Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery
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Date
2019-03-06
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Citation of Original Publication
Gopalakrishnan, R.; Kauffman, J.S.; Fagan, M.E.; Coulston, J.W.; Thomas, V.A.; Wynne, R.H.; Fox, T.R.; Quirino, V.F. Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery. Forests 2019, 10, 234
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Attribution 4.0 International (CC BY 4.0)
Attribution 4.0 International (CC BY 4.0)
Abstract
Sustainable forest management is hugely dependent on high-quality estimates of forest
site productivity, but it is challenging to generate productivity maps over large areas. We present
a method for generating site index (a measure of such forest productivity) maps for plantation
loblolly pine (Pinus taeda L.) forests over large areas in the southeastern United States by combining
airborne laser scanning (ALS) data from disparate acquisitions and Landsat-based estimates of forest
age. For predicting canopy heights, a linear regression model was developed using ALS data and
field measurements from the Forest Inventory and Analysis (FIA) program of the US Forest Service
(n = 211 plots). The model was strong (R² = 0.84, RMSE = 1.85 m), and applicable over a large
area (~208,000 sq. km). To estimate the site index, we combined the ALS estimated heights with
Landsat-derived maps of stand age and planted pine area. The estimated bias was low (0.28 m)
and the RMSE (3.8 m, relative RMSE: 19.7%, base age 25 years) was consistent with other similar
approaches. Due to Landsat-related constraints, our methodology is valid only for relatively young
pine plantations established after 1984. We generated 30 m resolution site index maps over a large
area (~832 sq. km). The site index distribution had a median value of 19.4 m, the 5th percentile value
of 13.0 m and the 95th percentile value of 23.3 m. Further, using a watershed level analysis, we ranked
these regions by their estimated productivity. These results demonstrate the potential and value of
remote sensing based large-area site index maps.