Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models

Chen, Xinyu; Avtar, Ram; Umarhadi, Deha Agus; Louw, Albertus Stephanus; Shrivastava, Sourabh; Yunus, Ali P.; Khedher, Khaled Mohamed; Takemi, Tetsuya; Shibata, Hideaki

Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models

Chen, Xinyu; Avtar, Ram; Umarhadi, Deha Agus et al.

Weather and Climate Extremes, 2022, 38, 100494

Permalink : https://hdl.handle.net/2115/87094

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アクセス権	metadata only access
URI	https://hdl.handle.net/2115/87094
タイトル	Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
著者	Chen, Xinyu ; Avtar, Ram ; Umarhadi, Deha Agus ; Louw, Albertus Stephanus ; Shrivastava, Sourabh ; Yunus, Ali P. ; Khedher, Khaled Mohamed ; Takemi, Tetsuya ; Shibata, Hideaki
著者名 Chen, Xinyu
e-Rad_Researcher 90648057 著者名 Avtar, Ram
著者名 Umarhadi, Deha Agus
著者名 Louw, Albertus Stephanus
著者名 Shrivastava, Sourabh
著者名 Yunus, Ali P.
著者名 Khedher, Khaled Mohamed
e-Rad_Researcher 10314361 著者名 Takemi, Tetsuya
e-Rad_Researcher 70281798 著者名 Shibata, Hideaki
言語	英語
キーワード	Forest damage
	Remote sensing
	Vegetation indices
	Multispectral classification
	CLASlite
発行日	2022-12
出版者	Elsevier
収録物名	Weather and Climate Extremes
巻(号)	38
ページ	100494
抄録	The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosys-tems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suit-able use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and & UDelta;EVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
PISSN	2212-0947
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.1016/j.wace.2022.100494