Comparing the potential of stereo aerial photographs, stereo very high-resolution satellite images, and TanDEM-X for estimating forest height
Sami Ullah, Matthias Dees, Pawan Datta, Petra Adler, Tahir Saeed, Muhammad Sadiq Khan, Barbara Koch
Abstract
Airborne laser scanning (ALS) is generally known as the most accurate and primary source providing the 3D structure of forest canopy model (CHM) information; however, in many countries, it is not updated as needed for continuous forest management planning due to its high costs. Image-based point clouds derived from airborne stereo aerial photographs, space-borne stereo very high-resolution WorldView-2, and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) can also be used for the generation of the CHM. In this study, we compared the performance of three types of 3D data, i.e. stereo aerial photographs, stereo very high-resolution WorldView-2, and TanDEM-X for the retrieval of forest CHM. Digital surface models (DSMs) generated from image-based point clouds produced from stereo aerial photographs and stereo WorldView-2 using image-matching algorithms and from TanDEM-X data using interferometry. Finally, three types of CHMs generated by subtracting ALS-based digital terrain model (DTM) from the above three DSMs. Plot-level heights and density metrics extracted from the CHMs and regressed with the field-based Lorey’s mean, and maximum height. Stereo aerial photographic CHM showed the most accurate results with root-mean-square error (RMSE) = 1.71 m, followed by high-resolution stereo WorldView-2 with RMSE = 2.04 m, and TanDEM-X with RMSE = 2.13 m, respectively. Also, for maximum height, we obtained higher accuracy for image-based point clouds with RMSE = 2.33 m, followed by high-resolution stereo WorldView-2 with RMSE = 2.71 m, and TanDEM-X with RMSE = 2.81 m, respectively. Our overall finding indicated that stereo aerial photography is the most accurate option followed by stereo very high-resolution WorldView-2 and TanDEM-X data for estimating forest heights. The study shares valuable information for the generation and regular update of forest structure information in the presence of pre-existing ALS DTM.