Modeling Aboveground Biomass in Tropical Forests Using Multi-Frequency SAR Data—A Comparison of Methods

Author

Englhart, Sandra ; Keuck, Vanessa ; Siegert, Florian

Author_Institution

Biol. Dept. II, Ludwig-Maximilians-Univ., Planegg-Martinsried, Germany

Volume

5

Issue

1

fYear

2012

Firstpage

298

Lastpage

306

Abstract

In the context of climate change mitigation mechanisms for avoiding deforestation, i.e., reducing emissions from deforestation and forest degradation (REDD+), comprehensive forest monitoring, especially in tropical regions, is of high relevance. A precise determination of forest carbon stocks or aboveground biomass (AGB) for large areas is of special importance.

Keywords

remote sensing by radar; vegetation; vegetation mapping; AGB model calibration; AGB model validation; ALOS PALSAR imagery; Indonesia; LiDAR measurements; REDD+; aboveground biomass modeling; artificial neural network; climate change mitigation mechanisms; comprehensive forest monitoring; field inventory AGB data; forest carbon stocks; forest degradation; high biomass range; multifrequency SAR backscatter data; multitemporal TerraSAR-X imagery; multivariate linear regression; peat swamp forests; support vector regression; tropical forests; tropical regions; Artificial neural networks; Backscatter; Biological system modeling; Biomass; Estimation; Laser radar; Support vector machines; ALOS PALSAR; Indonesia; REDD+; artificial neural network (ANN); biomass; forest; regression; support vector regression (SVR);

fLanguage

English

Journal_Title

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of

Publisher

ieee

ISSN

1939-1404

Type

jour

DOI

10.1109/JSTARS.2011.2176720

Filename

6104195