Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon

Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon

Previous research has explored the potential to integrate lidar and optical data in aboveground biomass (AGB) estimation, but how different data sources, vegetation types, and modeling algorithms influence AGB estimation is poorly understood. This research conducts a comparative analysis of differen...

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Bibliographic Details
Main Authors: Yunyun Feng, Dengsheng Lu, Qi Chen, Michael Keller, Emilio Moran, Maiza Nara dos-Santos, Edson Luis Bolfe, Mateus Batistella
Format: Article
Language:English
Published: Taylor & Francis Group 2017-10-01
Series:International Journal of Digital Earth
Subjects:
lidar
rapideye
aboveground biomass
moist tropical forest
support vector regression
random forest
linear regression
stratification
Online Access:http://dx.doi.org/10.1080/17538947.2017.1301581

Internet

http://dx.doi.org/10.1080/17538947.2017.1301581

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