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RSS FeedsRemote Sensing, Vol. 14, Pages 4714: Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review (Remote Sensing)

 
 

21 september 2022 15:10:08

 
Remote Sensing, Vol. 14, Pages 4714: Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review (Remote Sensing)
 


Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.


 
81 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 4722: Combining Deep Semantic Edge and Object Segmentation for Large-Scale Roof-Part Polygon Extraction from Ultrahigh-Resolution Aerial Imagery (Remote Sensing)
Remote Sensing, Vol. 14, Pages 4723: On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas (Remote Sensing)
 
 
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