Remote Sensing Applications-Society and Environment最新文献

{"title":"Deep-learning deforestation detection in the Legal Amazon area based on Sentinel-1 data","authors":"Renam Silva ,&nbsp;Ulisses S. Guimarães ,&nbsp;Diogo C. Garcia ,&nbsp;Hélcio Vieira Jr. ,&nbsp;Edson M. Hung","doi":"10.1016/j.rsase.2025.101747","DOIUrl":"10.1016/j.rsase.2025.101747","url":null,"abstract":"<div><div>The Amazon rainforest, the largest in the world, has been in the global spotlight for decades, given its size (more than half of the Brazilian territory), biodiversity and impact on global weather, economy, politics and on other ecosystems. Deforestation monitoring of the Amazon area can be a herculean task for governmental agencies, non-governmental organizations and other interested parties, especially during the region’s rainy season, roughly from October to May. During the drier season, it is possible to monitor large areas using optical satellite data to calculate temporal changes in the Normalized Difference Vegetation Index (NDVI), but during the rainy season the extremely clouded images render this method impractical. Synthetic Aperture Radar (SAR) imagery such as those from the Sentinel-1 mission, on the other hand, is insensitive to weather conditions, becoming a great candidate for deforestation monitoring, even though there is no NDVI equivalent for radar data. In this work, we propose a deep-learning method to detect new deforestation events in the Legal Amazon area using bi-temporal Sentinel-1 data, by segmenting former images for forest detection and latter images for deforestation. Results show that our solution is effective at drawing attention to areas that have undergone some sort of change consistent with deforestation patterns.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101747"},"PeriodicalIF":4.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of CNN architectures for satellite-based forest fire detection: A mobile-friendly approach using Sentinel-2 imagery","authors":"Cesilia Mambile,&nbsp;Judith Leo,&nbsp;Shubi Kaijage","doi":"10.1016/j.rsase.2025.101739","DOIUrl":"10.1016/j.rsase.2025.101739","url":null,"abstract":"<div><div>This study evaluates the performance of nine convolutional neural network (CNN) architectures for fire detection using Sentinel-2 satellite imagery from Mount Kilimanjaro National Park. It aims to identify the most effective models by balancing detection accuracy, computational efficiency, and deployment feasibility, especially in resource-constrained environments. The study employed a comparative analysis of traditional (AlexNet, VGG16, VGG19), advanced (ResNet-50, ResNet-101, Inception-v3), and mobile-friendly architectures (MobileNetV2, MobileNetV3, EfficientNet-B2). Despite a limited base set of 60 Sentinel-2 images, we derived 2940 image patches and applied augmentation to support robust model comparison. Performance was evaluated using metrics such as accuracy, precision, recall, and F1-score, while computational efficiency was assessed using FLOPs, inference time, and memory usage. Statistical validation using the Mann-Whitney <em>U</em> test ensured the reliability of the results. MobileNetV2 emerged as the optimal architecture for resource-constrained environments, achieving near-perfect performance metrics (precision, recall, and F1-score of 0.99) with minimal computational requirements (300M FLOPs, 12ms inference time). ResNet-101 demonstrated the highest accuracy (99 %) among advanced models but required substantial computational resources. The results highlight the importance of leveraging multi-spectral data, particularly Sentinel-2's short-wave infrared bands, for accurate fire detection. Statistical validation confirmed significant performance differences among models, with MobileNetV2 and ResNet-101 outperforming alternatives in their respective categories. While the evaluation focused on one ecological region and year, future work will extend this analysis across time and geography for broader generalization. This study bridges the gap between computational advancements and practical deployment needs by providing actionable insights into CNN model selection for real-time fire detection systems. It uniquely combines Sentinel-2's multi-spectral capabilities with advanced machine learning models, offering a scalable framework for addressing environmental challenges in resource-limited settings. The findings contribute to sustainable fire management practices and open new avenues for deploying CNNs in environmental monitoring.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101739"},"PeriodicalIF":4.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-based estimation of net radiation to support evapotranspiration modeling in agriculture","authors":"Chutimon Phoemwong,&nbsp;Rungrat Wattan,&nbsp;Somjet Pattarapanitchai,&nbsp;Serm Janjai","doi":"10.1016/j.rsase.2025.101746","DOIUrl":"10.1016/j.rsase.2025.101746","url":null,"abstract":"<div><div>Net radiation (<em>R</em>_<em>n</em>) is a fundamental variable in the surface energy balance and serves as a key input for estimating evapotranspiration (<em>ET</em>_<em>0</em>), which is critical for agricultural water management and irrigation planning. Accurate estimation of <em>R</em>_<em>n</em> is particularly important in large-scale agricultural regions where ground-based measurements are limited or unavailable. This study aims to investigate the spatiotemporal variation of surface <em>R</em>_<em>n</em> and to develop simplified multiple linear regression models for its estimation using satellite-derived atmospheric variables. The selected input variables chosen for their direct or indirect influence on <em>ET</em>_<em>0</em> include downward shortwave radiation (<em>S</em>_<em>d</em>) and the brightness temperature difference between bands 31 and 32 from MODIS, which indicates atmospheric water vapor content (<em>WP</em>). These are supplemented by relative humidity (<em>RH</em>), air temperature (<em>T</em>_<em>air</em>), and cloud cover (<em>C</em>), obtained from NCEP/NCAR reanalysis data. Ground-based observations of <em>R</em>_<em>n</em> were used as reference data to develop and validate the model. The dataset was divided into two parts: 2017–2021 for model development and 2022–2024 for validation. The resulting linear model showed high accuracy, with an <em>R</em>^<em>2</em> of 0.96, <em>RMSE</em> of 21.6 %, and <em>MBE</em> of −6.4 %. The validated model was applied to produce spatial <em>R</em>_<em>n</em> maps, which demonstrated strong agreement with in-situ data and effectively represented spatial and temporal variation. This modeling approach enhances the ability to estimate <em>R</em>_<em>n</em> over large agricultural areas, thereby supporting more reliable <em>ET</em>_<em>0</em> estimation and water resource management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101746"},"PeriodicalIF":4.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing future flood risk using remote sensing and explainable machine learning: A case study in the Beijing-Tianjin-Hebei region","authors":"Siying Li ,&nbsp;Yi Tang ,&nbsp;Yuting Zhao ,&nbsp;Xiaojun Ning ,&nbsp;Yifan Zhang ,&nbsp;Siran Lv ,&nbsp;Chenshu Liu","doi":"10.1016/j.rsase.2025.101742","DOIUrl":"10.1016/j.rsase.2025.101742","url":null,"abstract":"<div><div>Flood disasters pose increasingly severe threats to densely populated and economically critical regions under changing climate conditions. In this study, we conducted a comprehensive flood risk assessment of the Beijing-Tianjin-Hebei (BTH) region, integrating multi-source remote sensing data and explainable machine learning methods. First, flood inundation areas during the 2023 extreme rainfall event were identified using Sentinel-1 SAR imagery. Based on the assessment framework of hazard, exposure, and vulnerability, key factors influencing flood risk were quantified using a XGBboost model and SHAP (Shapley Additive Explanations) analysis. The results revealed that terrain ruggedness, elevation, precipitation, dependency ratio, and GDP (Gross Domestic Product) were the primary drivers of flood risk distribution. Subsequently, future flood risk patterns for 2030 were projected under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585), combining projected environmental and socio-economic variables with the established model. The findings indicate a clear trend of flood risk intensification under higher emission scenarios, with high-risk areas expanding significantly under SSP370 and SSP585. These results emphasize the urgent need for differentiated flood management strategies, combining climate mitigation, resilient urban planning, and adaptive infrastructure development to effectively reduce future flood risks. It provides a scientific basis for climate-resilient disaster risk governance in rapidly urbanizing regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101742"},"PeriodicalIF":4.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating stepwise residual refinement and explainable AI for interpretable forest volume modeling in Hokkaido, Japan","authors":"Kotaro Iizuka ,&nbsp;Nobuo Ishiyama ,&nbsp;Yasutaka Nakata","doi":"10.1016/j.rsase.2025.101740","DOIUrl":"10.1016/j.rsase.2025.101740","url":null,"abstract":"<div><div>Accurate estimation of forest stem volume is essential for effective forest resource management and carbon accounting. However, spatial heterogeneity in forest conditions often leads to systematic modeling errors, especially across ecological and operational gradients. This study proposes an integrated framework that combines XGBoost-based modeling with a novel Stepwise Residual Refinement (SRR) approach and explainable AI techniques utilizing SHapley Additive exPlanations (SHAP) to enhance both prediction accuracy and model interpretability. The framework was applied to forest inventory and remote sensing data across Hokkaido, Japan, incorporating topographic, climatic, structural, and socioeconomic variables. The initial XGBoost model achieved a root mean square error (RMSE) of 170.21 m³/ha and a percentage RMSE (%RMSE) of 36.90 %. Following the application of SRR corrections, the final model improved significantly, yielding an RMSE of 105.75 m³/ha and a %RMSE of 22.93 %. KernelSHAP analysis revealed region-specific patterns of variable influence, highlighting how environmental and human factors differentially shape forest volume across regions. SHAP-derived zoning delineated clusters of forest quality that aligned with workforce presence and ecological conditions, particularly in conifer-dominated areas. These results demonstrate the importance of integrating explainable AI and spatial refinement to uncover nuanced forest dynamics and support adaptive, data-driven forest management. This study highlights how interpretable machine learning can simultaneously improve predictive accuracy and reveal latent socio-ecological processes that drive forest conditions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101740"},"PeriodicalIF":4.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved soil moisture mapping using an integrated cyclic modeling and bias correction approach","authors":"Yajie Shi ,&nbsp;Wei Dai ,&nbsp;Guangsheng Chen ,&nbsp;Xi Zhang ,&nbsp;Nan Li ,&nbsp;Weijun Fu","doi":"10.1016/j.rsase.2025.101741","DOIUrl":"10.1016/j.rsase.2025.101741","url":null,"abstract":"<div><div>Soil moisture (SM) is crucial for climate change, crop growth estimation, and environmental hazard monitoring. Existing SM products often have low spatial resolution, limiting their use in local-scale studies. While various machine learning (ML) methods have been applied to downscale SM, few studies have explored multiple cyclic modeling or improved downscaling accuracy by recycling qualified stations. In this study, we performed quality control and bias correction on data from the International Soil Moisture Network (ISMN) stations. We obtained qualified sites by cyclic modeling using an extreme gradient boosting (XGBoost) regression model. The predicted bias from cyclic modeling was combined with dynamic environmental variables to correct errors at unqualified sites. Finally, a surface (0–5 cm) soil moisture product with a temporal and spatial resolution of 500 m/day was produced: (1) the XGBoost model described the relationship between SM and environmental variables well, achieving a correlation coefficient (R) of 0.98 and a root mean square error (RMSE) of 0.007 m³/m³ (2) The generated 500 m SM data was comparable to the Soil Moisture Active Passive Level 4 (SMAP-L4) SM data, with 83.2 % of the 1996 points having R &gt; 0.6. The downscaling accuracy is improved by robust cyclic modeling and bias correction techniques, with R, RMSE, and mean absolute error (MAE) improved by 6.5 %, 9.3 %, and 9.6 %, respectively, over single-shot modeling. The estimated results of the surface layer (0–5 cm) soil moisture at 500 m/day can supplement the regional soil moisture database and provide ideas for downscaling soil moisture research.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101741"},"PeriodicalIF":4.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-criteria based optimal niche analysis of seasonal productivity in the Bay of Bengal using MODIS data","authors":"Md Asif Hasan ,&nbsp;Mir Md Tasnim Alam ,&nbsp;Md Zayed Abdur Razzak ,&nbsp;Anika Nawar Mayeesha","doi":"10.1016/j.rsase.2025.101743","DOIUrl":"10.1016/j.rsase.2025.101743","url":null,"abstract":"<div><div>Despite receiving enormous riverine nutrient inputs, the Bay of Bengal (BoB) has a long-standing biogeochemical paradox of comparatively low open-ocean productivity. To understand its long-term trajectory, this study analyzes a consistent two-decade (2003–2022) satellite dataset of Sea Surface Temperature (SST) and Chlorophyll-a (Chl-a) from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua. Our analysis reveals a significant basin-wide warming trend of +0.019 °C/year and spatially heterogeneous changes in productivity, with a pronounced apparent increasing Chl-a trend in the northern coastal zone (&gt;1 μg/m³/year<strong>)</strong> and a slight decline in open-ocean regions. In areas of nutrient-driven productivity, a weak but statistically significant negative correlation (Pearson's r = −0.204, p &lt; 0.05) between SST and Chl-a anomalies confirms the role of upwelling. As a secondary objective, we developed and applied an exploratory seven-factor Multi-Criteria Evaluation (MCE) model to synthesize these biophysical drivers based on weighted overlay and identify potential productivity hotspots. The model integrates data on SST and Chl-a suitability, thermal and biological fronts, upwelling potential, habitat stability, and depth. The MCE framework successfully identified key productive zones, including persistent biological fronts along the northern coast, characterized by chlorophyll gradients exceeding 6 mg/m³/km, a threshold corresponding to the 90th percentile of all observed gradient values. Notably, with scores &gt;0.7, the spring pre-monsoon period stood out as a time of widespread high productivity, casting doubt on traditional theories of basin-wide oligotrophy. This integrated approach provides a robust quantification of climate-driven trends and offers an exploratory framework for mapping productivity suitability zones, serving as an essential tool for ecosystem-based management in the BoB.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101743"},"PeriodicalIF":4.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale mangrove mapping in Thailand using multi-sensor ensemble machine learning with sentinel-1/2 and SRTM data","authors":"Surachet Pinkaew ,&nbsp;Werapong Koedsin ,&nbsp;Jonathan Cheung-Wai Chan ,&nbsp;Alfredo Huete","doi":"10.1016/j.rsase.2025.101744","DOIUrl":"10.1016/j.rsase.2025.101744","url":null,"abstract":"<div><div>Mangrove ecosystems provide critical ecological services but face increasing pressure from anthropogenic activities and climate change. Accurate large-scale mapping is essential for effective conservation strategies. We produced a 2024 national mangrove map by merging Sentinel-2 multispectral imagery, Sentinel-1 synthetic-aperture radar (SAR) backscatter and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The analysis domain comprised all Global Mangrove Watch (2023) polygons with a 2 km buffer. From these layers we derived 23 predictors, including six spectral bands, six vegetation indices (e.g., Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Mangrove Vegetation Index, MVI), four radar texture metrics (VV, VH, VV/VH ratio, contrast) and terrain variables(elevation, slope, aspect). Five widely used machine-learning classifiers—Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), Gradient Tree Boosting (GTB) and XGBoost—were combined through soft voting after grid-based hyper-parameter tuning. The ensemble achieved an overall accuracy of 97.0 %, outperforming individual models (95.8–96.9 %). Feature-importance analysis identified MVI as the strongest discriminator (0.209–0.720), followed by VV contrast (0.052–0.097) and elevation (0.044–0.089). The final map shows 2557 km² of mangroves distributed across 24 provinces, with 75 % located along the Andaman Sea coast. By blending complementary optical, radar and topographic information in a fully script-based Google Earth Engine (GEE) workflow, the study delivers an operationally scalable tool for national monitoring that supports conservation planning, carbon accounting and climate-adaptation policies.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101744"},"PeriodicalIF":4.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating machine learning models for enhanced permafrost distribution mapping using rock glaciers: A case study in Shaluli Mountain, Southeast Tibetan Plateau","authors":"Tingting Wu ,&nbsp;Xiaowen Wang ,&nbsp;Hailun Yuan ,&nbsp;Xiangbing Kong ,&nbsp;Jiaxin Cai ,&nbsp;Xin Guo ,&nbsp;Guoxiang Liu","doi":"10.1016/j.rsase.2025.101745","DOIUrl":"10.1016/j.rsase.2025.101745","url":null,"abstract":"<div><div>Rock glaciers are widely used as indirect indicators for modeling permafrost distribution, particularly in remote mountain regions with limited in-situ observations. However, previous studies have often relied on empirically selected models and predictor variables, leaving their impacts on mapping accuracy unclear. In this study, we focus on the Shaluli Mountain region in the southeastern Tibetan Plateau to conduct permafrost distribution mapping driven by rock glaciers, with an emphasis on model evaluation. Using an interferometric synthetic aperture radar (InSAR)-assisted method, we compiled an inventory of 236 active and 229 relict rock glaciers in the study area. We then evaluated multiple machine learning models and environmental predictors, identifying logistic regression (LR) with mean annual air temperature (MAAT) and potential incoming solar radiation (PISR) as the most effective combination. The optimal model achieved 82 % accuracy (Kappa = 0.64), producing a 90 m resolution permafrost favorability index (RG-PFI) map. Our results estimate permafrost coverage at 1554 km² (20.2 % of the study area), primarily between 4750 and 5200 m elevation. Compared to four existing permafrost maps, RG-PFI demonstrated a 3 %–13 % improvement in classification accuracy. This study underscores the importance of integrating robust statistical modeling with high-quality rock glacier inventories to enhance permafrost mapping in data-scarce regions. Additionally, our findings highlight the urgent need to address permafrost degradation risks posed by climate warming, which threaten critical infrastructure such as the under-construction railway.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101745"},"PeriodicalIF":4.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Canopy spectral responses of temperate forests to late spring frost and hot drought events assessed with Sentinel-2 NDVI time series","authors":"Michele Dalponte ,&nbsp;Davide Andreatta ,&nbsp;David A. Coomes ,&nbsp;Luca Belelli Marchesini ,&nbsp;Daniele Marinelli ,&nbsp;Loris Vescovo ,&nbsp;Damiano Gianelle","doi":"10.1016/j.rsase.2025.101737","DOIUrl":"10.1016/j.rsase.2025.101737","url":null,"abstract":"<div><div><em>Extreme climatic events (ECEs) are projected to increase due to climate change, but we still have limited understanding of how these events affect the functioning of forest ecosystems. Each species may react differently to ECEs, depending on their ecology, but we lack a regional perspective on these responses. Here we tracked intra-annual changes in the canopy greenness (i.e. NDVI from Sentinel-2 imagery) of 16 tree species growing within</em> 3000 km² <em>of forests of the Italian Alps. The study region was subject to a late spring frost event in May</em> <em>2019, and a hot drought in July 2022, allowing us to quantify species responses to ECEs by comparison of seasonal trends in NDVI observed over the period 2018–2024. The effects of 2019 frost were very localized and mainly affected the canopy spectral response and phenology of</em> Fagus sylvatica L. <em>in areas around 1000 m a.s.l.. There, trees had developed buds and some juvenile leaves when frost occurred, resulting in the wilting or dropping of the earliest leaves, and slower green-up phase but no lasting impacts. The hot drought had its largest impact on</em> Quercus ilex L. <em>forests growing at low elevations: there was a clear decrease in canopy greenness from July onwards in 2022, but no residual impacts were observed the following years. At higher elevations, some species had unusually</em> <em>green</em> <em>canopies in response to the heatwave suggesting they benefitted from warmer conditions</em>.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101737"},"PeriodicalIF":4.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}