimating an integrated response of the drainage basin to large-scale land use changes over time and space. The results show that for the drainage basin of the Corumbá IV Hydroelectric Power Plant (HPP), the recovery of riparian areas occupied by annual crops has the potential to generate greater benefits to water use in the reservoir. In the studied catchment area, 14% of the total vegetation deficit in riparian areas is occupied by crops, while 86% is occupied by pastures. This highlights the buffering role of riparian vegetation as an effective practice for managing sediment flows originating from sheet erosion in agricultural landscapes. The methodology employed in this study allows for the quantification and mapping of the effects of soil and water conservation measures in river basins. This enables the prioritization of sectoral efforts for the restoration of native vegetation, considering the highest returns in benefits perceived by water users affected by sediment input in reservoirs. The study's results reinforce the importance of conserving vegetation in riparian areas and their surroundings for sediment retention, highlighting the role of these areas as assets in providing water-related ecosystem services.

: Kentucky is one of the largest coal producing states and surface coal mining has led to changes in natural land cover, soil loss, and water quality. This study explored relationships between actively mined and reclaimed areas and vegetation change and water quality parameters. The study site evaluated 58 watersheds with Landsat-derived variables (reclamation age, mining percentage, reclaimed forest percentage, and reclaimed woods percentage) as well as topographic variables (such as elevation, slope, drainage density, and infiltration). Water samples were collected in spring (n=9), summer (n=14), and fall (n=58) 2017 to study changes in water quality variables (SO42-, alkalinity, conductivity, Ca2+, Mg2+, Mn2+, Al3+, and Fe2+,Fe3+) in response to changes in land cover. Pearson correlation analyses indicated conductivity has strong to very strong relationships with other variables of water quality variables related to coal mining (except Al3+, Fe2+,Fe3+, Mn2+, elevation, slope, and drainage density) and land cover variables. In addition, separate multivariate regression analyses were performed with conductivity values based on samples collected in the fall. First, conductivity response to mining percentage, reclamation age and topographic variables were examined (adjusted R2=0.818, p < 0.01). Next, vegetation cover change parameters were added to the same model, which yielded slightly improved R2 (adjusted R2 = 0.826, p < 0.01). Finally, reclamation age and mining percentage were used to explain the quantity of reclaimed forested areas as a percentage of watersheds. The model was significant (p < 0.01) with an adjusted R2 value of 0.641. Results suggest that the quantity (area as a percentage) of reclaimed forests may be a predictor of mining percentage and reclamation age. This study indicated that conductivity is a predictable water quality indicator that is highly associated with Coal Mine Related Stream Chemistry in areas where agriculture and urban development are limited. These findings may help the scientific community and key state and federal agencies improve their understanding of water quality attributes in watersheds affected by coal mining as well as refining land reclamation practices.

The proposed research study explore the decision tool for cropwise variability of NDVI using RS techniques and GIS tool as a crucial indicators for monitoring vegetation status.A small case study for Parbhani district of Maharashtra, India was conducted using Landsat-9 satellite images of the Rabi season in the year 2022-23 were processed using ArcGIS 10.7 software to derive the NDVI values. During the analysis, the maximum NDVI in the study area was recorded at 0.5857 on February 03, 2023, while the minimum was observed as -0.1018 on February 19, 2023. For specific crops, the average NDVI ranged from 0.1183 to 0.3189 for wheat, 0.1679 to 0.2891 for jowar, and 0.1163 to 0.3367 for gram. The mean NDVI values for wheat, jowar, and gram crops during the Rabi season of 2022-23 were 0.218, 0.219, and 0.217, respectively.Furthermore, the average NDVI values exhibited an increasing from the initial stage to the mid-crop growth stage, followed by a decrease during the end-season stage for wheat, jowar, and gram crops. Interestingly, the mean NDVI values of major Rabi crops, wheat, jowar, and gram for the year 2022-23, is approximately similar to the mean NDVI values observed for of year 2022-23.

In this article, a method for the moisture mapping of the soil surface of agrophytocenosis was proposed using neural network based on synchronized radar and multispectral optoelectronic data of Sentinel-1,2. To verify the developed method, data from two experimental plots were used. These plots were located two on irrigated soybean crops. The first of them was located on the right bank (1st plot) and the second one on the left bank (2nd plot) of the down part of Volga River. Two experimental soil moisture geo-datasets were done by measurements and geo-referencing points using gravimetric method (1st plot) and with proximal sensing method (2nd plot) using Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict reflection coefficient of an electromagnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated on the basis of Sentinel-1 data) to the index (calculated on the basis of data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). Such way calculated index reveals showed a significantly greater dependence on soil moisture than on vegetation height that was been used in previous studies. The retrieved values of soil moisture were compared with the soil moisture measured in-situ. The proposed method with a determination coefficient of 0.44-0.65 and a standard deviation of 2.4%-4.2% for the 1st plot as well as with and of the same metrics for the 2nd allows predicting the soil moisture of both a test plots covered by soybean plants, relative to soil moisture measured in-situ. The conducted research created the scientific basis for a new technology for remote sensing the moisture content of the soil surface of agrophytocenosis as an element of the precision farming system and agroecology.

The purpose of this paper is to develop a phytosociological, ecological, cytogenetic, eco-protective and economic study of the vegetation of the wetland ecosystems in the Vlădeasa Massif built by the phytocoenoses of the association Carici echinatae-Sphagnetum (Balázs 1942) Soó 1955. In order to attain the aim and objectives put forward we carried out 10 phytocoenological surveys (surveys) in the phytocoenoses of the ecosystems during the optimal vegetation periods related to the summer-serotinal season in the timeframe 2020-2021. The taxonomically inventoried species were included in an association table by the criteria of their belonging to the basic coenotaxa of the association, alliance, order, vegetation class. The outcomes of the phytocoenosis research of the vegetation of the wetland ecosystems were processed, analysed, interpreted based on tables, histograms, diagrams, regarding the numerical and percentage weight of the species in the ecological categories of bioforms, phytogeographic elements (geoelements) and cytogenetic elements. The ecological behaviour of the species confined in ecosystems was also analysed by their relationship with ecological factors, edaphic soil moisture, air temperature and chemical reaction of the soil. The current state of ecosystems, potential threats, sustainable conservation of biodiversity, dynamics of phytocenoses, economic and scientific relevance were also subjected to research. The results obtained in the surveyed territory were analysed and compared numerically and as percentage with the data provided by two reference scientific works belonging to authors who independently carried out research with a similar topic in two different geographical regions of the Western Carpathians.

Weeds can have both positive and negative effects on agricultural environments. However, despite the growing interest in the ecology of weed communities in agricultural areas, few studies have been carried out in the northern region of the Andes of Colombia, where urban and agricultural expansion have gen-erated highly disturbed scenarios. The aim of this study is to analyze the diversity of vegetation and weed seed banks in three agricultural production systems and a forest ecosystem in the northern Andes of Colombia. Hill numbers were used to compare diversity, Beta diversity to assess changes in composition, and range-abundance-dominance curves at different sites. Likewise, indicator species were analyzed to find species associations to each system. The results revealed differences in the composition of weeds between the forest ecosystem and the agricultural production systems, with higher equitability in the forest ecosystem and higher dominance in agricultural systems. A higher differentiation was observed in the dominant species of each agricultural system, highlighting those considered pests due to their life history traits, giving them greater success against various anthropogenic selection pressures. These findings highlight the impact of anthropogenic disturbances on the ecological dynamics of weed com-munities in different ecosystems, which should be considered when planning integrated weed manage-ment techniques.

Grazing exclusion (GE) and rest grazing (RG) are important management to restore grassland ecosystems. In order to evaluate the effects and mechanisms of different grazing management on species diversity, vegetation community indices and soil variables were determined in the plots of Qilian Mountains in Gansu Province. The results showed that diversity effects and their regulating mechanisms had space-scale dependence under different grazing management. Species richness and species diversity indices of RG in grassland were significantly higher than that of GE at the regional scale. Additionally, three grazing management in mountain meadows had only a significant effect on species richness, but different management in mountain meadows and temperate steppes had a significantly different effect on species diversity indices. Meanwhile, soil variables only influenced species diversity at the regional scale. Most of community and soil variables at each scale had positive effects on species diversity, except that the direction of biodiversity effects was negative for species coverage, mean plant height, soil porosity (SK) and bulk density (BD) under two contrasting grazing management. In conclusion, choosing RG at the regional scale and select grazing management according to different grassland types at the local scale to restore degraded grassland vegetation.

This paper investigates the influence of different vegetation on the permeability of the shallow soil layers of slopes under rainfall infiltration. Firstly, four large slopes are filled in the outdoor natural environment, and the overburdens of the four slopes are Magnolia multiflora, Cynodon dactylon, Magnolia multiflora mixed with Cynodon dactylon, and no vegetation. Secondly, the four slopes are cultivated in the outdoor natural environment for one year. After the vegetation overburdens are matured, the field artificial rainfall test is carried out through the self-developed artificial rainfall device to monitor the water migration law inside the four slopes in real time. Finally, the unsaturated permeability coefficients of the shallow soil layers of slopes are calculated. The results show that the infiltration rate of rainwater in each overburden slope from fast to slow is Magnolia multiflora overburden slope, no vegetation slope, Cynodon dactylon overburden slope, Magnolia multiflora mixed with Cynodon dactylon overburden slope. In the early stage of rainfall, Magnolia multiflora increases the permeability coefficient of the shallow soil layer of the slope, thus weakening the anti-seepage ability of the slope, but the influence of Magnolia multiflora is not obvious in the later stage. Cynodon dactylon and Magnolia multiflora mixed with Cynodon dactylon can significantly reduce the permeability coefficient of the shallow soil layers of the slopes, thereby increasing the anti-seepage ability of the slopes, and the mixed planting of Magnolia multiflora and Cynodon dactylon can minimize the permeability coefficient of the shallow soil of the slope, resulting in the best anti-seepage effect.

The Mara River basin (MRB) has a world-famous ecosystem, but the vegetation has been damaged due to economic development in recent years, and there is little known about the area that will experience severe vegetation damage in the future. Based on the vegetation vulnerability system, principal component analysis, and three CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), vegetation vulnerability was calculated in the base period (2010-2019), near-term period (2020-2059) and long-term period (2060-2099) in the MRB. The spatial cluster of vegetation vulnerability was revealed by spatial correlation analysis, and the transition of vegetation vulnerability of different periods was analyzed by stochastic matrix. The results showed that in all periods, the vulnerability showed a high-high cluster in the east, and a low-low cluster midstream and downstream. From the stochastic matrix, the area of high vulnerability increased the least under the SSP1-2.6, while it increased the most under the SSP5-8.5. The vegetation vulnerability upstream increased the most from the base period to the near-term period and long-term period in the MRB. By comparing the vegetation vulnerability under different scenarios, and pointing out the areas with the highest vulnerability increase, this study can better provide comprehensive decision-making for vegetation protection in the MRB.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Coastal cliffs erode in response to short- and long-term environmental changes but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in 3-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face were compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.