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Drought is one of the abiotic stresses controlling plant function and ecological stability. In the context of climate change, drought is predicted to occur more frequently in the future. Despite numerous attempts to clarify the overall effects of drought stress on the growth and physiological processes of plants, a comprehensive evaluation on the impacts of drought stress on biomass allocation, especially on reproductive tissues, remains elusive. We conducted a meta‐analysis by synthesizing 164 published studies to elucidate patterns of plant biomass allocation in relation to drought stress. Results showed that drought significantly increased the fraction of root mass but decreased that of stem, leaf, and reproductive mass. Roots of herbaceous plants were more sensitive to drought than woody plants that reduced reproductive allocation more sharply than the former. Relative to herbaceous plants, drought had a more negative impact on leaf mass fraction of woody plants. Among the herbaceous plants, roots of annuals responded to drought stress more strongly than perennial herbs, but their reproductive allocation was less sensitive to drought than the perennial herbs. In addition, cultivated and wild plants seemed to respond to drought stress in a similar way. Drought stress did not change the scaling exponents of the allometric relationship between different plant tissues. These findings suggest that the allometric partitioning theory, rather than the optimal partitioning theory, better explains the drought‐induced changes in biomass allocation strategies. Drought significantly increased the fraction of root mass but decreased the mass fractions of stem, leaf, and reproductive parts. Roots of herbaceous plants are more sensitive to drought than woody plants that reduced reproductive allocation more sharply than the former. Drought stress did not alter the scaling exponents of the allometric relationship between different plant tissues.

With the rapid pace of urbanization, light pollution has emerged as a critical environmental issue. Evaluating and managing light pollution effectively is challenging, as traditional monitoring methods often fail to capture its spatial distribution and driving factors comprehensively. To address this limitation, this study integrates Sky Quality Meter (SQM) observational data from three diverse locations—Chaozhou (China), Urumqi (China), and Ghent (Belgium)—with multi-source remote sensing data to construct predictive models of night sky brightness (NSB) using machine learning approaches. Among the tested models, the voting ensemble model demonstrated superior performance, achieving high predictive accuracy and robust generalization across diverse regional datasets. The generated local-scale NSB distribution maps reveal substantial regional variations in light pollution, highlighting the critical influence of local environmental and anthropogenic factors. By combining remote sensing and machine learning, this study offers a scalable and efficient method for evaluating and monitoring light pollution levels at regional scales. The findings underscore the value of these methods in providing actionable insights for light pollution mitigation and management strategies, supporting efforts to reduce its adverse impacts on the environment and society.

Allocation of biomass to different organs is a fundamental aspect of plant responses and adaptations to changing environmental conditions, but how it responds to nitrogen (N) and phosphorus (P) availability remains poorly addressed. Here we conducted greenhouse fertilization experiments using , with five levels of N and P additions and eight repeat experiments, to ascertain the effects of N and P availability on biomass allocation patterns. N addition increased leaf and stem allocation, but decreased root and fruit allocation. P addition increased stem and fruit allocation, but decreased root and leaf allocation. Pooled data of the five levels of N addition relative to P addition resulted in lower scaling exponents of stem mass against leaf mass (0.983 vs. 1.226; = 0.000), fruit mass against vegetative mass (0.875 vs. 1.028; = 0.000), and shoot mass against root mass (1.069 vs. 1.324; = 0.001). This suggested that N addition relative to P addition induced slower increase in stem mass with increasing leaf mass, slower increase in reproductive mass with increasing vegetative mass, and slower increase in shoot mass with increasing root mass. Further, the levels of N or P addition did not significantly affect the allometric relationships of stem mass vs. leaf mass, and fruit mass vs. vegetative mass. In contrast, increasing levels of N addition increased the scaling exponent of shoot to root mass, whereas increasing levels of P addition exerted the opposite influence on the scaling exponent. This result suggests that increasing levels of N addition promote allocation to shoot mass, whereas the increasing levels of P addition promote allocation to root mass. Our findings highlight that biomass allocation of exhibits a contrasting response to N and P availability, which has profound implications for forecasting the biomass allocation strategies in plants to human-induced nutrient enrichment.

Aim Dryland ecosystems are exceedingly sensitive to climate change. Desertification induced by both climate changes and human activities seriously threatens dryland vegetation. However, the impact of climate change on distribution of dryland plant species has not been well documented. Here, we studied the potential distribution of four representative dryland plant species (Haloxylon ammodendron, Anabasis aphylla, Calligonum mongolicum, and Populus euphratica) under current and future climate scenarios in a temperate desert region, aiming to improve our understanding of the responses of dryland plant species to climate change and provide guidance for dryland conservation and afforestation. Location Junggar Basin, a large desert region in northwestern China. Methods Occurrence data of the studied species were collected from an extensive field investigation of 2,516 sampling sites in the Junggar Basin. Ensemble species distribution models using 10 algorithms were developed and used to predict the potential distribution of each studied species under current and future climate scenarios. Result Haloxylon ammodendron and A. aphylla were likely to lose most of their current suitable habitats under future climate scenarios, while C. mongolicum and P. euphratica were likely to expand their ranges or remain relatively stationary. Variable importance evaluation showed that the most important climate variables influencing species distribution differed across the studied species. These results may be explained by the different ecophysiological characteristics and adaptation strategies to the environment of the four studied species. Main conclusions We explored the responses of the representative dryland plant species to climate change in the Junggar Basin in northwestern China. The different changes in suitability of different species imply that policymakers may need to reconsider the selection and combination of the afforestation species used in this area. This study can provide valuable reference for the management and conservation of dryland ecosystems under future climate change scenarios. Using distribution data of four dominant dryland plant species in a temperate desert region in northwestern China obtained from extensive field survey, we evaluated their responses to future climate change based on ensemble species distribution modeling. We found that these species differed in the direction or extent of habitat change under future climate scenarios.

In recent years, Rwanda, especially its Eastern Province, has been contending with water shortages, primarily due to prolonged dry spells and restricted water sources. This situation poses a substantial threat to the country’s agriculture-based economy and food security. The impact may escalate with climate change, exacerbating the frequency and severity of droughts. However, there is a lack of comprehensive spatiotemporal analysis of meteorological and agricultural droughts, which is an urgent need for a nationwide assessment of the drought’s impact on vegetation and agriculture. Therefore, the study aimed to identify meteorological and agricultural droughts by employing the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI). VHI comprises the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), both derived from the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). This study analyzed data from 31 meteorological stations spanning from 1983 to 2020, as well as remote sensing indices from 2001 to 2020, to assess the spatiotemporal patterns, characteristics, and adverse impact of droughts on vegetation and agriculture. The results showed that the years 2003, 2004, 2005, 2006, 2013, 2014, 2015, 2016, and 2017 were the most prolonged and severe for both meteorological and agricultural droughts, especially in the Southern Province and Eastern Province. These extremely dry conditions led to a decline in both vegetation and crop production in the country. It is recommended that policymakers engage in proactive drought mitigation activities, address climate change, and enforce water resource management policies in Rwanda. These actions are crucial to decreasing the risk of drought and its negative impact on both vegetation and crop production in Rwanda.

Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Litterfall load using a BP neural network based on vegetation indices from Landsat 5 and 8 satellite images, litterfall inventory data, slope, and distance to major river tributaries. It also aims to analyze the spatiotemporal distribution pattern of litter in the research area by estimating and analyzing the spatiotemporal pattern of litterfall along the desert riparian forests of the lower Qarqan and Tarim Rivers from 2001 to 2021. The results show that the initiation of the ecological water transfer project has facilitated the decomposition of litterfall, leading to an initial decline. Subsequently, the vegetation gradually recovered, leading to an increase in leaf litter input. Since 2001, litterfall initially decreased until reaching its lowest value of 4.39 × 10 9 kg in 2005, followed by a subsequent increase, reaching its highest value of 12.5 × 10 9 kg in 2021. The study concludes that ecological water conveyance promotes both the decomposition and increase of litterfall. Initially, it accelerates litterfall decomposition, while later stages foster an increase in Litterfall load. Meanwhile, due to the ecological water transfer project and the higher vegetation cover along the Tarim River compared to the Qarqan River, the Tarim River basin experiences higher average Litterfall load and variation.

Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs.

Accurate, cost-efficient vegetation mapping is critical for managing afforestation projects, particularly in resource-limited areas. This study used a consumer-grade RGB unmanned aerial vehicle (UAV) to evaluate the optimal spatial and temporal resolutions (leaf-off and leaf-on) for precise, economically viable tree species mapping. This study conducted in 2024 in Kasho, Bannu district, Pakistan, using UAV missions at multiple altitudes captured high-resolution RGB imagery (2, 4, and 6 cm) across three sampling plots. A Support Vector Machine (SVM) classifier with 5-fold cross-validation was assessed using accuracy, Shannon entropy, and cost–benefit analyses. The results showed that the 6 cm resolution achieved a reliable accuracy (R2 = 0.92–0.98) with broader coverage (12.3–22.2 hectares), while the 2 cm and 4 cm resolutions offered higher accuracy (R2 = 0.96–0.99) but limited coverage (4.8–14.2 hectares). The 6 cm resolution also yielded the highest benefit–cost ratio (BCR: 0.011–0.015), balancing cost-efficiency and accuracy. This study demonstrates the potential of consumer-grade UAVs for affordable, high-precision tree species mapping, while also accounting for other land cover types such as bare earth and water, supporting budget-constrained afforestation efforts.

High-accuracy Digital Elevation Models (DEMs) are critical for hydrological and ecological applications in low-relief arid basins, yet Interferometric Synthetic Aperture Radar (InSAR)-derived DEMs suffer from significant altitudinal errors due to temporal decorrelation and phase unwrapping artifacts, particularly in flat terrains. To address these limitations, we developed a novel machine learning framework that synergizes Sentinel-1 InSAR, UAV photogrammetry, Sentinel-2 spectral indices, and ALOS topographic features to enhance DEM accuracy. The approach was validated in Northwest China’s Taitema Lake basin across 13 sample plots covering diverse arid surface types (dunes, wetlands, playas). Four algorithms – Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Polynomial Regression (PR) – were rigorously evaluated. Without topographic data, SVM achieved the highest accuracy (test-set R2 = 0.8564). Integrating terrain features with RF further improved performance (R2 = 0.8634, MAE = 1.0683 m), reducing errors from approximately [−10, 27] m to predominantly ±6 m. The RF-corrected DEM exhibited a 42.8% decrease in standard deviation (2.60 m → 1.49 m) and a substantial R2 increase (16.4% → 89.1%). Shapley Additive exPlanations (SHAP) interpretability analysis identified slope and near-infrared reflectance as dominant error-correction features. The corrected DEMs demonstrate enhanced terrain continuity, minimized elevation noise, and offer a scalable, efficient solution for InSAR post-processing in ecologically sensitive arid regions.

Climate change, rapid urbanization, and population growth are increasingly influencing the quality and quantity of surface water resources, especially in vulnerable reservoir systems. This study investigates the spatiotemporal changes in water features and quality of three key drinking water source lakes‐Rawal, Simly, and Khanpur (RSK), located in and around Islamabad, Pakistan. Using Level 2 Landsat 5, 7 and 8 satellite data from 1991 to 2020, changes in lake surface area were assessed through the Google Earth Engine (GEE) platform. Thresholding and geospatial analysis in ArcGIS 10.8 were used to extract and visualize water bodies and surface feature changes. The study found that lake surface areas were directly linked to rainfall levels and decreased with rising temperatures especially during 1991, 2000 2010, and 2020. Water quality was assessed using standard laboratory procedures. Notably, higher bacterial counts were recorded during the wet season, indicating increased microbial contamination likely due to surface runoff. Among the heavy metals analyzed (Fe, F, As, Cu, Zn, Mn, Cr, Pb, Ni, B, Cd, P, Hg), only boron (B), nickel (Ni), and chromium (Cr) were detected above background levels, though within permissible limits. The study highlights the significant influence of climatic variables on both the physical extent and microbial quality of drinking water lakes. These findings offer critical insights for policymakers and water resource managers, providing a replicable framework for monitoring and managing similar reservoirs in other climate‐sensitive regions. Plain Language Summary This study explores how climate change, urbanization, and population growth have impacted the size and quality of three key drinking water lakes like Rawal, Simly, and Khanpur located in and around the Islamabad region of Pakistan. Landsat satellite data from 1991 to 2020 and geospatial tools like Google Earth Engine, ArcGIS, and standard methods for water quality were used for the assessment. The study found that lake surface areas were directly linked to rainfall levels and decreased with rising temperatures during 1991, 2000 2010, 2020 and fluctuations in other years. Water quality testing showed higher bacterial contamination during the wet season, likely due to runoff, while most heavy metals remained within safe limits. The research highlights the strong influence of climate on water quantity and quality and provides a model for monitoring lakes in other climate‐sensitive areas. Key Points This study assessed 30 years of changes in the surface area of three lakes, revealing climate‐driven fluctuations in lake extent A positive correlation between the lake's surface area and rainfall, and a negative correlation with temperature was observed during 1991, 2000, 2010 and 2020 Higher bacterial contamination was detected during wet seasons, attributed to increased surface runoff, while heavy metals remained largely within permissible limits