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Using forest inventory data with Landsat 8 imagery to map longleaf pine forest characteristics in Georgia, USA

Posted date: August 19, 2019
Publication Year: 
2019
Authors: Hogland, JohnAnderson, Nathaniel (Nate); Affleck, David; St. Peter, Joseph
Publication Series: 
Scientific Journal (JRNL)
Source: Remote Sensing. 11: 1803.

Abstract

This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy > 0.80; square root basal area per ha residual standard errors

Citation

Hogland, John; Anderson, Nathaniel; Affleck, David; St. Peter, Joseph. 2019. Using forest inventory data with Landsat 8 imagery to map longleaf pine forest characteristics in Georgia, USA. Remote Sensing. 11: 1803.