Remote Sensing Vegetation Recovery after Forest Fires using Energy Balance Algorithm

Information on the temporal and spatial dynamics of post-fire vegetation recovery and water use is essential for establishing post-fire vegetation management and for evaluating reforestation programs to reduce the risk of landslides and soil erosion after forest fires. Remote sensing techniques have been increasingly used as a convenient tool for monitoring vegetation cover and water stress. Commonly used techniques include spectral analysis, such as the Normalized Vegetation Index (NDVI). However, the accuracy of the spectral analysis can be significantly affected by the illumination geometry and the optical properties of the soil background. Furthermore, spectral analysis can not estimate absolute water use of plants. Alternatively, satellite derived estimates of spatial evapotranspiration (ET) computed using a Surface Energy Balance Algorithm for Land (SEBAL©) provide a more accurate means for monitoring vegetation changes and water consumption. In this paper, we use a modified SEBAL© to estimate and compare ET at the burned and unburned areas at Los Alamos, New Mexico where the Cerro Grande Fire burned 43,000-acre area on May 8-15, 2000. By comparing our results to point measurements, we demonstrate that this method is appropriate for estimating spatial and temporal ET and vegetation recovery after forest fires.