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Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

1
Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA
2
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1906; https://doi.org/10.3390/rs11161906
Received: 2 July 2019 / Revised: 10 August 2019 / Accepted: 12 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Advances in Active Remote Sensing of Forests)
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Abstract

Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha−1. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R2 of 0.26 and RMSE of 70.1 Mg ha−1. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R2 of 0.40 and an RMSE of 108.2 Mg ha−1 compared to the full lidar coverage model with an R2 of 0.58 and an RMSE of 96.0 Mg ha−1. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies. View Full-Text
Keywords: systematic sampling; classification-based sampling; forest types; data fusion; regression; random forest systematic sampling; classification-based sampling; forest types; data fusion; regression; random forest
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Li, S.; Quackenbush, L.J.; Im, J. Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery. Remote Sens. 2019, 11, 1906.

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