&lt;title&gt;Relationships between leaf area index (LAI) and vegetation indices to analyze and monitor Mediterranean ecosystems&lt;/title&gt;

Manes, Fausto; Anselmi, Silvia; Giannini, Maria Teresa; Melini, Susanna

doi:10.1117/12.413942

Cited by 4 publications

(2 citation statements)

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“…First, as mentioned above, the acquisition date (28 July) certainly influences the accuracy of results [74]. Moreover, several authors [76][77][78] highlight that although NDVI is commonly used to retrieve LAI values, it is not always the VI with the highest correlation. In particular, for S-2, the newest of the three satellites, alternative Vis may be considered to improve results [32,[79][80][81].…”

Section: Discussionmentioning

confidence: 99%

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020

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In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.

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Section: Discussionmentioning

confidence: 99%

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020

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“…1c): sclerophyllous vegetation with holm-oak prevailing, reafforestations of Pinus pinea L., mixed woods (holm-oak and pine), deciduous oak forest with Carpinus orientalis Miller, deciduous oak forest with evergreen shrubs. The relations between measured PAI and NDVI values were performed according to Manes et al (2001).…”

Section: Plant Area Index Measurementsmentioning

confidence: 99%

Multiscale integration of satellite, airborne and field data for Mediterranean vegetation studies in the natural area of the Castelporziano Estate (Rome)

Allegrini

Anselmi

Cavalli³

et al. 2009

Annals of Geophysics

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A new experimental approach to land analysis has recently been developed, based on the integration of information acquired on different scales; it enables the structure and the functionality of the vegetation in natural ecosystems to be analysed. This research aims at assessing the potentiality of the experimental approach by the integration of airborne and satellite remotely sensed data with ground measurements of structural parameters. In July 1999 a joint campaign for the acquisition of airborne (MIVIS, spatial resolution 3 m) and satellite remotely sensed data (Landsat 5TM, spatial resolution 30 m) and measures taken at ground (PAI), was deployed in the Presidential Estate at Castelporziano (Rome, Italy). The spectral signatures of the main vegetational types of the Estate were examined and the PAI were related to NDVI values, calculated by means of satellite and airborne images. The adopted approach enabled PAI maps to be produced. The linear relation between measured PAI and estimated PAI showed a higher coefficient of determination when the MIVIS data were used. The sensor high spectral resolution has moreover allowed to better describe the structural characteristics of the main plant typologies at Castelporziano Estate.

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Metropolitan area of Rome: an integrated vegetation study by remote sensing and field data

Giannini

Anselmi

Melini

et al.

IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas (Cat. No.01EX482)

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<title>Relationships between leaf area index (LAI) and vegetation indices to analyze and monitor Mediterranean ecosystems</title>

Cited by 4 publications

References 0 publications

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

Multiscale integration of satellite, airborne and field data for Mediterranean vegetation studies in the natural area of the Castelporziano Estate (Rome)

Metropolitan area of Rome: an integrated vegetation study by remote sensing and field data

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