Comparing the potential of stereo aerial photographs, stereo very high-resolution satellite images, and TanDEM-X for estimating forest height

Abstract

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.