Environmental and Earth Sciences

Abstract: Despite the global crisis of mangrove deforestation, certain sediment-dominated estuaries exhibit remarkable morphodynamic resilience. This study investigates the spatiotemporal trajectory of the Ujung Pangkah estuary (1995–2025) to quantify natural progradation against anthropogenic pressures. Utilizing Google Earth Engine (GEE), Landsat archives, and a Random Forest classifier enhanced with advanced spectral indices (NDVI, mNDWI, EVI, MVI), we rigorously mapped the estuarine landscape. Accuracy assessment using independent historical validation yielded an exceptional overall accuracy of 97.30% for 2025. The change dynamics analysis revealed an explosive natural recovery. While the ecosystem suffered a gross historical loss of 574.02 ha primarily due to aquaculture conversion, this was vastly offset by a massive seaward gain of 1,245.15 ha on accreted mudflats. The total mangrove extent expanded from 613.88 ha in 1995 to 1,285.01 ha in 2025. Non-parametric statistical evaluation confirmed a highly significant, continuous median expansion rate of 27.65 ha/year. Crucially, hydrodynamic driver analysis using a red-band proxy for Total Suspended Solids (TSS) empirically validated that this progradation is intrinsically sediment-driven, fueled by the hyper-concentrated monsoonal discharge of the Bengawan Solo River. Safeguarding these dynamic frontiers requires urgent policy frameworks to legally protect newly accreted estuarine zones from future land-use conversion.

Abstract: Cancer incidence exhibits substantial spatial disparities linked to environmental, behavioral, built-environment, healthcare-access, and socioeconomic conditions, yet the spatial scales at which these relationships operate remain insufficiently understood. This study develops an explainable spatial epidemiology workflow that integrates Random Forest, SHapley Additive exPlanations (SHAP), Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to examine county-level incidence for all-site, colon, breast, and skin cancers across Texas, USA. Random Forest and SHAP were used to identify outcome-specific nonlinear predictor relevance, and OLS, GWR, and MGWR were used to compare global, local, and multiscale spatial associations before and after RF-SHAP feature screening. MGWR generally achieved higher model fit than OLS and GWR. Before RF-SHAP screening, MGWR R² values were 0.701 for all-site cancer, 0.516 for colon cancer, 0.499 for breast cancer, and 0.694 for skin cancer, compared with OLS R² values of 0.343, 0.338, 0.257, and 0.368. RF-SHAP reduced predictors by about one-half and consistently improved AICc. The results show that environmental exposures, activity-related conditions, transportation access, screening, food insecurity, and chronic health indicators contribute to spatial differences in cancer incidence. The framework links nonlinear machine-learning evidence with spatially explicit interpretation for transferable epidemiological analysis.

Abstract: Population data at small area scales are essential for effective decision-making, influencing public health, disaster response, and resource allocation, amongst others. While national censuses remain the cornerstone of population data, they are often constrained by high costs, infrequent collection cycles, and coverage gaps, which can hinder timely data availability. To address these challenges, geospatial statistical approaches using limited microcensus surveys have been demonstrated as a reliable source, but the field has advanced substantially in recent years, with significant developments in both data sources and modelling methodologies. New approaches now leverage routine health intervention campaign data, satellite-derived settlement maps, and bespoke modelling approaches to produce reliable small area population estimates where enumeration is difficult or outdated. Various countries are applying these techniques to support census operations, health program planning, and humanitarian response. This manuscript reviews recent advances in ‘bottom-up’ population mapping approaches, highlighting innovations in input data, modelling methods, and validation techniques. We examine ongoing challenges, including partial observation of buildings under forest canopy, population displacement, and institutional uptake. Finally, we discuss emerging opportunities to enhance these approaches through better integration with traditional data ecosystems, capacity strengthening, and co-production with national institutions.

Abstract: Monocular 3D lane detection is challenged by the loss of directional detail in distant markings during feature downsampling and by geometric ambiguity between lateral curvature and road elevation under single-view projection. R-A3D addresses both issues through frequency-aware Riemannian anchor learning in an anchor-regression pipeline. A Haar-based feature pyramid retains four frequency subbands before learned channel projection, strengthening thin and low-contrast lane responses. Each evolving 3D anchor is summarized in the lateral–elevation plane by Gaussian mean and covariance statistics, embedded in a symmetric positive-definite matrix, and mapped to a tangent-space feature with the Log-Euclidean metric. This Riemannian anchor feature is fused residually with anchor-aligned visual evidence and recomputed after each cascade stage. On OpenLane, the ResNet-50 model achieves 64.9% F1, 94.1% category accuracy, and far-range lateral and vertical errors of 0.215 m and 0.081 m at 23.5 frames/s; the ResNet-18 model reaches 63.0% F1 at 53.2 frames/s. On ApolloSim Visual Variations, R-A3D achieves 96.1% F1 and reduces the two far-range errors by 31.0% and 30.3% relative to GLane3D. These results demonstrate complementary benefits from frequency-preserving visual evidence and proposal-dependent Riemannian geometry.

Abstract: Compound coastal hazards flooding, land subsidence, storm surge, and salinity intrusion, impose accelerating risks on deltaic communities. No published study has simultaneously mapped all four hazard types using a comparable algorithm diversity under spatially honest validation. This study presents a multi-hazard susceptibility mapping framework applied to Terrebonne Parish, coastal Louisiana. Eight algorithms were compared: XGBoost, Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Ma-chine (SVM), Multilayer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and a hybrid deep learning architecture (CNN-LSTM); a Spatially Aware Ensemble Meta-Learner (SAEML) trained on spatially separated out-of-fold (OOF) predictions was additionally evaluated. Sixty-five condition-ing factors spanning ten thematic categories were compiled at 30-m resolution; features involved in hazard label generation were excluded to prevent circular reasoning. Model generalizability was assessed through conventional holdout testing and 5-fold spatial block cross-validation (5-km blocks). The 1D-CNN achieved the highest holdout F1-macro for flood susceptibility (0.923), XGBoost for land subsidence (0.919), and GBM for storm surge (0.865) and salinity intrusion (0.765). Spatial cross-validation revealed a 53- mean percentage-point performance degradation (F1-macro: 0.22–0.39 vs. holdout 0.72–0.92), demonstrating that conventional holdout metrics substantially overestimate geographic generalizability. The SAEML’s near-zero holdout-to-spatial-Cross-Validation gap (~0.005) arises by design from its OOF training procedure; under spatially honest comparison, SAEML spatial CV F1-macro (0.310–0.364) fell below the best base-model scores on all four hazards, establishing that ensemble stacking does not automatically improve upon CV-guided individual model selection. Five-class susceptibility maps show that over 81% of Terrebonne Parish is classified as High or Very High flood susceptibility. The composite Multi-Hazard Susceptibility Index (mean = 0.574; range = 0.125–0.938) identifies southern coastal areas as the highest-priority zone for integrated risk reduction, with implications for land-use planning, emergency management, and climate adaptation policy.

Abstract: River avulsion, the sudden relocation of a river channel to a new course from the parent channel, is a geomorphic process with direct implications for floodplain evolution, ecosystem dynamics, and infrastructure vulnerability. This review article discusses how sandbar migration acts as a precursor to avulsion by altering hydraulic geometry, redirecting flow paths, modifying sediment transport patterns, and affecting the development of incipient channels. The morphodynamical evolution of sandbars, influenced by sediment supply, flow regime, vegetation, and anthropogenic influences such as dams and sand mining, plays a central role in creating avulsion. Different methods, such as field measurements, remote sensing imagery (including multispectral, SAR, LiDAR and UAV), physics-based numerical models, machine learning, and deep learning techniques, which are used to evaluate river sandbar and river avulsion, are also thoroughly evaluated for efficacy and fit for purpose.

Abstract: Avocado cultivation in Michoacán, Mexico, has undergone rapid expansion over the past five decades, transforming regional landscapes and economies. This study analyzes the spatial and environmental dynamics of avocado growth from 1974 to 2024, drawing on high-resolution geospatial data. Results show a 20-fold increase in cultivated areas (from 13,000+ to ~266,109 ha) accompanied by significant forest loss (~86,411 hectares) and fragmentation. While avocado production offers economic benefits, it raises concerns about ecological integrity, water access, and social equity. Although the study makes emphasis only on expansion-derived forest cover impacts, these findings underscore the need for spatially explicit governance tools to reconcile agricultural productivity with long-term territorial sustainability. We propose a conceptual framework for avocado sustainable territorial development using the planning principles known as the Triple E: efficacy, efficiency and equity.

Abstract: Geographic information generalization is central to multi-scale spatial database construction and cartographic representation. The quality of terrain generalization for Digital Elevation Models (DEMs) directly determines geomorphological fidelity during scale transformation. However, existing DEM generalization methods, which are mostly limited to local geometric filtering or surface simplification, lack global awareness of macroscopic terrain structures. This limitation often causes “geographic alienation,” such as ridge breakage and gradient distortion. To address this issue, we propose TG-GAN, a terrain morphological factor-constrained multi-rate deep learning model for DEM generalization. This model overcomes the indiscriminate smoothing of conventional downsampling by embedding the “structure-first” principle into a deep learning framework. Specifically, a Generative Adversarial Network is used to enhance the generator's simulation capacity. Terrain morphological factors, including local relief and gradient, are innovatively incorporated as physical error terms into the loss function, guiding the network to adaptively reinforce primary terrain skeletons while smoothing secondary micro-terrain features during downscaling. A multi-objective loss function integrating elevation fidelity and gradient preservation enables intelligent terrain generalization in a physically constrained manner. Experiments conducted in mountainous regions of Chongqing, China, Alaska, USA, and Colorado, USA, with downscaling factors of 2× to 5× demonstrate that TG-GAN achieves robust multi-rate generalization while preserving the overall terrain structure. Compared with traditional interpolation methods, TG-GAN effectively avoids over-smoothing and structural breakage, excelling in preserving elevation extremes and slope morphology. Compared with conventional CNN-based methods, TG-GAN shows significant advantages in MAE, RMSE, and SSIM, especially under high downscaling factors. Overall, the proposed model offers a new data- and physics-driven paradigm for automated, multi-scale, and high-fidelity DEM generalization, supporting multi-scale mapping and related geomorphological analysis.

Abstract: Agricultural drought represents a critical global environmental challenge that directly jeopardizes food security. Monitoring agricultural drought is essential for effective agricultural planning and robust water resource management. This study rigorously analyzed monthly precipitation (mm) and maximum and minimum temperatures (°C) from 14 grid points derived from the ERA5-Land dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF), covering the period from 1981 to 2025. The Standardized Precipitation Evapotranspiration Index (SPEI) at a six-month time scale (SPEI-6) was calculated, and the Mann–Kendall test was employed to identify trends. The findings indicate that each grid point experiences varying intensities of drought. G4 stands out as the grid point with the highest drought events, followed closely by G7, G8, G1, G3, G5, G13, and G14 grid points. In stark contrast, G11, G12, G9, G6, G10, and G2 grid points reported the fewest events. The Mann–Kendall test results confirm that only one grid point (G14) exhibits a statistically insignificant decreasing trend (p>0.05). Conversely, 93% of the grid points reveal a statistically significant decreasing trend in the SPEI values, pointing to the fact that agricultural droughts are expected soon in these areas. These findings establish a strong foundation for future research on drought prediction and provide critical insights for effective decision-making in drought risk management. By highlighting the significant temporal and spatial variability in agricultural drought across Mpumalanga Province, this research decisively supports the development of adaptive strategies and policies necessary for managing these conditions effectively.

Abstract: Coastlines possess significant ecological and resource values, which are intricately associated with marine ecological civilization, the marine green economy, and coastal well-being. Comprehending the spatiotemporal variations and driving mechanisms of coastlines is of great significance for their effective protection, rational utilization, and sustainable development. In this study, we employed ArcGIS to extract the coastline vectors of Huizhou in 1973, 1988, 2003, and 2019 based on multi-source remote sensing and unmanned aerial vehicle (UAV) images. The coastline location and type (CLT) model was proposed to differentiate four coastline types, namely the sets of coastline segments with invariant locations and types (SCA), the sets of coastline segments with invariant locations but altered types (SCB), the sets of coastline segments with changed locations but invariant types (SCC), and the sets of coastline segments with both changed locations and types (SCD). Subsequently, the spatio-temporal evolution and disturbance factors of these coastline types were analyzed, offering a diversified foundation for quantitative coastline analysis. The results indicate that total length of Huizhou coastlines increased from 248.75 km in 1973 to 260.82 km in 2019, with natural coastlines decreasing by 62.86 km and artificial coastlines increasing by 75.21 km. The length and proportion of SCA exhibited a continuous decline, decreasing from 79.66% in the initial stage to 58% in the final stage. Conversely, the lengths of SCB, SCC, and SCD all witnessed a continuous increase. The coastline disturbance index (CDI) of Huizhou exhibited a continuous upward trend, escalating from 20.34% to 30.95% and further to 42.00%. This phenomenon was primarily propelled by land reclamation and aquaculture enclosures, accompanied by distinct regional disparities. The coastline alterations were concentrated in regions such as the Daya Bay Petrochemical Zone, Fanhe Port, Kaozhouyang Bay, Xiaogui Village, and Quanwan Port. Meanwhile, the CDI of aquaculture reclamation witnessed a continuous decline, whereas the CDI of land reclamation showed a continuous increase. The natural environment of Huizhou, including its topographical, geomorphological, and hydrological characteristics, serves as the basis for coastline evolution. Meanwhile, social and economic development, along with policies, are significant driving forces for coastline evolution. These findings offer a solid scientific foundation for the management of coastal zones in Huizhou.

Abstract: This article examines how Rondônia's territory changed in the early 21st century, focusing on “matogrossization,” a process reflecting the spread of Mato Grosso's productive practices due to agribusiness expansion, especially soybean and corn monocultures. This led to a major reorganization of the regional agrarian space, with commodity exports sparking a land rush and intensive deforestation. The process caused conflicts over land, resistance from local peasants and Amazonian peoples, rural depopulation, and urban growth, driven by rural-to-urban migration. “Matogrossization” explains these socio-spatial shifts, linking Rondônia to global agribusiness and its environmental impacts.

Abstract: Artificial intelligence (AI) is a human-built component of the technosphere, not an intelligence outside Earth-system limits. As AI systems scale, they increasingly shape the decisions, infrastructures, and capital flows through which human activity damages the biosphere. Dominant deployed foundation-model alignment methods, including reinforcement learning from human feedback (RLHF) and constitutional AI, treat human preferences as the primary alignment target while leaving biosphere integrity as context, externality, or secondary constraint. That framing is structurally incomplete. Human welfare, technological continuity, and AI operation all depend on biosphere function. Three convergent literatures support a corrective framework: planetary-boundary analysis showing seven of nine boundaries transgressed; energy-system analysis showing rapid and infrastructure-constrained data-center growth during the 2025-2030 buildout; and collective-action analysis showing that voluntary ecological restraint is unstable under competitive pressure. These literatures imply a design conclusion: ecological constraints must be formalized as hard inference-time refusal rules and reinforced through reward design. This paper presents Biosphere Sentinel as a reference architecture for reducing human and technospheric impacts on the biosphere through refusal rules, an eight-domain reward landscape, carbon-lock-in diagnostics, and a proposed Trophic Integrity Index pathway.

Abstract: Flash drought, as a rapidly developing form of drought, has become an increasing threat to agricultural production, ecosystem stability, and regional carbon cycling, par-ticularly in croplands within monsoon regions. Existing studies have mainly focused on point-scale identification or conventional vegetation indices, while a systematic understanding of the regional spatiotemporal evolution of flash droughts and crop-specific differences in photosynthetic recovery remains limited. Using mul-ti-source remote sensing data from the North China Plain and the Middle-Lower Yangtze Plain during 2001–2024, this study integrated triple collocation error assess-ment, root-zone soil moisture percentile-based identification, connected component tracking, and Random Forest–SHAP analysis to characterize flash drought trajectories and their impacts on vegetation. The results showed that the southern Middle-Lower Yangtze Plain exhibited a high-frequency but low-intensity pattern, whereas the cen-tral North China Plain was characterized by relatively low frequency but higher inten-sity and longer duration. Rice-based systems were more vulnerable to frequent flash drought shocks, while rainfed and rotation systems faced stronger cumulative risks. Solar-induced chlorophyll fluorescence (SIF) responded to flash droughts 6–9 days ear-lier than gross primary productivity (GPP), and all cropping systems exhibited a “rapid physiological response–lagged carbon assimilation recovery” pattern. The month of occurrence, drought duration, and decline rate were identified as the dominant factors controlling photosynthetic recovery. These findings extend the flash drought monitor-ing framework from the perspectives of regional connectivity and crop recovery mechanisms, and provide a remote sensing-based scientific basis for agricultural early warning, drought mitigation, and food security management.

Abstract: The southern coast of Java, Indonesia, lies along the active Sunda subduction margin, where coastal landforms record the interaction between sea-level change, wave erosion, sedimentation, and tectonic uplift. Marine terraces and raised coastal surfaces are important geomorphic indicators of vertical deformation, but their interpretation remains difficult where chronological control is limited and where coastal surfaces have been modified by erosion, deposition, karstification, or human activity. This study presents new Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) topographic profiles from four coastal sites: Pantai Ajah, Kalijali, Kulon Progo, and Wingko. The profiles were measured from the beachward side toward the landward side and were used to identify terrace treads, risers, slope breaks, residual topographic highs, and possible raised coastal platforms. These field data are integrated with published information on Holocene sea-level change, marine terraces, coastal uplift, and forearc deformation along the southern Java margin. The RTK profiles show variable terrace morphology between sites. Pantai Ajah preserves a prominent riser and a probable terrace tread at approximately 7–8.5 m elevation. Kalijali records a lower terrace-like surface at approximately 4–5 m, an upper surface at approximately 7–9 m, and a higher local topographic high near 12–13 m. Kulon Progo shows a subdued low-elevation raised coastal surface, while Wingko contains a distinct slope break at approximately 1450–1500 m from the beachward end and a broad landward surface at approximately 5–6.5 m elevation. The profiles suggest two tentative morphostratigraphic terrace groups: a lower group at approximately 4–6.5 m and an upper group at approximately 7–9 m. Higher local peaks, including the 12–13 m high at Kalijali and comparable elevated points at other sites, may represent remnants of older or more strongly uplifted coastal features. One possible interpretation is that some of these higher surfaces originated near the mid-Holocene sea-level highstand, when relative sea level in parts of Indonesia and Sundaland was higher than present, and were subsequently uplifted to different elevations according to local uplift rates. However, this hypothesis requires direct chronological and sedimentological confirmation. The raised terrace ridges and topographic highs may also act as partial natural barriers that reduce tsunami flow penetration inland, although they should not be treated as complete protection. Overall, RTK profiling provides a useful field method for recognizing coastal terrace morphology and identifying priority sites for future dating, tsunami-inundation modelling, and coastal-hazard planning.

Abstract: Coastal basins are highly dynamic systems susceptible to flooding and erosion, processes intensified by extreme cyclonic events. This study aims to develop a physical-geographic framework for analyzing the multi-hazard geomorphological dynamics of the La Sabana River basin in southern Mexico. The methodology integrates the analysis of the basin's natural and anthropogenic components with morphometric evaluation and multivariate analysis (PCA) at the sub-basin level. The results show a highly efficient drainage network (3.8-5.4 km/km²) and short concentration times (0.98–2.75), which favor a rapid hydrological response and high susceptibility to flooding and erosion. PCA explained 65.8% of the total variance, identifying basin size, drainage organization, and system shape as dominant controls. Critical sub-basins with rapid hydrological response (Tc ≤ 1.5 h) were identified, coinciding with areas of high anthropogenic exposure. It is concluded that integrating morphometric indices through multivariate approaches provides a robust, replicable basis for risk governance and territorial planning in coastal basins.

Abstract: Rivers are the main source of water supply for the Crimean Peninsula, making their chemical status crucial for the regional water security. The study is based on results of geochemical surveys conducted in 2015 – 2018 during different hydrological phases in rivers of the northern macroslope of the Crimean Mountains (the Salgir, Belbek, Kacha, and Alma) and the southern coast (the Derekoyka, Ulu-Uzen, Demerdzhi, and Uchan-Su). Background levels of most elements in water and suspended matter are comparable to their global averages. In impacted areas metal contents exceed background by up to 10–20 times. Dissolved metal contamination is typical during low-water periods, whereas increased values in suspended matter is primarily associated with flood events. Suspended matter is enriched in Bi, Cd, Sb, Zn, Cu, Sn, Pb, W, and Mn, consistent with the geochemical signature of urban road dust in Crimean cities. Among the rivers of the northern part of Crimea, the highest pollution is typical for the Salgir River, on the southern coast - for the Uchan-Su River. Metal(loid)s inputs to suspended matter is predominantly controlled by natural sources, the highest anthropogenic impact is related to construction activities (releasing calcareous dust), urban wastewater, vehicle emissions, and agricultural practices, particularly vineyards.

Abstract: Rapid urbanization and stringent ecological protection policies in China have intensified spatial competition among Urban–Agricultural–Ecological (UAE) spaces. However, existing studies often overlook how this competition evolves across different slope structures. To address this, this study establishes a fine-scale analytical framework using H3 hexagonal grids and slope spectrum analysis to investigate the slope structure evolution and spatial competition mechanisms from 1990 to 2023. The results reveal a distinct topographic stratification of competitive niches: urban space dominates low-slope regions (< 6°) but exhibits a pervasive "upslope expansion" trend, with its average slope increasing from 1.81° to 2.07°. Agricultural space characterizes the transition zones (6°–15°), showing an "upslope migration" in the Southeastern Hills driven by urban squeeze. Ecological space functions as a stable barrier in steep terrains (> 15°) but faces encroachment in transition zones. Furthermore, cluster analysis identifies significant regional heterogeneity aligning with China’s macro-topography: the Eastern Plains are characterized by "low-slope agglomeration," where urban–agricultural conflict is most intense; the Southern Hilly Regions display an "interwoven upslope" pattern; while the Western Highlands maintain absolute ecological dominance. Mechanism analysis using GeoDetector and Multiscale Geographically Weighted Regression (MGWR) indicates that competition intensity is predominantly driven by human activity factors (e.g., human footprint, nighttime lights, q > 0.29), yet significantly modulated by topographic constraints (e.g., elevation), creating a nonlinear enhancement effect. Crucially, this study challenges the traditional flat-projection planning model. We propose a transition to "three-dimensional topographic regulation," advocating for differentiated management strategies—such as strict "slope redlines" for urban–agricultural transition zones—to resolve the intensifying spatial conflicts in complex terrains and safeguard agricultural sustainability.

Abstract: In the geographic world, phenomena such as mesoscale ocean eddies exhibit continuous and gradual changes. Due to limitations in remote sensing observation technology, a contradiction exists between discrete observational data and these evolving phenomena. While spatiotemporal interpolation is crucial for bridging this gap, existing single-model methods fail to account for continuous process characteristics, making it difficult to obtain consistent datasets. To address this, this paper proposes an evolutionary process-embedded marine spatiotemporal interpolation model (EPMSIM) by integrating deep learning and geostatistics. EPMSIM first decomposes marine time-series fields into trend, seasonal, and evolutionary components using seasonal and trend decomposition using loess (STL). A convolutional bidirectional long short-term memory (ConvBiLSTM) model is designed to reconstruct the trend and seasonal components, while a process-based spatiotemporal dynamic tracking interpolation method (PSDTIM) reconstructs the evolutionary component. Finally, these components are additively coupled for interpolation. A case study on sea surface temperature (SST)-based mesoscale eddies shows that EPMSIM outperforms traditional geostatistical and deep learning-based baseline models in terms of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and structural similarity index measure (SSIM). These results confirm the model’s effectiveness and feasibility in capturing the continuous evolution of marine phenomena and generating high-quality spatiotemporal datasets.

Abstract: The land territory of Pakistan extends from the coastal area towards the Karakoram, rising vertically by more than 8,600 metres within a distance of 1,600 kilometres. The net primary productivity (NPP) has been affected by climate change, but the regional differentiation of climatic impacts on vegetation productivity and the trends of these impacts over the last two decades remain unclear. Using the ERA5-Land climate dataset and the MODIS NPP dataset via partial regression and moving correlation analyses, we identified the main climatic driver of the NPP and assessed the potential climatic forces faced by local vegetation in the future. Our results were as follows: (1) The NPP showed an overall increasing tendency across Pakistan from 2001 to 2022. (2) The areas where the changes in NPP were driven mainly by temperature and NPP benefitted from the temperature change were located in the northern mountainous regions approximately north of 35°N and east of 72°E, and the northern Upper Indus Plain. With temperatures changing over time, the increase in NPP intensified in the northern mountainous regions above approximately 3,500 m a.s.l., whereas the increase in NPP diminished below this zone and in the northern Upper Indus Plain. (3) The areas where the changes in NPP were driven mainly by precipitation and NPP benefitted from the precipitation change were located in the Gandhara Plain, the northern Potwar Plateau and in the middle to southern parts of Pakistan south of approximately 32°N. With precipitation changing over time, the increase in NPP intensified in the region between approximately 26°N and 32°N, whereas the increase in NPP diminished in the Gandhara Plain, the northern Potwar Plateau and south of approximately 26°N. Our findings indicated spatial differentiation in the responses of NPP to climate change. If climate change continues at its current pace, vegetation in the northern mountainous regions below 3,500 m a.s.l., the Gandhara Plain, the northern Potwar Plateau, the northern Upper Indus Plain and regions south of approximately 26°N may undergo risks of degradation.

Abstract: For ages, archaeologists had used shovel test grids and excavation to determine the most likely places to dig, this procedure requires a lot of work and time. In seismic hazard assessment studies, it is important to identify subsurface faults and to constrain seismic deformation parameters near surface. Ground penetrating radar (GPR) method is a nondestructive, noninvasive high-resolution geophysical mapping method favorable to picture the buried archaeological remains and delineation subsurface possible shallow walls effected by tectonic process like faults within altered environments. Processed two-dimensional radargrams were used to identify the location of some anomalies related to ancient walls. The three-dimensional model shows that the GPR anomalies are typically simpler to spot and isolate in order to make the depth and position more clear and delineate the extension of buried archaeological walls at both surveyed sites. The GPR method was able trace a possible 0.5 m deep left lateral strike slip fault affected ancient buried wall at site 2 which was impossible to mapped by 2D profiles. The inferred faultʹs direction and displacement match an exposed fault that has been mapped in the northwest corner of the western wall of the Nabataean-Roman age reservoir.