2020
DOI: 10.3390/land9110402
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Global Open Data Remote Sensing Satellite Missions for Land Monitoring and Conservation: A Review

Abstract: The application of global open data remote sensing satellite missions in land monitoring and conservation studies is in the state of rapid growth, ensuring an observation with high spatial and spectral resolution over large areas. The purpose of this study was to provide a review of the most important global open data remote sensing satellite missions, current state-of-the-art processing methods and applications in land monitoring and conservation studies. Multispectral (Landsat, Sentinel-2, and MODIS), radar … Show more

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Cited by 60 publications
(32 citation statements)
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“…Eucalyptus spp. (1), Conifers (2), Broadleaves (3), Crops and pastures (6), Shrubs (7), Bare soil (8), Anthropogenic areas (9) and Water (10). Table 10.…”
Section: Spatial Resolutionmentioning
confidence: 99%
See 1 more Smart Citation
“…Eucalyptus spp. (1), Conifers (2), Broadleaves (3), Crops and pastures (6), Shrubs (7), Bare soil (8), Anthropogenic areas (9) and Water (10). Table 10.…”
Section: Spatial Resolutionmentioning
confidence: 99%
“…Previously, Landsat was the most commonly used open-access satellite data for land cover mapping and monitoring studies. However, since the launch in 2015 of Sentinel-2A, an increasing number of studies are shifting away from Landsat in favor of data from this newer satellite [5,6] or to using a combination of both [7]. Several authors have even reported the superiority of Sentinel-2 for land cover mapping and monitoring purposes when compared to other similar sensors [5] like Landsat-8 [8].…”
Section: Introductionmentioning
confidence: 99%
“…The values of the producer's accuracy, user's accuracy, and the kappa coefficient within a particular class indicate a high agreement between classified and reference data (Table 10). It should be noted that the visual inspection [93,96] of the classification map determined that there are smaller additional areas (polygons) that are not included in the statistical analysis and that are incorrectly classified. These are boundary polygons between two classes, such as misclassification of unstocked timberland with agricultural land, misclassification of non-flooded lowland forests with hill forests, or misclassification of smaller settlements (which are not the whole polygon) with agricultural land.…”
Section: Resultsmentioning
confidence: 99%
“…This implies that despite the uniform composition of tree species as well as similar orientation, size, and leaf shape, the spatial diversity (variation) in the basal area, number of trees, and height of trees can be significant factors in enabling flood detection in forests [43]. In line with the first recommendation, S1 data (and data from other Sentinel missions) are free and available to all citizens and organizations worldwide, without any restrictions on the distribution, processing, and exchange of data [96,99]. These qualities made this sensor, among other purposes, available for the monitoring and mapping of natural disasters, which was conducted in this study on the example of flood mapping in the area of lowland forests.…”
Section: Discussionmentioning
confidence: 99%
“…Remotely sensed images have been used lately by researchers in machine vision applications such as object identification [1,2], detection [3], or extraction [4]. At the same time, deep learning algorithms proved to be useful for classification tasks and land use analysis [5] in satellite imagery data [6,7]-an important remote sensing application, where semantic segmentation techniques (based on supervised learning) are applied to assign a land cover class to every pixel of an image. This extraction task is generally carried out by means semantic segmentation and can be considered very challenging due to complex nature of geospatial objects, due to defects present in imagery (noise, occlusions, etc.…”
Section: Introductionmentioning
confidence: 99%