WANG Xuecheng, YANG Fei, GAO Xing, WANG Wei, ZHA Xinjie. Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing[J]. Chinese Geographical Science, 2019, 20(3): 405-416. doi: 10.1007/s11769-019-1041-3
Citation: WANG Xuecheng, YANG Fei, GAO Xing, WANG Wei, ZHA Xinjie. Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing[J]. Chinese Geographical Science, 2019, 20(3): 405-416. doi: 10.1007/s11769-019-1041-3

Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing

doi: 10.1007/s11769-019-1041-3
Funds:  Under the auspices of National Key Research and Development Program of China (No. 2017YFA0604804), Advanced Scientific Research Projects of Chinese Academy of Sciences (No. QYZDY-SSW-DQC007-34), National Natural Science Foundation of China (No. 41301607), Innovation Project of LREIS (State Key Laboratory of Resources and Environmental Information System) of Chinese Academy of Sciences (No. O88RAA02YA)
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  • Corresponding author: YANG Fei. E-mail:yangfei@igsnrr.ac.cn
  • Received Date: 2018-06-07
  • Publish Date: 2019-06-27
  • The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer (MODIS) 13Q1 products are used, which include two vegetation indices data of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). Furtherly, after Quality Screening (QS) and Savizky-Golay (S-G) filtering of MODIS 13Q1 data, four evaluation indices are obtained, which are NDVI with QS (QSNDVI), EVI with QS (QSEVI), NDVI with S-G filtering (SGNDVI) and EVI with S-G filtering (SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters.
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Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing

doi: 10.1007/s11769-019-1041-3
Funds:  Under the auspices of National Key Research and Development Program of China (No. 2017YFA0604804), Advanced Scientific Research Projects of Chinese Academy of Sciences (No. QYZDY-SSW-DQC007-34), National Natural Science Foundation of China (No. 41301607), Innovation Project of LREIS (State Key Laboratory of Resources and Environmental Information System) of Chinese Academy of Sciences (No. O88RAA02YA)
    Corresponding author: YANG Fei. E-mail:yangfei@igsnrr.ac.cn

Abstract: The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer (MODIS) 13Q1 products are used, which include two vegetation indices data of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). Furtherly, after Quality Screening (QS) and Savizky-Golay (S-G) filtering of MODIS 13Q1 data, four evaluation indices are obtained, which are NDVI with QS (QSNDVI), EVI with QS (QSEVI), NDVI with S-G filtering (SGNDVI) and EVI with S-G filtering (SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters.

WANG Xuecheng, YANG Fei, GAO Xing, WANG Wei, ZHA Xinjie. Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing[J]. Chinese Geographical Science, 2019, 20(3): 405-416. doi: 10.1007/s11769-019-1041-3
Citation: WANG Xuecheng, YANG Fei, GAO Xing, WANG Wei, ZHA Xinjie. Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing[J]. Chinese Geographical Science, 2019, 20(3): 405-416. doi: 10.1007/s11769-019-1041-3
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