Title :
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
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);
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
DOI :
10.1109/JSTARS.2011.2176720
