Monitoring of River oil Spill Using UAV Hyperspectral Remote Sensing
author: Gavin
2022-01-06
Oil spill accidents are sudden, and understanding the current situation and development trend of the disaster in the first time is the basis for all emergency decision-making. However, remote sensing satellites fly on a basic fixed orbit and at a fixed time. It is difficult to quickly obtain images of the oil spill accident scene and cannot provide real-time information for decision-making departments. UAV remote sensing is a combination of unmanned aircraft and remote sensing technology, comprehensively using advanced unmanned flying technology, remote control technology and remote sensing application technology to quickly obtain land resources and environment and other space remote sensing information application technology. As a low-altitude remote sensing technology, UAV remote sensing has the characteristics of low cost, high safety, high maneuverability and high resolution. It has advantages in the field of environmental protection and is the direction of future environmental monitoring.
Principle
The key of UAV remote sensing oil spill information monitoring is to determine the response characteristics of the oil film in the electromagnetic spectrum. Collect hyperspectral images of different crude oil film thicknesses in a river environment, and then carry out oil film characteristic response spectrum analysis and oil film thickness estimation modeling, in order to realize the quantitative application of UAV hyperspectral remote sensing in river oil spill monitoring.
Solution
Spectral collection uses ATH9010(400-1000nm), It consists of eight main parts of six-rotor UAV, high-stable cloud platform, hyperspectral imager, big memory storage, GPS navigation system, ground receiver station, and ground control system.
The purpose of this flight experiment is to obtain hyperspectral data of different crude oil film thicknesses. The specific steps are as follows:
In the hyperspectral image, there are crude oil films of different thicknesses inside the red swimming ring. Using ENVI software, according to the on-site sun exposure angle and the shadow shielding surface of the swimming ring, the unshaded area is delineated inside the swimming ring (Figure 2), and each spectrum is counted. Reflectance average value, the corresponding spectrum curve of the oil film thickness in the range of 400～1000nm is obtained (Figure 3). The river water spectrum curve is dark blue, and then as the thickness increases, the blue tone gradually fades. In general, the oil film is similar to the river water spectrum. The reflectance of the oil film in the visible part is less than that of the water body, and the reflectance of the near-infrared part is greater than that of the water body (see Figure 3). The spectral line is quite different from the indoor pure water experiment, which is mainly related to the turbidity of the river water, the complex composition, and the shallower water depth.
The reflectance spectra of oil films with different thicknesses are the most direct response characteristics of oil films in different wavelength bands. From Figure 3, the spectral curves of different thicknesses are intertwined with each other, and it is difficult to determine the best response spectrum. By calculating the correlation coefficients between the reflectance of different spectral bands and the thickness of the oil film, it is found that the reflectance of the spectral bands around 399 nm and 984 nm have the best correlation with the thickness of the oil film (Figure 4).
The curve fitting results show that the 984nm spectral band reflectivity and oil film thickness modeling effect is better (see Figure 5). Among them, 6.29~12.58μm, the oil film is too thin and irregular; 18.87~37.74μm, the reflectivity increases with the increase of the oil film thickness, the correlation coefficient is 0.9876; 44.04~125.8μm, the reflectivity decreases with the increase of the oil film thickness, and the correlation coefficient is 0.8433.
Conclusion
Using the abnormal information of hyperspectral altered minerals, combined with the mineralization geological background, we carried out model construction and prospecting predictions, and achieved good prospecting results. The follow-up plan is to strengthen the research on the internal relationship between hyperspectral remote sensing alteration information and ore-forming elements. The Hongshan area provides a new polymetallic prospecting prediction area, and also provides new prospects for breakthroughs in polymetallic mineral exploration in other areas.
Related products
A six-rotor UAV is selected as the carrying platform, and a hyperspectral imaging spectrometer (400-1000nm) is used as the sensor to collect hyperspectral images of different crude oil film thicknesses in a river environment.
Hyperspectral Camera:ATH1010
Airborne Hyperspectral Remote Sensing System:ATH9010
Drone Hyperspectral Imaging System:ATHL9010
Related articles
[1] ZAREIS, GANJIH, SADIM, et al. Kinetic modeling and opti-mization of Claus reaction furnace [J]. Journal of Natural Gas Sci-Encore and Engineering, 2016, 31: 747-757.
[2] HAIMOURN, EL-BISTAWIR. Efects of impurities in Claus Feed, II. CO2 [J]. Energy Sources, Part A: Recovery, Utiliza-time, and Environmental Efecs, 2007, 29(2):169-178.[3] Kruse FA, Hauff PL. Identification of illite polytype zoning in dis- seminated gold deposits using reflectance spectroscopy and X － ray diffraction － potential for mapping with imaging spectrometer［J］． IEEE Trans actions on Geoscience Remote Sensing, 1991, 29(1): 101-104.
[3] ZARENEZHAD B, HOSSEINPOUR N. Evalution of Different Alternativities for increasing the reaction furnace temperature of Claus SRU by chemcal calculations [J]. Appled Thermal Engineering, 2008, 28(7): 738-744.
Principle
The key of UAV remote sensing oil spill information monitoring is to determine the response characteristics of the oil film in the electromagnetic spectrum. Collect hyperspectral images of different crude oil film thicknesses in a river environment, and then carry out oil film characteristic response spectrum analysis and oil film thickness estimation modeling, in order to realize the quantitative application of UAV hyperspectral remote sensing in river oil spill monitoring.
Solution
Spectral collection uses ATH9010(400-1000nm), It consists of eight main parts of six-rotor UAV, high-stable cloud platform, hyperspectral imager, big memory storage, GPS navigation system, ground receiver station, and ground control system.
The purpose of this flight experiment is to obtain hyperspectral data of different crude oil film thicknesses. The specific steps are as follows:
- Place 5 round swimming rings in the river, and use an injector to inject 1 to 5 mL of crude oil from north to south. The amount of oil in the circle is 1 mL, 2 mL, 3 mL, 4 mL and 5 mL in sequence. After the oil film in the circle basically diffuses, from north to south Collect hyperspectral data of one route from south to north.
- Add 5 mL of crude oil to the five swimming circles. The oil volume in the circles is 6 mL, 7 mL, 8 mL, 9 mL, and 10 mL. When the oil film spreads, the same Collect hyperspectral data of one route from north to south and from south to north.
- continue the previous experiment method, obtain hyperspectral data of 8 routes in total, remove invalid data, and finally obtain 30 hyperspectral data of different crude oil thicknesses, corresponding to oil volume 1-20mL, the volume method (h=v/πr2) is used to estimate the crude oil thickness to be approximately 6.29～125.82μm. Before data acquisition, the instrument performs dark current correction.
In the hyperspectral image, there are crude oil films of different thicknesses inside the red swimming ring. Using ENVI software, according to the on-site sun exposure angle and the shadow shielding surface of the swimming ring, the unshaded area is delineated inside the swimming ring (Figure 2), and each spectrum is counted. Reflectance average value, the corresponding spectrum curve of the oil film thickness in the range of 400～1000nm is obtained (Figure 3). The river water spectrum curve is dark blue, and then as the thickness increases, the blue tone gradually fades. In general, the oil film is similar to the river water spectrum. The reflectance of the oil film in the visible part is less than that of the water body, and the reflectance of the near-infrared part is greater than that of the water body (see Figure 3). The spectral line is quite different from the indoor pure water experiment, which is mainly related to the turbidity of the river water, the complex composition, and the shallower water depth.
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The curve fitting results show that the 984nm spectral band reflectivity and oil film thickness modeling effect is better (see Figure 5). Among them, 6.29~12.58μm, the oil film is too thin and irregular; 18.87~37.74μm, the reflectivity increases with the increase of the oil film thickness, the correlation coefficient is 0.9876; 44.04~125.8μm, the reflectivity decreases with the increase of the oil film thickness, and the correlation coefficient is 0.8433.
Conclusion
Using the abnormal information of hyperspectral altered minerals, combined with the mineralization geological background, we carried out model construction and prospecting predictions, and achieved good prospecting results. The follow-up plan is to strengthen the research on the internal relationship between hyperspectral remote sensing alteration information and ore-forming elements. The Hongshan area provides a new polymetallic prospecting prediction area, and also provides new prospects for breakthroughs in polymetallic mineral exploration in other areas.
Related products
A six-rotor UAV is selected as the carrying platform, and a hyperspectral imaging spectrometer (400-1000nm) is used as the sensor to collect hyperspectral images of different crude oil film thicknesses in a river environment.
Hyperspectral Camera:ATH1010
Airborne Hyperspectral Remote Sensing System:ATH9010
Drone Hyperspectral Imaging System:ATHL9010
Related articles
[1] ZAREIS, GANJIH, SADIM, et al. Kinetic modeling and opti-mization of Claus reaction furnace [J]. Journal of Natural Gas Sci-Encore and Engineering, 2016, 31: 747-757.
[2] HAIMOURN, EL-BISTAWIR. Efects of impurities in Claus Feed, II. CO2 [J]. Energy Sources, Part A: Recovery, Utiliza-time, and Environmental Efecs, 2007, 29(2):169-178.[3] Kruse FA, Hauff PL. Identification of illite polytype zoning in dis- seminated gold deposits using reflectance spectroscopy and X － ray diffraction － potential for mapping with imaging spectrometer［J］． IEEE Trans actions on Geoscience Remote Sensing, 1991, 29(1): 101-104.
[3] ZARENEZHAD B, HOSSEINPOUR N. Evalution of Different Alternativities for increasing the reaction furnace temperature of Claus SRU by chemcal calculations [J]. Appled Thermal Engineering, 2008, 28(7): 738-744.
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