Burned Areas Mapping Using Sentinel-2 Data and a Rao’s Q Index-Based Change Detection Approach: A Case Study in Three Mediterranean Islands’ Wildfires (2019–2022)

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 830 - 830

Published: Feb. 27, 2025

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 2022, namely Sardinia (2019, Italy), Thassos (2022, Greece), Pantelleria Italy). Applying Rao’s Q Index-based approach Sentinel-2 spectral data derived indices, we evaluate their effectiveness accuracy identifying mapping burned wildfires. Our methodological implies processing analysis pre- post-fire imagery extract relevant indices such as Normalized Burn Ratio (NBR), Mid-infrared Index (MIRBI), Difference Vegetation (NDVI), Burned area for (BAIS2) then use (the classic approach) or combine them (multidimensional detect using a technique. The Copernicus Emergency Management System (CEMS) were used assess validate all results. lowest overall (OA) classical mode was 52%, BAIS2 index, while multidimensional mode, it 73%, combining NBR NDVI. highest result reached 72% with MIRBI 96%, NBR. combination consistently achieved across areas, demonstrating its improving classification regardless characteristics.

Language: Английский

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Enhancing forest fire susceptibility mapping in Xichang City, China using DBSCAN-based non-fire point selection integrated with deep neural network

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 2, 2025

Forest fire susceptibility mapping plays a crucial role in forest management and disaster prevention. However, existing research often neglects the selection of non-fire data during model construction, resulting limited prediction accuracy. To address this issue, we propose an innovative DBSCAN-DNN that optimizes to enhance precision. Using VIIRS GLC_FCS30D datasets, created spatial database for Xichang's dry seasons from 2012 2022, incorporating topography, meteorology, vegetation, human activities. Based on this, employed DBSCAN algorithm cluster points accurately delineated affected areas. Subsequently, selected samples outside these regions training DNN model. Through comparative experiments, found exhibited excellent performance predicting Xichang City, with AUC value 0.925 significant improvements accuracy (0.834), precision (0.800), recall (0.891), F1-score (0.843), Kappa coefficient (0.669). Additionally, conducted SHAP analysis delve into contributions interactions various factors influencing susceptibility. This finding offers valuable insights selecting sample

Language: Английский

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Study on Small-Scale Forest Fire Risk Zoning Based on Random Forest and the Fuzzy Analytic Network Process

Forests, Journal Year: 2025, Volume and Issue: 16(1), P. 97 - 97

Published: Jan. 9, 2025

Forest fire risk mapping is an essential measure for forest management. Quickly and precisely assessing risks, rationally planning zones, scientifically allocating firefighting resources are of great significance mitigating the increasingly severe threat fires. This study utilized random (RF) algorithm Fuzzy Analytic Network Process (FANP) to conduct a risk-zoning in protection development belt Wuyishan National Park. The findings revealed that some areas western southern parts this region have relatively high levels. Particularly, prevention control area need be strengthened prevent potential hazards accuracy FANP model was as 88.5%; with levels grade 3 above could 98.44% fires, proportion 4 33.41%, which 65.63% finding indicates has preferable applicability small-scale zoning can offer more reliable decision-making support reference basis regional

Language: Английский

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Monitoring Forest Disturbances and Associated Driving Forces in Guangdong Province Using Long-Term Landsat Time Series Images

Forests, Journal Year: 2025, Volume and Issue: 16(1), P. 189 - 189

Published: Jan. 20, 2025

Research on monitoring forest disturbances and analyzing its driving factors is crucial for the sustainable management of ecosystems. To quantitatively identify spatial distribution dynamic changes disturbance in Guangdong Province from 1990 to 2019, long-term Landsat time series imagery LandTrendr change detection algorithm were utilized. The impact four types landscape fragmentation (attrition, perforation, shrinkage, subdivision) was analyzed using Forman index. Geodetector model used analyze human activity natural environment. results showed that achieved a Kappa coefficient 0.79, with an overall accuracy approximately 82.59%. findings indicate consistent increase shrinkage patches, both quantity area. Spatially, centroids processes exhibited clear inland migration trend, reflecting growing ecological pressures faced by Furthermore, interactions among factors, particularly between population density economic significantly amplified their combined impacts. correlation socio-economic revealed distinct regional variations, highlighting significant differences dynamics across cities varying levels development. This study provides critical insights into spatiotemporal under rapid urbanization It lays groundwork strategies may contribute global discussions managing ecosystems during periods transformation.

Language: Английский

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Post-Fire Forest Ecological Quality Recovery Driven by Topographic Variation in Complex Plateau Regions: A 2006–2020 Landsat RSEI Time-Series Analysis

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 502 - 502

Published: March 12, 2025

Forest fires are an important disturbance that affects ecosystem stability and pose a serious threat to the ecosystem. However, recovery process of forest ecological quality (EQ) after fire in plateau mountain areas is not well understood. This study utilizes Google Earth Engine (GEE) Landsat data generate difference indices, including NDVI, NBR, EVI, NDMI, NDWI, SAVI, BSI. After segmentation using Simple Non-Iterative Clustering (SNIC) method, were input into random (RF) model accurately extract burned area. A 2005–2020 remote sensing index (RSEI) time series was constructed, post-fire EQ evaluated through Theil–Sen slope estimation, Mann–Kendall (MK) trend test, analysis, integration with topographic information systems. The shows (1) from 2006 2020, improved year by year, average annual increase rate 0.014/a. exhibited overall “decline initially-fluctuating increase-stabilization”, indicating RSEI can be used evaluate complex mountainous regions. (2) Between forests significant increasing spatially, 84.32% showing notable growth RSEI, while 1.80% regions experienced declining trend. (3) coefficient variation (CV) area 0.16 during period 2006–2020, good recovery. (4) Fire has impact on low-altitude areas, steep slopes, sun-facing slow. offers scientific evidence for monitoring assessing also inform restoration management efforts similar areas.

Language: Английский

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Wildfire response of forest species from multispectral LiDAR data. A deep learning approach with synthetic data

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102612 - 102612

Published: April 22, 2024

Forests play a crucial role as the lungs and life-support system of our planet, harbouring 80% Earth's biodiversity. However, we are witnessing an average loss 480 ha forest every hour because destructive wildfires spreading across globe. To effectively mitigate threat wildfires, it is to devise precise dependable approaches for forecasting fire dynamics formulating efficient management strategies, such utilisation fuel models. The objective this study was enhance classification that considers only structural information, Prometheus model, by integrating data on responses various tree species other vegetation elements, ground litter shrubs. This distinction can be achieved using multispectral (MS) Light Detection Ranging (LiDAR) in mixed forests. methodology involves novel approach semantic classifications forests generating synthetic with labels regarding reflectance information at different spectral bands, real MS scanner device would detect. Forests, which highly intricate environments, present challenges accurately classifying point clouds. address complexity, deep learning (DL) model trained clouds formats achieve best performance when leveraging data. Forest plots region were scanned Terrestrial Laser Scanning sensors wavelengths 905 1550 nm. Subsequently, interpolation process applied generate each plot, DL classify them. These surpassed thresholds 90% 75% accuracy intersection over union, respectively, resulting more categorisation models based distinct elements fire. results reveal potential LiDAR improving retrieval ecosystems enhancing wildfire efforts.

Language: Английский

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Post-fire vegetation dynamic patterns and drivers in greater Hinggan Mountains: Insights from long-term remote sensing data analysis

Ecological Informatics, Journal Year: 2024, Volume and Issue: 83, P. 102850 - 102850

Published: Oct. 9, 2024

Language: Английский

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Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data

Forests, Journal Year: 2024, Volume and Issue: 15(9), P. 1523 - 1523

Published: Aug. 29, 2024

Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses health threats human societies. Therefore, it is imperative map forest-fire risk mitigate the likelihood of occurrence. In this study, we utilized hierarchical analysis process (AHP), comprehensive weighting method (CWM), random forest Anning River Valley Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic (PV), normalized difference index (NDVI), plant species, land use, type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance road, residential predisposing factors. derived following conclusions. (1) Overlaying historical fire points with mapped revealed an accuracy that exceeded 86%, indicating reliability results. (2) primarily occur February, March, April, typically months characterized by very low rainfall dry conditions. (3) Areas high medium were mainly distributed Dechang Xide counties, while low-risk areas most prevalent Xichang city Mianning country. (4) Rainfall, NPV emerged main influencing factors, exerting dominant role occurrence fires. Specifically, higher coverage correlates increased fire. conclusion, study represents novel approach incorporating PV key factors triggering By mapping risk, have provided robust scientific foundation decision-making support for effective management strategies. This research significantly contributes advancing ecological civilization fostering sustainable development.

Language: Английский

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Spatiotemporal evolution and driving mechanism of Dongting Lake based on 2005–2020 multi-source remote sensing data

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102822 - 102822

Published: Sept. 1, 2024

Language: Английский

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Integration of the AHP Method and GIS Techniques for Mapping Areas Susceptible to Forest Fires in the Southern Amazon Region (Peru)

International Journal of Design & Nature and Ecodynamics, Journal Year: 2024, Volume and Issue: 19(3), P. 769 - 778

Published: June 25, 2024

In recent decades, the occurrence of forest fires has increased, causing damage to wild flora and fauna.For this reason, it is necessary determine areas susceptible phenomenon thus implement policies for its management.In study, AHP GIS method were used map in province Rodrí guez de Mendoza located southern Amazon region Peru, using climatic variables (Temperature, Precipitation Wind Speed), topographic (altitude, slope aspect), socioeconomic (proximity roads distance populated centers) biological (NDVI).The results indicate that 23.65% area high-risk class 19.05% very class.These risk levels are directly related topographic, meteorological, social variables, could trigger large-scale fires, generating losses diversity economic losses.It concluded 42.70% study classified as high areas, which makes take relevant measures reduce natural disasters; Furthermore, methodology research can be other provinces have similar conditions.

Language: Английский

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