Abstract
The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in tropical forests have been inconsistent and further knowledge on the accuracy of models linking AGB and InSAR height data is crucial for the development of large scale forest monitoring programs. This study provides an example of the use of TanDEM-X WorldDEM data to model AGB in Tanzanian woodlands. The primary objective was to assess the accuracy of a model linking AGB with InSAR height from WorldDEM after the subtraction of ground heights. The secondary objective was to assess the possibility of obtaining InSAR height for field plots when the terrain heights were derived from global navigation satellite systems (GNSS); i.e., as an alternative to using airborne laser scanning (ALS). The results revealed that the AGB model using InSAR height had a predictive accuracy of R M S E = 24.1 t·ha−1; or 38.8% of the mean AGB when terrain heights were derived from ALS. The results were similar when using terrain heights from GNSS. The accuracy of the predicted AGB was improved when compared to a previous study using TanDEM-X for a sub-area of the area of interest and was of similar magnitude to what was achieved in the same sub-area using ALS data. Overall; this study sheds new light on the opportunities that arise from the use of InSAR data for large scale AGB modelling in tropical woodlands.
Highlights
Forests play a critical role in the global carbon budget with deforestation accounting for 10% of the anthropogenic carbon emissions, as estimated in the fifth assessment report of the IntergovernmentalPanel on Climate Change (IPCC) [1]
These revealed that the ∆z airborne laser scanning (ALS) model presented in complementary knowledge to an area of interest (AOI) which had been the object of extensive research efforts in the the current study had a predictive accuracy of similar magnitude to a model where seven predictor past fivevariables years
Despite the need of confirming these results in other forest types and with more suitable sampling designs, the findings of this study suggest that TanDEM-X WorldDEM data can provide valuable above ground biomass (AGB) explanatory variables
Summary
Forests play a critical role in the global carbon budget with deforestation accounting for 10% of the anthropogenic carbon emissions, as estimated in the fifth assessment report of the IntergovernmentalPanel on Climate Change (IPCC) [1]. Forests play a critical role in the global carbon budget with deforestation accounting for 10% of the anthropogenic carbon emissions, as estimated in the fifth assessment report of the Intergovernmental. The United Nations Framework Convention on Climate Change (UNFCCC) established a program aimed at reducing emissions from deforestation and land degradation in tropical countries (i.e., REDD+ program) [2]. In order to ensure a successful implementation of REDD+, the UNFCCC has proposed a system based on financial compensations for reducing emissions. In order to become operational, these methods in addition to being robust and precise should be cost-efficient. In the past decade, great effort has been dedicated to better understanding the possibility of using remotely sensed data for monitoring changes in forest carbon and above ground biomass (AGB) in tropical countries [4]
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