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Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USAAuthor(s): Ram K. Deo; Grant M. Domke; Matthew B. Russell; Christopher W. Woodall; Hans-Erik Andersen
Source: Environmental Research Letters
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionAboveground biomass (AGB) estimates for regional-scale forest planning have become cost-effective with the free access to satellite data from sensors such as Landsat andMODIS. However, the accuracy of AGB predictions based on passive optical data depends on spatial resolution and spatial extent of target area as fine resolution (small pixels) data are associated with smaller coverage and longer repeat cycles compared to coarse resolution data. This study evaluated various spatial resolutions of Landsat-derived predictors on the accuracy of regional AGB models at three different sites in the eastern USA:Maine, Pennsylvania-New Jersey, and South Carolina.We combined national forest inventory data with Landsat-derived predictors at spatial resolutions ranging from 30–1000m to understand the optimal spatial resolution of optical data for large-area (regional) AGB estimation. Ten generic models were developed using the data collected in 2014, 2015 and 2016, and the predictions were evaluated (i) at the county-level against the estimates of the USFS Forest Inventory and Analysis Program which relied on EVALIDator tool and national forest inventory data from the 2009–2013 cycle and (ii) within a large number of strips (∼1 km wide) predicted via LiDAR metrics at 30m spatial resolution. The county-level estimates by the EVALIDator and Landsat models were highly related (R2 >0.66), although the R2 varied significantly across sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of coarser resolution. The Landsat-based total AGB estimates were larger than the LiDAR-based total estimates within the strips, however the mean of AGB predictions by LiDAR were mostly within one-standard deviations of the mean predictions obtained from the Landsat-based model at any of the resolutions. We conclude that satellite data at resolutions up to 1000m provide acceptable accuracy for continental scale analysis of AGB.
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CitationDeo, Ram K.; Domke, Grant M.; Russell, Matthew B.; Woodall, Christopher W.; Andersen, Hans-Erik. 2018. Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA. Environmental Research Letters. 13(5): 055004. 10 p. https://doi.org/10.1088/1748-9326/aabcd5.
Keywordsabove-ground forest biomass, large-area estimation, Landsat data, spatial resolution of predictors, LiDAR, design-based estimates
- Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations
- Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA
- Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data
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