Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

• Published in: Forests Vol. 10; no. 3; p. 222
• Main Authors: Blinn, Christine, House, Matthew, Wynne, Randolph, Thomas, Valerie, Fox, Thomas, Sumnall, Matthew
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 02.03.2019
• Subjects: Atmospheric models; Competition; Dependent variables; Economic models; Empirical analysis; Estimates; Forest management; forestry; Landsat; Landsat satellites; Leaf area; Leaf area index; Least squares method; Leaves; Measuring instruments; Moisture index; phenology; Pine trees; Pinus taeda; Plantations; Reflectance; Remote sensing; Seasons; Sensors; silviculture; Software; Summer; Vegetation; Winter; United States > US; Virginia
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f10030222

Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included examination of the impact of georegistration accuracy, comparison of top-of-atmosphere and surface reflectance, development of a new empirical model for the species and region, and comparison of the new empirical model with the current operational standard. Permanent plots for the collection of ground LAI measurements were established at two locations near Appomattox, Virginia and Tuscaloosa, Alabama in 2013 and 2014, respectively. Each plot is thirty by thirty meters in size and is located at least thirty meters from a stand boundary. Plot LAI measurements were collected twice a year using the LI-COR LAI-2200 Plant Canopy Analyzer. Ground measurements were used as dependent variables in ordinary least squares regressions with ETM+ and OLI-derived vegetation indices. We conclude that accurately-located ground LAI estimates at minimum and maximum LAI in loblolly pine stands can be combined and modeled with Landsat-derived vegetation indices using surface reflectance, particularly simple ratio (SR) and normalized difference moisture index (NDMI), across sites and sensors. The best resulting model (LAI = −0.00212 + 0.3329SR) appears not to saturate through an LAI of 5 and is an improvement over the current operational standard for loblolly pine monitoring, modeling, and management in this ecologically and economically important region.