Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning

Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning

Accurately mapping urban above-ground vegetation carbon density presents challenges due to fragmented landscapes, mixed pixels, and shadows induced by buildings and mountains. To address these issues, a novel methodological framework is introduced, utilizing a linear spectral unmixing analysis (LSUA...

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Bibliographic Details
Main Authors: Guangping Qie, Jianneng Ye, Guangxing Wang, Minzi Wang
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Forests
Subjects:
urban
vegetation carbon density
mapping
de-shadow
spectral unmixing
machine learning
Online Access:https://www.mdpi.com/1999-4907/15/3/480

Internet

https://www.mdpi.com/1999-4907/15/3/480

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