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Changes Analysis of Post-Fire Vegetation Spectrum and Index Based on Time Series GF-1 WFV Images |
SUN Gui-fen1, QIN Xian-lin1*, YIN Ling-yu1, LIU Shu-chao1, LI Zeng-yuan1, CHEN Xiao-zhong2, ZHONG Xiang-qing2 |
1. Research Institute of Forest Resources Information Technique, Chinese Academy of forestry,Key Laboratory of Forestry Remote Sensing and Information Techniques, State Forestry Administration, Beijing 100091, China
2. Forestry Information Center of Sichuan Province, Chengdu 610081, China |
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Abstract To explore the ability of domestic high-resolution satellite remote sensing technology monitoring the effect of fire disturbance on vegetation growth and the characterize vegetation index, two burned sites formed by forest fire in 2014 in Yajiang county and Mianning county of Sichuan province were selected as the study area. The change of vegetation spectral features of burned area at different burned severity between pre-fire and post-fire has been analyzed using the selected GF-1 WFV data. At the same time, post-firetime series GF-1 WFV data has been used to analyze the monthly variation of Normalized Difference Vegetation Index(NDVI), Enhanced Vegetation Index (EVI) and Global Environment Monitoring Index (GEMI) which can characterize vegetation growth status of fire disturbance vegetation at different fire severity during two years after the forest fires taking place. With Combination of the latitude, altitude and climatic conditions of the study area, vegetation recovery pattern of post-fire vegetation was analyzed. Results showed that the vegetation pigments and cell structure were destroyed by the fire, which made its spectral features no longer show the unique spectral characteristics of normal vegetation. In the visible region, spectral reflectance of fire-disturbed vegetation at different fire severity was higher than that of normal vegetation and its value increased withthe severity. In the near infrared band, the reflectance of vegetation decreased after fire disturbance and its value was much lower than that of normal vegetation. NDVI, EVI and GEMI were highly correlated in the characterization of vegetation restoration process and sensitive to vegetation seasonal variation, which made it capability to reflect vegetation restoration process and they had the ability to describe the dynamic process of vegetation restoration. The changes of vegetation index of disturbed vegetation in vegetation restoration process were basically same as that of normal vegetation. Growing and non-growing season existed in the restoration process of affected vegetation as well. NDVI, EVI and GEMI of the vegetation at burned area were always lower than those of the normal vegetation and the higher the vegetation burned severity, the lower the vegetation index value was.
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Received: 2017-03-31
Accepted: 2017-07-24
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Corresponding Authors:
QIN Xian-lin
E-mail: noaags@ifrit.ac.cn
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[1] Gouveia C, DaCamara C C, Trigo R M. Natural Hazards & Earth System Sciences, 2010, 10(4): 673.
[2] MIAO Qing-lin, TIAN Xiao-rui, ZHAO Feng-jun(苗庆林, 田晓瑞, 赵凤君). Scientia Silvae Sinicae(林业科学), 2015, 2(51): 90.
[3] Beck P S, Goetz S J, Mack M C, et al. Glob. Chang. Boil, 2011, 17:2853.
[4] Vila G,Barbosa P. Ecological Modelling, 2010, 221: 75.
[5] Van Leeuwen W, Casady G, Neary D, et al. International Journal of Wildland Fire, 2010, 19: 75.
[6] Veraverbeke S, Gitas I, Katagis T, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 68: 28.
[7] XIAN Wei, JI Jian-wan, HE Bin-bin, et al(仙 巍, 季建万, 何彬彬,等). Journal of Southwest China Normal University·Natural Science Edition(西南师范大学学报·自然科学版), 2016, 41(9): 1.
[8] WANG Xin, WANG Rui-ting(王 鑫, 王锐婷). Chinese Agricultural Science Bulletin(中国农学通报), 2014, 30(29): 155.
[9] Kashian D M, Corace R G I, Shartell L M, et al. For. Ecol. Manag., 2012, 263: 148. |
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