Abstract:This paper studies the oil spill, which occurred in the Norilsk and Taimyr region of Russia due to the collapse of the fuel tank at the power station on May 29, 2020. We monitored the snow, ice, water, vegetation and wetland of the region using data from the Multi-Spectral Instruments (MSI) of Sentinel-2 satellite. We analyzed the spectral band absorptions of Sentinel-2 data acquired before, during and after the incident, developed true and false-color composites (FCC), decorrelated spectral bands and used the… Show more
“…Consequently, SPM has great potential for monitoring oil-spill areas experiencing rapid land cover changes, which can be undertaken without the need for images with both fine spatial and temporal resolution. For example, Rajendran et al [55] used historical images with relatively coarse spatial resolution from May 23 to June 8 for analyzing the oil-spill event on the Ambarnaya river. By applying spatio-temporal SPM (e.g., FSSTSPM developed in this paper) to those multi-temporal coarse images, oil velocity and direction can be further analyzed at a finer spatial resolution, which may contribute more to oil cleanup and underwater biological conservation.…”
Section: F Application To Oil-spill Mappingmentioning
Subpixel mapping (SPM) is a technique to tackle the mixed pixel problem and produce land cover and land use (LCLU) maps at a finer spatial resolution than the original coarse data. However, uncertainty exists unavoidably in SPM, which is an ill-posed downscaling problem. Spatio-temporal SPM methods have been proposed to deal with this uncertainty, but current methods fail to explore fully the information in the time-series images, especially more rapid changes over a short-time interval. In this paper, a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method is proposed to account for fast LCLU changes over a short-time interval and slow changes over a long-time interval. Namely, both fast and slow change-based temporal constraints are proposed and incorporated simultaneously into the FSSTSPM to increase the accuracy of SPM. The proposed FSSTSPM method was validated using two synthetic datasets with various proportion errors. It was also applied to oil-spill mapping using a real PlanetScope-Sentinel-2 dataset and Amazon deforestation mapping using a real Landsat-MODIS dataset. The results demonstrate the superiority of FSSTSPM. Moreover, the advantage of FSSTSPM is more obvious with an increase in proportion errors. The concepts of the fast and slow changes, together with the derived temporal constraints, provide a new insight to enhance SPM by taking fuller advantage of the temporal information in the available time-series images.
“…Consequently, SPM has great potential for monitoring oil-spill areas experiencing rapid land cover changes, which can be undertaken without the need for images with both fine spatial and temporal resolution. For example, Rajendran et al [55] used historical images with relatively coarse spatial resolution from May 23 to June 8 for analyzing the oil-spill event on the Ambarnaya river. By applying spatio-temporal SPM (e.g., FSSTSPM developed in this paper) to those multi-temporal coarse images, oil velocity and direction can be further analyzed at a finer spatial resolution, which may contribute more to oil cleanup and underwater biological conservation.…”
Section: F Application To Oil-spill Mappingmentioning
Subpixel mapping (SPM) is a technique to tackle the mixed pixel problem and produce land cover and land use (LCLU) maps at a finer spatial resolution than the original coarse data. However, uncertainty exists unavoidably in SPM, which is an ill-posed downscaling problem. Spatio-temporal SPM methods have been proposed to deal with this uncertainty, but current methods fail to explore fully the information in the time-series images, especially more rapid changes over a short-time interval. In this paper, a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method is proposed to account for fast LCLU changes over a short-time interval and slow changes over a long-time interval. Namely, both fast and slow change-based temporal constraints are proposed and incorporated simultaneously into the FSSTSPM to increase the accuracy of SPM. The proposed FSSTSPM method was validated using two synthetic datasets with various proportion errors. It was also applied to oil-spill mapping using a real PlanetScope-Sentinel-2 dataset and Amazon deforestation mapping using a real Landsat-MODIS dataset. The results demonstrate the superiority of FSSTSPM. Moreover, the advantage of FSSTSPM is more obvious with an increase in proportion errors. The concepts of the fast and slow changes, together with the derived temporal constraints, provide a new insight to enhance SPM by taking fuller advantage of the temporal information in the available time-series images.
“…Meanwhile, the environmental risks are especially pronounced in Northern Arctic and sub-Arctic regions, where natural ecosystems are highly susceptible to human impacts [2,3]. The greatest risks for Russia's Arctic ecosystems today are accidents at fuel and energy facilities [4]; use of dirty fuels (heavy oil, diesel fuel, etc.) in cargo transport and heating [5]; prevalence of extraction industries in the economy of Arctic regions [6]; and the obsolete energy systems [5].…”
Due to the depletion of traditional energy sources, the rising costs of their operation and the need to transition to a sustainable economy, it becomes relevant to increase the share of renewable energy sources in total consumption. The purpose of this study is to determine the role of renewable energy and the establishment of factors determining pro-environmental behavior. The data of the author’s sociological survey of the population of the Arctic regions of Russia and methods of descriptive statistics were used, and regression analysis was carried out. The study shows the ecological and energy characteristics of the Arctic regions of Russia. The main advantages and possibilities of transition to renewable energy sources have been identified. A relationship has been established between the degree of involvement in pro-environmental behavior and knowledge about renewable energy, the perceived importance of environmental problems, age, income, education, amount of waste produced and current electricity costs. It is shown that the degree of involvement in pro-environmental behavior affects the willingness to pay more for renewable energy. A number of institutional measures to promote renewable energy, increase willingness to pay for renewable energy and spread pro-environmental behavior are proposed.
“…To overcome this issue and validate the results of SAR [ 4 , 5 ], data of optical sensors have been used. The high spatial resolution (10, 20, and 60 m) multispectral bands of Sentinel-2 available since June 2015 have been used to map the oil spill very effectively [ 1 , 6 ]. The band combinations using MSI bands of Sentinel-2 for different applications are found in the Sentinel Application Platform (SNAP) program ( https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/indexdb/ ).…”
Although several indices have been constructed and available at the Index database (IDB) for Sentinel-2 satellite to map and study several earth resources, no indices have been developed to map oil spill. We constructed band ratios (5 + 6)/7, (3 + 4)/2, (11+12)/8 and 3/2, (3 + 4)/2, (6 + 7)/5 using the high-resolution MSI (multi-spectral instrument) visible-near infrared-shortwave infrared spectral bands of Sentinel-2 by summing-up the bands representing the shoulders of absorption features as numerator and the band located nearest to the absorption feature as denominator to discriminate oil spill, and demonstrate the potential of this method to map the Wakashio oil spill which occurred in the Indian Ocean, off Mauritius. The resulted images discriminated the oil spill well. We also decorrelated the spectral bands 4, 3 and 2 by studying the spectral band absorptions and discriminated the spill as very thick, thick and thin. The results of decorrelation stretch method exhibited the distribution of types of oil spill in a different tone, distinctly. Both the image transformation methods (band ratios and decorrelation stretch methods) showed their capability to map oil spills, and these methods are recommended to use for similar spectral bands of other sensors to map oil spills.
This study demonstrated the application of band ratios and decorrelation stretch methods to map oil spill.
The methods discriminated the oil spill off Mauritius, and showed spill thicknesses from the Sentinel-2 data.
The new methods are recommended to use for the spectral bands of other sensors to map oil spill.
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