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Abstract
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.
Highlights
Remote sensing is a technique for acquiring information on the Earth using different types of sensors located at variable distances [1]
The aim of this study was to test the performance of three different resolution imagery, Landsat 8, Sentinel-2, and Pleiades 1A, in detecting the Leaf Area Index (LAI) in a Mediterranean area characterized by different land cover types
This study assessed the potential of three satellite images with different resolutions (Landsat 8, Sentinel-2, and Pleiades 1A) in studying vegetation features in a mountain riparian environment of Southern Italy
Summary
Remote sensing is a technique for acquiring information on the Earth using different types of sensors located at variable distances [1]. Ever since the first remote sensing imagery became available, it has been used for environmental applications, such as land use, hydrology, geology, weather, climate, and vegetation studies [2,3]. Satellites and aircrafts equipped with optical, radar, or laser sensors (i.e., satellite platforms) are the most common means for collecting data on and studying Earth’s surface [4]. Along with these traditional remote sensing technologies, drones have recently been used to conduct environmental studies [5,6,7,8]. Satellite remote sensing is still used to monitor large areas at regular intervals of time [9,10]. The use of satellite imagery is, essential in contexts where the size of the area makes in-field monitoring expensive and time-consuming, as in the case of watersheds [12]
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