A Multi-Scale Approach to Mapping Canopy Height
Mapping vegetation height over large areas presents a problem of scale: height varies with the individual tree or stand, but the resolution of available datasets is too low to characterize this variability sufficiently for many applications. We address this problem by fusing 1 km resolution canopy height data derived from satellite-based laser altimetry with higher-resolution land-cover data, resulting in 30 m resolution estimates of canopy height. These are downscaled further to 1 m resolution by simulating individual trees. A web service architecture is used, which allows processing to occur on demand without preprocessing large datasets. We compared the resulting canopy volumes to reference airborne lidar data from 262 randomly located 1 km2 areas within nine study sites. Results at 30 m resolution show an RMSE of 33 percent of the mean reference volume and an R2 of 0.77; at 1 m the RMSE is 66 percent and the R2 is 0.38.
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Document Type: Research Article
Publication date: February 1, 2013
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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