| dc.contributor |
Forest Resources and Environmental Conservation |
|
| dc.creator |
Gopalakrishnan, Ranjith |
|
| dc.creator |
Kauffman, Jobriath S. |
|
| dc.creator |
Fagan, Matthew E. |
|
| dc.creator |
Coulston, John W. |
|
| dc.creator |
Thomas, Valerie A. |
|
| dc.creator |
Wynne, Randolph H. |
|
| dc.creator |
Fox, Thomas R. |
|
| dc.creator |
Quirino, Valquiria F. |
|
| dc.date |
2019-03-18T12:28:06Z |
|
| dc.date |
2019-03-18T12:28:06Z |
|
| dc.date |
2019-03-06 |
|
| dc.date |
2019-03-15T13:54:40Z |
|
| dc.date.accessioned |
2023-03-01T18:51:12Z |
|
| dc.date.available |
2023-03-01T18:51:12Z |
|
| dc.identifier |
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. |
|
| dc.identifier |
http://hdl.handle.net/10919/88468 |
|
| dc.identifier |
https://doi.org/10.3390/f10030234 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/281486 |
|
| dc.description |
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 (<i>Pinus taeda</i> 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 (<i>n</i> = 211 plots). The model was strong (<i>R</i><sup>2</sup> = 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. |
|
| dc.description |
Published version |
|
| dc.format |
application/pdf |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
MDPI |
|
| dc.rights |
Creative Commons Attribution 4.0 International |
|
| dc.rights |
http://creativecommons.org/licenses/by/4.0/ |
|
| dc.subject |
forestry |
|
| dc.subject |
forest site productivity |
|
| dc.subject |
site index |
|
| dc.subject |
Landsat |
|
| dc.subject |
airborne laser scanning |
|
| dc.subject |
forest productivity mapping |
|
| dc.subject |
FIA |
|
| dc.title |
Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery |
|
| dc.title |
Forests |
|
| dc.type |
Article - Refereed |
|
| dc.type |
Text |
|