Fusion of NASA Airborne Snow Observatory (ASO) Lidar Time Series over Mountain Forest Landscapes

Abstract

Mountain ecosystems are among the most fragile environments on Earth. The availability of timely updated information on forest 3D structure would improve our understanding of the dynamic and impact of recent disturbance and regeneration events including fire, insect damage, and drought. Airborne lidar is a critical tool for monitoring forest change at high resolution but it has been little used for this purpose due to the scarcity of long-term time-series of measurements over a common region. Here, we investigate the reliability of on-going, multi-year lidar observations from the NASA-JPL Airborne Snow Observatory (ASO) to characterize forest 3D structure at a fine spatial scale. In this study, weekly ASO measurements collected at~1 pt/m 2 , primarily acquired to quantify snow volume and dynamics, are coherently merged to produce high-resolution point clouds (∼12 pt/m 2 ) that better describe forest structure. The merging methodology addresses the spatial bias in multi-temporal data due to uncertainties in platform trajectory and motion by collecting tie objects from isolated tree crown apexes in the lidar data. The tie objects locations are assigned to the centroid of multi-temporal lidar points to fuse and optimize the location of multiple measurements without the need for ancillary data or GPS control points. We apply the methodology to ASO lidar acquisitions over the Tuolumne River Basin in the Sierra Nevada, California, during the 2014 snow monitoring campaign and provide assessment of the fidelity of the fused point clouds for forest mountain ecosystem studies. The availability of ASO measurements that currently span 2013-2017 enable annual forest monitoring of important vegetated ecosystems that currently face ecological threads of great significance such as the Sierra Nevada (California) and Olympic National Forest (Washington).

Uav-Based Lidar High-Resolution Snow Depth Mapping in the Swiss Alps: Comparing Flat and Steep Forests

“…As we were flying during wind-free conditions and when the snow intercepted in the tree branches was limited, the trees were stable features within all the acquisitions. The points corresponding to the branches and the treetops were therefore used for applying different feature-based 3D registration algorithms: point-to-point manual alignment, ICP algorithm, and 3D transformation proposed by Ferraz et al (2018) were applied to our data. The first two point-based methods did not identify pairing points in the trees (more details in section 3).…”

“…The first two point-based methods did not identify pairing points in the trees (more details in section 3). Alternatively, we used the registration workflow proposed by Ferraz et al, (2018). Since this is a registration methodology based on the treetop positions within the scans that need to be registered, we first had to segment the treetops.…”

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

“…Over the last decades, optical remote sensing techniques have been used for monitoring forest changes at regional scales with the support of field surveys (e.g., [4,5]). However, airborne laser scanning (ALS) is better adapted to characterize forest structure [6] and estimate forest inventory parameters, providing accurate information to perform forest management and planning [3]. Furthermore, costs of ALS-based inventories are comparable to those associated with traditional ground-based ones [7,8].…”

“…Furthermore, costs of ALS-based inventories are comparable to those associated with traditional ground-based ones [7,8]. Despite the great potential of this technology, multi-temporal ALS data have been utilized less, as the availability of two or more surveys in the same area has been limited by acquisition costs as well as by the need of temporal-concomitant field data (e.g., [3,6,9,10]). Recently, organizations, companies, and countries have made an effort to gather multi-temporal datasets in different years (e.g., [11][12][13]) allowing the estimation of biophysical properties in forested areas over time.…”

Snow Depth Distribution Patterns and Consistency from Airborne Lidar Time Series

“…The basin is a snow-dominated system where 80-90% of the precipitation input from the mid to high elevations (2100-4000 m) occurs during the winter months and is stored as snow until the melt season when streamflow peaks (Rice et al, 2011). The lowest elevation in the basin is at 1150 m, just above California's Central Valley floor, and extends to the crest of the Sierra at 4000 m. The tree line is near 2900 m and the predominant forest cover is firs and pines (Hedrick et al, 2018;Ferraz et al, 2018).…”