Central Asian vegetation is more sensitive to soil moisture drought than to heat and meteorological drought.

Moon-based synthetic aperture radar (SAR) offers a promising platform for long-term monitoring with global coverage of Earth's vegetation tipping elements. However, wind-induced vegetation motion during aperture synthesis can degrade image stability and spatial resolution. To address this, we integrated multi-year wind speed records, a parameterized internal clutter motion model, and Moon-based SAR geometry to systematically analyze the dynamic scattering behavior across representative vegetation types under prevailing wind conditions. The results reveal two distinct degradation mechanisms: high-latitude sensitivity, driven by orbital geometry, and high-biomass severity, driven by vegetation structure. Crucially, we demonstrate that the unique ultra-high orbit enables Moon-based SAR to overcome these challenges, achieving sub-100 m resolution across all representative ecosystems by surpassing the traditional half-antenna length limit. Furthermore, the analysis indicates that C-band may be preferentially considered in system design, as it achieves a comparatively favorable trade-off between temporal coherence required for imaging stability and structural sensitivity across diverse vegetation conditions. These findings support the design and capability assessment of Moon-based SAR systems optimized for vegetation observation in the context of ecosystem transition monitoring.

Enhanced urban CO2 monitoring and understanding its spatiotemporal patterns and driving factors are essential for emission management and climate change mitigation. This study developed a high-resolution framework for predicting and mapping CO2 concentrations within the road network of Shenzhen by integrating vehicle-cruising observations, street-view panoramas, and multisource remote sensing data. The proposed machine learning model demonstrated strong predictive performance (R² = 0.92). Furthermore, the effects of urban function, urban development intensity, traffic conditions, and environmental factors on CO2 concentrations were systematically examined using explainable machine learning techniques. The results indicate that in urban centres, human activities exert a substantial influence on CO2 levels. Effective non-motorway planning, accessible public transport systems, and diversified urban functions are associated with lower CO2 concentrations. Notably, areas with high vegetation cover were also found to exhibit elevated CO2 concentrations, and the influence of greenery on Shenzhen's CO2 levels was positive in November. By integrating multisource data from the perspectives of the urban landscape and street configuration, this study provides an interpretable approach for analysing the complex drivers of urban CO2 dynamics. Overall, the findings establish a cost-effective methodological framework for refined urban carbon monitoring and offer actionable insights to support low-carbon urban planning and evidence-based policy formulation.

Spatiotemporal dynamics of vegetation cover in Egypt and its nonlinear climate responses: Insights from interpretable machine learning

Stormwater management ponds (SWPs) are engineered systems designed primarily for flood control and sediment capture in urban landscapes. Despite their intended function, these ponds are often colonized by aquatic biota and can contribute to urban biodiversity. We investigated the ecological condition of 21 SWPs in a highly urbanized city in southern Ontario, Canada, by assessing the composition of periphytic diatom and aquatic macroinvertebrate assemblages and relating these to water quality, pesticide contamination, physical habitat features, and surrounding land use. Assemblages were dominated by pollution-tolerant taxa. Water quality parameters were good predictors of variation in community composition for both assemblages, whereas pesticide contamination was associated with diatom relative abundances, and local vegetation cover was associated with macroinvertebrate relative abundances. Landscape variables within a 300-m buffer surrounding the SWPs were not associated with community composition or taxonomic richness for either assemblage, suggesting that site-level conditions exert stronger ecological influence. These findings highlight the importance of using multiple biological assemblages to capture different aspects of ecological condition and the value of integrating biological monitoring into stormwater infrastructure planning. Enhancing emergent and riparian vegetation, reducing pollutant inputs, and managing contaminant pathways may improve biodiversity potential in urban SWPs.

Survey of equine pasture best management practices and soil fertility in southeast Pennsylvania after the grazing season.

Shifting relative importance of vegetation filtering and cold trapping in PCB deposition along a 1000 km transect toward the Qinghai-Tibet Plateau.

Climate-hydrology-topography-anthropogenic factors jointly drive the evolution of vegetation coverage in semi-arid regions: A downscaling approach based on random forest and nonlinear residual correction

Development of a river habitat quality index (RHQI) framework for dry-hot valleys: Unraveling driving mechanisms and management implications.

Stage-transition mechanism of forest-grass vegetation coverage in machine-learning simulation of event-scale suspended sediment concentration processes.

Urbanization is one of the major land transformation processes taking place worldwide, particularly in developing countries such as India. In recent decades, secondary cities have expanded rapidly across the globe. In this context, a comprehensive understanding of Land Use Land Cover and Urban Expansion Intensity dynamics is crucial for rational urban planning and policy formulation. This study examined the spatiotemporal pattern of urban expansion using Remote Sensing and GIS techniques. Maximum Likelihood Supervised classification and Urban Expansion Intensity Index have been utilised through satellite imagery of Moradabad district for 2004 and 2024. LULC categories help quantify the growth of various land use classes, whereas Urban Expansion Intensity measures the rate and magnitude of urban growth in Moradabad District. The results reveal a significant increase in built-up area. Particularly in Moradabad tehsil, with a 176% growth over 20 year period, contrasted by decline in agricultural land, water bodies and vegetation cover. The Tehsil-level analysis exposes the uneven growth of built-up area within the district, with very high growth in Moradabad tehsil compared to Bilari, Kanth and Thakurdwara. The findings underscore the need for urban planning, afforestation, river buffer protection and sustainable development and the integration of geospatial monitoring in developmental policies.

Peat-based soil amendments enhance long-term soil-plant-microbe recovery in boreal mine reclamation.

Abstract Determining long‐term soil erosion and deposition rates and understanding landform evolution are important for managing both natural and human‐modified landscapes, including postmining rehabilitation sites. Although various landform evolution models (LEMs) have been developed to simulate erosion processes and landscape change, relatively few studies have directly compared modelled outputs with field‐based erosion estimates. This study evaluates and compares two LEMs, (i) SIBERIA, which is widely used in the Australian mining sector, and (ii) SSSPAM, a newer coupled soilscape–LEM, together with the well‐established soil erosion model (the revised universal soil loss equation). A formerly grazed hillslope in the Upper Hunter region of New South Wales was used as the case study, and a high‐resolution light detection and ranging (LiDAR)‐derived digital elevation model was used as the landscape input. Field‐based erosion rates were quantified using sediment yield data from a catchment dam and 137 Cs isotopic analysis. Sediment trap measurements indicated erosion rates ranging from 0.43 to 0.61 t/ha/year, while 137 Cs results showed erosion and deposition rates of up to 1.5 and 1.1 t/ha/year, respectively. SIBERIA predicted erosion rates of 1.07 t/ha/year under dense vegetation cover and 3.74 t/ha/year under moderate cover, while SSSPAM estimated 0.35 and 2.43 t/ha/year for the same conditions. The RUSLE model predicted an average erosion rate of 2.23 t/ha/year, with values ranging from 0.58 to 4.65 t/ha/year. Overall, the modelled erosion estimates were broadly consistent with the field observations, demonstrating the ability of both SIBERIA and SSSPAM to reproduce realistic hillslope erosion rates. These findings support the use of LEMs as valuable tools for guiding sustainable land management and rehabilitation practices in both natural and constructed landscapes.

ABSTRACT Fractional vegetation cover (FVC) is a crucial biophysical indicator for monitoring vegetation abundance and distribution. Existing FVC estimation methods based on synthetic aperture radar (SAR) images often overlook the heterogeneity of scattering mechanisms across vegetation types, limiting accuracy in complex and topographically varied regions. To address this, this study proposes a vegetation-type-adaptive approach that integrates tailored feature selection with ensemble learning algorithms optimized by the dung beetle optimizer (DBO), based on dual-polarized SAR images. The study area is classified into cropland, woodland, and wetland using the European Space Agency’s (ESA’s) WorldCover 10-m product to address vegetation heterogeneity. Following this classification, 25 SAR features – including backscatter coefficients, polarimetric decomposition parameters, and radar vegetation indices – are extracted from Sentinel-1 SAR data over the Dongting Lake region, China. The minimum redundancy maximum relevance (mRMR) algorithm identifies optimal feature subsets for each vegetation type, effectively capturing unique scattering characteristics. Five ensemble models, adaptive boosting (Adaboost), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM), are trained separately for each vegetation type, with hyperparameters tuned via DBO. To mitigate terrain-induced layover effects, an adaptive OTSU-based thresholding strategy is applied. Experimental results demonstrate high-precision FVC estimation, with R 2 values of 0.8916, 0.8313, and 0.9303 for cropland, woodland, and wetland, respectively. Compared with conventional uniform models, this vegetation-type-specific approach significantly improves accuracy by addressing feature heterogeneity and geometric distortions. This method provides a robust and physically consistent solution for FVC mapping in heterogeneous-vegetated regions, significantly advancing SAR-based vegetation monitoring under complex terrain conditions.

ABSTRACTBackgroundAvian haemosporidians (Haemoproteus, Plasmodium, and Leucocytozoon) and erythrocytic bacteria (e.g., Aegyptianella) are globally widespread, yet their epidemiology in Iran's semi‐arid ecosystems remains understudied.ObjectiveThis study provides the first survey of avian blood parasites in Semnan Province, Iran, integrating microscopic and spatial analyses to identify host and environmental factors influencing infection patterns.MethodsFrom September 2022 to December 2023, 263 healthy birds from ten species were examined. Giemsa‐stained blood smears were screened microscopically. Infection hotspots and risk factors were identified using Kruskal–Wallis tests, logistic regression, and spatial mapping.ResultsOverall infection prevalence was 22.1% and was dominated by Haemoproteus spp. (18.6%), followed by Plasmodium spp. (1.5%), Leucocytozoon spp. (1.1%), and Aegyptianella spp. (0.8%). Pigeons exhibited the highest infection rate and were nearly three times more likely to be infected than other species (OR = 2.78, 95% CI: 1.38–5.60, p = 0.004). Spatial analysis revealed infection clustering in Shahroud County, which exhibits relatively higher humidity and vegetation cover compared with other parts of the predominantly semi‑arid Semnan Province. These findings highlight the potential influence of local climatic and ecological factors on the distribution of avian haemosporidian parasites. Although infection appeared slightly higher during spring and summer (9.6% and 7.3%, respectively) compared with autumn (3.4%) and winter (1.9%), the observed differences were not statistically significant (p > 0.05).ConclusionsThis baseline study provides important epidemiological data and establishes a framework for future molecular and vector‑based investigations of avian blood parasites in the Middle East.

Different elevational gradients and anthropogenic pressures between island and mainland systems mediate discrete climatic sensitivities and distribution trends in butterfly lineages. We assess the comparative distributional changes of Pieridae (Pierinae and Coliadinae) on Taiwan Island and mainland China. Across Taiwan Island, the geographic range of Pieridae is mainly limited to lowlands due to the sharp decline in temperature with rising elevation. In mainland China, both reduced temperature and low precipitation at upper elevations shape Pieridae's spatial pattern. Across Taiwan, both subfamilies showed positive associations with human disturbances at greater elevations, while Pierinae showed a positive relationship with thick vegetation cover at lower elevations. Throughout mainland China, Pieridae exhibited negative associations with human disturbances and vegetation density at higher elevations. According to the MaxEnt results, across Taiwan Island, elevation is mainly responsible (80.6%) for the distribution of Pierinae, while maximum temperature of warmest month (Bio5) is mainly influencing (53.7%) the distribution of Coliadinae. Initially Pierinae exhibited an increase in highly suitable regions across the three historical periods, followed by a decrease. Conversely, Coliadinae showed an initial contraction, followed by an expansion. Future global warming may result in a reduction of highly appropriate habitats for both subfamilies without significant evidence of upslope migration. Most Pieridae species are projected to undergo habitat contraction, resulting in decreased species diversity. These outcomes indicate stronger ecological constraints and higher vulnerability to Pieridae diversity in island systems, highlighting the necessity of implementing regional conservation strategies.

Tuberculosis (TB) remains a major public health burden in China. Although meteorological and environmental factors are recognized to influence TB transmission, their non-linear effects and spatiotemporal heterogeneity have not been fully elucidated. Based on monthly TB incidence data from 31 provinces in China during 2005-2020, this study systematically investigated these effects by integrating nine meteorological and air pollution variables within a combined machine learning and spatial statistical modeling framework. The results indicated that the Extreme Gradient Boosting (XGBoost) model effectively captured the complex non-linear relationships between environmental exposure and TB incidence. SHAP interpretability analysis identified surface pressure (SP), vegetation coverage, and PM2.5 as the key drivers and revealed pronounced nonlinear response patterns and threshold effects. In particular, the promoting effect of PM2.5 on TB incidence increased sharply at medium-to-high concentration levels. To further investigate spatial and temporal non-stationarity, Geographically and Temporally Weighted Regression (GTWR) was applied. The results demonstrated strong spatiotemporal heterogeneity in driver effects across provinces. The influence of PM2.5 showed a consistently positive association with TB incidence and exhibited a distinct temporal evolution characterized by an initial strengthening before 2015 followed by a weakening thereafter, closely aligning with China's air pollution control process. These findings provide new insights into the nonlinear and spatiotemporally heterogeneous effects of meteorological and environmental factors on TB incidence and support the development of more targeted, region-specific TB prevention strategies.

Urbanization and climate extremes amplify upstream-downstream water quality disparities across Chinese urban watersheds.

Dune morphology and migration in the Nitzanim coastal dunes: Integrating ground penetrating radar and satellite-based analysis.

ABSTRACT Timely and accurate crop mapping is important for agricultural monitoring and resource management. In this study, multi-temporal Sentinel-2 imagery acquired during 2024 and 2025 was analysed using an unsupervised classification approach to generate 10 m spatial resolution distribution maps of makhana cultivation in selected districts of North Bihar, India. Temporal spectral reflectance patterns of makhana were examined across its growth period using Sentinel-2 multispectral observations. Vegetation indices showed progressive temporal variation, with NDVI, EVI, and NDRE increasing from 0.11 to 0.53, 0.10 to 0.77, and 0.03 to 0.32, respectively, between April and July, consistent with increasing vegetation cover. In contrast, NDWI, MNDWI, and NDBI decreased from −0.11 to −0.41, −0.10 to −0.25, and −0.01 to −0.19, respectively, indicating reduced open-water spectral contribution during canopy development. District-level mapping estimated makhana cultivation areas of 31,325 ha in 2024 and 37,878.7 ha in 2025 across the study region. The classification achieved an overall accuracy of 78.4%, demonstrating the potential of multi-temporal Sentinel-2 data for regional-scale identification of makhana cultivation. The derived spectral reflectance profiles and index trajectories provide observational insights into seasonal crop behaviour and contribute to the development of remote sensing approaches for monitoring floating aquatic cropping systems.