Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models-Reference-Cited by-同舟云学术

Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models

Published:2015-10-07 Issue:21 Volume:36 Page:5384-5402
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Wang Cong¹²,Du Huaqiang¹²,Xu Xiaojun¹²,Han Ning¹²,Zhou Guomo¹²,Sun Shaobo¹²,Gao Guolong¹²

Affiliation:

1. Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A & F University, Lin’an, 311300, China

2. School of Environmental and Resources Science, Zhejiang A & F University, Lin’an, 311300, China

Funder

Natural Science Foundation of Zhejiang province

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2015.1095371

Reference33 articles.

1. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

2. Chen, C. 2011. “Retrieval of Forest Canopy Closure in Ji’an, Jiangxi Province Using Remote Sensing Data.” PhD thesis. Nanjing University, Jiangsu, China. [in Chinese]

3. Retrieval of Canopy Closure and LAI of Moso Bamboo Forest Using Spectral Mixture Analysis Based on Real Scenario Simulation

4. Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique

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