E 37 studies using satellites (“satellite only” and “satellite other” in Figure two). Please note that some studies use information from more than one particular satellite. From this analysis, WorldView satellites appear to be probably the most normally utilized ones for coral mapping, confirming that high-resolution multispectral satellites are extra appropriate than low-resolution ones for coral mapping.Figure 3. Most utilized satellites in coral reef classification and mapping amongst 2018 and 2020.3. Image Correction and Preprocessing Even though satellite imagery can be a exceptional tool for benthic habitat mapping, supplying remote pictures at a relatively low expense over significant time and space scales, it suffers from a variety of limitations. A few of they are not exclusively related to satellites but are shared with other remote sensing methods such as UAV. The majority of the time, existing image correction strategies can overcome these issues. Within the identical way, preprocessing solutions usually lead to enhanced accuracy of classification. Having said that, the efficiency of these algorithmsRemote Sens. 2021, 13,7 ofis still not excellent and may in some cases induce noise when trying to produce coral reef maps. This part will describe one of the most common processing that will be performed, at the same time as their limitations. 3.1. Clouds and Cloud Shadows One important difficulty of remote sensing with satellite imagery is missing information, primarily brought on by the presence of clouds and cloud shadows, and their impact on the atmosphere radiance measured on the pixels near clouds (adjacency effect) [115]. For example, Landsat7 images have on typical a cloud coverage of 35 [116]. This dilemma is globally present, not merely for the ocean-linked subjects but for each and every study using satellite images, such as land monitoring [117,118] and forest monitoring [119,120]. As a result, many algorithms happen to be created within the literature to face this concern [12128]. One particular widely employed algorithm for cloud and cloud shadow detection is Function of mask, called Fmask, for pictures from Landsat and Sentinel-2 satellites [12931]. Given a multiband satellite image, this algorithm supplies a mask giving a probability for each pixel to become cloud, and performs a segmentation from the image to segregate cloud and cloud shadow from other components. Nonetheless, the cloudy parts are just masked, but not replaced. A popular method to get rid of cloud and clouds shadows is usually to build a composite image from multi-temporal photos. This entails taking various images at different time periods but close adequate to assume that no change has occurred in among, as an illustration over a handful of weeks [132]. These images are then combined to take the top cloud-free Compound 48/80 Biological Activity components of each image to form a single final composite image without clouds nor cloud shadows. This procedure is extensively utilized [13336] when a enough variety of photos is out there. 3.2. Water Penetration and Benthic Tasisulam Apoptosis Heterogeneity The situation of light penetration in water happens not just with satellite imagery, but with all sorts of remote sensing imagery, which includes these offered by UAV or boats. The sunlight penetration is strongly limited by the light attenuation in water as a consequence of absorption, scattering and conversion to other forms of power. Most sunlight is as a result unable to penetrate below the 20 m surface layer. Therefore, the accuracy of a benthic mapping will lower when the water depth increases [137]. The light attenuation is wavelength dependent, the stronger attenuation being observed either at quick (ultraviolet) or extended (infrared) w.