Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

AUTOMATIC GENERATION OF INDOOR NAVIGABLE SPACE USING A POINT CLOUD AND ITS SCANNER TRAJECTORY

Automatic generation of indoor navigable models is mostly based on 2D floor plans. However, in many cases the floor plans are out of date. Buildings are not always built according to their blue prints, interiors might change after a few years because of modified walls and doors, and furniture may be repositioned to the user’s preferences. Therefore, new approaches for the quick recording of indoor environments should be investigated. This paper concentrates on laser scanning with a Mobile Laser Scanner (MLS) device. The MLS device stores a point cloud and its trajectory. If the MLS device is operated by a human, the trajectory contains information which can be used to distinguish different surfaces. In this paper a method is presented for the identification of walkable surfaces based on the analysis of the point cloud and the trajectory of the MLS scanner. This method consists of several steps. First, the point cloud is voxelized. Second, the trajectory is analysing and projecting to acquire seed voxels. Third, these seed voxels are generated into floor regions by the use of a region growing process. By identifying dynamic objects, doors and furniture, these floor regions can be modified so that each region represents a specific navigable space inside a building as a free navigable voxel space. By combining the point cloud and its corresponding trajectory, the walkable space can be identified for any type of building even if the interior is scanned during business hours.

SATELLITE STEREO BASED DIGITAL SURFACE MODEL GENERATION USING SEMI GLOBAL MATCHING IN OBJECT AND IMAGE SPACE

This paper presents methodology and evaluation of Digital Surface Models (DSM) generated from satellite stereo imagery using Semi Global Matching (SGM) applied in image space and georeferenced voxel space. SGM is a well known algorithm, used widely for DSM generation from airborne and satellite imagery. SGM is typically applied in the image space to compute disparity map corresponding to a stereo image pair. As a different approach, SGM can be applied directly to the georeferenced voxel space similar to the approach of volumetric multi-view reconstruction techniques. The matching in voxel space simplifies the DSM generation pipeline because the stereo rectification and triangulation steps are not required. For a comparison, the complete pipeline for generation of DSM from satellite pushbroom sensors is also presented. The results on the ISPRS satellite stereo benchmark using Worldview stereo imagery of 0.5m resolution shows that the SGM applied in image space produce slightly better results than its object space counterpart. Furthermore, a qualitative analysis of the results on Worldview-3 stereo and Pleiades tri-stereo images are presented.

SATELLITE STEREO BASED DIGITAL SURFACE MODEL GENERATION USING SEMI GLOBAL MATCHING IN OBJECT AND IMAGE SPACE

This paper presents methodology and evaluation of Digital Surface Models (DSM) generated from satellite stereo imagery using Semi Global Matching (SGM) applied in image space and georeferenced voxel space. SGM is a well known algorithm, used widely for DSM generation from airborne and satellite imagery. SGM is typically applied in the image space to compute disparity map corresponding to a stereo image pair. As a different approach, SGM can be applied directly to the georeferenced voxel space similar to the approach of volumetric multi-view reconstruction techniques. The matching in voxel space simplifies the DSM generation pipeline because the stereo rectification and triangulation steps are not required. For a comparison, the complete pipeline for generation of DSM from satellite pushbroom sensors is also presented. The results on the ISPRS satellite stereo benchmark using Worldview stereo imagery of 0.5m resolution shows that the SGM applied in image space produce slightly better results than its object space counterpart. Furthermore, a qualitative analysis of the results on Worldview-3 stereo and Pleiades tri-stereo images are presented.