Login   |      Register
English    中文
A rapid assessment method for forest disaster based on MODIS /NDVI time series: a case study from Guizhou Province

Date: 2018-06-08      View counts: 120    

Label:

Language
Chinese
Author
SHI Hao, WANG Xiao, XUE Jianhui1, et al
Journal
ACTA ECOLOGICA SINICA
Class
The damaged vegetation detection
Year
2012
Paper Keyword
snow disaster; MODIS /NDVI; time series; damage threshold; consistency check
Abstract
Snow disasters are one of the major natural disturbances to forest ecosystems in China. The increased frequency and severity of forest disturbances in recent years requires rapid and accurate regional forest damage assessment to support post-disturbance forest management, hazardous fuel management, post-hazard relief activities, and government compensation claims. Interpretation of MODIS /NDVIs to construct Guizhou Province's remote sensing images time series between 2005 and 2008 was involved in this analysis. Specifically, the mean value of usefulness index was applied to identify key MODIS / NDVI images and the Savitzky-Golay filter was used to reconstruct key images first. Then,the ratio of forest pixels NDVI value ( R2005—2007 ) was computed before and after the corresponding period of snow disaster in 2008,the respective mean and mean absolute of R2005—2007 were derived to determine undisturbed forest pixels and forest damage threshold ( DT ) . Ultimately,with the support of remote sensing and geographical information system,disaster areas and damage ranks were identified by using this method. Light,moderate and severe damage were classified in the light of the R2007-2008 of forest damage pixels at county level. Further,in conjunction with field surveys of forest resources implemented by the Department of Forestry of Guizhou Province,the consistency between our derivations and field surveys was assessed. The preliminary results were as follows: ( 1) With the aid of usefulness index,MODIS /NDVI products captured on September 30( September 30 2005,September 30 2006 and September 30 2007) and May 9( May 9 2006,May 9 2007 and May 9 2008) were respectively chosen as key images corresponding to the period of the snow disasters,which had the lower mean value of usefulness index ( 2. 06 and 2. 41,respectively) form 2005 to 2008. After reconstructing the chosen MODIS / NDVI products using the Savitzky-Golay filter at the specified parameters ( No. of the envelope iterations: 3,adaption strength: 2 and the window size: 5) ,the mean NDVI value of six key images increased by about 0. 04. ( 2 ) Based on statistical analysis,the R2005—2007 and δall ( damage threshold) were estimated at 0. 044 and 0. 048 respectively without largescale natural disturbance observed during September 30 and May 9,but there was distinct shift amplitude( 0. 041) between μ2005—2007 and μ2007-2008 after snow disasters. According to forest damage threshold,damaged pixels accounted for 28. 6% of the total forest pixels,which was above the result based on subcompartment investigation ( 17. 7%) . ( 3 ) The snow disasters in the southeast and northeast Guizhou Province were the worst,containing Autonomous Prefectures of South Guizhou Province,Autonomous Prefectures of Southeast Guizhou Province,Tongren City,etc. And then,in terms of different damage levels,11 Severe damage counties ( Dejiang,Yanhe,Duyun,etc) and 10 moderate damage counties ( Meitan,Rongjiang,Tongzi,etc) were determined at county level. The derived forest damage maps due to disasters from the current methods were in consistency with field surveys in some extent,with a kappa coefficient of 0. 86( above 0. 75) derived from a statistical test. The adopted analytical flow for assessing forest losses due to snow disasters in Guizhou Province was based on remote sensing and geographical information systems operations. This method also provides a new idea for quick assessment on forest disasters at a regional scale,without relying on ground inventory or sampling.

You can post a comment after logging in!

Comment list

 

Information evaluation
ICP备案号:京ICP备14021735号-1    © 2008 - 2018 IKCEST All rights reserved