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Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data

1
Faculty of Forest Sciences, Swedish University of Agricultural Sciences, SLU Skogsmarksgränd 17, SE-90183 Umeå, Sweden
2
Inventory and Monitoring, United States Department of Agriculture (USDA) Forest Service, 1400 Independence Ave, SW, Washington, DC 20250-1111, USA
3
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
4
School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1832; https://doi.org/10.3390/rs10111832
Received: 11 October 2018 / Revised: 7 November 2018 / Accepted: 14 November 2018 / Published: 19 November 2018
(This article belongs to the Special Issue Applications of Full Waveform Lidar)
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Abstract

Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches. View Full-Text
Keywords: carbon monitoring; GEDI; Landsat 7 ETM+; model-based inference; superpopulation models; variance estimation carbon monitoring; GEDI; Landsat 7 ETM+; model-based inference; superpopulation models; variance estimation
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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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Saarela, S.; Holm, S.; Healey, S.P.; Andersen, H.-E.; Petersson, H.; Prentius, W.; Patterson, P.L.; Næsset, E.; Gregoire, T.G.; Ståhl, G. Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data. Remote Sens. 2018, 10, 1832.

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