Forest leaf area index determination : a multiyear satelliteindependent method based on within-stand normalized difference vegetation index spatial variability

The Leaf Area Index (LAI) and its spatial distribution are key features to describe the forest ecophysiological processes. A stable and reproducible relationship is obtained between the LAI and the standard deviation sNDVI of the pixel-based satellite-derived normalized difference vegetation indices...

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Bibliographic Details
Published in:Journal of Geophysical Research 2006, Vol.111 (G2)
Main Authors: Le Maire, G., François, C., Soudani, Kamel, Davi, H., Le Dantec, Valérie, Saugier, Bernard, Dufrene, E.
Format: Article
Language:English
Subjects:
Ecology, environment
Ecosystems
Life Sciences
Online Access:Get full text
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Summary:The Leaf Area Index (LAI) and its spatial distribution are key features to describe the forest ecophysiological processes. A stable and reproducible relationship is obtained between the LAI and the standard deviation sNDVI of the pixel-based satellite-derived normalized difference vegetation indices (NDVI) of forest stands. In situ measurements of LAI have been performed with the LAI-2000 Plant Canopy Analyser over 8 years in the managed Fontainebleau forest (France) on about 31 stands each year, including oak, beech, and mixed oak-beech stands. Simultaneous satellite images have been acquired, atmospherically and geometrically corrected, and included into a geographic information system to get the mean NDVI and the sNDVI for each stand. A total of six different satellites with a 20- to 30-m spatial resolution have been considered over the eight studied years: SPOT1, SPOT2, SPOT4, LANDSAT ETM+, IKONOS, and HYPERION. The mean LAI of a stand is linked to the sNDVI with a unique relationship that appears to be mostly year- and satellite-independent, because the sNDVI is nearly insensitive to additive or proportional shifts on NDVI. The theoretical bases of the sNDVI-LAI relationship are investigated. The results show the combined importance of the shape of the within-stand LAI distribution (following a Weibull probability density function) and the shape of the within-stand LAI-NDVI curves (showing a saturation). The root mean square error of the predicted LAI over the 259 samples is 1.14 m2/m2 when all years and satellites are considered, using the following equation: LAI = 2.45 ln(sNDVI) 5.58 (r2 = 0.63).
ISSN:0148-0227
2156-2202
DOI:10.1029/2005JG000122