# The Application of Satellite Image Analysis in Oil Spill Detection
## 1 Introduction  
Scientists believe that recent, more frequent oil spills may result from climate change in addition to human error. Permafrost is a phenomenon in which parts of the Earth's crust remain below the freezing point of water [1]. Since the early 20 th century, the severity of glacier freezing has been recorded. In the high-latitude regions of Fursia, temperatures are rising by around 0.12 \({}^{\circ}\)C per year, which is seen as a much greater increase compared to other parts of the world [2]. In addition, along with permafrost thaw, large sinkholes open, releasing small amounts of greenhouse gases into the environment. Released carbon dioxide and methane are factors in environmental biodegradation [3]. Satellite imagery plays an important role in the study of the modern world. It allows us to better understand the changes taking place on our planet. [PERSON] [4] claims that \"satellite technologies have led to one of the most productive periods in the history of cartography.\" In turn, another author, [PERSON] [5], claims that \"Landsat revealed entirely new worlds hidden in the folds of a familiar world that we thought I knew so well.\" A review of the literature on the use of remote sensing technology for oil spill detection and mapping confirms the high efficiency of remote sensing technology [6, 7, 8, 9, 10, 11, 12]. Research shows that active and passive sensors can be used to respond more quickly and precisely to such disasters [13, 14, 15]. To understand this phenomenon, Sentinel-2 [16] and Sentinel-1 [17]data are used, as well as images from Landsat 5, Landsat 7, and Landsat 8 [18]. Based on satellite images, many researchers have analysed oil spills that occurred in water reservoirs. [PERSON] et al. [19] studied the spread of oil in the East China Sea as a result of a collision between two ships, a tanker and a freighter, using multisensory satellite images. They showed that by combining remote sensing and modelling techniques used in post-processing, they could provide an effective means of monitoring sea accidents. The same author [18] focused her subsequent research on the northern part of the Gulf of Mexico, which was destroyed in September 2004 by Hurricane Ivan. The hurricane caused oil leaks from nearby plants. The Landsat 5, Landsat 7, and Sentinel-2A satellites have been used for the first time to estimate damage caused by the above-mentioned natural disaster. Consequently, we found that many data can be used to understand the movement of oil residues. On the other hand, [PERSON] [20], using the innovative floating debris index algorithm, separated microplastics and other elements, not being the natural environment of water, from algae and marine plants with an overall accuracy of 86%. The study was carried out at four different locations: Vietnam, Canada, Great Britain, and Ghana. [PERSON] [20] prepared action strategies for the following years. Australian researchers [21] assessed the sensitivity of the type and thickness of hydrocarbon oil on the remote sensing signal. Using the results of the collected data, they calculated the theoretical detection limit of each oil for two sensors: HYMAP hyperspectral and Quickbird multispectral. In their study, they found that the type of oil was critical for the application of optical remote sensing to detect and identify oil leaks. Currently, there are two methods available for remote oil thickness measurements, namely passive microwave radiometry and acoustic travel time with laser measurements [17]. [PERSON], together with [PERSON], carried out a review of remote sensing achievements in oil spill detection [22]. They found that satellite radar technology has dominated the detection and mapping of sea backwaters. Furthermore, the development of leakage detection on rivers is less than the development of maritime leakage detection technology. It is important to note that oil films have a reflective contrast between the surface of the sea and background water, ranging from 400 to 700 nanometres (nm) [23].