Search Results (149)

Accurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ET_a) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R² = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ET_a variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Rapid urbanization and climate change are reshaping land-use systems, intensifying conflicts among urban growth, cultivated land conservation, and ecosystem protection. Understanding how land-use change affects carbon balance is important for designing sustainable land management and climate-resilient spatial planning. Taking Nanjing, China, as a case study, this study investigates how land-use change shaped carbon emissions, carbon sequestration, and net carbon emissions from 2000 to 2020 and further evaluates their future changes in 2030 under SSP–RCP scenarios. By integrating land-use simulation, carbon accounting, and contribution–sensitivity analysis, this study distinguishes land-use conversion effects from intra-type intensity change effects associated with changes in carbon emission or sequestration intensity within unchanged land categories. From 2000 to 2020, Nanjing experienced a substantial increase in net carbon emissions, with construction land expansion and higher emission intensity of construction land serving as the primary drivers. Although the carbon sink function was still mainly supported by cultivated land and forest land, land conversion and changes in sequestration intensity weakened the regional carbon balance. Under all SSP–RCP scenarios, simulated net carbon emissions for 2030 exceed the 2020 level, even though lower carbon intensity under SSP1–2.6 can partially mitigate emission growth. Conversion to construction land shows the highest carbon cost, especially when cultivated or ecological land is occupied. These findings highlight the need to coordinate urban expansion control, farmland protection, ecological restoration, and low-carbon industrial transformation. The study offers empirical support for improving sustainable land management and guiding spatial planning toward low-carbon development. Full article

Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived, multi-modal geospatial data. In contrast to existing reactive systems that rely on thermal anomaly detection (e.g., MODIS or VIIRS-SNPP), our approach is fully predictive, generating pixel-wise fire risk maps a day in advance. Our study focuses on Uttarakhand, India, which is an ecologically sensitive region that experiences frequent and severe forest fires. We curated a domain-specific geospatial dataset spanning 1 April to 29 May 2016. It includes daily 30 m GeoTIFF images with 10 bands comprising weather (e.g., temperature, wind, precipitation), topography (slope, aspect), fuel map, and fire mask. We constructed this dataset from diverse sources and aligned all bands spatially and temporally. To demonstrate the usefulness of this dataset, we implement a deep convolutional neural network (CNN) using the ResUNet-A architecture, chosen for its robust performance in the semantic segmentation of high-resolution remote sensing data. Our model is trained from scratch to produce high-resolution fire probability maps and classify fire/no-fire pixels. Our solution helps with planning and decision-making for early intervention, especially in areas with high risk. It supports the UN’s SDG 13 (Climate Action) and SDG 15 (Life on Land) by enhancing resilience and conserving ecosystems. The presented dataset and methodology can serve as a benchmark for future research on wildfire risk prediction using Earth observation data. Full article

Investigating the spatiotemporal dynamics of urban heat islands and their responses to urban forest (UF) landscape patterns is crucial for mitigating urban thermal stress. However, the nonlinear influence and conditional constraints of UF landscape composition and configuration on the warming effects across varying urbanization gradients remain inadequately understood. By integrating land use/cover data, MODIS-derived land surface temperature (LST), and meteorological datasets, this study employed the XGBoost-SHAP model to quantify the nonlinear and interaction effects of UF landscape patterns on developed and developing urban regions of the Pearl River Delta. The results indicate that (1) spatial clustering patterns of warming varied significantly between the two regions, with substantial seasonal heterogeneities (p < 0.05). Summer exhibited the most intense warming, characterized by more rapid temperature increase in developed areas than in developing regions. (2) Relative to UF landscape metrics, the proportion of impervious surfaces, precipitation, and temperature exerted greater influence on regional warming. Coverage area, fragmentation, and connectivity of UFs emerged as the primary landscape drivers modulating warming. In developed areas, spatial configuration metrics exerted greater influence on LST than compositional metrics. (3) The responses of LST to diverse UF landscape patterns are characterized by nonlinearity and pronounced threshold effects. These landscape thresholds vary by season, revealing critical tipping points for warming suppression; however, this regulatory effect is highly context-dependent. Specifically, under high percentages of impervious surface, the warming-suppression capacity of UFs intensifies with increasing percentage of UF area or core. Our findings highlight the necessity of strategic UF planning and forest fragmentation mitigation for developing effective climate resilience strategies. These results provide a foundation for adaptive planning tailored to specific urbanization stages and the implementation of targeted UF cooling strategies. Full article

Small Mediterranean islands relying on the sun–sea–sand (3S) tourism model face growing climate risks that threaten their tourism-dependent economies. This study evaluates climate suitability for 3S tourism on the Island of Vis by integrating the Climate Index for Tourism (CIT) with land- use and land-cover (LU/LC) spatial analysis. The integration is operationalized by overlaying CIT-derived seasonal suitability windows with LU/LC-based spatial vulnerability maps, enabling identification of micro-zones where natural buffers (forest cover and elevation) can offset thermal discomfort during peak heat stress periods. Observed data reveals declining ideal 3S conditions from July to October, with the island already exceeding 50 days per year of Physiologically Equivalent Temperature (PET) above 35.1 °C, increasing by 0.7 days per year. Regional climate models tend to exhibit a cold bias over small Adriatic islands, largely related to their limited spatial horizontal resolution (12.5 km grid spacing). However, they robustly reproduce the direction of recent and projected warming trends. Future projections indicate that the annual number of strong heat stress days with PET above 35.1 °C increase from approximately one per year in the reference period to six under RCP4.5 and nine under RCP8.5, with both scenarios reducing ideal peak-summer conditions while extending favorable periods into transitional seasons. Spatial analysis shows that coastal zones have higher sealed surfaces and less forest cover, reducing natural shade and cooling capacity, while the island interior offers higher elevations, forest buffers, hiking trails, and a UNESCO Global Geopark. Drawing on social–ecological resilience theory, we conceptualize the island’s tourism system as an adaptive unit whose long-term viability depends on spatially diversified resource use and temporally extended seasonality. The integrated analytical framework identifies not only when conditions deteriorate but where alternative tourism resources exist, enabling more targeted adaptation planning and supporting diversification toward outdoor tourism forms. The novelty of this study lies in the systematic spatial integration of bioclimatic suitability assessments (CIT and PET) with LU/LC analysis at the micro-island scale. Such an approach moves beyond temporally focused climate–tourism indices to produce actionable, location-specific adaptation strategies. Full article

Dryland sustainability depends on how vegetation productivity and water-use processes respond to climatic variability and human intervention. Focusing on Xinjiang, China, this study assessed eco-hydrological change from 2000 to 2023 using multi-source remote sensing and climatic datasets. We integrated vegetation productivity and water-use efficiency into a composite EcoIndex, combined anomaly-based diagnostics with eco-hydrological synchrony analysis, and used pixel-level random forest attribution to identify dominant climatic and anthropogenic controls. The results show clear regional differentiation. Northern Xinjiang remained primarily climate-driven and maintained relatively stronger vegetation–water coupling, whereas Southern Xinjiang exhibited more pronounced human-induced restructuring, especially in oasis and cultivated areas. Eastern Xinjiang functioned as a transitional zone with weak coupling and high sensitivity to multiple pressures. Across Xinjiang, 63.27% of the area was classified as climate-dominated, 22.41% as human-dominated, and 14.32% as mixed influence. The results indicate that improvements in vegetation condition do not necessarily imply improved eco-hydrological coordination, and that mixed-influence zones may represent early-warning areas of sustainability risk. This study provides a spatial diagnostic framework for supporting sustainable land and water management, regional adaptation planning, and resilience-oriented governance in arid and semi-arid regions. Full article

Climate change poses an increasing threat to the functionality of forest transportation infrastructure, particularly in northern regions where seasonal access and ground conditions are critical for wood mobilization. The objective of this study was to assess how projected changes in temperature and precipitation may compromise accessibility to forest resources. In addition, it aimed to develop targeted adaptation recommendations to support resilient transportation systems. These actions are essential to ensure the continuity of wood supply under future climatic conditions. Climate projections were extracted from the climatedata.ca platform based on the CMIP6 (CanDCS-M6) model under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Using a GIS-based workflow, projected temperature and precipitation data were spatially matched to the selected Forest Management Units (FMUs) in Quebec, Canada, and the study area was divided into three latitudinal subregions to capture spatial temperature variation. Classified road network maps were then overlaid with projected climate data for 2020, 2040, 2060, and 2080 to evaluate winter road usability, precipitation-related exposure of road classes, and changes in effective winter road density. Results showed a consistent shortening of the winter road operational period under all scenarios, with the most severe reductions under SSP5-8.5. In highly affected areas, the winter road usability window may decrease from 90 days in 2020 to only 21 days by 2080. Increased precipitation is also expected to affect numerous road segments, raising risks of erosion, sedimentation, and loss of accessibility. A reduction of approximately 7% in effective winter road density is projected across the study area under the high-emission scenario (SSP5-8.5), reflecting the most severe impact of future temperature increases. Based on these findings, targeted road upgrades, climate-informed infrastructure design, and alternative access planning are proposed to help sustain wood flow and support year-round forest operations under future climatic conditions. Full article

Climate change poses significant threats to Mediterranean plant species, including Laurus nobilis L., an ecologically and economically important tree. This study evaluates potential shifts in its suitable distribution areas across Türkiye under future climate scenarios [Shared Socioeconomic Pathway 2-4.5 (SSP2-4.5) and 5-8.5 (SSP5-8.5)] using an ensemble species distribution model incorporating ten algorithms. Key environmental drivers—elevation, annual mean temperature (Bio1), and evaporation including sublimation and transpiration (evspsbl)—were identified as critical factors influencing habitat suitability. Results indicate substantial spatial redistributions, with habitat losses projected in inland transition zones toward continental climates, particularly in parts of the Aegean and Black Sea regions. The current suitable distribution area across the country, approximately 18.48%, could rise to 18.55% by 2040 under the SSP2-4.5 scenario and to 18.76% by 2060 under the SSP5-8.5 scenario. However, without human intervention, the species’ establishment in these new suitable distribution areas is not considered possible. Moreover, it has been determined that the suitable distribution area of the species could decrease to 17.48% by 2060 under the SSP2-4.5 scenario and to 17.31% by 2080 under the SSP5-85 scenario. This result indicates that there could be a loss of more than 8% of the suitable distribution area between 2060 and 2080, according to the SSP5-8.5 scenario. Conversely, limited expansions may occur in specific areas, including the northern Aegean and the Hatay-Antep region. By 2100, despite periodic fluctuations, a net decline in suitable habitats is expected under both scenarios. Notably, spatial analysis reveals that while some newly suitable areas may emerge, natural migration will likely be insufficient for population persistence, necessitating human-assisted adaptation strategies. These findings underscore the need for proactive conservation measures, such as identifying climate-resilient provenances, assisted migration, and targeted reforestation in future suitable zones. Given that most Turkish forests are state-managed, collaboration with the General Directorate of Forestry is essential to integrate climate adaptation into long-term management plans. This study provides a framework for mitigating climate-induced habitat loss in L. nobilis while offering insights applicable to other vulnerable Mediterranean species facing similar threats. Full article

The accelerated growth of cities has intensified interest in the ecosystem services provided by urban forests, increasingly conceptualized as socio-ecological systems (SESs). This study presents a structured narrative review combined with bibliometric analysis of research published between 2010 and 2025 to examine how urban forests are addressed in relation to ecosystem service provision and climate change adaptation. The literature search and screening process followed procedures informed by the PRISMA framework to enhance transparency in the identification and selection of relevant studies. The results reveal a marked increase in scientific production during the last decade, with approximately 70% of publications concentrated in five countries: the United States, China, Italy, Canada, and Brazil. Although research methodologies are diverse, a strong bias toward quantitative ecological models—particularly tools such as i-Tree—persists, often prioritizing carbon sequestration while overlooking social dimensions of urban forest governance. A key finding is the disconnect between objectively modeled ecosystem services and the benefits perceived by citizens, which may influence the long-term sustainability and acceptance of urban green infrastructure. In addition, emerging research highlights the importance of considering ecosystem disservices, such as allergenic pollen, infrastructure conflicts, or maintenance costs, within urban forest planning. Finally, the review identifies a significant research gap in Latin America and the Caribbean, where rapid urbanization requires context-specific socio-ecological approaches. Advancing urban forest management therefore requires transdisciplinary frameworks that integrate ecological processes, social perception, governance, and climate adaptation to support more resilient and equitable cities. Full article

Pinyon–juniper (PJ) woodlands, one of the most extensive mature and old-growth woodland types in the Western United States, provide critical ecological, cultural, and economic benefits but face increasing threats from climate change, altered disturbance regimes, invasive species, and pests. We developed the PJ Woodland Climate Adaptation Management Menu, a decision support tool designed to guide adaptive, climate-informed management of PJ ecosystems, particularly within the Colorado Plateau ecoregion. The menu was created through an iterative, collaborative process involving literature review, integration of strategies from existing adaptation frameworks, and extensive input from scientists, land managers, and community partners during workshops and focus groups. The menu links specific, evidence-based approaches to each of six broad strategies, including soliciting community input, mitigating disturbance, enhancing and maintaining biodiversity, conserving ecotones, timing actions for optimal outcomes, and accepting climate-driven changes when appropriate. It is intended for use with the Adaptation Workbook to help managers connect local goals and climate vulnerabilities to tailored management tactics. Hypothetical scenarios demonstrate the menu’s application to contrasting PJ woodland conditions, from die-off events to old-growth maintenance. Lessons learned during development underscore the value of early stakeholder engagement, cross-sector collaboration, and balancing diverse ecological objectives. This menu offers a flexible, transferable framework to strengthen climate resilience in PJ woodlands and serves as a model that could improve adaptation planning in other dryland forest ecosystems. Full article

Forests worldwide are increasingly affected by climate-driven stressors and large-scale disturbances that impair tree physiology, disrupt water and carbon balance, and increase mortality risk. In this context, successful natural and artificial regeneration is essential for maintaining forest continuity, carbon storage, and biodiversity. However, regeneration outcomes depend not only on site conditions but also on biotic pressures, especially browsing by cervids in temperate and boreal forests. The aim of this review was to identify and synthesize evidence on how silvicultural methods can reduce browsing damage in forest regeneration and to assess how these methods influence the underlying drivers of cervid pressure through stand structure and forage availability. We examine mechanisms operating at two spatial scales: at the microscale, regeneration type, planting density, structural heterogeneity, planting stock, and how species mixture influences browsing probability and intensity; at the macroscale, how cutting systems and the spatial and temporal arrangement of harvests shape foraging landscapes by concentrating or dispersing browse resources and edge habitats. The reviewed evidence shows that dense, structurally diverse natural regeneration can dilute browsing pressure, whereas uniform artificial regeneration may increase repeated damage, and that species composition and mixture patterns can either protect or expose palatable species. We conclude that integrating microscale regeneration design with landscape-level harvest planning can strengthen stand resilience, reduce dependence on fencing, and support climate-adaptive forest development. To the best of our knowledge, no previous review has synthesized this evidence across both micro- and macroscale silvicultural contexts. Although most of the studies included in this review originate from Europe, we believe that the knowledge presented here is relevant to the majority of boreal and temperate forests worldwide. Full article

Urbanisation reshapes Land Cover and Land Use (LCLU) by driving deforestation, wetland loss, and the conversion of natural and agricultural areas into built environments. However, integrated analyses of LCLU change in response to climate variability in topographically complex, rapidly urbanising African cities remain limited. Therefore, this study examined 2000–2024 LCLU changes in hilly Gasabo District (Kigali, Rwanda) using 30 m Landsat imagery and a Random Trees classifier (92.7% accuracy, 70/30 train-test split), with 2032 projections via a population-driven hybrid trend model. Population estimates/projections 320,516 in 2002 to 967,512 in 2024, 1.41 million by 2032, were derived from Rwanda’s census data and exponential growth modelling (calibrated to 5.05% annual growth). Rapid population growth has driven a 539% expansion of Built-up Areas, accompanied by notable declines in cropland and Forest. Local climate trends (Meteo Rwanda stations) aligned with global datasets (ERA5-Land and CHIRPS): rainfall fluctuation and temperature rose, with strong correlations between population-driven Built-up Areas expansion. From 2024 to 2032, LCLU projections indicate that Built-up Areas will continue to expand by 29.5%. Cropland was forecast to decline to 15.9%, while Forest loss slowed to 5.7%. MLR analysis revealed strong correlations between population-driven expansion of Built-up Areas, cropland/forest loss, warming, and rainfall fluctuations in Gasabo. An ARDL model was applied to address multicollinearity among LCLU predictors, which limited the interpretation of individual coefficients, and confirmed the core MLR correlation trends, with statistically significant (p < 0.05) coefficients. The results highlight the need for data-driven spatial planning in Gasabo (stricter zoning, high-rise buildings, targeted reforestation, climate-resilient green infrastructure) to mitigate population and urbanisation-driven environmental degradation. Full article

Compound Drought and Heatwave (CDH) events increasingly threaten terrestrial carbon uptake, yet the spatiotemporal heterogeneity of Gross Primary Productivity (GPP) responses in urban agglomerations remains unclear. This study analyzed CDH impacts in China’s three major urban agglomerations, namely the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, using ERA5 and satellite GPP data (GOSIF and FluxSat) for representative CDH years (2007 for BTH; 2022 for YRD and PRD). CDH conditions exhibited a coherent hot–dry coupling, with temperature anomalies of 0.46–1.26 K and soil moisture deficits of −0.042 to −0.169 m³ m⁻³, accompanied by enhanced atmospheric dryness. Pronounced spatial heterogeneity in GPP responses aligned with regional climatic regimes and ecosystem types. The water-limited BTH region exhibited significant GPP deficits, with anomalies of −1.13 Standard Deviations (STD) and −0.96 STD for GPP_FluxSat and GPP_GOSIF, respectively. Conversely, the energy-limited regions showed positive anomalies: the YRD recorded +0.32 and +1.79 STD, while the PRD reached +1.86 and +1.06 STD for GPP_FluxSat and GPP_GOSIF, respectively. Mechanistically, the north–south contrast suggests a transition from water-limited vulnerability to energy-limited resilience, with vegetation traits and management (e.g., potential irrigation buffering in croplands and deeper water access in forests) modulating sensitivity to atmospheric dryness. These findings provide quantitative benchmarks for improving regional carbon-cycle assessments and adaptation planning under increasing compound extremes. Full article

Mediterranean cork oak forests provide essential ecosystem services but face increasing threats from climate change, ecosystem simplification, and oak decline. Ensuring their long-term sustainability requires governance approaches that integrate regional planning frameworks with international certification standards. This study presents a pioneering case of public cork oak forest management in Alà dei Sardi, Sardinia (Italy), where municipal forest planning was aligned with national and regional regulations and further enhanced through Forest Stewardship Council^® (FSC^®) certification. The FSC system offers internationally recognized standards and the Ecosystem Services Procedure (FSC-PRO-30-006 v2-1) to verify responsible forest management and quantify key ecosystem benefits. The Alà dei Sardi forest is the first publicly owned municipal cork oak forest to achieve FSC Forest Management certification, with demonstrated positive impacts of its management activities on biodiversity conservation, carbon sequestration and storage, water protection, soil conservation, and recreational services. The certification process integrated management planning, stakeholder engagement, monitoring, and adaptive interventions, showing that public institutions can combine legal frameworks with voluntary standards to enhance ecological performance, accountability, and socio-economic value. This case illustrates a potentially scalable and replicable model for sustainable forest governance, linking territorial planning with market-based mechanisms, and provides a practical example of governance for resilient and multifunctional forest systems. Full article