http://www.cnr.it/ontology/cnr/individuo/prodotto/ID183111

Forest leaf area index in an Alpine valley from medium resolution satellite imagery and in situ data (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Forest leaf area index in an Alpine valley from medium resolution satellite imagery and in situ data (Articolo in rivista) (literal)

Anno

2012-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1117/1.JRS.6.063528 (literal)

Alternative label

Stroppiana D.; M. Boschetti; P.A. Brivio; L. Nizzetto; A. Di Guardo (2012)
Forest leaf area index in an Alpine valley from medium resolution satellite imagery and in situ data
in Journal of applied remote sensing; SPIE-INT SOC OPTICAL ENGINEERING, BELLINGHAM, WA 98227-0010 (Stati Uniti d'America)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Stroppiana D.; M. Boschetti; P.A. Brivio; L. Nizzetto; A. Di Guardo (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

6 (literal)

Rivista

Journal of applied remote sensing (Rivista)

Note

ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

Stroppiana D.; M. Boschetti; P.A. Brivio: CNR-IREA; L. Nizzetto: University of Lancaster, Lancaster LA1 4YQ, England - Norwegian Institute for Water Research, Oslo, Norway; A. Di Guardo: University of Insubria, Como, Italy (literal)

Titolo

Forest leaf area index in an Alpine valley from medium resolution satellite imagery and in situ data (literal)

Abstract

Leaf area index (LAI) is a key variable for modeling the interaction between vegetation and the atmosphere. We collected field LAI measurements over 12 sites in 2005 in the Lys valley, Northern Italy, to calibrate regressive models using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and wide dynamic range vegetation index (WDRVI) products derived from 250-m moderate resolution imaging spectroradiomete (MODIS) imagery. Field data were compared to the 1 km MODIS leaf area index--fraction of photosynthetically active radiation (LAI/fAPAR) product to show that regressive techniques are better suited for local applications. We investigated these LAI-vegetation index (VI) regressive models to 1. test the sensitivity of the model to forest type and phenology, 2. identify the most suitable VI for LAI retrieval, and 3. verify the feasibility of using a linear model. Results show that in our experimental conditions the LAI-VI relationship is primarily influenced by phenology and that the leaf constant period (maximum LAI) is significantly different compared to the other phenological phases. Among the indices, EVI yielded the poorest performance (R2<0.41) and the linear regressive models for NDVI and WDRVI derived by pooling together data from different phenological phases show a good correlation with field data (R2>0.65); the use of a logarithmic model does not improve the performance. The LAI-WDRVI and LAI-NDVI models were inverted and applied to 2004 MODIS data and model performance was assessed by comparing predicted and measured LAI. Results show that WDRVI performs best in a linear regressive model, yielding a relative root mean square error <23%. (literal)

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