Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6346730 | Remote Sensing of Environment | 2014 | 17 Pages |
Abstract
Considering tree class alone, deciduous and evergreen trees could be classified with an overall accuracy of 94% using validation data generated using aerial imagery from a 60-km strip across Central Jutland. The lower values of ALS-based percentage tree cover were overestimated and the higher values underestimated by MODIS VCF data, with a root-mean-square (RMS) deviation of 18.26%. The tree heights estimated using ALS data were generally lower than the global estimates of tree height with an RMS deviation of 5.1Â m. The ALS intensity values were useful for classifying evergreen and deciduous trees. These findings show that ALS datasets collected for generating national DTMs can be used for tree cover and tree height mapping, as well as for regional classification of trees if data over the whole area are collected within a few months in the leaf-off season.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Cici Alexander, Peder Klith Bøcher, Lars Arge, Jens-Christian Svenning,