Co-ordinating Editor: Geoffrey Henebry
Decadal-scale aspen changes: evidence in remote sensing and tree ring data
Version of Record online: 28 JUN 2011
© 2011 International Association for Vegetation Science
Applied Vegetation Science
Volume 15, Issue 1, page no, February 2012
How to Cite
Sankey, T. T. (2012), Decadal-scale aspen changes: evidence in remote sensing and tree ring data. Applied Vegetation Science, 15: no. doi: 10.1111/j.1654-109X.2011.01141.x
Sankey, T.T. (corresponding author, firstname.lastname@example.org) Research Assistant Professor, Boise Center Aerospace Laboratory, Idaho State University, 322 E. Front Street Suite 240, Boise, ID 83702-7359, USA.
- Issue online: 18 JAN 2012
- Version of Record online: 28 JUN 2011
- Received 19 July 2010, Accepted 6 May 2011
- Active sensors;
- Aspen decline;
- Laser data;
- Vegetation height
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.