Abstract
Abstract. Accurate forest resources maps are needed in diverse applications ranging from the local forest management to the global climate change research. In particular, it is important to have tools to map changes in forest resources, which helps us to understand the significance of the forest biomass changes in the global carbon cycle. In the task of mapping changes in forest resources for wide areas, Earth Observing satellites could play the key role. In 2013, an EU/FP7-Space funded project “Advanced_SAR” was started with the main objective to develop novel forest resources mapping methods based on the fusion of satellite based 3D measurements and in-situ field measurements of forests. During the summer 2014, an extensive field surveying campaign was carried out in the Evo test site, Southern Finland. Forest inventory attributes of mean tree height, basal area, mean stem diameter, stem volume, and biomass, were determined for 91 test plots having the size of 32 by 32 meters (1024 m2). Simultaneously, a comprehensive set of satellite and airborne data was collected. Satellite data also included a set of TanDEM-X (TDX) and TerraSAR-X (TSX) X-band synthetic aperture radar (SAR) images, suitable for interferometric and stereo-radargrammetric processing to extract 3D elevation data representing the forest canopy. In the present study, we compared the accuracy of TDX InSAR and TSX stereo-radargrammetric derived 3D metrics in forest inventory attribute prediction. First, 3D data were extracted from TDX and TSX images. Then, 3D data were processed as elevations above the ground surface (forest canopy height values) using an accurate Digital Terrain Model (DTM) based on airborne laser scanning survey. Finally, 3D metrics were calculated from the canopy height values for each test plot and the 3D metrics were compared with the field reference data. The Random Forest method was used in the forest inventory attributes prediction. Based on the results InSAR showed slightly better performance in forest attribute (i.e. mean tree height, basal area, mean stem diameter, stem volume, and biomass) prediction than stereo-radargrammetry. The results were 20.1% and 28.6% in relative root mean square error (RMSE) for biomass prediction, for TDX and TSX respectively.
Highlights
1.1 Remote Sensing in mapping forest resourcesThe amount of forest Above Ground Biomass (AGB) is under of intense international discussion related to the global climate change and carbon cycle
The main objective of this study is to compare the accuracy of InSAR and stereo-radargrammetry 3D metrics in forest inventory attribute prediction using X-band synthetic aperture radar (SAR) satellite data from TSX and TDX satellites
Using TDX InSAR data the prediction accuracy of all forest attributes was better than when the TSX stereo-radargrammetric height data is used
Summary
1.1 Remote Sensing in mapping forest resourcesThe amount of forest Above Ground Biomass (AGB) is under of intense international discussion related to the global climate change and carbon cycle. In order to contribute this discussion, as the remote sensing scientific community, we need to find techniques to measure AGB effectively and accurately for wide areas. The use of Airborne Laser Scanning (ALS) data in measuring forest resources has been a success story and has undergone though a commercial breakthrough globally (Hyyppä et al, 2008). There is still need for wide-area forest resources maps and in this scope satellite data is expected to play a very important role (Houghton et al, 2009). Alternative techniques to lidars exist, and there are examples on the use of stereo-photogrammetry (both airborne and spaceborne) and imaging radars in mapping of forests. Scientific studies in the past couple of years have shown that elevation information extracted from satellite SAR data (Synthetic Aperture Radar) have potential in estimation of forest canopy height even close to accuracy of ALS data, under the assumption that the elevation model of the underlying terrain is known (e.g. Solberg et al, 2013, Solberg et al, 2014, Karjalainen et al, 2012)
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