IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p4893-d544213.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/4893/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/9/4893/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.
    3. Sergiusz Pimenow & Olena Pimenowa & Piotr Prus & Aleksandra Niklas, 2025. "The Impact of Artificial Intelligence on the Sustainability of Regional Ecosystems: Current Challenges and Future Prospects," Sustainability, MDPI, vol. 17(11), pages 1-42, May.
    4. Sweta Chatterjee & Oishani Chatterjee & Gupinath Bhandari, 2025. "Impact assessment of very severe cyclonic storm (VSCS) amphan over mangrove cover of indian part of sundarbans using geospatial techniques," 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. 121(11), pages 13305-13336, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Javad Seyedmohammadi & Mir Naser Navidi & Ali Zeinadini & Richard W. McDowell, 2024. "Random forest, an efficient smart technique for analyzing the influence of soil properties on pistachio yield," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 2615-2636, January.
    2. Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
    3. Florian Schierhorn & Max Hofmann & Taras Gagalyuk & Igor Ostapchuk & Daniel Müller, 2021. "Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages," Climatic Change, Springer, vol. 169(3), pages 1-19, December.
    4. Puyu Feng & Bin Wang & De Li Liu & Hongtao Xing & Fei Ji & Ian Macadam & Hongyan Ruan & Qiang Yu, 2018. "Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia," Climatic Change, Springer, vol. 147(3), pages 555-569, April.
    5. Li Fan & Shibo Fang & Jinlong Fan & Yan Wang & Linqing Zhan & Yongkun He, 2024. "Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
    6. Martin Kuradusenge & Eric Hitimana & Damien Hanyurwimfura & Placide Rukundo & Kambombo Mtonga & Angelique Mukasine & Claudette Uwitonze & Jackson Ngabonziza & Angelique Uwamahoro, 2023. "Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize," Agriculture, MDPI, vol. 13(1), pages 1-19, January.
    7. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, "undated". "Estimation of the Farm-Level Yield-Weather-Relation Using Machine Learning," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317075, German Association of Agricultural Economists (GEWISOLA).
    8. Pavithra Mahesh & Rajkumar Soundrapandiyan, 2024. "Yield prediction for crops by gradient-based algorithms," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-20, August.
    9. Timsina, Jagadish & Dutta, Sudarshan & Devkota, Krishna Prasad & Chakraborty, Somsubhra & Neupane, Ram Krishna & Bishta, Sudarshan & Amgain, Lal Prasad & Singh, Vinod K. & Islam, Saiful & Majumdar, Ka, 2021. "Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency," Agricultural Systems, Elsevier, vol. 192(C).
    10. Jaturong Som-ard & Savittri Ratanopad Suwanlee & Dusadee Pinasu & Surasak Keawsomsee & Kemin Kasa & Nattawut Seesanhao & Sarawut Ninsawat & Enrico Borgogno-Mondino & Filippo Sarvia, 2024. "Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms," Land, MDPI, vol. 13(9), pages 1-19, September.
    11. Li, Siyi & Wang, Bin & Feng, Puyu & Liu, De Li & Li, Linchao & Shi, Lijie & Yu, Qiang, 2022. "Assessing climate vulnerability of historical wheat yield in south-eastern Australia's wheat belt," Agricultural Systems, Elsevier, vol. 196(C).
    12. Jihong Sun & Chen Sun & Zhaowen Li & Ye Qian & Tong Li, 2024. "Prediction method of sugarcane important phenotype data based on multi-model and multi-task," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-25, December.
    13. Kouame, Anselme K.K. & Bindraban, Prem S. & Kissiedu, Isaac N. & Atakora, Williams K. & El Mejahed, Khalil, 2023. "Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach," Agricultural Systems, Elsevier, vol. 209(C).
    14. Jinhui Zheng & Shuai Zhang, 2025. "Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020," Agriculture, MDPI, vol. 15(5), pages 1-17, February.
    15. Matthew Smith & Francisco Alvarez, 2025. "Machine Learning for Applied Economic Analysis: Gaining Practical Insights," Working Papers 2025-03, FEDEA.
    16. Mohsen Shahhosseini & Guiping Hu, 2020. "Machine Learning Models for Corn Yield Prediction A Survey of Literature," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 25(3), pages 80-83, July.
    17. Che-Hao Chang & Jason Lin & Jia-Wei Chang & Yu-Shun Huang & Ming-Hsin Lai & Yen-Jen Chang, 2024. "Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data," Agriculture, MDPI, vol. 14(4), pages 1-21, March.
    18. Jung Ryeol Park & Yituo Feng, 2023. "Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-20, February.
    19. Chen, Kefei & O'Leary, Rebecca A. & Evans, Fiona H., 2019. "A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool," Agricultural Systems, Elsevier, vol. 173(C), pages 140-150.
    20. van der Velde, M. & Nisini, L., 2019. "Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015," Agricultural Systems, Elsevier, vol. 168(C), pages 203-212.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4893-:d:544213. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.