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Remote Sens. 2018, 10(2), 164; https://doi.org/10.3390/rs10020164

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

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
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Received: 13 November 2017 / Revised: 18 January 2018 / Accepted: 22 January 2018 / Published: 24 January 2018
(This article belongs to the Special Issue Mountain Remote Sensing)
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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/m2, primarily acquired to quantify snow volume and dynamics, are coherently merged to produce high-resolution point clouds ( ~ 12 pt/m2) 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). View Full-Text
Keywords: NASA Airborne Snow Observatory; lidar; mountain ecosystems; multi-temporal data; co-registration; low density point clouds; forest vegetation structure; surface hydrology NASA Airborne Snow Observatory; lidar; mountain ecosystems; multi-temporal data; co-registration; low density point clouds; forest vegetation structure; surface hydrology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ferraz, A.; Saatchi, S.; Bormann, K.J.; Painter, T.H. Fusion of NASA Airborne Snow Observatory (ASO) Lidar Time Series over Mountain Forest Landscapes. Remote Sens. 2018, 10, 164.

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