Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery-Reference-Cited by-同舟云学术

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Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

Published:2016-08-08 Issue:3 Volume:10 Page:035010
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:en
Short-container-title:J. Appl. Remote Sens

Author:

Wu Chaofan¹,Shen Huanhuan¹,Shen Aihua²,Deng Jinsong¹,Gan Muye¹,Zhu Jinxia³,Xu Hongwei¹,Wang Ke¹

Affiliation:

1. Zhejiang University, Institute of Applied Remote Sensing and Information Technology, Hangzhou 310058, Zhejiang, China

2. Zhejiang Forestry Academy, Hangzhou, Zhejiang Province 310023, China

3. Zhejiang University of Finance and Economics, Institute of Economic and Social Development, Hangzhou 310018, China

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Earth and Planetary Sciences

Reference58 articles.

1. Satellite remote sensing of forest resources: three decades of research development

2. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon

3. Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods;Olander,2008

4. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

5. Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest

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1. Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region;Ecological Informatics;2024-09

2. Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning;Frontiers in Plant Science;2024-08-29

3. A Comparative Analysis of Remote Sensing Estimation of Aboveground Biomass in Boreal Forests Using Machine Learning Modeling and Environmental Data;Sustainability;2024-08-22

4. Synergizing sustainability: a critical review on harnessing agroforestry for biomass, carbon sequestration, and water-food-energy nexus;Energy, Ecology and Environment;2024-08-06

5. Combination of hyperspectral and LiDAR for aboveground biomass estimation using machine learning;Transactions in GIS;2024-07-10

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