Decadal‐scale aspen changes: evidence in remote sensing and tree ring data

Abstract

Abstract Question: (1) Which remote sensing classification most successfully identify aspen using multitemporal Landsat 5 TM images and airborne lidar data? (2) How has aspen distribution changed in southwestern Idaho? (3) Are topographic variables and conifer encroachment correlated with aspen changes? Location: Reynolds Creek Experimental Watershed in southwestern Idaho, USA. Methods: Multi‐temporal Landsat 5 TM and lidar data were used individually and fused together. The best classification model was compared with a 1965 aspen map and tree ring data. Conifer encroachment was examined via image‐based change detection and field mapping. Lidar‐derived topographic variables were correlated with aspen change patterns using quantile regression models. Results: The best Landsat 5 TM classification was a normalized difference vegetation index (NDVI)‐based approach with 92% overall accuracy. The lidar classification of tree presence/absence performed with 100% overall accuracy. Fusing the lidar classification with various Landsat 5 TM classifications improved overall accuracies 3 to 6%. Among the fusion models, the NDVI‐lidar fusion performed best with 96% overall accuracy. Change detection indicated 69% decline in aspen cover, but 179% increase in aspen cover in other areas of the watershed. Conifers have completely replaced 17% of the aspen, while 93% of the remaining aspen stands have young Douglas‐fir and western juniper trees underneath the aspen canopy. Aspen significantly decreased (P‐values <0.05) with increasing elevation (up to 2150 m) and decreasing slope. Conclusions: Landsat 5 TM data used with a NDVI‐based approach provide an accurate method to classify aspen distribution. Landsat 5 TM classifications can be further improved via fusion with lidar data. Aspen change patterns are spatially variable: while aspen is drastically declining in some parts of this watershed, aspen is increasing in other areas.