Using InSAR to determine above-ground biomass in tropical forests

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Tropical forest

Using InSAR to determine above-ground biomass in tropical forests

Using InSAR to determine above-ground biomass in tropical forests

Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR

Tropical forests are under severe anthropogenic pressure. Indonesia became one of the largest greenhouse gas emitters worldwide due to deforestation and forest degradation and is therefore in the focus of REDD+ projects, which aim at reducing emissions from deforestation and forest degradation. These projects require a precise monitoring of forest carbon stocks and its changes, which are generally, derived from aboveground biomass (AGB) estimations.

Forest height and above-ground biomass

The international research project Pol-InSAR4AGB focused on the development of algorithms and the creation of process chains for the integrative use of multi-sensor X- and C-band SAR data using Pol-InSAR techniques for the determination of forest height and above-ground biomass in tropical forests. The main study area was located in Central Kalimantan in Indonesia. Based on a synergistic use of TerraSAR-X, TanDEM-X, Radarsat-2 and Sentinel-1 Pol-InSAR data we evaluated the effect of various beam modes, baselines and polarizations on the accuracy of estimated forest heights using Pol-InSAR techniques. Above-ground biomass was estimated based on the height of the forest canopy.


  • Collecting forest inventory data for forest height and above ground biomass
  • Determination of forest canopy height from SAR interferogramms using the Random Volume over Ground and the Random Motion over Ground Model
  • Testing of the algorithms, validation and accuracy assessment
  • Overall project management

Transferability to other tropical forest ecosystems

Reference data for forest heights and biomass were both available from a comprehensive in-house database. This database includes forest canopy height and biomass data from LiDAR measurements and field inventories, as well as newly collected forest inventory and aerial survey data from the study site. Furthermore, the change in forest height and above-ground biomass was detected between two time periods (2012-2015). The developed algorithms were tested for transferability to other tropical forest ecosystems.

Project partners

A.U.G. Signals Ltd. (AUG), Canada

Funding source

Federal Ministry of Economics and Technology – BMWi via the German Aerospace Center (DLR)