2006 journal article

Using multispectral satellite imagery to estimate leaf area and response to silvicultural treatments in loblolly pine stands

CANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE, 36(6), 1587–1596.

By: F. Flores, H. Allen*, H. Cheshire*, J. Davis*, M. Fuentes* & D. Kelting

TL;DR: The results suggest that stand LAI of loblolly pine plantations can be accurately estimated from readily available remote sensing data and provide an opportunity to apply the findings from ecophysiological studies in field plots to forest management decisions at an operational scale. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
15. Life on Land (Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018

The relationship between leaf area index (LAI) of loblolly pine plantations and the broadband simple ratio (SR) vegetation index calculated from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data was examined. An equation was derived to estimate LAI from readily available Landsat 7 ETM+ data. The equation developed to predict LAI with Landsat 7 ETM+ data was tested with ground LAI measurements taken in 12 plots. The root mean square error of prediction was 0.29, an error of approximately 14% in prediction. The ability of Landsat 7 ETM+ data to consistently estimate SR over time was tested using two scenes acquired on different dates during the winter (December to early March). Comparison between the two images on a pixel-by-pixel basis showed that approximately 96% of the pixels had a difference of <0.5 units of SR (approximately 0.3 units of LAI). When the comparison was made on a stand-by-stand basis (average stand SR), a maximum difference of 0.2 units of SR (approximately 0.12 units of LAI) was found. These results suggest that stand LAI of loblolly pine plantations can be accurately estimated from readily available remote sensing data and provide an opportunity to apply the findings from ecophysiological studies in field plots to forest management decisions at an operational scale.