Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data

Abstract

Accurate and up-to-date data about growing stock volume are essential for forest management planning. Airborne Laser Scanning (ALS) is known for producing accurate wall-to-wall predictions but the data are at present collected at long time intervals. Digital Photogrammetry (DP) is cheaper and often more frequently available but known to be less accurate. This study investigates the potential of using contemporary DP data together with older ALS data and compares this with the case when only old ALS data are trained with recent field data. Combining ALS data from 2010 to 2011 with DP data from 2015, both trained with National Forest Inventory (NFI) field plot data from 2015, improved predictions of growing stock volume. Validation using data from 100 stands inventoried in 2015 gave an RMSE of 24.3% utilizing both old ALS data and recent DP data, 26.0% for old ALS only and 24.9% for recent DP only. If information about management actions were assumed available, combining old ALS and recent DP gave RMSE of 23.0%, only ALS 23.3% and only DP 23.8%.

Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning

“…Remote sensing data are a promising and potentially highly informative data source for the task of biodiversity estimation ( Gholizadeh et al, 2020 ; Moat et al, 2021 ). These data have been successfully applied in several recent studies for modeling vegetation attributes such as biomass ( Breidenbach et al, 2021 ), growing stock volume ( Lindgren et al, 2021 ), and plant size ( Söderberg et al, 2021 ), and can be applied for global inventory of habitats and for estimating the trait diversity within these habitats ( Cavender-Bares et al, 2020 ). These data sources, which are already successfully applied for many biodiversity-related purposes (see overview in Cavender-Bares et al, 2020 ), will likely play a key role for future developments in the field of automated biodiversity assessments, and can be readily added as additional features to neural network models as the ones presented in this study.…”

Monitoring Solutions for Remote Locations: A Data Gathering Approach for Remote Nature-Based Solution Sites

“…The high-resolution DTMs obtained from UAV flights highlight a modern low-cost data collection method that is highly applicable to the remote settings of NbS sites. This approach can better represent the smaller-scale features at NbS sites, update boundary conditions and elevation changes for use in flood modelling, and address areas with outdated or unmapped high-resolution LiDAR [56]. Conducting flights during periods of low flows or low vegetation cover will help gather the most accurate ground elevation values.…”

Remotely Sensed Tree Characterization in Urban Areas: A Review

“…PCCs are products derived from aerial images from which, through image matching techniques of the overlapping stereo images, a 3D image or point cloud can be obtained [101]. As with LiDAR data, some metrics such as tree height can be obtained from a digital elevation model (DEM) or a canopy height model (CHM) [88].…”