Search Results (6,359)

This study examines the landscape characteristics of high-suitability habitats for the Black-tailed Godwit (Limosa limosa) during the non-breeding season in inland and coastal wetlands of the middle and lower Yangtze River regions, and seeks to elucidate the distribution patterns and their drivers. Using the MaxEnt model and landscape analysis, the following conclusions were obtained: (1) High-suitability habitats for the Black-tailed Godwit cover approximately 128,800 km² and are primarily distributed across the middle and lower Yangtze River regions. (2) The dominant environmental variables were identified as elevation, distance to water source, slope, distance to paddy field, land use classification, and minimum temperature of the coldest month. (3) Landscape fragmentation, habitat connectivity, human disturbance, and climate change were found to be associated with the shift in the Black-tailed Godwit’s distribution from coastal to inland areas. (4) The distribution of the Black-tailed Godwit in the Nanji Wetland showed significant moderate positive correlation with shallow-water area (r = 0.38, p < 0.05) and significant moderate negative correlation with deep-water area (r = −0.48, p < 0.01). (5) At large spatial scales (coastal and inland wetlands), habitat connectivity and fragmentation were found to exert a greater influence, whereas at smaller spatial scales (Nanji Wetland) land use areas (wetlands and shallow-water areas) and food resources were found to exert greater influence on the Black-tailed Godwit’s distribution. This study synthesizes findings from multiple sources and aims to provide a reference for the conservation of the Black-tailed Godwit. Full article

Mangrove ecosystems provide critical coastal protection, biodiversity support, and carbon storage, yet they remain vulnerable to degradation caused by coastal development, pollution, and climate-related pressures. This study monitors mangrove dynamics in Al-Birk, Asir Region, Saudi Arabia, using Google Earth Engine (GEE), multi-temporal Landsat imagery, spectral indices, and Random Forest (RF) classification. Landsat imagery from 2016 to 2021 was processed to derive NDVI, MSAVI2, EVI, and NDWI, and supervised RF classification was applied to map annual mangrove extent and associated land-cover classes. The RF classifier achieved an overall accuracy of 92.5% and a Kappa coefficient of 0.89. Results indicate that classified mangrove extent increased from approximately 1069 ha in 2016 to 1540 ha in 2021, representing a net gain of 471 ha and a 44% increase over the study period. A localized decline was detected between 2020 and 2021, indicating spatially uneven vegetation dynamics. The findings provide a spatial baseline for monitoring mangrove change and supporting coastal conservation planning in Saudi Arabia. While the detected expansion is temporally consistent with ongoing restoration initiatives, the study does not establish direct causality between policy interventions and observed spatial changes. Full article

Coastal cities are typical regions where economic growth, population agglomeration, and eco-environmental pressures are strongly coupled. Assessing the coordination between urban development and the eco-environment is therefore important for regional sustainability. This study selected seven representative coastal cities in China—Dalian, Qinhuangdao, Qingdao, Shanghai, Fuzhou, Xiamen, and Zhuhai—and integrated multisource remote sensing data with statistical yearbook data to construct a comprehensive evaluation system for urban development level (UDL) and eco-environmental quality (EEQ). An ecologically enhanced indicator system incorporating vegetation condition index (VCI), biological richness index (BRI), normalized difference vegetation index (NDVI), and dynamic habitat index (DHI) was developed. The coupling coordination degree (CCD) model was then used to evaluate urban sustainable development from 2014 to 2023. In addition, an EWM–MLP adaptive weighting strategy was applied to refine entropy-derived weights, and Random Forest was used to identify variables associated with CCD prediction. The results show that CCD values generally increased during the study period, indicating improved coordination between urban development and the eco-environment. However, the evolutionary pathways differed markedly among cities, and UDL and EEQ changes were not fully synchronized. The EWM–MLP strategy introduced adaptive numerical refinements to CCD values while maintaining the overall stability of coordination-level classification. Random Forest analysis showed that CCD prediction was mainly associated with a limited number of high-contribution indicators. For all indicators combined, approximately 7–10 top-ranked variables were generally required to exceed 80% of the total importance, whereas the UDL and EEQ subsystems reached this threshold with fewer indicators. UDL-related variation was mainly associated with land-use structure, population agglomeration, and economic activity, whereas EEQ-related variation was related to ecological conditions, land-cover composition, and environmental pressure. The high-importance indicators exhibited clear inter-city heterogeneity, suggesting the need for differentiated governance strategies. The proposed framework provides methodological support for sustainable development assessment and differentiated governance in coastal cities. Full article

Intensive agriculture, deforestation, and frequent land-use changes contribute to increased soil erosion and sediment transport from both arable and non-arable lands into minor river channels. These factors directly and indirectly influence riverbank erosion and, in turn, sediment transport in rivers. Evidence on anthropogenic land-use/land-cover (LU-LC) change impact remains limited in both quantitative and spatial terms within the Danube River Basin. The study area includes research results from 17 locations concerning satellite-derived LU-LC changes along the Romanian sector of the Danube River, as well as validation results with particular highlighting on the Corabia area, Romania. According to results derived from combining LU-LC products based on Copernicus satellite data (comparing the years 2000 and 2018) and validated in the field through UAV flights conducted in 2025, the conversion of riparian vegetation into cultivated or uncultivated land accelerates bank failure. This is particularly evident where agricultural areas are located in the immediate vicinity of riverbanks. Such bank failures can be attributed to a reduction in root cohesion and a decrease in soil–bank structural stability. As a consequence, sediment delivery to the river channel increases via overland flow. The workflow proposed in this study offers a transferable and adaptable solution for areas with similar characteristics for a multitemporal approach regarding the influence of agricultural lands especially on sediment transport and riverbank erosion. Full article

Soil degradation driven by inappropriate soil management is a serious global challenge, while climate change-induced yield declines are increasing the conversion of natural ecosystems to agricultural land. This review examines how soil structure influences soil health, focusing on organo-mineral complexes derived from microbial biomass and soil organic carbon-to-clay (SOC/Clay) ratio as an indicator of structural quality. Regenerative agriculture based on conservation farming practices helps mitigate SOC depletion and aligns with the nature-based solutions framework. In Hokkaido, Japan, 10 years of clean agricultural applications (cover crops and organic matter application) increased SOC storage in farmland affected by volcanic eruption. This was associated with improved bulk density, porosity, cation exchange capacity, and phosphate absorption capacity, indicating improved soil health. The increased SOC rose SOC/Clay ratio to levels comparable with unaffected farmland (≥1/13). When the SOC/Clay ratio exceeded 1/13 (soil carbon storage level of 30 t C/ha/15 cm), carbon sequestration rate became negative. This suggests that improved soil health and structural quality may promote carbon saturation and stimulate microbial decomposition of existing SOC. While the threshold for SOC/Clay ratio varies depending on soil type, vegetation type, climatic conditions, and land use, changes in the SOC/Clay ratio can provide insights into changes in soil health and structural quality. Full article

The normalized difference vegetation index (NDVI) is widely used to track vegetation cover and ecological change. However, in arid watersheds where irrigated farmland and natural vegetation coexist, it remains unclear how irrigation changes the relative effects of climate, terrain, and soil on vegetation growth. Using the Hetao irrigation district watershed in Inner Mongolia, this study analyzed NDVI dynamics and their environmental controls from 2001 to 2024 through trend analysis, spatial autocorrelation, XGBoost-SHAP, GeoDetector, and geographically weighted regression. NDVI increased significantly across the watershed at 0.0035 yr⁻¹, but the increase was much stronger inside the irrigation district (mean NDVI = 0.58; slope = 0.0061 yr⁻¹) than outside it (mean NDVI = 0.26; slope = 0.0015 yr⁻¹). Global Moran’s I values remained above 0.86, showing persistent spatial clustering. The main drivers also differed by zone. DEM, SOC, and precipitation were most important for the whole watershed; SOC, TP, pH, and TN were more important inside the irrigation district; and precipitation and DEM were more important outside it. GeoDetector confirmed that paired drivers strengthened each other, including SOC ∩ DEM at the watershed scale and DEM ∩ TP outside the irrigation district. GWR further showed that rainfall effects were stronger outside the irrigation boundary, while soil-related effects were stronger in the irrigated agricultural belt. These results show that irrigation not only increases NDVI but also changes how vegetation responds to environmental conditions by weakening direct rainfall limitation and strengthening soil-related controls in managed landscapes. The findings provide evidence for zone-specific vegetation restoration and land-water management in dryland irrigation watersheds. Full article

Woody vegetation growing outside officially designated forest land represents a significant but poorly quantified resource in many countries, where institutional and methodological limitations hinder its systematic accounting. This study develops and applies a multi-stage remote sensing-based framework to identify and characterize forest-eligible areas (FEAs) in Lithuania by integrating airborne LiDAR, Sentinel-2 time series, historical orthophotos, and national geospatial datasets. The workflow combines (i) LiDAR-derived canopy height model generation and object-based segmentation, (ii) rule-based aggregation of vegetation segments according to legal forest criteria, (iii) multi-index Sentinel-2 change detection to exclude recent disturbances, and (iv) deep learning-based classification of historical orthophotos to assess stand age. Three detection approaches were evaluated—LiDAR-based, land parcel identification system (LPIS)-based, and their combination. A total of 111,754.4 ha of FEAs were identified outside official forest land, of which 76,204.6 ha meet the minimum age criterion for classification as forest land under national legislation. The designation of these areas as forest land would increase national forest cover from 33.9% to 35.0%. The LiDAR-based approach achieved the highest overall accuracy after dataset refinement (91.5%), while the combined approach yielded the highest precision (97.1%). Accuracy improved notably when reference points affected by definitional conflicts and temporal inconsistencies were excluded, indicating that apparent detection errors were largely attributable to reference data limitations rather than algorithmic failure. The proposed framework offers a scalable solution for wall-to-wall identification and monitoring of unregistered forest resources, with direct applications for national forest inventories and LULUCF reporting. Full article

Enormous land exploitation is triggering strong urban growth, and this phenomenon is exacerbating the already existing problem of rising land surface temperatures. This leads to increased human activities and a disruption of the balance of natural ecosystems. The application of thermal remote sensing techniques is, in this context, helpful in learning about the condition of the earth’s surface and monitoring how it changes over time. This paper utilizes thermal data from 2000, 2010 and 2020, with supplementary data from 2024, to assess current trends in two different seasonal conditions (rainy period and low rainy period). Two different areas (urban and rural) of the central Vietnamese Province of Thua Thien-Hue have been analyzed to compare them. Processing Landsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Sentinel-2 satellite images, a heat map of the study area was defined, considering hot spots and cold spots. As support for this analysis, spectral indexes have been developed for a better comprehension of the land cover change over the years and to provide a validation of the thermal analysis. This paper aims to assess the threat posed by the intensification of the urban heat island effect on cultural heritage sites. The case studies are represented by areas where there are urban growing and cultural heritage sites to be preserved, such as the UNESCO-listed Hue Citadel. Full article

The Niger Delta region of Nigeria experiences multiple environmental stresses due to intensive oil exploration and pervasive gas flaring, both of which contribute to local and regional climate changes, extreme weather events, and excessive and erratic rainfall. Consequently, flooding remains a recurrent natural disaster, disproportionately impacting the low-lying states of Delta, Bayelsa, and Rivers. This study employs remotely sensed geospatial data and a GIS-based weighted overlay analysis to delineate flood-prone areas on a regional scale in the central Niger Delta states. Flood susceptibility was determined through a weighted overlay of digital elevation model (DEM), slope, proximity to streams, rainfall, and LULC data, among others. Weights of criteria were derived through an analytical hierarchy process (AHP) with a very good consistency ratio of 2.5%. Land use and land cover (LULC) and rainfall data were further analyzed to detect trends of changes between 2012 and 2022. The results show that relatively 77% of the study region is prone to flooding. Areas prone to very high flooding are about 16%, high is 29%, moderate is 32%, while low and very low flood-prone areas cover 18% and 5% of the study region, respectively. There is also a notable increase in average annual rainfall and land cover changes. Average rainfall increased by 58.1% between 2012 and 2017, and by 11.5% between 2017 and 2022. Land cover change analysis further indicates that approximately 1.3% of the study area was converted predominantly to flooded zones and water bodies from 2017 to 2022. The results of this study could be useful for urban regional planning, flood mitigation, and resettlement policies aimed at reducing flood vulnerability and enhancing resilience in the central Niger Delta, as well as other places where similar challenges exist. Full article

Riparian ecosystems provide numerous services that are critical to integrated, sustainable water management. Their ecological functions face various threats, however, including the construction of impervious surfaces that alter watershed hydrology. The understanding of risks and the design of adequate solutions to the threats posed by impervious cover requires assessment throughout entire watersheds. Yet few assessments have considered parcel-scale changes over larger extents, particularly using readily available public data. Seeking to better characterize recent patterns and to understand how characterizations differ with alternative spatial resolutions and assumptions, we assessed statewide change in impervious land cover within riparian areas in Washington State, USA. Leveraging open data from a public decision-support application, we generated estimates based on high-resolution (1 m) change detections for 2011 to 2017, intersected with riparian areas defined from the current management guidance. As an illustrative contrast, we constructed estimates based on the 2011 to 2016 change in a national dataset of 30 m resolution land cover within a fixed buffer on a coarser stream network. Complementing these depictions of change, we also estimated the 2021 standing impervious area using an independent 1 m land cover layer within the management-based riparian extent for the western portion of the state. The “best available” high-resolution estimate of change indicated that riparian and floodplain impervious cover increased by hundreds of hectares a year statewide during the early and middle 2010s. New impervious cover was more prevalent within reaches associated with urban growth areas (UGAs) and in portions of the assessed extent used by highly valued Pacific salmon. The coarser contrasting approach yielded a similar overall magnitude of change, but this served to clarify methodological sources of uncertainty rather than to confirm accuracy. Notably, in addition to capturing larger blocks of impervious increase, high-resolution data revealed many individual changes that were smaller than a single 30 m × 30 m pixel. In 2021, standing impervious cover was also concentrated in UGA-associated reaches, which contained 43.5% of the impervious area despite being 5.2% of the assessed extent. Much of the observed change within the assessed extent was likely outside of the local riparian regulatory jurisdiction at the time, but the patterns revealed by high-resolution monitoring data underscore the importance of continuing to strengthen riparian protections to maintain ecosystem function. Full article

Green and blue-green infrastructures are key for reducing the effects of urban heat islands driven by rapid city expansion. However, the spatial relationship between land-cover patterns and air-temperature distribution, plus the combined cooling effects of green and blue spaces, remains insufficiently explored. This study applies the InVEST Urban Cooling Model to analyze the spatiotemporal changes in land use and their impact on the heat-mitigation service provided by green and blue spaces in the city of Arequipa, Peru, between 2006 and 2024. Furthermore, land-use change is projected for 2030 using the CA-Markov model and the InVEST Scenario Generator tool. These projections enabled the evaluation of two heat-mitigation scenarios by modifying the spatial distribution of green, blue-green, and urbanized areas. The findings indicate that urbanized areas doubled over the measurement period. The greatest loss of agricultural land and tree-covered areas occurred between 2020 and 2024, with a decline of up to 5%. Correspondingly, the percentage of low heat mitigation index areas (0.1–0.2 and ≤0.1) increased by 3.8%, reaching a total increase of up to 6.7%. Scenario simulations showed that reducing both green and blue-green infrastructure had similar impacts on the heat-mitigation index, providing valuable insights for urban planning and environmental management. Full article

Bangladesh is an extremely climate-exposed country, with erosion, accretion, tidal surges, and cyclones continuously modifying coastal districts. Shoreline change in Bangladesh is crucial for sustainable coastal management and disaster resilience. Therefore, the objectives of this research are as follows: (i) to assess accretion- and erosion-based shoreline changes of the Bangladesh delta adjacent to the Bay of Bengal for 2021–2025 using a fixed 2021 reference shoreline and a 2025 shoreline proxy extracted from Landsat 8/9 imagery, and (ii) to explore onshore change dynamics from satellite-derived NDVI, NDBI, and NDWI for 2022–2025. The study covers 14 coastal districts and integrates the 2021 baseline shoreline, Survey of Bangladesh geospatial datasets, and 17,055 Ground Reference Points (GRPs) to support geometric consistency and spatially explicit reporting at the delta scale. Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to assess vegetation health, surface water distribution, and built-up/exposed land characteristics. Results indicate spatial variability in coastal change, with 383.49 km² of land gained through accretion and 124.12 km² lost to erosion, resulting in a neat accretion of 259.37 km² between 2021 and 2025; 8747.91 km² remained geomorphologically stable. Spectral index trends show minimal inter-annual NDVI and NDWI variability, suggesting stable vegetation cover and no long-term expansion of surface water. In contrast, a slight increase in NDBI indicates localized exposure of new sediments or small-scale land-use transitions along emerging coastal zones. Spearman correlation analysis highlights consistent negative relationships between NDVI and NDWI and moderate contrasts between NDVI and NDBI, reinforcing the coexistence of vegetation recovery, water withdrawal, and sediment-driven land emergence. The novelty of this study lies in the provision of consistent, near-real-time coastal change inventory for the full ~710 km Bangladesh delta coastline by combining a common 2021 baseline shoreline with harmonized Landsat 8/9 OLI surface reflectance (2022–2025) and linked onshore spectral-index dynamics over the same period. Overall, this short-term assessment reveals a sedimentary system that is active but balanced, with accretion surpassing erosion despite cyclone-affected disturbances, underscoring the value of operational satellite monitoring for coastal management, hazard preparedness, and climate-adaptive planning. Full article

The Source Region of the Yellow River (YRSR) is a key ecological barrier and a major water supply area, where water conservation is highly sensitive to ongoing climate change (CC) and land use/cover change (LUCC). However, the relative roles of CC and LUCC in regulating water conservation remain insufficiently quantified. In this study, we applied the Soil and Water Assessment Tool (SWAT) to simulate the spatiotemporal dynamics of water conservation in the YRSR and to disentangle the respective contributions of CC and LUCC using a fixing–changing approach, in which one driver is fixed and the other is varied across paired scenarios, followed by projections driven by CMIP6 forcing under SSP2–4.5 and SSP5–8.5. Water conservation showed a pronounced southeast–northwest contrast and increased over 2000–2019 (+4.56 mm/year). Attribution analysis revealed that CC dominated changes in water conservation, whereas LUCC exerted a weak net negative influence. Most increasing regions were precipitation-driven, whereas declining regions were concentrated where evapotranspiration and surface runoff increased concurrently. Under SSP2–4.5, water conservation is projected to continue increasing (+1.16 mm/year). In contrast, under SSP5–8.5, water conservation is projected to slightly decline (−0.26 mm/year). These findings highlight the primary role of climate in regulating water conservation in the YRSR and provide scientific support for adaptive watershed management under a changing climate. Full article

The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land–atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15–29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements. Full article