| dc.description.abstract | Climate change projections have predicted more frequent and severe droughts that may lead to major loss of trees and subsequent range shifts. Drought-induced tree mortality leaves both dead & live trees intermixed that remain standing rather than leaving clearings that result from acute disturbances such as fire. Thus this disturbance is difficult to detect at a regional scale, but is a harbinger of range shifts so its detection is high priority. During the summer of 2011, the southwestern US including Texas was impacted by an extreme drought. Statewide tree mortality was observed and thus provided an opportunity to test the efficacy of moderate to coarse resolution remotely-sensed indicators to detect, map and enumerate drought-induced tree mortality. Calibration models of 250-m ΔNDVI and 1-km VegDRI with 599 field data plots of tree mortality were developed to produce predictive maps. ΔNDVI, ΔPV, and NPV mortality indices were derived from 30-m Landsat 7 and compared to each other and 250-m ΔNDVI. ΔNDVI predicted tree mortality best (Khat = 0.15), with an estimate of 9% mortality that was primarily concentrated in East and Central Texas. However at 30-m resolution for East Texas, ΔPV matched the validation data best (Khat = 0.21).
Maximum entropy models were used with the field data to test the relative importance of 2011 drought conditions versus historical climate drivers of the distribution of drought-induced tree mortality. 2011 drought conditions explained 57% of the resulting model (AUC = 0.84) and bioclimate variables explained 43%. Mean annual precipitation explained 17% of tree mortality, followed by 2011 isothermality (16%). Models were run to test the contribution of edaphic, biotic, and climatic factors toward explaining dead tree distribution, and also test of effects of scale and location (East vs. Central Texas). Climate was the highest contributor at the state scale (42%) and also in Central Texas (48%). In East Texas, edaphic factors were the major driver (47%). As drought frequency and intensity increase as predicted, a refinement of detection techniques and understanding of the drivers of tree mortality are needed to understand and predict the nature of drought consequences for forests. | en |
