Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

Abstract

The accurate detection of burned forest area is essential for post-fire management and assessment, and for quantifying carbon budgets. Therefore, it is imperative to map burned areas accurately. Currently, there are few burned-area products around the world. Researchers have mapped burned areas directly at the pixel level that is usually a mixture of burned area and other land cover types. In order to improve the burned area mapping at subpixel level, we proposed a Burned Area Subpixel Mapping (BASM) workflow to map burned areas at the subpixel level. We then applied the workflow to Sentinel 2 data sets to obtain burned area mapping at subpixel level. In this study, the information of true fire scar was provided by the Department of Emergency Management of Hunan Province, China. To validate the accuracy of the BASM workflow for detecting burned areas at the subpixel level, we applied the workflow to the Sentinel 2 image data and then compared the detected burned area at subpixel level with in situ measurements at fifteen fire-scar reference sites located in Hunan Province, China. Results show the proposed method generated successfully burned area at the subpixel level. The methods, especially the BASM-Feature Extraction Rule Based (BASM-FERB) method, could minimize misclassification and effects due to noise more effectively compared with the BASM-Random Forest (BASM-RF), BASM-Backpropagation Neural Net (BASM-BPNN), BASM-Support Vector Machine (BASM-SVM), and BASM-notra methods. We conducted a comparison study among BASM-FERB, BASM-RF, BASM-BPNN, BASM-SVM, and BASM-notra using five accuracy evaluation indices, i.e., overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), intersection over union (IoU), and Kappa coefficient (Kappa). The detection accuracy of burned area at the subpixel level by BASM-FERB’s OA, UA, IoU, and Kappa is 98.11%, 81.72%, 74.32%, and 83.98%, respectively, better than BASM-RF’s, BASM-BPNN’s, BASM-SVM’s, and BASM-notra’s, even though BASM-RF’s and BASM-notra’s average PA is higher than BASM-FERB’s, with 89.97%, 91.36%, and 89.52%, respectively. We conclude that the newly proposed BASM workflow can map burned areas at the subpixel level, providing greater accuracy in regards to the burned area for post-forest fire management and assessment.

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

“…The impact of severe forest fires on humans is evident in the forest's ecological environment and the difficulty of short-term recovery of forest resources. [4][5][6] After a forest fire, the burned area can provide a lot of information, including the distribution, extent, timing, frequency, and location of fire events, which is necessary for forest recovery and management, carbon emission estimation, etc. Vegetation recovery of burned areas is the basis of forest ecosystem structure and function restoration, while forest type is one of the key driving factors affecting vegetation succession in burned areas.…”

“…One of the main factors affecting changes in forest resources is forest fire, 3 including notable incidents, such as the Greater Khingan Mountains forest fire in China on May 6, 1987, the Liangshan forest fire in Sichuan Province on March 30, 2019, the Australian forest fire in July 2019, and the California wildfires in the United States on March 2018. The impact of severe forest fires on humans is evident in the forest’s ecological environment and the difficulty of short-term recovery of forest resources 4 – 6 After a forest fire, the burned area can provide a lot of information, including the distribution, extent, timing, frequency, and location of fire events, which is necessary for forest recovery and management, carbon emission estimation, etc.…”

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

“…Xu produces a sub-pixel mapping model incorporating a pixel-swapping algorithm for timely monitoring of forest fire sources [41]. Xu shows a Burned-Area Subpixel Mapping (BASM) workflow to position fire-trail areas at the sub-pixel-level [42]. Li X improves sub-pixel mapping results based on random forest classification results for forest fire smoke identification and sub-pixel positioning [43].…”