Integration of Landsat time-series vegetation indices improves consistency of change detection  

作　　者：Mingxing Zhou Dengqiu Li Kuo Liao Dengsheng Lu 

机构地区：[1]Institute of Geography,Fujian Normal University,Fuzhou,People’s Republic of China [2]Fujian Provincial Key Laboratory for Subtropical Resources and Environment,Fujian Normal University,Fuzhou,People’s Republic of China [3]School of Environmental&Resource Sciences,Zhejiang A&F University,Hangzhou,People’s Republic of China [4]Wuyi Mountain National Meteorological Observation Station,Wuyishan,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期1276-1299,共24页国际数字地球学报（英文）

基　　金：supported by Natural Science Foundation of Fujian Province[grant number 2022J01640,2022J011076];Public welfare projects of Fujian Provincial Science and Technology Department[grant number 2021R 1002008];National Natural Science Foundation of China[grant number 41701490].

摘　　要：Vegetation indices(VIs)were used to detect when and where vegetation changes occurred.However,different VIs have different or even diametrically opposite results,which obstructed the in-depth understanding of vegetation change.Therefore,this study examined the spatial and temporal consistency offive VIs(EVI;NBR;NDMI;NDVI;and NIRv)in detecting abrupt and gradual vegetation changes,and provided an ensemble algorithm which integrated the change detection results of thefive indices to reduce the uncertainty of change detection using a single index.The spatial consistency of thefive indices in abrupt change detection accounted for 50.6%of the study area,but the temporal consistency was low(21.6%).Wetness indices(NBR,NDMI)were more sensitive to negative abrupt changes,greenness indices(EVI,NDVI,NIRv)were more sensitive to positive abrupt changes;and both types of indices were similar in detecting gradual and total changes.The overall accuracy of the ensemble method was 81.60%and higher than that of any single index in abrupt change detection.This study provides a comprehensive evaluation of the spatial and temporal inconsistencies of change detection in model-fitting errors and various types of vegetation changes.The proposed ensemble method can support robust change-detection.

关 键 词：Breaks for Additive Season and Trend ensemble algorithm consistence of vegetation change vegetation index 

分 类 号：TQ3[化学工程]