High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data-Reference-Cited by-同舟云学术

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High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

Published:2020-10 Issue: Volume:92 Page:102163
ISSN:0303-2434
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Li Wang,Niu Zheng,Shang Rong,Qin Yuchu,Wang Li,Chen Hanyue

Funder

China Natural Science Foundation

Youth Innovation Promotion Association Chinese Academy of Sciences

China’s Special Funds for Major State Basic Research Project

100 Talents Program of the Chinese Academy of Sciences

National Key R&D Program of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference73 articles.

1. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm;Ahmed;ISPRS J. Photogramm. Remote Sens.,2015

2. Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models;Alexander;Int. J. Appl. Earth Obs. Geoinf.,2018

3. Monitoring the structure of forest restoration plantations with a drone-lidar system;Almeida;Int. J. Appl. Earth Obs. Geoinf.,2019

4. kerasR: R Interface to the Keras Deep Learning Library;Arnold;J. Open Source Software,2017

5. Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo;Asner;Biol. Conserv.,2018

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