Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India-Reference-Cited by-同舟云学术

Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India

Published:2021-07-26 Issue:14 Volume:48 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophys Res Lett

Author:

Nandy Subrata¹^ORCID,Srinet Ritika¹^ORCID,Padalia Hitendra¹

Affiliation:

1. Indian Institute of Remote Sensing Indian Space Research Organisation Department of Space Government of India Dehradun India

Funder

Indian Space Research Organisation

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL093799

Reference64 articles.

1. Estimating standing biomass in papyrus (Cyperus papyrus L.) swamp: exploratory of in situ hyperspectral indices and random forest regression

2. Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models

3. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps

4. Random forest in remote sensing: A review of applications and future directions

Cited by 102 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. Evaluating machine learning approaches for aboveground biomass prediction in fragmented high-elevated forests using multi-sensor satellite data;Remote Sensing Applications: Society and Environment;2024-11

2. Estimating aboveground biomass of urban trees based on ICESat-2 LiDAR and Zhuhai-1 hyperspectral data;Physics and Chemistry of the Earth, Parts A/B/C;2024-10

3. High-resolution mapping of forest structure and carbon stock using multi-source remote sensing data in Japan;Remote Sensing of Environment;2024-10

4. Regional mangrove vegetation carbon stocks predicted integrating UAV-LiDAR and satellite data;Journal of Environmental Management;2024-09

5. 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