This chapter is about collecting three-dimensional (3D) information from lidar and radar and turning that information into valuable forest informatics. For the first time, we present that the processing of all these data, whether lidar or radar, should go into the same pipeline. Today, it can be seen that many of the future remote sensing processes for forestry will be based on point cloud processing or on elevation models (3D techniques). These required forestry data can be provided not only by both the lidar and the radar but also by photogrammetry. For example, analogous to photogrammetric spatial intersection, a stereo pair of SAR images with different off-nadir angles can be used to calculate the 3D coordinates for corresponding points on the image pair producing point clouds from radar imagery. Also, in SAR interferometry (InSAR), pixel-by-pixel phase difference between two complex SAR images acquired from slightly different perspectives can be converted into elevation differences of the terrain/object. Thus, both lidar and radar can provide data that can be processed in a similar way either using original points or using surface models in a raster form. From the point clouds, you can calculate digital terrain model (DTM), digital surface model (DSM), and canopy height model, normalized digital surface model (CHM/nDSM). The idea is to provide surface model (DSM) and subtract the ground elevation (DTM) from it in order to get a canopy height. Intensity, coherence (in interferometry SAR) and texture can be used to improve the estimates in 3D-based inventory.
|Title of host publication||Land Resources Monitoring, Modeling, and Mapping with Remote Sensing|
|Number of pages||32|
|Publication status||Published - 1 Jan 2015|
|MoE publication type||A3 Part of a book or another research book|