Paper
23 January 2001 Relationships between leaf area index (LAI) and vegetation indices to analyze and monitor Mediterranean ecosystems
Fausto Manes, Silvia Anselmi, Monica Giannini, Susanna Melini
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Abstract
The analysis of LAI is of great importance to evaluate the structural characteristics of a plant community. Vegetation types remote sensed analysis is a useful tool for the investigation of structural and functional characteristics of ecosystems. The integration of data acquired at these two different spatial scales contributes to the management of the land use and in particular of protected areas for the conservation of biodiversity. Field measuraments (LAI) and remote sensed data (Landsat 5 TM) were performed in the Circeo National Park and in the Presidential Estate of Castelporziano (located along the coast of Lazio, Italy, and characterised by Mediterranean climate). In this study the relationships between LAI and vegetation indices (NDVI, PVI, SAVI, GVI) were analysed; the NDVI showed the best correlation with the LAI. Using NDVT/LAI low power we estimated the LAI for the entire study areas on a multitemporal basis and plotted different LAI maps. The results obtained permitted to define a useful approach to analyse the canopy conditions of Mediterranean ecosystems on a multitemporal scale and also to highlight the effects of biotic and abiotic causes on vegetative growth as phytophagous attac and climatic stress due to local annual variability and to potential climatic change.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fausto Manes, Silvia Anselmi, Monica Giannini, and Susanna Melini "Relationships between leaf area index (LAI) and vegetation indices to analyze and monitor Mediterranean ecosystems", Proc. SPIE 4171, Remote Sensing for Agriculture, Ecosystems, and Hydrology II, (23 January 2001); https://doi.org/10.1117/12.413942
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Cited by 3 scholarly publications.
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KEYWORDS
Vegetation

Ecosystems

Climatology

Error analysis

Climate change

Remote sensing

Earth observing sensors

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