Search Results (187)

With the intensification of global climate change, frequent extreme temperature events pose increasing challenges to agricultural production. The aim of this study is to characterize the spatiotemporal evolution of county-level agricultural total factor productivity (ATFP) under extreme temperature events, reveal key driving factors and crop-specific heterogeneity, and predict potential high-risk areas, which is crucial for providing scientific basis for risk management and adaptive policy formulation in globally climate-sensitive agricultural regions. This paper selects Jiangsu Province as a typical case study, uses the DEA-Malmquist model to measure agricultural total factor productivity (ATFP), systematically analyzes the spatiotemporal dynamic evolution characteristics of ATFP at the county scale, and selects the random forest and XGBoost ensemble models with optimal accuracy through model comparison for prediction, assessing the evolution trends of ATFP under different climate scenarios. The results showed that: (1) From 1993 to 2022, the average ATFP increased from 0.7460 to 1.1063 in the province, though development showed uneven distribution across counties, exhibiting a “high in the south, low in the north” gradient pattern. (2) Mechanization, agricultural film and land inputs are the core elements driving the overall ATFP increase but there are obvious crop differences: mechanization has a more prominent role in promoting the productivity of wheat and maize, while labor inputs have a greater impact on the ATFP of rice. (3) The negative impacts of extreme climate events on agricultural production will be significantly amplified under high-emission scenarios, while moderate climate change may have a promotional effect on certain crops in some regions. Full article

In semi-arid areas like Southeastern Anatolia, where agricultural productivity and water supply are extremely climate-sensitive, drought is a significant environmental and socioeconomic problem. Comprehensive assessment of drought and soil moisture dynamics is fundamental to sustainable agriculture and water security in semi-arid regions. This study analyzes drought patterns across seven provinces in the Southeastern Anatolia (GAP) region of Türkiye (Adıyaman, Diyarbakır, Gaziantep, Kilis, Mardin, Siirt, and Şanlıurfa) from 1963 to 2022, employing four drought indices (SPI, SPEI, CZI, and RDI) at multiple timescales (1-, 3-, and 12-month) to support evidence-based strategies for sustainable water and agricultural resource management. A more thorough evaluation is made possible by this multi-index and multi-scale method, which is rarely used concurrently at the provincial level. Additionally, the drought characterization was validated and enhanced through the analysis of ERA5-Land soil moisture data (1950–2022). According to the findings, the provinces with the lowest median index values and the highest frequency of extreme drought episodes are Diyarbakır and Şanlıurfa. The SPEI-12 (THW) median values showed a neutral long-term drought–wetness balance with seasonal changes, ranging from −0.0714 (Adıyaman) to 0.188 (Şanlıurfa). Particularly after 2009, soil moisture levels decreased to as low as 2–3 mm during the summer, indicating heightened evapotranspiration stress. RDI-12’s reliability in long-term drought evaluation was confirmed by its strongest correlation with other indices (r = 0.87–0.97). According to spatial research, the frequency of moderate droughts in the southwest was as high as 39%, whilst the eastern provinces experienced severe and intense droughts as high as 8%. However, with frequency above 53%, wet occurrences were more common in the east, particularly in Siirt. By clarifying long-term drought and soil moisture patterns, this study provides essential insights for sustainable irrigation planning and agricultural water allocation in the GAP region. Full article

Floods rank among the most frequent and destructive natural hazards, threatening ecosystems, human settlements, and national economies. This study delineates flood-prone areas across Kosovo by developing a national-scale Flood Risk Database (FRDB) and a comprehensive mapping framework integrating Geographic Information Systems (GIS), Multi-Criteria Decision-Making (MCDM), and the Analytical Hierarchy Process (AHP). Eight hydrological and topographic conditioning factors—slope, elevation, flow accumulation, distance to rivers, land use/land cover, soil type, precipitation, and drainage density—were analyzed. AHP was employed to assign factor weights based on their relative influence on flood susceptibility, while MCDM aggregated these weighted spatial layers to generate a national flood risk map. Model validation, based on historical flood points, achieved an AUC of 0.909, confirming its high predictive accuracy. The resulting flood risk map classifies Kosovo’s territory into five risk levels: very high (0.56%), high (14.44%), moderate (36.68%), low (46.46%), and very low (1.88%). This research provides the first systematic national-scale FRDB for Kosovo, offering a reliable scientific basis for flood management, spatial planning, and climate resilience policy. Full article

The significant transformation of agricultural production and operation models has reshaped the supply-demand structure of rural land, providing growth opportunities for new agricultural management entities characterized by large-scale operation. Their large-scale land demand has directly driven an upward trend in the transfer prices of contracted land management rights. By analyzing this practical phenomenon, this study explores the intrinsic logic behind the rising transfer prices of contracted land management rights under the participation of new agricultural management entities, aiming to provide references for further regulating the formation mechanism of transfer prices and promoting the healthy development of the land transfer market. Based on the sample survey data of farmers from the Songnen Plain and Sanjiang Plain in Northeast China, this study adopts the cluster-robust Ordinary Least Squares (OLS) model and moderating effect model for analysis. The results show that the participation of new agricultural management entities exerts a positive impact on the transfer price of contracted land management rights; the impact of new agricultural management entities’ participation on the transfer price is positively moderated by agricultural production efficiency; and the impact also presents heterogeneity across different villages and land parcels. Compared with remote villages and paddy parcels, the participation of new agricultural management entities has a more significant impact on the transfer price of contracted land management rights in township-adjacent villages and dryland parcels. Therefore, to reasonably standardize the transfer price of contracted land management rights, efforts should focus on further strengthening policy guidance to standardize the participation mechanism of new agricultural management entities, regulating the transfer market to establish a dynamic monitoring mechanism for transfer prices, and strengthening the training and guidance for new agricultural management entities to connect and drive farmers so as to improve the agricultural production efficiency of individual farmers. Full article

Urban green space is essential for ecological functions, environmental quality, and human well-being, yet campus expansion can reduce vegetated areas. This study assessed UGS dynamics at Universitas Padjadjaran’s Jatinangor campus from 2015 to 2025 and evaluated an object-based machine learning approach for fine-scale land cover mapping. High-resolution WorldView-2, WorldView-3, and Legion-03 imagery were pan-sharpened, geometrically corrected, normalized, and used to compute NDVI and NDWI indices. Object-based image analysis segmented the imagery into homogeneous objects, followed by random forest classification into six land cover classes; UGS was derived from dense and sparse vegetation. Accuracy assessment included confusion matrices, overall accuracy 0.810–0.860, kappa coefficients 0.747–0.826, weighted F1 scores 0.807–0.860, and validation with 43 field points. The total UGS increased from 68.89% to 74.69%, bare land decreased from 13.49% to 5.81%, and building areas moderately increased from 10.36% to 11.52%. The maps captured vegetated and developed zones accurately, demonstrating the reliability of the classification approach. These findings indicate that campus expansion has been managed without compromising ecological integrity, providing spatially explicit, reliable data to inform sustainable campus planning and support green campus initiatives. Full article

This study investigates the morphometric and anthropogenic controls governing the occurrence and spatial distribution of tributary–junction fans (TJFs) along the Chaudière River, Québec, Canada. Using GIS-based morphometric analysis, field validation, and multivariate statistics (PCA, CART, LDA), 142 tributary watersheds were analyzed, of which 41 display fan-shaped depositional features. Basin relief, drainage density, contributing area, and slope–area coupling emerge as the dominant predictors of TJF development, delineating an intermediate energy domain where sediment supply and transport capacity become balanced enough to allow partial geomorphic coupling at confluence nodes. CART analysis identified approximate slope and area thresholds (slope < 9°, area > 20 km²; 66% accuracy), while LDA achieved 76%, indicating that morphometry provides useful but incomplete predictive power. These moderate performances reflect the additional influence of event-scale hydrological forcing and unquantified Quaternary substrate heterogeneity typical of postglacial terrain. Beyond morphometry, anthropogenic disturbance exerts a secondary but context-dependent influence, with moderately disturbed watersheds (10–50% altered) showing higher frequencies of fans than both highly engineered (>50%) and minimally disturbed (<10%). This pattern suggests that land-use modification can locally reinforce or offset morphometric predisposition by altering sediment-routing pathways. Overall, TJFs function as localized sediment-storage buffers that may be periodically reactivated during high-magnitude floods. The combined effects of basin geometry, land-use pressures, and hydroclimatic variability explain their spatial distribution. The study provides an indicative, process-informed framework for evaluating sediment connectivity and depositional thresholds in cold-region fluvial systems, with implications for geomorphic interpretation and hazard management. Full article

The quality of groundwater is essential to sustain human and environmental activities now and in the future. However, the current intensification of anthropogenic activities has increased the magnitude of contaminant sources. When those contaminants reach a saturated zone (groundwater), their levels of presence may make their use for various purposes unfeasible. Therefore, research into the vulnerability degree is essential for estimations of potential for contamination and possible risks. This manuscript presents the results obtained by applying a parametric procedure for mapping groundwater vulnerability based on a set of attributes related to contaminant sources, transport, and natural attenuation of contaminants. In addition to vulnerability zoning, the set of attributes supports the adoption of measures and recommendations related to territorial and environmental planning guidelines and orientations about land uses. The open source Geographical Information System—QGIS open source version 3.22.4 was used for spatially integrating different attribute maps and obtaining partial indices for contaminant introduction, transport, and attenuation; hence, the specific vulnerability index. The results promoted the division of the region into six classes of specific vulnerability, namely, extremely high, accounting for around 23% vulnerability, very high (20%), moderate (24%), very low (23%), and high and low together accounting for 10%. Such categories were associated with measures and recommendations aimed at territorial and environmental planning and protection and control of environmental functions. Approximately 50% of the study area requires restrictive measures regarding buildings, sustainable drainage systems, waste disposal, chemical storage, and petrol stations, and other measures are necessary for the protection of wells and natural springs. The method employed can produce results that enable areas to be categorized and ranked in terms of specific vulnerability; however, it requires a large quantity of data and spatial details according to the scale adopted. The specific vulnerability map produced will help planners make more appropriate territorial and environmental planning decisions and risk management, avoiding groundwater contamination. Full article

Tree seedling and understory vegetation re-establishment following wildfires is fundamental to landscape recovery but highly variable, depending strongly on biophysical context at small spatial scales. Onsite regeneration surveys and monitoring have been traditionally viewed as a crucial part of sustainable forest management but can be extremely difficult and time-consuming. The objectives of this study were to use a combination of ground measurements and nonparametric hypothesis tests to quantify the ecological relationship between seedling abundance and microclimate by identifying optimal ranges of vapor pressure deficit (VPD) and sun for seedling abundance in postfire environments in the McKenzie River watershed in Oregon. We followed this effort by evaluating how wildfire severity alters these optimal conditions, informing concepts of conifer regeneration under shifting fire regimes in the Pacific Northwest. LOESS modeling, nonparametric statistics, and geospatial analysis quantified the top–down relationship between wildfire severity, site factors, and seedling abundance in our case study. Using LOESS models, optimal VPD ranges were found at 1.1–1.7 kPa and optimal sun ranges were found at 31.2%–47.8% (PAR). Kruskal–Wallis tests were used to compare differences in seedling abundance and optimal VPD and sun ranges (p = 0.027; p = 0.045). Their combined effect on seedling abundance was also evaluated using a Wilcoxon rank-sum test (p = 0.012). Fire severity was not significant to seedling abundance occurrence, but high-severity areas had a higher occurrence of optimal environments. However, given seed source availability, moderate-fire-severity events are still favored for predictable postfire regeneration. These results give insight into the resilience of ecosystems postfire and can be used to assess reforestation needs and monitor forest recovery. Measurements and resulting applications will benefit land managers serving as prefire data for when, inevitably, the next wildfire burns. These concepts can help repair the relationship between humans and wildland fire. Full article

Hailstorms represent one of the most damaging convective hazards for agriculture, yet quantifying their impacts at a landscape scale remains challenging due to their localized and short-lived nature. In this study, we combine weather radar parameters and Sentinel-2 multispectral imagery to assess vegetation damage caused by two major hail events in northeastern Romania: Rădăuți (17 July 2016) and Dolhasca (30 July 2020). Radar-derived hail kinetic energy (HKE) was used as a rapid temporal indicator of hail occurrence, with a threshold of 300 J m⁻² applied to delineate potentially affected areas. Sentinel-2 Level-1C imagery, selected under strict temporal and cloud cover criteria, was processed to generate pre- and post-event Normalized Difference Vegetation Index (NDVI) maps, from which NDVI differences (ΔNDVI) were computed. Thresholds of 0.10 and 0.20 were applied to identify moderate and severe vegetation stress, respectively. The results demonstrate strong spatial correspondence between radar-derived HKE cores and Sentinel-2 ΔNDVI reductions. In Rădăuți, where only one post-event image was available, ΔNDVI thresholds identified between 2236 and 5856 ha of affected vegetation within the HKE > 300 J m⁻² zone. In Dolhasca, where three post-event images were available (5, 8, and 15 days), the analysis revealed 6200–9100 ha affected at 5 days, decreasing to 4800–7200 ha at 8 days, and further to 3100–5600 ha at 15 days post-event. This temporal gradient highlights both the recovery of vegetation and the diminishing sensitivity of the ΔNDVI signal with increasing time elapsed since the event. Analysis by land use classes showed arable fields to be the most sensitive, followed by orchards and pastures, while forests exhibited smaller but persistent declines. This study demonstrates the robustness of integrating radar-derived hail kinetic energy with Sentinel-2 NDVI differencing for the spatiotemporal assessment of hail damage. The approach provides both rapid detection and temporally resolved mapping of hail damage, underlining the critical role of time as a determining factor in impact assessments. These findings have strong implications for operational crop monitoring, disaster response, and risk management in hail-prone regions. Full article

With the intensification of global warming, surface thermal environment issues have become increasingly prominent, particularly in the ecologically fragile Yellow River Basin (YRB). However, most studies neglect the synergistic effects of underlying surface composition and geomorphological context, limiting the understanding of regional thermal contribution patterns. Based on MODIS-derived land surface temperature and Landsat 8-based land use and Fathom DEM-derived geomorphological datasets, this study constructs an integrated assessment framework combining a dual “quality–quantity” perspective with land use–geomorphology coupling, systematically analyzing the comprehensive thermal contributions of different underlying surfaces. Results show that (1) the YRB features diverse underlying surfaces, transitioning from natural (forest, grassland) to human-dominated (cropland, construction land) land uses, and from high-altitude, large undulating mountains to low-altitude, small undulating plains along the source-to-downstream gradient. (2) The average LST is 17.97 °C, displaying a south–north and east–west gradient. Human disturbance intensity drives thermal responses at the land use level, with natural surfaces contributing to cooling regulation, while artificial and desert surfaces generate heat accumulation. Geomorphology jointly shapes the thermal distribution, with high mountains acting as cold sources and plains/hills as heat sources. (3) Dual “quality–quantity” dimensional evaluation reveals that temperature-based assessments alone overestimate localized extremes (e.g., towns, extremely high mountains) and underestimate broad, moderate surfaces (e.g., drylands, large and medium undulating high mountains). This “area-neglect effect” may lead to biased regional thermal assessments and unbalanced resource allocation. (4) Coupled land use–geomorphology analysis uncovers the multi-scale composite mechanisms of thermal formation and mitigates single-factor assessment biases. Geomorphology defines macro-scale energy exchange, while land use regulates local heat responses. The results provide scientific support for large-scale thermal assessment and refined management. Full article

Understanding the evolutionary dynamics of land use and habitat quality (HQ) under climate change scenarios is pivotal for formulating science-based biodiversity conservation policies and promoting climate-resilient urban development in arid regions. By integrating the SD–PLUS–InVEST framework with SPEI-driven drought scenarios, this study introduces a novel coupling mechanism that links climate variability, land-use transitions, and HQ evolution in the Northern Slope of the Tianshan Mountains (UANSTM) under SSP–RCPs scenarios. The HQ assessment was validated using the Remote Sensing Ecological Index (RSEI). Simultaneously, the Optimal Multivariate-Stratification Geographical Detector (OMGD) was applied to identify scale-optimized drivers of HQ changes. The results indicated the following: (1) From 2000 to 2020, cultivated and construction land in the UANSTM expanded, while forest and water areas declined, with unused land remaining dominant from 2000 to 2020. (2) HQ decreased from 0.36 to 0.33 (2000–2020), significantly correlating with RSEI (Pearson r = 0.329, Spearman ρ = 0.446, p < 0.001), with climatic, vegetation, and coupled natural-social factors remaining the dominant drivers. (3) From 2020 to 2050, under all climate scenarios, the areas of farmland, grassland, and construction land are expected to grow, while HQ is projected to improve through the conversion of low-quality areas into moderate- and high-quality habitats (greatest under SSP119, least under SSP585). The framework advances predictive insights for arid-region ecological planning, supporting practical applications in habitat management and sustainable land-use planning, while providing a methodological paradigm for dryland habitat resilience assessment. Full article

Monitoring groundwater dynamics and basin-scale water budget closure is critical for sustainable water resource management, especially in regions facing climate stress and overexploitation. This study examines the temporal variability of total water storage and groundwater trends in Türkiye’s Kızılırmak River Basin by integrating GRACE/GRACE-FO satellite gravimetry, GLDAS-Noah land surface model outputs, ERA5-Land reanalysis products, and local meteorological observations. Groundwater storage anomalies (GWSAs) were derived from the difference between GRACE-based total water storage anomalies (TWSAs) and GLDAS-modeled surface storage components, revealing a long-term groundwater depletion trend of −9.55 ± 2.6 cm between 2002 and 2024. To investigate the hydrological drivers of these changes, lagged correlation analyses were performed between GRACE TWSA and ERA5-Land variables (precipitation, evapotranspiration, runoff, soil moisture, and temperature), showing time-shifted responses from −3 to +3 months. The strongest correlations were found with soil moisture (CC = 0.82 at lag −1), temperature (CC = −0.70 at lag −3), and runoff (CC = 0.71 at lag 0). A moderate correlation between GRACE TWSA and ERA5-based water storage closure (CC = 0.54) indicates partial alignment. These findings underscore the value of satellite gravimetry in tracking subsurface water changes and support its role in basin-scale hydrological assessments. Full article

Wildfire-induced disturbances to soil and vegetation can significantly impact streamflows for years, depending upon the degree of burn severity. Accurately predicting the effects of wildfire on streamflow at the watershed scale is essential for effective water budget management. This study presents a novel approach to generating a burn severity map on a small scale by integrating unmanned aerial vehicle (UAV)-based thermal imagery with Landsat-derived Differenced Normalized Burn Ratio (dNBR) and upscaling burned severity to the entire burned area. The approach was applied to the Thompson Ridge Fire perimeter, and the upscaled UAV-Landsat-based burn severity map achieved an overall accuracy of ~73% and a kappa coefficient of ~0.62 when compared with the Burned Area Emergency Response’s (BAER) fire product as a reference map, indicating moderate accuracy. We then tested the transferability of burn severity information to a Beaver River watershed by applying Random Forest models. Predictors included topography, spectral bands, vegetation indices, fuel, land cover, fire information, and soil properties. We calibrated and validated the Distributed Hydrology Soil Vegetation Model (DHSVM) against observed streamflow and Snow Water Equivalent (SWE) data within the Beaver River watershed and measured model performance using Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS) metrics. We adjusted soil (maximum infiltration rate) and vegetation (fractional vegetation cover, snow interception efficiency, and leaf area index) parameters for the post-fire model setup and simulated streamflow for the post-fire years without vegetation regrowth. Streamflow simulations using the upscaled and transferred UAV-Landsat burn severity map and the Burned Area Emergency Response’s (BAER) fire product produced similar post-fire hydrologic responses, with annual average flows increasing under both approaches and the UAV-Landsat-based simulation yielding slightly lower values, by less than 6% compared to the BAER-based simulation. Our results demonstrate that the UAV-satellite integration method offers a cost- and time-effective method for generating a burn severity map, and when combined with the transferability method and hydrologic modeling, it provides a practical framework for predicting post-fire streamflow in both burned and unburned watersheds. Full article

Mediterranean relict forests, including Laurisilva and other humid forest refugia, are rare and ecologically distinctive habitats often embedded in fire-prone landscapes. Understanding how these ecosystems respond to disturbance is essential for biodiversity conservation and land management under increasing fire risk. However, the effects of fire on key components of these forests, such as macrofungi, remain poorly understood. Here, we examined how fine-scale spatial heterogeneity in fire severity, topography and vegetation shapes post-fire macrofungal communities in a Laurisilva relict forest in central Portugal. Fire severity reduced mycorrhizal richness while having negligible effects on saprotrophs, leading to shifts in the mycorrhizal-to-saprotrophic richness ratio along severity gradients. A similar shift toward saprotrophs also occurred from low to moderate–high elevations, consistent with more exposed, drier conditions at higher elevations. Aspect, topographic ruggedness, and wetness showed weaker, guild-specific associations with macrofungal richness, while vegetation cover and richness had more limited influence, possibly reflecting the complexity and vulnerability of post-fire plant–fungus interactions. Overall, these results highlight the importance of conserving humid and structurally complex environments to foster post-fire fungal diversity in relict forests. More broadly, our findings suggest that fine-scale environmental heterogeneity may help sustain relict forest resilience under intensifying wildfires and other disturbances associated with land-use and climate change. Full article

In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices. A Long Short-Term Memory (LSTM) model combined with Random Forest gap-filling achieved strong predictive performance (R² = 0.84; RMSE = 0.103 cm³ cm⁻³), outperforming SMAP satellite estimates by approximately 30% across key accuracy metrics. The model was applied to 222 field sites in northern Ghana to quantify the effects of stone bunds on soil moisture retention. The results revealed that fields with stone bunds maintained 4–6% higher moisture than non-bunded fields, particularly on steep slopes and in areas with low to moderate topographic wetness. These findings demonstrate the capability of combining remote sensing and deep learning for fine-scale soil-moisture prediction and provide quantitative evidence of how nature-based solutions enhance water retention and climate resilience in dryland agricultural systems. Full article