Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream
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 (...
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| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/20/3376 |
| _version_ | 1827704239710797824 |
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| author | Giovanni Romano Giovanni Francesco Ricci Francesco Gentile |
| author_facet | Giovanni Romano Giovanni Francesco Ricci Francesco Gentile |
| author_sort | Giovanni Romano |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:35:54Z |
| format | Article |
| id | doaj.art-d5f8db2c80ab4d37a869c0431cead6a5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:35:54Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d5f8db2c80ab4d37a869c0431cead6a52023-11-20T17:16:43ZengMDPI AGRemote Sensing2072-42922020-10-011220337610.3390/rs12203376Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain StreamGiovanni Romano0Giovanni Francesco Ricci1Francesco Gentile2Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70032 Bari, ItalyDepartment of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70032 Bari, ItalyDepartment of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70032 Bari, ItalyIn 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.https://www.mdpi.com/2072-4292/12/20/3376riparian vegetationLeaf Area IndexLandsatSentinelPleiades |
| spellingShingle | Giovanni Romano Giovanni Francesco Ricci Francesco Gentile Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream Remote Sensing riparian vegetation Leaf Area Index Landsat Sentinel Pleiades |
| title | Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream |
| title_full | Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream |
| title_fullStr | Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream |
| title_full_unstemmed | Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream |
| title_short | Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream |
| title_sort | influence of different satellite imagery on the analysis of riparian leaf density in a mountain stream |
| topic | riparian vegetation Leaf Area Index Landsat Sentinel Pleiades |
| url | https://www.mdpi.com/2072-4292/12/20/3376 |
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