Abstract
Huajun Liang, Qiang Bie, Wenyu Yao
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10.1109/tgrs.2017.2786659 · doi-reference
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A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data
10.1016/j.jag.2025.104474 · doi-reference
Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery
10.1016/j.isprsjprs.2019.03.016 · doi-reference
Feature analysis of LIDAR waveforms from forest canopies
10.1007/s11430-011-4212-3 · doi-reference
A high-resolution canopy height model of the Earth
10.1038/s41559-023-02206-6 · doi-reference
Combining high-resolution images and LiDAR data to model ecosystem services perception in compact urban systems
10.1016/j.ecolind.2017.05.014 · doi-reference
A 30 m global map of elevation with forests and buildings removed
10.1088/1748-9326/ac4d4f · doi-reference
Terrain slope effect on forest height and wood volume estimation from GEDI data
10.3390/rs13112136 · doi-reference
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10.1016/j.rse.2007.07.024 · doi-reference
High-resolution canopy height mapping: integrating NASA’s global ecosystem dynamics investigation (GEDI) with multi-source remote sensing data
10.3390/rs16071281 · doi-reference