Multispectral Satellite Images Research Articles

Impact of mining on vegetation cover: A case study of Prestea Huni-Valley municipality

Land Use Land Cover (LULC) changes are the direct and indirect consequences of human actions to secure essential resources for a successful livelihood. Therefore, it has become essential to analyze such changes for effective management of natural resources and protection of our environment. Anthropogenic factors such as urbanization, mining, and population increase in the Prestea Huni-Valley municipality are causing rapid changes to the land cover. These factors among others are putting a lot of pressure on the vegetation and this has dire consequences on the availability and protection of the vegetative cover. Given this, the study focused on assessing the impact of mining on vegetation cover with a particular focus on Prestea Huni-Valley municipality. Multi-spectral satellite images of the study area from 1986 to 2016 were spatially analyzed to identify the LULC change patterns. Modeling and analysis of these images were performed using IDRISI and R software. Six land cover classes were identified including forest, open vegetation, cultivated areas, bare lands, built-up, and mine sites. The result showed that the forest, open vegetation, and bare areas decreased by 14.59 %, 3.86 %, and 8.36 % respectively whereas cultivated, built up and mines increased by 6.31 %, 3.14 %, and 7.36 % respectively.

A high-resolution map of coastal vegetation for two Arctic Alaskan parklands: An object-oriented approach with point training data.

Bering Land Bridge National Preserve and Cape Krusenstern National Monument in northwest Alaska have approximately 1600 km of predominantly soft-sediment coastlines along the Chukchi Sea, a shallow bay of the Arctic Ocean. Over the past decade, marine vessel traffic through the Bering Strait has grown exponentially to take advantage of new ice-free summer shipping routes, increasing the risk of oil spills in these fragile ecosystems. We present a high-resolution coastal vegetation map to serve as a baseline for potential spill response, restoration, and change detection. We segmented 663 km2 of high-resolution multispectral satellite images by the mean-shift method and collected 40 spectral, topographic and spatial variables per segment. The segments were classified using photo-interpreted points as training data, and verified with field based plots. Digitizing points, rather than polygons, and intersecting them with the segmentation allows for rapid collection of training data. We classified the map segments using Random Forest because of its high accuracy, computational speed, and ability to incorporate non-normal, high-dimensional data. We found creating separate classification models by each satellite scene gave highly similar results to models combining the entire study area, and that reducing the number of variables had little impact on accuracy. A unified, study area-wide Random Forest model for both parklands produced the highest accuracy of various models attempted. We mapped 18 distinct classes, with an out-of-bag error of 11.6%, resulting in an improvement to the past per-pixel classification of this coast, and in higher spatial and vegetation classification resolution. The resulting map demonstrates the utility of our point-based method and provides baseline data for incident preparedness and change detection. Elevation is highly correlated with the ordination of the vegetation types, and was the most important variable in all tested classification models. The vegetation classification brings together the largest amount of vegetation data for the Chukchi Sea coast yet documented.

Monitoring System of the Mar Menor Coastal Lagoon (Spain) and Its Watershed Basin Using the Integration of Massive Heterogeneous Data.

The tool created aims at the environmental monitoring of the Mar Menor coastal lagoon (Spain) and the monitoring of the land use of its watershed. It integrates heterogeneous data sources ranging from ecological data obtained from a multiparametric oceanographic sonde to agro-meteorological data from IMIDA’s network of stations or hydrological data from the SAIH network as multispectral satellite images from Sentinel and Landsat space missions. The system is based on free and open source software and has been designed to guarantee maximum levels of flexibility and scalability and minimum coupling so that the incorporation of new components does not affect the existing ones. The platform is designed to handle a data volume of more than 12 million records, experiencing exponential growth in the last six months. The tool allows the transformation of a large volume of data into information, offering them through microservices with optimal response times. As practical applications, the platform created allows us to know the ecological state of the Mar Menor with a very high level of detail, both at biophysical and nutrient levels, being able to detect periods of oxygen deficit and delimit the affected area. In addition, it facilitates the detailed monitoring of the cultivated areas of the watershed, detecting the agricultural use and crop cycles at the plot level. It also makes it possible to calculate the amount of water precipitated on the watershed and to monitor the runoff produced and the amount of water entering the Mar Menor in extreme events. The information is offered in different ways depending on the user profile, offering a very high level of detail for research or data analysis profiles, concrete and direct information to support decision-making for users with managerial profiles and validated and concise information for citizens. It is an integrated and distributed system that will provide data and services for the Mar Menor Observatory.

Machine Learning Approaches to Automatically Detect Glacier Snow Lines on Multi-Spectral Satellite Images

Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers is challenging; recent developments in machine learning approaches show promise to tackle this issue. This manuscript presents a proof of concept of machine learning approaches applied to multi-spectral images to detect the snow line and quantify its average altitude. The tested approaches include the combination of different image processing and classification methods, and takes into account cast shadows. The efficiency of these approaches is evaluated on mountain glaciers in the European Alps by comparing the results with manually annotated data. Solutions provided by the different approaches are robust when compared to the ground truth’s snow lines, with a Pearson’s correlation ranging from 79% to 96% depending on the method. However, the tested approaches may fail when snow lines are not continuous or exhibit a strong change of elevation. The major advantage over the state of the art is that the proposed approach does not require one calibration per glacier.

Sentinel 2 analysis of turbidity retrieval models in inland water bodies: The case study of Jordanian dams

AbstractThis study investigates the potential of using Sentinel 2 multispectral satellite images to develop turbidity retrieval models and further estimate the turbidity values of inland water bodies in Jordan. Traditionally, laboratory analysis has been used to assess surface water quality, which is expensive, time‐consuming, and requires accessing the field physically. In contrast, remote sensing technologies can detect the water contaminant level at a consistent spatial and temporal coverage. Turbidity is an essential indicator of inland water quality as it directly reflects under‐water light penetration. This study was held in three Jordanian dams, King Talal Dam, Mujib Dam, and Wadi Al Arab Dam, which vary in their water quality level. Twenty water samples were collected from each dam. Forty samples were used to calibrate the models, and the rest samples were used to validate the predictive models. The results show that Sentinel 2 near‐infrared band to green band (B8/B3) achieved high fitting accuracies with R2 = 0.832 and root mean square error (RMSE) = 1.123 NTU. Overall, this study has demonstrated the ability of Sentinel 2 data to estimate the turbidity in different ranges of inland water bodies’ quality and indicates that remote sensing can be used as an efficient tool for monitoring inland water quality. This study presents empirical data that could act as a platform to extend future work to cover more sites and contexts.

End-to-End Prediction of Lightning Events from Geostationary Satellite Images

While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180 min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.

MONITORING LANDSLIDE DISPLACEMENTS THROUGH MAXIMUM CROSS-CORRELATION OF SATELLITE IMAGES

Abstract. Landslides are one of the most dangerous and disastrous geological hazard worldwide, posing threats to human life, infrastructures and to the natural environment. Consequently, monitoring active landslides is crucial in order to reduce the risk of damages and casualties. With this aim, this work proposes a way to compute landslide displacements through time, by exploiting the great availability of high quality multispectral satellite images. The developed procedure produces maps of displacement magnitude and direction by means of local cross-correlation of Earth Observation data from the Sentinel-2 and PlanetScope missions. The Ruinon landslide, an active landslide in Northern Lombardy, Italy, was selected as a case study. The workflow of the developed procedure is described, and the results are presented and discussed. Moreover, a validation of the analysis by comparison with UAV surveys of the landslide is reported, along with a discussion on future developments and improvements of this technique. This work was designed to be entirely based on free and open-source GIS software and to rely mainly on open data. These characteristics allow the proposed analysis to be easily replicated, customized, and empowered.

What Is a Cloud? Toward a More Precise Definition

Abstract There are a couple of reasons to stimulate a discussion about the definition of clouds. One reason is that the American Meteorological Society (AMS) and the World Meteorological Organization (WMO) define clouds differently, and that in two aspects. AMS defines clouds as visible objects, and WMO as perceivable objects. Furthermore, AMS includes all minute particles independent of their nature and composition whereas WMO considers only such minute particles that consist of water, ice, or a mixture of both. Additionally, the so-called invisible or subvisible clouds are perceivable objects in the definition of WMO but not of AMS. Clouds can be observed by humans and be detected by active and passive sensors from ground and space. The detection limits of instruments span more than two orders of magnitude in visible optical thickness ranging from about 0.001 from satellite lidar (in case of horizontal and vertical averaging or 0.07 for single lidar shots) to about 0.3 to 0.45 for multispectral satellite imaging spectrometers. Human observers see clouds when their optical thickness is larger than about 0.03 at day and 0.05 at night. This paper gives a brief overview of cloud detection from ground and space and of the occurrence, characteristics, and impacts of subvisible clouds. Pros and cons of these definitions are discussed, followed by a proposal for a more precise definition. This definition is, like those of AMS and WMO, a one-parametric definition and does not cover all aspects of clouds.

The Determination of Priority Areas for the Restoration of Degraded Tropical Peatland Using Hydrological, Topographical, and Remote Sensing Approaches

Degraded peatland is caused by forest clearing and the construction of artificial water networks. When water management is not implemented across land uses in the entire peatland landscape, then it will be a big issue that causes a water deficit and leads to increasing droughts and fires. Effective restoration must first identify the part of Peatland Hydrological system Units (PHUs) with insufficient water storage and resources. This study used intercorrelated factors of water balance, deficit months, NDMI-NDVI indices, dry periods, recurrent fires, peat depth, and water loss conditions, as the evaluation parameters, within individual sub-PHUs to determine the most degraded areas that require intervention and restoration. Sub-PHU was determined based on the peat hydrological unity concept by identifying streamline, outlet channels, peat-depth, slopes, and network connectivity. Global hydrological data using TerraClimate and CHIRPS, combined with field observations, were used to validate and calculate each sub-PHU’s water balance and dry periods. Soil moisture (NDMI), vegetation density (NDVI), and fire frequency were extracted from multispectral satellite images (e.g., Landsat 8, MODIS-Terra, and MODIS-Aqua). Each parameter was ranked by the score for each sub-PHU. The parameters that can be ranked are only the ordinal type of number. The lowest ranks indicated the most degraded sub-PHUs requiring peat rewetting interventions.

METHOD OF AUTOMATED CORRECTION OF INSTRUMENT DISTORTIONS ON LANDSAT-7 MULTISPECTRAL SATELLITE IMAGES

An alternative method of automated correction of instrumental distortions of the ETM + scanner on Landsat-7 multispectral satellite images is proposed. The method is based on the application of filtering in the field of spatial frequencies using fast Fourier transform and spectral masks. The parameters of the mask are determined by a known spatial period of interference or experimentally by the results of the filtration. To filter the area of spatial frequencies to be removed, a filtering mask is applied, consisting of pixels with zero values. Periodic components whose spatial period corresponds to the spatial frequencies filtered on the Fourier image will be removed from the filtered image. The quality of filtration is determined by the mask used. When using multispectral images, a filter mask based on the Fourier image of one of the spectral channels is applied to filter other channels. The results of visual analysis of the processed images showed a fairly high-quality correction and elimination of instrumental distortions of the ETM + scanner in comparison with other methods. The main advantages of the proposed method are the next: the ability to work without the use of metadata and masks of instrumental distortion; work with single-channel and multispectral images; ability to work with small fragments of the scene; minimum number of manual settings for processing procedures; possibility to work without additional pictures for other dates; high stability of the used algorithms when using images from different satellites; fairly good repeatability of the results on satellite images taken in different seasons and for different areas. Experimental testing of the proposed method on a large number of images also confirmed the good repeatability and high stability of the algorithms used. It is expected that the developed technology will also be successfully used to cor

Hydro-morphological investigations of Neeru watershed using DEM and geospatial techniques

Morphometric analysis is very popular among the hydro-geomorphological investigation of the river basins that deals with the linear, areal, shape and landscape aspects of river catchment. Neeru watershed, a left-bank tributary of the Chenab River (Doda, Jammu and Kashmir, India). Carto-DEM, Sentinel-2B multispectral satellite image and Topographical maps taken into account for extracting and computing watershed’s various parameters adopting the geospatial techniques. This northern aligned watershed has a length, width, and perimeter of 65 km, 22.4 km, and 106.03 km, respectively, and covers 415.19 sq. km area. It is a 5th stream order watershed, with lower-order streams predominating. The drainage density of 0.81 km/km2 indicates that it is highly permeable and thus has increased subterranean water storage capacity. The bifurcation ratio varies from 2 to 4.74, with a mean of 3.67, demonstrating strong structural control over drainage development. The study area's elongation ratio (0.67) indicates that it has an elongated shape with high relief, steep slope, high sediment load discharge, and susceptible soil erosion. The assessment indicates the role of geospatial techniques in quick appraisal of ecological indicators they will be helpful for decision makers in managing natural resource, planning, and watershed management.

Potential of Multispectral Satellite Data for Superficial Iron Oxide Detection in Sulaimaniyah, Iraqi Kurdistan Region

This study primarily investigates the total (Fe) iron presence in Sulaimaniyah Governorate, the Iraqi Kurdistan Region (IKR), which has an abundance of iron mines. Spatial quantification and frequent monitoring of mineral existence in the soil are essential in the mining regions. To achieve this goal, a remote sensing technique was utilized to predict soil minerals, particularly iron, in the study area using a multispectral satellite image, Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A robust methodology was perceived and developed from image processing to estimate and map iron oxide-rich soils, and the soil’s spectral indices were obtained after algorithms were applied in processing the bands of the Landsat image. Soil samples were collected and analyzed in the laboratory to determine the chemical, physical, and mineralogical characteristics of soils. Correlation coefficients were carried out between soil properties and spectral band values retrieved from image analysis to examine the band potentials of Landsat. The statistical results showed that there was a significant relationship between the 3rd band of the ETM+ image and each of the total iron (R2 = 0.643), the free iron oxide (R2 = 0.659), and sand particles (R2 = 0.561). The predicted soil mineral maps were generated for the study area to visualize the study site's soil characterization and total iron spread. This study's results could help primarily identify the spatial distribution of some soil properties in Sulaimaniyah, Iraq.

Prediction of regional vegetation cover using spatial image features and semantic segmentation

Vegetation is an essential part of our ecosystem; it also determines health of our planet. According to “The State of the World’s Forests 2020” only 31% of the global land area is covered with vegetation. This paper presents a superior way of representing vegetation cover in a region by utilizing Atmospherically Resistant Vegetation Index (ARVI) as vital features out of multispectral satellite images. These images comprise Green, Blue, Red and Near Infrared bands data which was further utilized by U-Net to efficiently segment satellite images. In remote sensing greenery of environment is traditionally determined using NDVI, one of the critical imperfections with this method is that it is liable to compute inaccurate values as a consequence of variations in soil, air moisture and shadowing affected by varying incidence angle of sunlight. On the other hand, ARVI is immune to such flaws and integration with deep learning provides a better solution for segmenting regional vegetation and predicting its coverage up to some extent. This method can be employed for predicting vegetation in any region along with assisting in events such as repopulating trees and urban planning while conserving beauty of our nature.

Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetles.

Land cover change analysis between 1990 and 2021 using Landsat images and object-based classification: A case study in Bodrum peninsula, Aegean Region, Turkey

Bodrum Peninsula is one of the most important tourism centers of Turkey with its geographical location, coastal and marine tourism, natural and cultural features. It has been determined that the winter population has also increased in Bodrum in recent years, and it is thought that this may cause an increasing permanent resident population and urbanization. The objective of this study is to determine the changes in land cover due to the rapid increase in urbanization in Bodrum Peninsula. For this purpose, object-based classification analysis was applied to Landsat 4-5 TM 1990, 2000, 2010 and Landsat 8 OLI 2021 multispectral satellite images. Within the scope of the analysis, the objects were created by applying the segmentation process to satellite images. Secondly, land cover classes were determined according to the Corine land cover classification with levels 1-2-3. Thirdly, the classification process based on a decision tree was carried out with the classes defined using the threshold values determined for spectral and texture properties of the objects using multiresolution segmentation. In the last stage, accuracy assessment analysis was applied to the classification results. According to the results, it is obtained that while Urban Fabric and Burnt Areas are increased in 32 years, Forest and semi-natural areas are decreased. As a result of population pressure due to tourism, Urban Fabric areas have moved closer to Forests and Semi-Natural Areas. Wildfires with the effect of heatwaves were increased, biodiversity has been endangered in the study area located in the Mediterranean basin, where human-related climate change is most clearly detected. Significantly, there has been a wildfire in Bodrum in August 2021, which lasted for days and caused severe degradation on the land cover. For this, sustainable land cover management is recommended to protect the natural ecosystem by minimizing the risks that cause land degradation in the Bodrum peninsula.

Exploring the potential of multispectral satellite images for estimating the contents of cadmium and lead in cropland: The effect of the dimidiate pixel model and random forest

Rapid and accurate predictions of heavy metal contents in farmland are of great significance to ensure the safety of agricultural products and maintain ecosystem balance. Combining multispectral images and chemometric modeling provides a feasible means of estimating cadmium (Cd) and lead (Pb) contents in farmland. We collected 640 samples from the surface soils of farmland in Xiangzhou District, Hubei Province, China. The Cd, Pb, soil organic matter (SOM), pH, and Fe of the soil samples were measured in a laboratory. In this study, the dimidiate pixel model was used to process remote sensing images. In particular, we used random forest (RF) to screen the best spectral indices for use as input variables. Partial least squares regression (PLSR), backward propagation neural network (BPNN), and RF were used to calibrate the spectral data with Cd and Pb contents, and the optimal model was used for the regional mapping of soil Cd and Pb contents. Additionally, we explored the potential of using spectral estimation mechanisms to estimate Cd and Pb contents. The mechanism for estimating Cd and Pb contents with multispectral images depended mainly on the covariance of Cd and Pb contents with that of SOM. For both Cd and Pb estimations, the double-date image estimation model performed better than the single-date image estimation model, and the unmixed image estimation model was more accurate than the original image estimation model. Overall, the estimation model using the best spectral indices as input variables performed better than the model using full-band data as input variables. The RF model outperformed the PLSR and BPNN models in all cases. Relatively satisfactory estimates of Cd and Pb contents in farmland of the study area (maximum R2val (determination coefficient of the validation set) = 0.60 for Cd and R2val = 0.63 for Pb) were obtained. Our results show that areas of farmland contaminated by Cd and Pb throughout the study area have increased and that contamination levels have worsened since 1990. In addition, the spatial patterns of Cd and Pb contents in farmland throughout the study area were analyzed and validated using field survey results. The results of the study provide a theoretical basis and methodological reference for the rapid prediction of Cd and Pb contents in regional farmland.

Evaluation of the results of pansharpening multispectral images

When processing digital images obtained by remote sensing of the Earth, various methods are used to increase their resolution. However, in this case, some distortions of a different nature may appear on the images. For example, luminance distortion (color, contrast, sharpness) and geometric (object boundary deformations). Developers of automated image processing systems face the task of choosing from dozens of methods the one that introduces the least visually noticeable distortions, i.e. creates images of the best quality.In this article, the following problem was solved: to determine the functions for assessing the quality of images formed as a result of multispectral satellite image pansharpening. The pansharped image cannot be compared with the template one, since it does not exist. To assess quality of such images, we proposed to use the so-called no-reference evaluation measures.The article briefly describes methods for synthesizing a new high-resolution color image from four images of Earth remote sensing. Functions for calculating quantitative estimates of the quality of the resulting images are discussed. Results of some space image pansharpening by different methods are presented. Graphs of these assessments of image quality are constructed. To evaluate panchromatic fusion results, the following non-reference quality scores are recommended: FISH, LOCC, LOEN, NATU, SHAR, and WAVS. The clearest boundaries and natural colors of objects were demonstrated by the P+XS pansharpening algorithm based on a linear combination of spectral channels.

Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with outstanding performance. Since the physical and visual manifestation of slums significantly varies with geographical region and comprehensive slum maps are rare, it is important to quantify the uncertainty of predictions for reliable and confident application of models to downstream tasks. In this study, we train a U-Net model with Monte Carlo Dropout (MCD) on 13-band Sentinel-2 images, allowing us to calculate pixelwise uncertainty in the predictions. The obtained outcomes show that the proposed model outperforms the previous state-of-the-art model, having both higher AUPRC and lower uncertainty when tested on unseen geographical regions of Mumbai using the regional testing framework introduced in this study. We also use SHapley Additive exPlanations (SHAP) values to investigate how the different features contribute to our model’s predictions which indicate a certain shortwave infrared image band is a powerful feature for determining the locations of slums within images. With our results, we demonstrate the usefulness of including an uncertainty quantification approach in detecting slum area changes over time.

Changes in the rehabilitation status of irrigated lands in the Volgograd Oblast during 2001–2018

A comparative assessment of the rehabilitation status of irrigated lands, which are located in different natural areas of the Volgograd Oblast, is presented for 2001–2018. It was found that during the specified period there were recorded significant changes in the condition of irrigated lands: the total irrigation area has dramatically decreased since 2001 (-31%), especially at local runoff (-44.5%), the groundwater level has decreased (the level >5 m remains on 78% of the area). Also on this background, the areas of secondary salinized soils were also decreased (-3.9%). The main problems at the present are: the presence of fallow lands and rain-fed lands initially cultivated for irrigation, the lack of drainage on the most of irrigation systems and significant areas of saline soils requiring rehabilitation. Differences in natural conditions have largely determined the current rehabilitation state of irrigated soils and the intensity of their changes. Thus, the largest areas of secondary saline soils are preserved on irrigation systems located on the initially highly saline, poorly drained soils of the Khvalyn clay plain (Pallasovka, Svetloyarsk irrigation systems). Reconstruction of a number of irrigation systems with areas of secondary saline soils gave a positive result, after which secondary saline soils on reclaimed lands were not detected (Large Volgograd, Tyazhin irrigation systems). Significant areas of saline soils can be found on irrigated lands in the distribution areas of natural saline soils and chestnut, light chestnut saline soils – in the south of the Volga Upland, in the area of the Northern Yergeni and on the Khvalyn clay plain (Gorodishуsche, Generalovskoye, Pallasovka, etc. irrigation systems). Inherent research on the Volga-Don irrigation system with the involvement of remote sensing data has shown that this system reflects the general features of the modern rehabilitation condition of irrigated lands of the Volgograd Oblast – fallow lands, lack of drainage, and widespread saline soils are specific for this area. The use of high-resolution multispectral satellite images (Landsat-8, Sentinel-2) for the purposes of detection of fallow and irrigated lands in the current season demonstrates the possibility of clarifying information on this category of land. The determination of the distribution of salinized and solonetzic soils on irrigated lands of the Volgograd Oblast by remote methods can be carried out indirectly, i. e. according to the state of vegetation: when areas of sparse vegetation cover are being identified on images, targeted routes for soil surveys and soil sampling are selected.

Detecting hydrocarbon micro-seepage and related contamination, probable prospect areas, deduced from a comparative analysis of multispectral and hyperspectral satellite images

Detecting mineralogical alteration by hydrocarbon micro-seepage from trap to surface across the deformed structure, through Comparative analysis study of hyperspectral image (EO-1, Hyperion), and multispectral (landsat7 and Advanced land imager (Ali) to map soil alteration by petroleum seepage with applying remote sensing techniques as (band ratios, supervised, SAM classification, and hydrocarbon detection and index (HD and HI) calculation to detect the potentiality of hydrocarbon seepage in the area. The type of spectral resolution accuracy available for each space imaging platform can be used to choose which is best in determining the target and whether exploration yields oil. Hyperspectral remote sensing data are integrated with a GIS framework Weighted Sum (Spatial Analyst) classified hydrocarbon seepage prospects into five potential zones very good, good, intermediate, poor, and very poor probable potentiality. The spatial lithologic carbonaceous alteration appears closely coincident along reactivated leaking faults that cause hydrocarbon micro-seepage and altered surface lithology and mineralogical cement. The hyperspectral images have proven their worth in identifying and studying hydrocarbon leaks and the resulting environmental pollution and mineral alteration, as well as use micro-seeps as a pathfinder to locations of new oil explorations discoveries that can use as a pre-drill prediction of hydrocarbon occurrence and detected a prospect area for hydrocarbon drill.