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Remote Sens. 2017, 9(6), 598; doi:10.3390/rs9060598

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

1
Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave. North, St. Paul, MN 55108, USA
2
Northern Research Station, Forest Inventory and Analysis, USDA Forest Service, 1992 Folwell Ave., St. Paul, MN 55108, USA
3
Pacific Northwest Research Station, USDA Forest Service, University of Washington, Seattle, WA 98195, USA
4
USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97731, USA
5
Northern Research Station, Center for Research on Ecosystem Change, USDA Forest Service, 271 Mast Road, Durham, NH 03824, USA
*
Author to whom correspondence should be addressed.
Academic Editors: L. Monika Moskal, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 14 April 2017 / Revised: 10 June 2017 / Accepted: 11 June 2017 / Published: 13 June 2017
View Full-Text   |   Download PDF [5862 KB, uploaded 14 June 2017]   |  

Abstract

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA—Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)—in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R2) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA. View Full-Text
Keywords: aboveground biomass; large-area estimation; eastern USA; LiDAR strip samples; Landsat time-series imagery; site-specific spatial models; generic spatial model aboveground biomass; large-area estimation; eastern USA; LiDAR strip samples; Landsat time-series imagery; site-specific spatial models; generic spatial model
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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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MDPI and ACS Style

Deo, R.K.; Russell, M.B.; Domke, G.M.; Andersen, H.-E.; Cohen, W.B.; Woodall, C.W. Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA. Remote Sens. 2017, 9, 598.

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