Forest Inventory Parameters from LiDAR Radar and Optical Data: Case Study Lubrecht Experimental Forest in California

Abstract

Active remote sensing technologies, including airborne laser scanning (LIDAR), interferometric radar (InSAR) have the capability to provide accurate information relating to three-dimensional forest canopy structure over extensive areas of the landscape. Detailed mapping of forests height and terrain features is now possible through 3D remote sensing. Consequently, forest inventory parameters are evaluated through point cloud and normalized surface models. Practically, forest resource mapping is carried out through Airborne Laser Scanner (ALS). In the modern age, ALS achieve considerable popularity in alternative 3-D techniques along with the apace-borne and air-borne sensors. Point clouds are generated through radargrammetry, photogrammetry and interferometry. Photogrammetric point clouds are derived through airborne stereo imagery while radargrammetry and interferometry utilize very-high-resolution Synthetic Aperture Radar (SAR) data. Similarly, high resolution optical data is also used for forest inventory. ALS has the ability of mapping tree heights and terrain in even mixed forest scenarios, which is advantageous over SAR data or aerial imagery. To put it simply, in the forest scenarios, height of the canopy, single tree heights and canopy density can be mapped. Forest zoning, crown density, crown height and forest area has been mapped out using these different datasets in comparison. In this paper, first we review experiences of the use of ALS data in estimation of forest inventory parameters in Lubrecht forest in California, and we compare techniques to SAR and optical imagery. In addition, on the basis of our experiences, we aim to present new implications