Satellite Images In Order Research Articles

Multi-Temporal Assessment of Soil Erosion After a Wildfire in Tuscany (Central Italy) Using Google Earth Engine

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha−1 × yr−1 to 1.29 ton × ha−1 × yr−1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha−1 × yr−1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.

INFORMATION EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES

The article analyzes various methods and approaches of modern remote sensing that can be used in the processing of multispectral satellite images in order to effectively extract visual information about territories for which preliminary data is not available. Attention is paid to the creation of new derivative images (synthesized and indexed) and to performing pixel-oriented computer non supervised classification. A series of experiments have been made that clearly reveal the advantages and conveniences of remote retrieval of information from multispectral satellite images in a territory for which reference objects and other data acquired in situ are not available.

REMOTE SENSING OF THE STATE OF THE VEGETATION COVER OF THE TERRITORY OF THE BULGARIAN BLACK SEA

This paper is devoted to the multispectral processing of satellite images in order to establish the actual state of the vegetation cover. As a result of conducted experiments, the vegetation on the territory of the Bulgarian Black Sea coast was studied. For this purpose, new indexed images have been generated from appropriately selected multispectral images, which visually reveal the actual state of the vegetation cover and provide objective and timely data. They could serve to manage and other decisions (in the field of ecology, forestry, agriculture, etc.), for environmental assessments and expertise, for scientific research and other projects.

GEOINFORMATION MAPPING OF ANTHROPOGENICALLY TRANSFORMED LANDSCAPES OF BILA TSERKVA (UKRAINE)

Aim of the study: The main purpose of the study is the analysis and assessment of anthropogenically transformed landscapes of Bila Tserkva (Ukraine) based on a combination of remote sensing methods and GIS mapping Material and methods: Usage of geoinformatics methods for mapping anthropogenically transformed landscapes of Bila Tserkva is studied. The data was downloaded and processed using the Semi-Automatic Classification Plugin QGIS for the supervised classification of remote sensing data. Satellite images were radiometrically calibrated and atmospherically corrected, followed by a controlled classification with signature creation, visualization of spectral profiles, quality assessment and post-processing Results and conclusions: The main methods of landscape research are analyzed. The conclusion is made about the expediency of using spectrophotometry of satellite images in order to identify different types of landscapes based on satellite data. An supervised classification of satellite images different-time images was performed, as a result of which the main Bila Tserkva landscape types were identified. Those identified types are: water bodies, vegetation (grass, forest, parks) urban areas and bare soils. Spatio-temporal changes of landscapes are studied and these changes are described in quantitative indicators

Atmospheric dispersion simulation of an accidental smoke plume using a heat diffusion algorithm into a LES-STO coupled model

A high resolution earth satellite image with the visible smoke plume of a huge fire in the city of La Plata, Argentina, is used to verify the simulated plume with the fluid particle dispersion model LES-STO (large-eddy simulation - lagrangian stochastic one particle model). The initial and boundary conditions are provided by a boundary layer model forecast, and local meteorological observations validate the wind and temperature forecast with good results. The smoke plume is modeled by the continuous emission of fluid particles, initiated 4 h before the time of the satellite image in order to allow the model spin-up. Due to the lack of technical information about the fire, the emission parameters required by the model are estimated considering the dimensions of the burnt area and the products stored there. The spatial orientation and horizontal lateral extension of the modeled plume coincide with the smoke plume clearly visible in the satellite image. The conclusion of the study is that it is possible to use LES-STO with initial and boundary conditions provided by operational meteorological forecasts to make a diagnosis of those areas that could be affected by accidental emissions. • The new smoke plume simulation algorithm is performed into LES-STO for obtained the geographical plume dispersion of smoke. • High resolution satellite image is used to verify the result of the smoke plume simulation. • The initial and boundary conditions determine the dispersion of the smoke plume and its geographical distribution. • LES-STO can be used with boundary conditions provided by operational weather forecasts for diagnosis of fire affected areas.

НОВЫЕ ПОДХОДЫ К ОТОБРАЖЕНИЮ МЕРЗЛОТНЫХ ФОРМ РЕЛЬЕФА НА ЦИФРОВЫХ ТОПОГРАФИЧЕСКИХ КАРТАХ СИБИРСКОЙ ЗОНЫ АРКТИКИ

The analysis of traditional topographic maps and plans of the Siberian Arctic zone shows that their relevance and completeness of content do not fully meet the modern requirements for the further de-velopment of this region. There is a need for targeted specialization of a new generation of topograph-ic maps and plans of Arctic territories, taking into account their physical and geographical specifics. The purpose of the work was to determine the possibility of such specialization based on the use of remote sensing materials from space and computer technologies.To achieve this goal, studies have been conducted to determine the visual and automated decryption capabilities of Landsat Sentinel sat-ellite images in order to expand and refine the existing system of conventional signs of cryogenic re-lief. Based on these studies, new conventional signs for polygonal, bumpy, solifluctional and thermo-karst forms are proposed. The conclusion is made about the need for specialization of other specific elements of Arctic maps – vegetation, hydrography, soils.

Monitoring Olive Oil Mill Wastewater Disposal Sites Using Sentinel-2 and PlanetScope Satellite Images: Case Studies in Tunisia and Greece

Mediterranean countries are known worldwide for their significant contribution to olive oil production, which generates large amounts of olive mill wastewater (OMW) that degrades land and water environments near the disposal sites. OMW consists of organic substances with high concentrations of phenolic compounds along with inorganic particles. The aim of this study is to assess the effectiveness of satellite image analysis techniques using multispectral satellite data with high (PlanetScope, 3 × 3 m) and medium (Sentinel-2, 10 × 10 m) spatial resolution to detect Olive Mill Wastewater (OMW) disposal sites, both in the SidiBouzid region (Tunisia) and in the broader Rethymno region on the island of Crete, (Greece). Documentation of the sites was carried out by collecting spectral signatures of OMW at temporal periods. The study integrates the application of a variety of spectral vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), in order to evaluate their efficiency in detecting OMW disposal areas. Furthermore, a set of image-processing methods was applied on satellite images to improve the monitoring of OMW ponds including the false-color composites (FCC), the Principal Component Analysis (PCA), and image fusion. Finally, different classification algorithms, such as the ISODATA, the maximum likelihood (ML), and the Support Vector Machine (SVM) were applied to both satellite images in order to assist in the overall approach to effectively detect the sites. The results obtained from different approaches were compared, evaluating the efficiency of Sentinel-2 and PlanetScope images to detect and monitor OMW disposal areas under different morphological environments.

Metodología para la estimación de pérdidas económicas de cultivos de maíz siniestrados por inundaciones usando teledetección

The flooded areas of corn cultivation were determined by means of satellite images in order to know the economic losses due to floods. As a case study, a flood that occurred in 2016 that affected 4 municipalities in the State of Tabasco, Mexico, was analyzed. The methodology included the use of Landsat satellite images (L8), calculation of spectral indices (water, vegetation and humidity), identification of corn growing areas through supervised classification and the estimation of economic losses. The results show that the spectral indices that best managed to identify the flooded areas were: MNDWI and NDWI. To report damages, values in USD (US dollars) were standardized in 2016 prices using exchange rates adjusted for purchasing power parity (PPP). For the case study, the results of damages per hectare were established between 728.57 USD / ha in PPP and 317.25 USD / ha in PPP, which agree with the estimates made in other countries for corn crops.

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”.

THE USE OF SPECTRAL AND TEXTURAL FEATURES IN CROP TYPE MAPPING USING SENTINEL-2A IMAGES: A CASE STUDY, ÇUKUROVA REGION, TURKEY

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.

Synoptic Characteristics of Torrential Rains in Southwest and Southeast Iraq: A Case Study

Torrential rains cause many losses in city infrastructure, crops, and deaths in several regions of the world including Iraq as in the case that we will discuss in this work, on January 28 and 29, 2019. Torrential rain caused the flow of torrents in several areas of Iraq and the neighboring areas. This research work aims to identify the synoptic characteristics of torrential rains and the causes of this case. This will be done by analyzing and interpreting the weather maps at different pressure levels with focusing on the troughs and fronts locations, relative vorticity, polar jet stream effect as well as the moisture flux. The Geographic Information System (GIS) was used to analyze the satellite images in order to calculate the Normalized Difference Water Index (NDWI) to confirm the heavy rain case. The weather maps were obtained from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2). As for the satellite images we used the satellite imagery from Sentinel-2 and EMUTSAT.

SATELLITE DATA AND SPATIAL ANALYSIS OF LAND USE IN KANDAHAR PROVINCE

The article addresses the application of geoinformation analysis technologies to solve problems of land management systems development in the Islamic Republic of Afghanistan. Using an example of the study of the territory of Kardagar province, it is shown how space survey materials and spatial analysis technologies can be utilized in assessing the prospects for the development of irrigated agriculture in the Arghandab River valley. The study uses decoding of multitime satellite images in order to map the structure of land utilization. This allows us to obtain uniform in quality and objective information simultaneously for vast territories. The approach makes up for a significant lack of data from the ground surveys of the region. Electronic thematic maps and digital models of the topography of the studied territory have been developed, which helps to determine the undeveloped lands suitable for agricultural use. The effectiveness and reliability of satellite data in analysis of poorly studied territories is demonstrated. The results of the study are of practical importance for the development of the informational basis for the land management systems in Afghanistan.

Spatio-Temporal Dynamics of Algae and Macrophyte Cover in Urban Lakes: A Remote Sensing Analysis of Bellandur and Varthur Wetlands in Bengaluru, India

Rapid urbanization processes and indiscriminate disposal of urban wastewaters are major causes for anthropogenic lake-sediment deposition and eutrophication. However, information about the spatial and temporal variation of macrophyte and phytoplankton distribution as indicators for water contamination is limited. To gain insights into the dynamics, we analyzed lake-cover changes of Bellandur and Varthur Lake in the S-Indian megacity of Bengaluru for the post-rainy seasons of the years 2002–2019. Supervised maximum likelihood classifications were conducted on 62 freely available, true-color satellite images in order to distinguish between macrophytes, algae, and free water surface. The image-derived results were verified by supervised classification and manual mapping of two simultaneously recorded multispectral satellite images (Sentinel-2 and WorldView-2). Seasonal interrelations between macrophytes and algae distribution were similar for both lakes. The increase in macrophyte cover during post-rainy season negatively correlated with algal abundance. Macrophyte expansion progressively suppressed algae development at both lakes, reflective of increasing eutrophication caused by on-going wastewater input. Seasonal variation in precipitation, wind direction, and temperature seemed to trigger intra-annual shifts of macrophytes and algae while similar macrophyte spread intensities during the post-monsoon season indicated independence of nutrient loads in the lake water.

Lineament Analysis and Inference of Geological Structures in Bansara-Boki Area, Southeastern Nigeria

The purpose of this study is to extract lineaments from satellite images in order to contribute to the understanding of the structural geology of parts of Boki and its environs. Shuttle Radar Topographic Mission (SRTM) and Landsat 7 ETM images of path 187 and row 056 were used for the analysis which is processed for automated extraction, validated through ground-truthing of planar and linear geological features displaying altitude of about 233 for Bansara sheet 304. Lineament extraction processing was done using PCI Geomatica version 2016 for Landsat imagery and ArcGIS 10.5 used to generate Digital Elevation Model (DEM) and Slope Map for SRTM imagery. Statistically a total of 3191 count of highly dense lineament were generated ranging between 0.86 to 4.33 km in length with the mean of 1.22 and a standard deviation of 0.83 intersecting at low percentage of 3-6%. The DEM display a range of 1335 to -1335 m sloping in the range of 0-2.81 and 61.224-89.725 m for topographic analysis. The lineament extracted were trending majorly in NW/SE and other minor ones in NE/SW directions some which were agreement with the altitude of the ground-truth data. The variation is possibly as a result of influence from regional process such as deformation, metamorphism, magmatism and method of data acquisition and analysis. Lineament analysis are profound index parameters for engineering of dams, economic mineral and water resources exploration, exploitation, planning and development. It is also useful in geohazard studies and its mitigation as the areas are prone to rockfalls, rockslides, landslides, mudslides and flooding due to high rainfall and human activities at the foot of the highlands.

The influence of man-induced land-use change on the upper forest limit in the Romanian Carpathians

The Romanian Carpathians are an important part of Europe’s mountain areas, the actual pattern of forest cover reflecting a long history of land-use practices and management. In the present work, we analyse the recent changes (1945–2018) in the upper forest cover along the 37 mountain units using old topographic maps and Sentinel-2A high-resolution satellite images in order to quantify several spatial indicators related to the upper forest cover and upper forest limit changes. In order to understand the regional variation, we have tested the correlations between the location of the upper forest limit, the local topography and the geographical position. Furthermore, with a view to evaluate the upper forest limit change according to its potential climatic limit, the 10 °C July isotherm was estimated based on WorldClim climate data. In addition, the situations inside and outside the major protected areas were also analysed and discussed so as to assess the possible regional differences as related to human effects. The results revealed a continued downward shift in the mountain areas located below the 1600 m a.l.s., but significant advancement above these values, indicating regional differences in land-use intensity and land management. This study is aimed to increase further knowledge as to the upper forest limit in the European mountains and to provide a baseline for modelling future potential change according to the environmental and anthropogenic variability in the area.

Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images

Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.

Molecular identification of different trypanosome species in tsetse flies caught in the wildlife reserve of Santchou in the western region of Cameroon.

Addressing the problems linked to tsetse-transmitted trypanosomiases requires considerable data on tsetse distribution and trypanosome infections. Although efforts to map tsetse and trypanosome infections have been undertaken at continental level, published data are still rare in wildlife reserves of West and Central Africa. To fill this gap, data on tsetse distribution and trypanosome infections were generated in the wildlife reserve of Santchou. For this study, each tsetse caught was identified and its DNA extracted. Different trypanosome species were identified by PCR. Entomological and parasitological data were transported onto a satellite image in order to visualize their distributions. From 195 Glossina palpalis palpalis that were caught, 33.8% (66/195) carried trypanosome infections with 89.4% (59/66) of single infections and 10.6% (7/66) mixed infections. From the 66 flies with trypanosome infections, 54.5% (36/66), 27.3% (18/66) and 18.2% (12/66) were respectively due to Trypanosoma congolense, Trypanosoma brucei s.l. and Trypanosoma vivax. The global infection rates were 18.5% (36/195) for Trypanosoma congolense (forest and savannah), 9.2% (18/195) for Trypanosoma brucei s.l. and 6.1% (12/195) for Trypanosoma vivax. The maps generated show the distribution of tsetse and trypanosome infections. This study showed an active transmission of trypanosomes in the wildlife reserve of Santchou. The maps enabled to identify areas with high transmission risk and where control operations must be implemented in order to eliminate tsetse and the diseases that they transmit.

Feature extraction from high resolution satellite images using K-means and colour threshold approach

In recent year developments in satellite sensors tend to the availability of high spatial and spectral resolution images. The motivation of this research paper is to obtain maximum benefits of different bands from high resolution satellite images in order to put into practice an image processing algorithm solution for extraction and classification of land cover and manmade objects can be used by non-professionals. In this research paper a novel approach for image classification is presented by applying k-means algorithm and colour threshold approach onto high resolution World View 2 (WV2) image. K-means algorithm is applied on a reflectance image to extract land cover classes and manmade objects based on a colour-based segmentation method. The proposed technique is applied through MATLAB environment. The user is asked to select few points of the desired classes and the algorithm do the rest and produce vector layers of the selected classes. The experimental results prove the effectiveness of our framework to enhance the quality of classification in aspects of computational time and precision. The preliminary results are considered promising.

Tectonic geomorphology and structural architecture of eastern Sulaiman Fold Thrust Belt (SFTB) and adjacent Sulaiman Foredeep (SF), northwest Pakistan

This investigation combines a number of datasets to provide an enhanced and improved tectono-geomorphological framework with which to identify new possible structural lineaments in northwest Pakistan. The dataset includes satellite images and geographic information system (GIS) methods. The present study was carried out using the USGS ASTER DEM (30 m), Landsat 8 (OLI/TIRS) and surface field geology of the study area. Different band combinations, directional filters and principle component analysis (PCA) methods were used to identify the major structural styles and tectonic geomorphological pattern. The panchromatic band (Band-8) was applied to the Landsat 8 satellite images in order to increase the spatial resolution from 30 m to 15 m which aided in the investigation and comparative analysis of tectonic geomorphology and structural styles. Drainage patterns of Zindapir Anticline (ZA), Sakhi Sarwar (SS), Sulaiman Foredeep (SF) and Kingri Fault (KF) areas were generated using Strahler order of streams. The results suggested a combination of the dendritic network with a minor component of trellis pattern for ZP and SF areas, hence proposing a combined structural and stratigraphic/lithological control, whereas the KF area indicated an element of a rectangular pattern suggesting a more structural influence in the western part in comparison to the eastern region. The presence of water gaps (WaGs) and active drainage network indicate lateral growth and propagation directions of the ZP and SS frontal anticlines following the Paleocene Epoch. The geomorphic evidence coupled with satellite data show that the SFTB is tectonically active along with the adjacent SF. The stream courses are deflected or guided due to active folds and tectonics in the area characterized by the distinct fluvial erosional pattern on the flanks/limbs of ZA. Evidence from the study suggests that compressional deformation, uplift and erosion played a significant role in the shaping of the structural and depositional architecture of the basin. It is proposed that the deformation in the SFTB during the late Paleogene is related to the Indian-Eurasian oblique collision, while the subsequent late Tertiary collision of Indian Plate with the Afghan Block (an accreted part of southern Eurasia) further ensued compressive deformation. The results from this study elucidate the possible models proposed for this compressional regime with prevailing wrenching by presenting new data. The findings from this research reveal new tectono-geomorphological features and lineaments in the area that were not documented previously.

COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER

Abstract. The purpose of the study was to compare performance of the classification methods, that are Rule Based (RB) classifier and Support Vector Machine (SVM), of Planetscope and Worldview-3 satellite images in order to produce land use / cover thematic maps. Six classes, which are deep water, shallow water, vegetation, agricultural area, soil and saline soil, were considered. After performing the classification process, accuracy assessment was employed based on the error matrices. The results showed that, both of the classification methods and satellite data were adequate to classify the area. Besides, classification accuracy was improved when Worldview-3 satellite and SVM method were used. The classification accuracies of RB classification of Planetscope and Worldview-3 were %87 and %94 respectively and the classification accuracies of SVM classification of Planetscope and Worldview-3 were %93 and %96 respectively.