Spatial scale and pattern dependences of aboveground biomass estimation from satellite images: a case study of the Sierra National Forest, California

Spatial scale and pattern play important roles in forest aboveground biomass (AGB) estimation in remote sensing. Changes in the accuracy of satellite images-estimated forest AGBs against spatial scales and pixel distribution patterns has not been evaluated, because it requires ground-truth AGBs of fine resolution over a large extent, and such data are difficult to obtain using traditional ground surveying methods. We intend to quantify the accuracy of AGB estimation from satellite images on changing spatial scales and varying pixel distribution patterns, in a typical mixed coniferous forest in Sierra Nevada mountains, California. A forest AGB map of a 143 km(2) area was created using small-footprint light detection and ranging. Landsat Thematic Mapper images were chosen as typical examples of satellite images, and resampled to successively coarser resolutions. At each spatial scale, pixels forming random, uniform, and clustered spatial patterns were then sampled. The accuracies of the AGB estimation based on Landsat images associated with varying spatial scales and patterns were finally quantified. The changes in the accuracy of AGB estimation from Landsat images are not monotonic, but increase up to 60-90 m in spatial scale, and then decrease. Random and uniform spatial patterns of pixel distributions yield better accuracy for AGB estimation than clustered spatial patterns. The corrected NDVI (NDVIc) was the best predictor of AGB estimation. A spatial scale of 60-90 m is recommended for forest AGB estimation at the Sierra Nevada mountains using Landsat images and those with similar spectral resolutions.National Natural Science Foundation of China [41471363, 41401505]; National Key Basic Research Program of China [2013CB956604]; Sierra Nevada Adaptive Management Project (SNMAP)SCI(E)[email protected]