Search Results (151)

Wildfires threaten ecosystems, biodiversity, and human livelihood while exacerbating climate change. Accurate identification and monitoring of burned areas (BA) are critical for effective post-fire recovery and management. Although satellite multi-spectral imagery offers a practical solution for BA monitoring, existing methods often prioritize specific spectral bands while neglecting full spectral shape information, which encapsulates overall spectral characteristics. This limitation compromises adaptability to diverse vegetation types and environmental conditions, particularly across varying spatial scales. To address these challenges, we propose the time-series spectral-angle-normalized burn index (TSSA-NBR). This unsupervised BA extraction method integrates normalized spectral angle and normalized burn ratio (NBR) to leverage full spectral shape and temporal features derived from Sentinel-2 time-series data. Seven globally distributed study areas with diverse climatic conditions and vegetation types were selected to evaluate the method’s adaptability and scalability. Evaluations compared Sentinel-2-derived BA with moderate-resolution products and high-resolution PlanetScope-derived BA, focusing on spatial scale and methodological performance. TSSA-NBR achieved a Dice Coefficient (DC) of 87.81%, with commission (CE) and omission errors (OE) of 8.52% and 15.58%, respectively, demonstrating robust performance across all regions. Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. The method effectively captured fire scars and outperformed band-specific and threshold-dependent approaches by integrating spectral shape features with fire indices, establishing a data-driven framework for BA detection. These results underscore its potential for fire monitoring and broader applications in detecting surface anomalies and environmental disturbances, advancing global ecological monitoring and management strategies. Full article

Accurate and rapid delineation of wildfire-affected areas is essential in the era of climate-driven increases in fire frequency. This study compares and analyzes four techniques for identifying wildfire-affected areas using Sentinel-2 satellite imagery: (1) calibrated differenced Normalized Burn Ratio (dNBR); (2) differenced NDVI (dNDVI) with empirically defined thresholds (0.04–0.18); (3) supervised SVM classifiers applying linear, polynomial, and RBF kernels; and (4) unsupervised ISODATA clustering. In particular, this study proposes an SVM-based classification method that goes beyond conventional index- and threshold-based approaches by directly using the SWIR, NIR, and RED band values of Sentinel-2 as input variables. It also examines the potential of the ISODATA method, which can rapidly classify affected areas without a training process and further assess burn severity through a two-step clustering procedure. The experimental results showed that SVM was able to effectively identify affected areas using only post-fire imagery, and that ISODATA enabled fast classification and severity analysis without training data. This study performed a wildfire damage analysis through a comparison of various techniques and presents a data-driven framework that can be utilized in future wildfire response and policy-oriented recovery support. Full article

Semi-Mediterranean (SM) and semi-arid (SA) regions, exemplified by the Kurdo-Zagrosian forests in western Iran and northern Iraq, have experienced frequent wildfires in recent years. This study proposes a modified Non-Negative Matrix Factorization (NMF) method for detecting fire-prone areas using satellite-derived data in SM and SA forests. The performance of the proposed method was then compared with three other already proposed NMF methods: principal component analysis (PCA), K-means, and IsoData. NMF is a factorization method renowned for performing dimensionality reduction and feature extraction. It imposes non-negativity constraints on factor matrices, enhancing interpretability and suitability for analyzing real-world datasets. Sentinel-2 imagery, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Zagros Grass Index (ZGI) from 2020 were employed as inputs and validated against a post-2020 burned area derived from the Normalized Burned Ratio (NBR) index. The results demonstrate NMF’s effectiveness in identifying fire-prone areas across large geographic extents typical of SM and SA regions. The results also revealed that when the elevation was included, NMF_L1/2-Sparsity offered the best outcome among the used NMF methods. In contrast, the proposed NMF method provided the best results when only Sentinel-2 bands and ZGI were used. Full article

The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions. Full article

Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE) accessed in January 2025, we applied remote sensing techniques to assess burn severity and post-fire regrowth. Sentinel-2 imagery was used to compute the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI); burn severity was derived from differenced NBR (dNBR), and vegetation recovery was monitored via differenced NDVI (dNDVI) and multi-year NDVI time series. We uniquely compare recovery across four zones with different fire histories—unburned, single-burn (2017 or 2021), and repeated-burn (2017 and 2021)—providing a novel perspective on post-fire dynamics in Mediterranean ecosystems. Results show that low-severity zones recovered more quickly than high-severity areas. Repeated-burn zones experienced the slowest and least complete recovery, while unburned areas remained stable. These findings suggest that repeated fires may shift vegetation from forest to shrubland. This study highlights the importance of remote sensing for post-fire assessment and supports adaptive land management to enhance long-term ecological resilience. Full article

This study investigates vegetation dynamics in boreal forests of Interior Alaska, focusing on topography, fire history, and climate influences. The study area includes Bonanza Creek Experimental Forest (BCEF) and surrounding region, categorized by topography (upland, floodplain, lowland) and fire history. Using Mann–Kendall trend and Theil–Sen slope analyses on Landsat-derived spectral metrics: Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), we observed a shift from browning to greening trends, particularly in historically burned areas. The photosynthetic activity in burned upland converged with unburned areas ~30 years post-fire, coincident with a shift towards deciduous dominance during post-fire succession. Normalized Difference Moisture Index (NDMI) trends revealed a significant increase in vegetation moisture content across all topographies. We introduce Effective Seasonal Precipitation Index (ESPI), which combines prior-year annual precipitation with current-year spring snow depth. Its positive correlation with NDMI highlights its potential for monitoring vegetation moisture dynamics at the landscape scale. Furthermore, by correlating dendrochronology-based climate indices, we found strong correlation between NDMI and normalized Supplemental Precipitation Index (nSPI), across topographies. Overall, this research provides critical insights into how climate and fire influence interior boreal vegetation, highlighting the effects of increased precipitation, and topography on shaping differential vegetation responses across the landscape. Full article

High-resolution Sentinel-2 imagery combined with a deep learning (DL) segmentation model offers a promising approach for accurate mapping of small and fragmented agricultural burn areas. Initially, the model was trained using ICNF burn area data from Portugal to capture large fire and burn area delineation, thereby achieving moderate accuracy. Subsequent fine-tuning using annotated data from Punjab improved the model’s ability to detect small burn patches, demonstrating higher accuracy than the baseline Normalized Burn Ratio (NBR) Index method. On-ground validation using buffer zone analysis and crop field images confirmed the effectiveness of DL approach. Challenges such as cloud interference, temporal gaps in satellite data, and limited reference data for training persist, but this study underscores the methodogical advancements and potential of DL models applied for small burn area detection in agricultural settings. The model achieved overall accuracy of 98.7%, a macro-F1 score of 97.6%, IoU 0.54, and a Dice coefficient of 0.64, demonstrating its capability for detailed burn area delineation. The model can capture burn area smaller than 250 m², but the model at present is less efficient at representing the full extent of the fires. Overall, outcomes demonstrate the model’s applicability to generalize to a new domain despite regional differences among research areas. Full article

Rocky land cover provides vital habitat for many different species, including endemic, vulnerable, or threatened plants and animals; thus, various land management organizations prioritize the conservation of rocky habitat. Despite its importance, land cover classification maps rarely classify rocky land cover explicitly, and if they do, they are limited in spatial resolution or extent. Consequently, we used random forest models in Google Earth Engine (GEE) to classify rocky land cover at a high spatial resolution across a broad spatial extent in the Cascade Mountains and Columbia River Gorge in Washington, USA. The spectral indices derived from Sentinel-2 satellite data and NAIP aerial imagery, the specialized multi-temporal predictors formulated using time series of normalized burn ratio (NBR) and normalized difference in vegetation index (NDVI), and topographical predictors were especially important to include in the rocky land cover classification models; however, the predictors’ relative variable importance differed regionally. Beyond evaluating random forest models and developing classification maps of rocky land cover, we conducted three case studies to highlight potential avenues for future work and form connections to land management organizations’ needs. Our replicable approach relies on open-source data and software (GEE), aligns with the goals of land management organizations, and has the potential to be elaborated upon by future research investigating rocky habitats or other rare habitat types. Full article

Droughts present a significant global challenge, particularly to forest ecosystems in regions such as eastern New South Wales, Australia, which is known for its dry climate and frequent, intense droughts. Recent studies have indicated a notable increase in tree mortality and canopy browning across this area, especially during the recent extreme drought period culminating in the Black Summer of 2019–2020. Our study investigates the impacts of drought on eucalypt forests by leveraging remote sensing and field observation data to detect and analyse vegetation health and stress indicators. Utilising data from Sentinel-2, alongside historical Landsat observations, we applied multiple spectral vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), and Tasseled Cap Transformation, to assess the extent of drought impacts. We found NBR to show the most consistent agreement with ground-based observations of drought-related tree mortality. Additionally, by integrating ground-based data from the “Dead Tree Detective” citizen science project, we were able to validate the remote sensing outcomes with a 90.22% consistency, providing confirmation of the extensive spatial distribution and severity of the inferred impacts. Our findings reveal that 13.16% of eucalypt forests and woodlands across eastern New South Wales experienced severe stress associated with drought during the 2019–2020 Black Summer drought. This study demonstrates the utility of satellite-derived drought indicators in monitoring forest health and highlights the necessity for continuous monitoring and research to understand the factors that trigger tree vitality loss. Full article

This study explores the application of remote sensing-based land cover change detection techniques to identify and map areas affected by three distinct wildfire events that occurred in Mediterranean islands between 2019 and 2022, namely Sardinia (2019, Italy), Thassos (2022, Greece), and Pantelleria (2022, Italy). Applying Rao’s Q Index-based change detection approach to Sentinel-2 spectral data and derived indices, we evaluate their effectiveness and accuracy in identifying and mapping burned areas affected by wildfires. Our methodological approach implies the processing and analysis of pre- and post-fire Sentinel-2 imagery to extract relevant indices such as the Normalized Burn Ratio (NBR), Mid-infrared Burn Index (MIRBI), Normalized Difference Vegetation Index (NDVI), and Burned area Index for Sentinel-2 (BAIS2) and then use (the classic approach) or combine them (multidimensional approach) to detect and map burned areas by using a Rao’s Q Index-based change detection technique. The Copernicus Emergency Management System (CEMS) data were used to assess and validate all the results. The lowest overall accuracy (OA) in the classical mode was 52%, using the BAIS2 index, while in the multidimensional mode, it was 73%, combining NBR and NDVI. The highest result in the classical mode reached 72% with the MIRBI index, and in the multidimensional mode, 96%, combining MIRBI and NBR. The MIRBI and NBR combination consistently achieved the highest accuracy across all study areas, demonstrating its effectiveness in improving classification accuracy regardless of area characteristics. Full article

With the rising frequency and severity of wildfires that cause significant threats to ecosystems, public health and livelihoods, it is essential to have tools for evaluating and monitoring their impacts and the effectiveness of policy initiatives. This paper presents the development and implementation of a new calculation pipeline integrated with a web-based platform designed to provide georeferenced data on the burn severity of wildfires in mainland Portugal. The platform integrates a modular architecture that comprises a module in R and Google Earth Engine to compute standardized satellite-derived datasets on observed/historical severity for burned areas, integrated with a web portal module to facilitate the access, search, visualization, and downloading of the generated data. The platform provides open-access, multisource data from satellite missions, including MODIS, Landsat-5, -7, and -8, and Sentinel-2. It offers multitemporal burn severity products, covering up to 12 months post-fire, and incorporates three severity indicators, the delta NBR, relative difference NBR, and relativized burn ratio, derived from Normalized Burn Ratio (NBR) quarterly median composites. The platform’s modular and scalable framework also allows the integration of more spectral indices, burn severity indicators, and other wildfire perimeter databases. These design features also enable the platform to adapt to other contexts or regions beyond its current scope and regularly update burn severity products. Results from exploratory data analyses revealed the ability of satellite-based severity products to diagnose trends, assess interannual variability, and enable regional comparisons of burn severity, providing a basis for further research. In the face of climate change and societal challenges, the platform aims to support decision-making processes by providing authorities with standardized and updated information while promoting public awareness of wildfire challenges and, ultimately, contributing to the sustainability of rural landscapes. Full article

The intensity of wildfires has increased dramatically in recent decades; thus, better understanding the impact of land-management efforts in biodiversity conservation on post-wildfire recovery could highlight the value of these interventions. Field assessments are often costly; therefore, monitoring the effectiveness of applied conservation practices using remote sensing tools is critical. The main goal of this study is to develop and apply a remote sensing framework to assess the impact of conservation practices on post-fire recovery. We focused on a study area in northern California and southern Oregon, a region with diverse conservation practices and increased wildfire activity in the past decade. The proposed framework uses the MODIS dataset to identify fire burn events and Landsat to analyze the time series of an area-aggregated vegetation index, the Normalized Burn Ratio (NBR). Using the remote sensing framework, we confirmed our hypothesis that in areas lacking conservation protection practices, post-fire recovery is slower and more lingering. The median 5-year dNBR recovery for unprotected burn events was around 27%, compared to 37% across all other burn areas. Along with our primary goal of recovery analysis, we also examined fire severity across different conservation practices to identify moderate-to-severe fire events and to capture differences in fire characteristics for the areas under different conversation practices. This analysis revealed that unprotected areas experienced more severe fire events. We also investigated the impact of conservation practices across three dominant forest types in our study area: Dry-Mesic Conifer, Mesic Conifer, and Evergreen Forests. The disparity in post-wildfire recovery between protected and non-protected areas was most pronounced in burn areas dominated by Evergreen Forests. Using the proposed aggregated remote sensing framework, this study highlights the importance of conservation practices in wildfire recovery. This approach could provide a cost-efficient tool for assessing the effectiveness of land-management practices on wildfire recovery across the globe. Full article

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold. Full article

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies. Full article

Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to highly valued resources and assets (HVRAs)—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and HVRAs impacted by the 2017 West Mims event, which burned across both prescribed-fire treated and untreated areas within the Okefenokee National Wildlife Refuge. Wildfire severity was quantified using the differenced normalized burn ratio (dNBR) index, while treatment records were used to calculate the prescribed frequency and post-treatment duration, which is defined as the time elapsed between the last treatment and the West Mims event. A generalized additive model (GAM) was fit to model dNBR as a function of post-treatment duration, fire frequency, and vegetation type. Although dNBR exhibited considerable heterogeneity both within and between HVRAs and vegetation classes, areas treated with prescribed fire demonstrated substantial reductions in burn severity. The beneficial effects of prescribed fire were most pronounced within approximately two years post-treatment with up to an 88% reduction in mean wildfire severity. However, reductions remained evident for approximately five years post-treatment according to our model. The mitigating effect of prescribed fire was most pronounced in Introduced Upland Vegetation-Shrub, Eastern Floodplain Forests, and Longleaf Pine Woodland when the post-treatment duration was within 12 months. Similar trends were observed in areas surrounding red-cockaded woodpecker nesting sites, which is an HVRA of significant ecological importance. Our findings support the frequent application of prescribed fire (e.g., one- to two-year intervals) as an effective strategy for mitigating wildfire severity to HVRAs. Full article