Urban carbon stock estimation based on deep learning and UAV remote sensing: a case study in Southern China-Reference-Cited by-同舟云学术

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Urban carbon stock estimation based on deep learning and UAV remote sensing: a case study in Southern China

Published:2023-08-30 Issue:1 Volume:35 Page:272-286
ISSN:2766-9645
Container-title:All Earth
language:en
Short-container-title:All Earth

Author:

Wu Zijian¹^ORCID,Jiang Mingfeng²^ORCID,Li Huaizhong¹,Shen Yang¹,Song Junfeng¹,Zhong Xuyang³,Ye Zhen¹^ORCID

Affiliation:

1. School of Mathematics and Computer Science, Lishui University, Lishui, China

2. School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China

3. Department of Civil Engineering, School of Engineering, Lishui University, Lishui, China

Funder

Zhejiang Provincial Natural Science Foundation of China

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Management, Monitoring, Policy and Law,Global and Planetary Change

Link

https://www.tandfonline.com/doi/pdf/10.1080/27669645.2023.2249645

Reference64 articles.

1. Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought

2. Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

3. Predators help protect carbon stocks in blue carbon ecosystems

4. Short and Long-Term Effect of Land Use and Management on Soil Organic Carbon Stock in Semi-Desert Areas of North Africa-Tunisia

5. Comparison of high-resolution NAIP and unmanned aerial vehicle (UAV) imagery for natural vegetation communities classification using machine learning approaches

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