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Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery

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  • Xu Zhang

    (State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
    College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China
    Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Hangzhou 311300, China)

  • Guangsheng Chen

    (State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
    College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China
    Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Hangzhou 311300, China)

  • Lingxiao Cai

    (Bureau of Agriculture and Rural Affairs of Lin’An District, Hangzhou 311300, China)

  • Hongbo Jiao

    (Forestry Industry Development Administration, Xinyu 338000, China)

  • Jianwen Hua

    (State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
    College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China
    Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Hangzhou 311300, China)

  • Xifang Luo

    (East China Forest Inventory and Planning Institute of National Forestry and Grassland Administration, Hangzhou 310019, China)

  • Xinliang Wei

    (State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
    College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China
    Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Hangzhou 311300, China)

Abstract

Wind damage is one of the major factors affecting forest ecosystem sustainability, especially in the coastal region. Typhoon Lekima is among the top five most devastating typhoons in China and caused economic losses totaling over USD 8 billion in Zhejiang Province alone during 9–12 August 2019. However, there still is no assessment of its impacts on forests. Here we detected forest damage and its spatial distribution caused by Typhoon Lekima by classifying Landsat 8 OLI images using the random forest (RF) machine learning algorithm and the univariate image differencing (UID) method on the Google Earth Engine (GEE) platform. The accuracy assessment indicated a high overall accuracy (>87%) and kappa coefficient (>0.75) for forest-damage detection, as evaluated against field-investigated plot data, with better performance using the RF method. The total affected forest area by Lekima was 4598.87 km 2 , accounting for 8.44% of the total forest area in Zhejiang Province. The light-, moderate- and severe-damage forest areas were 2106.29 km 2 , 2024.26 km 2 and 469.76 km 2 , respectively. Considering the damage severity, the net forest canopy loss fraction was 2.57%. The affected forest area and damage severity exhibited large spatial variations, which were affected by elevation, slope, precipitation and forest type. Our study indicated a larger uncertainty for affected forest area and a smaller uncertainty for the proportion of damage severity, based on multiple assessment approaches. This is among the first studies on forest damage due to typhoons at a regional scale in China, and the methods can be extended to examine the impacts of other super-strong typhoons on forests. Our study results on damage severity, spatial distribution and controlling factors could help local governments, the forest sector and forest landowners make decision on tree-planting planning and sustainable management after typhoon strikes and could also raise public and governmental awareness of typhoons’ damage on China’s inland forests.

Suggested Citation

  • Xu Zhang & Guangsheng Chen & Lingxiao Cai & Hongbo Jiao & Jianwen Hua & Xifang Luo & Xinliang Wei, 2021. "Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4893-:d:544213
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    1. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
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    1. Xunan Liu & Yao Zhang & Chenbin Liang & Yayu Yang & Wanru Huang & Ning Jia & Bo Cheng, 2022. "Storm surge damage interpretation by satellite imagery: case review," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 349-365, May.
    2. Tania Nasrin & Mohd Ramiz & Md Nawaj Sarif & Mohd Hashim & Masood Ahsan Siddiqui & Lubna Siddiqui & Sk Mohibul & Sakshi Mankotia, 2023. "Modeling of impact assessment of super cyclone Amphan with machine learning algorithms in Sundarban Biosphere Reserve, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(2), pages 1945-1968, June.

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