Forest canopy height estimation using LiDAR and optical multi-angler data
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Graphical Abstract
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Abstract
Tree height is an important parameter in the field of forest resource management, so it is significant
in forest resource monitoring and carbon cycling research to obtain accurate, large-scale and spatially
continuous tree height. The paper uses the waveform data from space-borne Lidar ICESat/GLAS, and
chooses proper algorithms with topographic correction to extract forest tree height average values from
GLAS foot-print data. Then, we apply the random forest machine learning algorithm to process tree height
values from GLAS foot-print data and MISR/BRF data from multi-angle optical remote sense, and get the
tree height map of Tahe Forest Farm, namely, which makes the scale of extracted tree height values
extend from point to plane. Finally, the field measured data was utilized to test the results, which showed
that R2, RMSE and precision were 0.72, 1.83 m and 85.22%, respectively. This approach integrates
Lidar data with multi-angle imagines of optical remote sense,which makes up of the shortcomings of each.
In addition, the results also provide accurate reference guidelines for the prediction of forest biomass and
carbon storage.
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