Experimental study on the use of unmanned aerial vehicles for the prediction of oil and gas facilities

The article is devoted to the topical problem of forecasting oil and gas promising objects using the latest remote sensing technologies. The proposed new approach to obtaining field verification data is an essential component of the methodology for solving oil and gas prospecting problems on land (satellite technology).Experimental field studies were carried out using the Ocean Optics STS-VIS Developers kit spectroradiometer installed on a quadcopter. Using the example of the Vostochnorogintsevska area, which is part of the Talalaevska-Rybaltsy oil and gas region of the Dnieper-Donetsk oil and gas region, the main stages of the developed method are demonstrated: a model of the fault-block structure was created, the neotectonic features of this area and its local blocks were estimated, photometric measurements of a multispectral satellite image along the route were carried out, birch leaves were sampled again along the same route for spectrometry using the ASD FieldSpec 3 FR instrument.The main objective of the experiment was to carry out field measurements with the Ocean Optics STS-VIS Developers kit spectroradiometer along a route that repeats the routes of measurements with spectrometers carried out earlier. The results showed that the optical anomaly, which is identified with a hydrocarbon accumulation, along the profile at the Vostochnorogintsevska area corresponds to the segment between points 15-26.The same anomaly has been established with the spectrometry device ASD FieldSpec 3 FR (2009 and 2021), the instrument SF-18 (1999 and 2004). Sufficiently accurately allocated transition from object to background, which corresponds to the boundary of the deposit on the drilling data (point 16 on the profile), i.e. has been confirmed in principle the possibility of allocating a low-intensity optical anomalies over hydrocarbon reservoirs using spectroradiometer STS-VIS Developers kit, mounted on quadrocopter.

Shallow Water Bathymetry Mapping of Xinji Island Based on Multispectral Satellite Image using Deep Learning

Nearshore bathymetry is a basic parameter of the ocean, which is crucial to the research and management of coastal zones. Previous studies have demonstrated that remote sensing techniques can be employed in estimating bathymetric information. In this paper, we propose a deep belief network with data perturbation (DBN-DP) algorithm for shallow water depth inversion from high resolution multispectral data, and applying it in Xinji Island of Malacca Strait and Yongxing Island in China. Results show that the DBN-DP method can produce more accurate water depth estimations than other traditional methods particularly for deeper water, which reaches 1.2 m of mean absolute error (MAE) and 12.8% of mean relative error (MRE) in Xinji Island. Most of the estimated bathymetry meet the category of zone of confidence C level defined by the International Hydrographic Organization. These findings are encouraging for employing deep learning in bathymetry, which may become a novel approach for bathymetric inversion in the future.

Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning By SAGA GIS

Vegetation of Cameroon includes a variety of landscape types with high biodiversity. Ecological monitoring of Yaoundé requires visualization of vegetation types in context of climate change. Vegetation Indices (VIs) derived from Sentinel-2 multispectral satellite image were analyzed in SAGA GIS to separate wetland biomes, as well as savannah and tropical rainforests. The methodology includes computing 6 VIs: NDVI, DVI, SAVI, RVI, TTVI, CTVI. The VIs shown correlation of data with vegetation distribution rising from wetlands, grassland, savanna, and shrub land towards tropical rainforests, increasing values along with canopy greenness, while also being inversely proportional to soils, urban spaces and Sanaga River. The study contributed to the environmental studies of Cameroon and demonstration of the satellite image processing.

S-DolLion-MSVNN: A Hybrid Model for Developing the Super-Resolution Image From the Multispectral Satellite Image

Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using the hybrid model. The hybrid model is developed using the support vector regression model and multi-support vector neural network (MSVNN), and the weights of the MSVNN is tuned optimally using the proposed algorithm. The proposed DolLion algorithm is the integration of the dolphin echolocation algorithm and lion optimization algorithm that exhibits better convergence and offers a global optimal solution. The experimentation is performed using the datasets taken from the multi-spectral scene images. The optimal and effective formation of the super-resolution image using the proposed hybrid model outperforms the existing methods, and the analysis using the second-derivative-like measure of enhancement (SDME) ensures that the proposed method is better and yields a maximum SDME of 67.6755 dB.

Characterization of Multispectral Satellite Image for Land Surface Temperature Interpretation

The increase in population is of course also accompanied by the development process. Conversion of land use from the vegetation area to non-vegetation, such as settlements for example will affect the surface temperature in the area. In addition, it will directly or indirectly affect the occurrence of global warming. Information about soil surface temperature needs to be known. Given that SPT as a factor that affects global climate change. To avoid urban heat, information about SPT is needed. Whereas in this study an identification process was carried out by utilizing thermal waves (thermal bands) found in Lands at 8. The identification process was carried out by the conversion method Algorithms produced from bands 10 and bands on lands at satellite images 8. The results of the research were high category was interpreted at most in the 2016 recording year with the majority distribution in urban areas, namely Kemuning District, IlirTimur I District and Bukit Kecil District.