A brief overview and perspective of using airborne Lidar data for forest biomass estimation-Reference-Cited by-同舟云学术

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A brief overview and perspective of using airborne Lidar data for forest biomass estimation

Published:2024-01-02 Issue:1 Volume:15 Page:1-24
ISSN:1947-9832
Container-title:International Journal of Image and Data Fusion
language:en
Short-container-title:International Journal of Image and Data Fusion

Author:

Lu Dengsheng¹²³,Jiang Xiandie¹²

Affiliation:

1. Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, China

2. Institute of Geography, Fujian Normal University, Fuzhou, China

3. Fujian Provincial Engineering Research Center for Forest Carbon Metering, Fujian Normal University, Fuzhou, China

Funder

National Natural Science Foundation of China

Fujian Provincial Science and Technology Department

China Scholarship Council

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19479832.2024.2309615

Reference109 articles.

1. Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

2. Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest

3. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

4. Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

5. Influence of Sampling Design Parameters on Biomass Predictions Derived from Airborne LiDAR Data

Cited by 3 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. Improving extraction of forest canopy height through reprocessing ICESat-2 ATLAS and GEDI data in sparsely forested plain regions;GIScience & Remote Sensing;2024-08-27

2. Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China;Remote Sensing;2024-06-09

3. The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests;GIScience & Remote Sensing;2024-04-26

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