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.
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References
Ahammad R, Stacey N, Sunderland T, 2019. Use and perceived importance of forest ecosystem services in rural livelihoods of Chittagong Hill Tracts, Bangladesh. Ecosystem Services, 35: 87–98. doi: https://doi.org/10.1016/j.ecoser.2018.11.009
Blume-Werry G, Kreyling J, Laudon H et al., 2016. Short-term climate manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants. Journal of Ecology, 104(6): 1638–1648. doi: https://doi.org/10.1111/1365-2745.12636
Bragg D C, Shelton M G, Zeide B, 2003. Impacts and management implications of ice storms on forests in the southern United States. Forest Ecology & Management, 186(1–3): 99–123. doi: https://doi.org/10.1016/S0378-1127(03)00230-5
Brandt M, Hiernaux P, Rasmussen K et al., 2016. Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics. Remote Sensing of Environment, 183: 215–225. doi: https://doi.org/10.1016/j.rse.2016.05.027
Broxton P, Harpold A, Biederman J et al., 2015. Quantifying the effects of vegetation structure on snow accumulation and ablation in mixed-conifer forests. Ecohydrology, 8(6): 1073–1094. doi: https://doi.org/10.1002/eco.1565
Brugger S, Gobet E, Sigl M et al., 2018. Ice records provide new insights into climatic vulnerability of Central Asian forest and steppe communities. Global and Planetary Change, 169(4): 188–201. doi: https://doi.org/10.1016/j.gloplacha.2018.07.010
Busseau B, Royer A, Roy A et al., 2017. Analysis of snow-vegetation interaction in the low Arctic-Subarctic transition zone (northeastern Canada). Physical Geography, 38(2): 159–175. doi: https://doi.org/10.1080/02723646.2017.1283477
Chen J, Jünsson P, Tamura M et al., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91(3-1): 332–344.
Chen J, Sun L, 2010. Using MODIS EVI to detect vegetation damage caused by the 2008 ice and snow storms in south China. Journal of Geophysical Research Biogeosciences, 115(G3): 1400–1416. doi: https://doi.org/10.1029/2009JG001246
Degaetano A T, 2010. Climatic perspective and impacts of the 1998 northern New York and New England ice storm. Bulletin of the American Meteorological Society, 81(2): 237–254.
Forestry of China Editorial Board, 2008. Forestry system fully launches reconstruction after the ice and snow disaster. Forestry of China, (2B): 1. (in Chinese)
Fujihara Y, Takase K, Chono S et al., 2017. Influence of topography and forest characteristics on snow distributions in a forested catchment. Journal of Hydrology, 546: 289–298. doi: https://doi.org/10.1016/j.jhydrol.2017.01.021
Gleason K, Nolin A, 2016. Charred forests accelerate snow albedo decay: parameterizing the post-fire radiative forcing on snow for three years following fire. Hydrological Processes, 30(21): 3855–3870. doi: https://doi.org/10.1002/hyp.10897
He Qian, Li Jiyue, Chen Xiaoyang et al., 2010. Types and extent of damage to Cunninghamia lanceolata plantations due to unusually heavy snow and ice in southern China. Chinese Journal of Plant Ecology, 34(2): 195–203. (in Chinese)
Huete A, Justice C, Leeuwen W V, 1999. MODIS Vegetation Index (MOD13). Algorithm theoretical basis document, 3th ed.
Ice-snow Disaster Investigation Team, 2008a. Assessment Report on the Loss of Forest Resources in the Ice-snow Disaster in Daoxian County. Yongzhou City, China: Daoxian County Forestry Bureau. (in Chinese)
Ice-snow Disaster Investigation Team, 2008b. Assessment Report on the Loss of Forest Resources in the Ice-snow Disaster in Jiangyong County. Yongzhou City, China: Jiangyong County Forestry Bureau. (in Chinese)
Ice-snow Disaster Investigation Team, 2008c. Assessment Report on the Loss of Forest Resources in the Ice-snow Disaster in Liuyang County. Changsha City, China: Liuyang County Forestry Bureau. (in Chinese)
Ice-snow Disaster Investigation Team, 2008d. Assessment Report on the Loss of Forest Resources in the Ice-snow Disaster in Xintian County. Yongzhou City, China: Xintian County Forestry Bureau. (in Chinese)
Isaacs R E, Stueve K M, Lafon C W et al., 2014. Ice storms generate spatially heterogeneous damage patterns at the watershed scale in forested landscapes. Ecosphere, 5(11): 1–14. doi: https://doi.org/10.1890/ES14-002341
Kim J, Guo Q, Baldocchi D et al., 2006. Upscaling fluxes from tower to landscape: Overlaying flux footprints on high-resolution (IKONOS) images of vegetation cover. Agricultural and Forest Meteorology, 136(4): 132–146. doi: https://doi.org/10.1016/j.agrformet.2004.11.015
King D J, Bemrose R, 2008. Impact of the 1998 ice storm on the health and growth of Sugar Maple (Acer saccharum Marsh.) dominated forests in Gatineau Park, Quebec. Journal of the Torrey Botanical Society, 135(4): 530–539. doi: https://doi.org/10.3159/08-RA-053R.1
Millward A A, Kraft C E, 2004. Physical influences of landscape on a large-extent ecological disturbance: the northeastern North American ice storm of 1998. Landscape Ecology, 19(1): 99–111.
Millward A A, Kraft C E, Warren D R, 2010. Ice storm damage greater along the terrestrial-aquatic interface in forested landscapes. Ecosystems, 13(2): 249–260. doi: https://doi.org/10.1007/s10021-010-9314-9
Levkoev E, Kilpeläinen A, Luostarinen K et al., 2017. Differences in growth and wood density in clones and provenance hybrid clones of Norway spruce. Canadian Journal of Forest Research, 47(3): 389–399. doi: https://doi.org/10.1139/cjfr-2016-0285
Olthof I, King D J, Lautenschlager R A, 2004. Mapping deciduous forest ice storm damage using Landsat and environmental data. Remote Sensing of Environment, 89(4): 484–496. doi: https://doi.org/10.1016/j.rse.2003.11.010
Orr H G, Wilby R L, Hedger M M et al., 2008. Climate change in the uplands: a UK perspective on safeguarding regulatory ecosystem services. Climate Research, 37(1): 77–98. doi: https://doi.org/10.3354/cr00754
Saarinen T, Rasmus S, Lundell R et al., 2016. Photosynthetic and phenological responses of dwarf shrubs to the depth and properties of snow. Oikos, 125(3): 364–373. doi: https://doi.org/10.1111/oik.02233
Shao Q, Huang L, Liu J, et al., 2011. Analysis of forest damage caused by the snow and ice chaos along a transect across southern China in spring 2008. Journal of Geographical Sciences, 21(2): 219–234.
Shi Hao, Wang Xiao, Xue Jianhui et al., 2012. A rapid assessment method for forest disaster based on MODIS/NDVI time series: a case study from Guizhou Province. Acta Ecological Sinica, 32(11): 3359–3367. (in Chinese)
Spruce J P, Sader S, Ryan R E et al., 2011. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment, 115(2): 427–437. doi: https://doi.org/10.1016/j.rse.2010.09.013
Streher A, Sobreiro J, Morellato L et al., 2017. Land Surface Phenology in the Tropics: The role of climate and topography in a snow-free mountain. Ecosystems, 20(8): 1436–1453. doi: https://doi.org/10.1007/s10021-017-0123-2
Sun Y, Gu L, Dickinson R E et al., 2012. Forest greenness after the massive 2008 Chinese ice storm: integrated effects of natural processes and human intervention. Environmental Research Letters, 7(3): 035702–035708.
Tsalyuk M, Kelly M, Getz W, 2017. Improving the prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 77–91. doi: https://doi.org/10.1016/j.isprsjprs.2017.07.012
Walker J, De-Beurs K, Wynne R, 2014. Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data. Remote Sensing of Environment, 144: 85–97. doi: https://doi.org/10.1016/j.rse.2014.01.007
Wang Zhuxiong, Yan Hongwei, Mo Mo, 2008. Thoughts on rehabilitation and rebuilding of the forest areas after the snow disaster in south China. Forest Resources Management, (2): 1–5. (in Chinese)
Wu J S, Wang T, Pan K et al., 2016. Assessment of forest damage caused by an ice storm using multi-temporal remote-sensing images: a case study from Guangdong Province. International Journal of Remote Sensing, 37(13): 3125–3142.
Xiao Fuming, Chen Hongxing, Jiang Xiangmei et al., 2008ly. Investigation on the Damage of Moso Bamboo Caused by Freezing Rain and Snow in Anfu, Jiangxi Province. Scientia Silvae Sinicae, 44(11): 32–35. (in Chinese)
Xu Fenglan, Qian Guoqin, Yang Lunzeng, 2008. Economical assessment of the lose’s value brought by the blizzard and frozen disasters to the forest in the ecosystem services——Take the disaster forest of Fujian Province as the example. Scientia Silvae Sinicae, 44(11): 193–201. (in Chinese)
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Foundation item: 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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Wang, X., Yang, F., Gao, X. et al. Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing. Chin. Geogr. Sci. 29, 405–416 (2019). https://doi.org/10.1007/s11769-019-1041-3
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DOI: https://doi.org/10.1007/s11769-019-1041-3