Estimating canopy height change using machine learning by coupling WorldView-2 stereo imagery with Landsat-7 data-Reference-Cited by-同舟云学术

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Estimating canopy height change using machine learning by coupling WorldView-2 stereo imagery with Landsat-7 data

Published:2023-01-17 Issue:2 Volume:44 Page:631-645
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Wang Jian¹^ORCID,Liu Desheng¹^ORCID,Quiring Steven M.¹²,Qin Rongjun²³⁴^ORCID

Affiliation:

1. Department of Geography, The Ohio State University, Columbus, OH, USA

2. Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA

3. Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA

4. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2169596

Reference39 articles.

1. The ICESat-2 Laser Altimetry Mission

2. Comparing geometric and radiometric information from GeoEye-1 and WorldView-2 multispectral imagery

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

4. What controls tropical forest architecture? Testing environmental, structural and floristic drivers

5. A Review of Methods for Mapping and Prediction of Inventory Attributes for Operational Forest Management

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Forest Canopy Height Retrieval and Analysis Using Random Forest Model with Multi-Source Remote Sensing Integration;Sustainability;2024-02-20

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