Integration of Landsat time-series vegetation indices improves consistency of change detection
Mingxing Zhou, Dengqiu Li, Kuo Liao, Dengsheng Lu
Abstract
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 of five 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 the five indices to reduce the uncertainty of change detection using a single index. The spatial consistency of the five 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.