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 (Sentinel-1), and digital elevation model missions (SRTM, ASTER) were analyzed, as the most often used global open data satellite missions, according to the number of scientific research articles published in Web of Science database. Processing methods of these missions’ data consisting of image preprocessing, spectral indices, image classification methods, and modelling of terrain topographic parameters were analyzed and demonstrated. Possibilities of their application in land cover, land suitability, vegetation monitoring, and natural disaster management were evaluated, having high potential in broad use worldwide. Availability of free and complementary satellite missions, as well as the open-source software, ensures the basis of effective and sustainable land use management, with the prerequisite of the more extensive knowledge and expertise gathering at a global scale.

Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models

“…Eucalyptus spp. (1), Conifers (2), Broadleaves (3), Crops and pastures (6), Shrubs (7), Bare soil (8), Anthropogenic areas (9) and Water (10). Table 10.…”

“…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].…”

Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an Object-Based Approach

“…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.…”

“…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.…”

Generative Learning for Postprocessing Semantic Segmentation Predictions: A Lightweight Conditional Generative Adversarial Network Based on Pix2pix to Improve the Extraction of Road Surface Areas

“…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.…”