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Soil erosion is an important concern due to the steepness of the terrain and the significant elevation differential between the upstream and downstream regions of the basin. Revised Universal Soil Loss Equation (RUSLE) was integrated with Remote Sensing (RS) and Geographic Information System (GIS) in the current work establish the annual soil erosion map in the Bulango Ulu Reservoir basin. The RUSLE model incorporated soil erosion zonation features such as rainfall erosivity, soil erodibility, topographic, vegetation cover, and conservation support practices. The results show that 0 and 110.31 t year−1 are the minimum and maximum soil erosion, with the average rate of soil erosion is 17.30 t year−1 in the present study area. Soil erosion risk regions were divided into five categories: very slight, slight, moderate, severe and extremely severe soil erosion areal extent and area percentages is 229.17 km2 (94.48%), 7.83 km2 (3.23%), 4.25 km2 (1.75%), 1.20 km2 (0.50%), and 0.12 km2 (0.05%), respectively. The Area Under Curve was indicated that RUSLE model had good performance (75.1%). This study demonstrates the utility of GIS and remote sensing for predicting soil erosion, allowing important information to be extracted for implementing soil conservation programs in the Bulango Ulu reservoir basin.

Nowadays, satellite systems are experiencing noticeable disruptive issues due to the single functionality, high cost of operations, and limited adaptability. This paper presents a satellite mission control system with RF duplex communication, which gives multiple functionalities and health monitoring systems with solar tracking capability. This aims to present a flexible and affordable solution called the Remote Sensing Mission Sentinel Pro Platform. The quantitative approach is used and implemented to trans-receive data from space and pass it to the ground station. Arduino-based prototype uses the nRF24L01 module with an antenna at the ground station to trans-receive satellite data has been developed. Testing and verifications of the developed platform prototype indicated that satellites could monitor key health parameters, including temperature, humidity, and battery charge capacity.

Forest fires are a type of natural catastrophe that poses a risk to the vegetation and fauna of the hill stations. Due to the presence of vast enormous areas of land adorned with aged trees, vegetation, and wild life, safeguarding the ecosystem is both critical and arduous. A Geographic Information System and Remote Sensing assist in resolving this issue through the continuous monitoring of the forest using satellite, aerial, and drone-based imagery gathered from a variety of sources in India and Abroad. Landsat8 series Band 4, Band 5, Band 6 and Band 7 is used for the study. Following the correction and analysis of reflectance values for pre-fire and post-fire imagery, the Normalized Burn Ratio (NBR) is computed and processed. Finally, the difference in NBR based on the pre-fire event and post-fire event for the Yercaud hill station is analyzed and the severity level is classified for the chosen area of interest.

This paper describes the use of NDVI analysis to assess the health condition of the rangeland in a specific study area. NDVI values were used to determine the vegetation cover and its condition by dividing the values into different classes based on field samples. The study found that the rangeland health condition could be classified into four classes based on the NDVI values ranging from -0.26 to 0.63. The results indicated the presence of both healthy and unhealthy vegetation cover in the study area. However, the study emphasizes that NDVI values can vary depending on different factors, such as season, weather conditions, and vegetation type, and therefore, these factors must be considered when interpreting the results. Overall, the study highlights the importance of using NDVI analysis as a valuable tool to assess the health condition of rangelands and to inform management practices. The paper presents the results of a study on rangeland health classification in a particular study area. The study utilized remote sensing and GIS technologies to classify the rangeland into healthy and degraded categories. The results showed that only 4.4% of the land area was classified as healthy rangeland, while most of the study area was classified as degraded. Specifically, 36.1% of the land area was classified as moderately degraded, 24.3% as low degraded, and 35.2% as highly degraded. These findings suggest that the health of rangelands in the study area is poor and requires effective management and restoration practices to improve productivity. The study demonstrates the usefulness of remote sensing and GIS technologies in monitoring rangeland health and supporting sustainable management practices.

Ecological damage caused by human activities poses a significant threat to ecosystems worldwide. Assessing and monitoring this damage is essential for effective environmental management and conservation. Geographic Information Systems (GIS) and Remote Sensing (RS) technologies have emerged as valuable tools for assessing ecological damage, providing timely, accurate, and spatially explicit information. This literature review comprehensively examines the use of GIS and RS in assessing ecological damage, focusing on factors such as deforestation, land degradation, habitat loss, and biodiversity decline. A systematic search of online databases and manual searches of relevant journals were conducted to identify studies that met the inclusion criteria. Key findings include the identification of land use change, urbanization, industrialization, and agricultural expansion as major drivers of ecological damage. GIS and RS have been instrumental in mapping and quantifying changes in land cover, vegetation health, and other environmental parameters, enabling more informed decision-making and targeted conservation efforts. However, challenges remain, including the need for high-quality data and standardized methodologies. Future research should focus on integrating socioeconomic factors into ecological assessments and developing more advanced remote sensing technologies. By continuing to innovate and collaborate, researchers, policymakers, and practitioners can work together to address the challenges of ecological damage and promote sustainable environmental management practices.

The article addresses the task of adapting the third-generation mathematical wind-wave model, WAVEWATCH III, for constructing predictive scenarios of the Azov Sea development based on satellite imagery data. In addition to the effective utilization of remote sensing data, WAVEWATCH III enables modeling using multicellular grids, thereby reducing the temporal constraints required for forecasting hydrophysical phenomena, including hazardous and catastrophic events. Within the article, modeling of the dynamics of the coastal system of the Sea of Azov is conducted based on remote sensing data. The article presents the results of modeling the direction, period, height, and wavelength of waves in the Azov Sea. A multicellular grid with varying levels of detail of coastal zone sections was employed for modeling, contingent upon the complexity of the coastline. To generate a grid with a complex structure utilized for modeling hydrodynamic processes in the Azov Sea, an algorithm is proposed. This algorithm not only generates a refined grid near the shore at any level but also refines the grid in arbitrary areas.

Remote sensing (RS) and geographical information systems (Geographical Information System) are among the major ecological spatial analysis tools, which has boosted the efficiency of relevant research. Thus, it is of great significance to reflect on this method. This paper aims to analyze the application of Remote Sensing and Geographical Information System in ecological assessment, which contributes to the practice of sustainable development. The paper reviews relevant studies and further reflects on the existing literature, with several recommendations. The paper shows that the combined use of Remote Sensing and Geographical Information System constitutes the dominant approach in small-scale studies with a very consistent research methodology. The paper analyses how these relatively fixed research materials and frameworks make the research efficient and widely applicable. This paper summarises the basic model of integrated application of Remote Sensing and Geographical Information System and points out some future development directions and research directions, including a few on land degradation neutralization and urbanization, as well as several views on the use of methods.

This study aims to assess the urban heat island (UHI) effect in Guangzhou City using remote sensing technology and to explore its potential impact on urban spatial layout and measures for mitigating the UHI effect. By analyzing Landsat 8 satellite data, this paper focuses on the changes in land surface temperature in Guangzhou from 2018 to 2023, using a weekly scale as the primary time analysis unit. The research method is based on the Radiative Transfer Equation (RTE) for the inversion of land surface temperature, utilizing ArcMap software for the related data calculations and processing. The research findings reveal the weekly scale distribution of land surface temperature in Guangzhou City from 2018 to 2023, showing that the UHI effect is mainly concentrated in the southwestern part of the city. In contrast, due to less economic activity, lower population density, and a larger area of green coverage, the northeastern part experiences a relatively weaker UHI effect. The discoveries of this paper provide important references and bases for spatial planning and mitigation of the UHI effect in Guangzhou City and other similar cities, emphasizing the application value of remote sensing technology in the study of urban climate change.

This study focuses on the ecological and environmental quality of the Yellow River Delta. Utilizing remote sensing data from 2013 to 2021, we employed the Remote Sensing Ecological Index (RSEI) derived from Landsat 8 OLI_TIRS graphics to gauge the ecological health of the region. The study reveals significant spatial heterogeneity in the ecological environment quality, with the coastal areas exhibiting the most degraded conditions. Temporal analysis indicates a fluctuating trend, underscoring the region’s ecological vulnerability and the imperative for strategic interventions to balance ecological preservation with economic progress. The findings provide crucial insights for policymakers to devise sustainable development strategies that address the delicate equilibrium between environmental stewardship and economic growth in the Yellow River Delta. This study’s comprehensive approach, integrating multiple ecological indices and principal component analysis, enhances the understanding of the dynamic interplay between human activities and natural processes, offering a robust framework for future ecological assessments and environmental management in the region.

Precision agriculture and Smart farming have become the essential backbone for sustainable agricultural production by leveraging cutting-edge remote sensing and communication technologies. With the remarkable development in recent decades, Unmanned Aerial Vehicles (UAV) based sensing technologies have gained rapid proliferation and exploitation in precision agriculture. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. Using thermal infrared or hyperspectral cameras, UAV remote sensing takes extensive pictures of farmland. It also collects the reflection bands of various crops, monitors and analyzes crop growth trends, locates disease and insect pests, and determines the moisture content of the soil based on variations in the bands. This paper will discuss the basic principles of UAV remote sensing technology for monitoring crop growth, disease, and insect pests, the application of soil vegetation index monitoring, and the development prospects of UAV remote sensing in agricultural monitoring.

Currently, remote sensing techniques assist in various environmental applications and facilitate observation and spatial analysis. Machine learning algorithms allow researchers to find dependencies in satellite data and vegetation cover properties. One of the significant tasks for ecological assessment is associated with estimating forest characteristics and monitoring changes over time. In contrast to the general computer vision domain, remote sensing data and forestry measurements have their own specific requirements and necessitate tailored approaches that involve processing multispectral satellite data, creating feature spaces, and selecting training samples. In this study, we focus on extracting primary forest characteristics, including forest species groups, height, basal area, and timber stock. We utilise Sentinel-2 multispectral data to develop a machine learning-based solution for vast and remote territories. Timber stock is calculated using empirical formulas based on measurements of forest species groups, height, and basal area. These intermediate forest parameters are estimated using individually trained machine learning algorithms for each parameter. As a case study, we examine the Sakhalin region (Russia), which encompasses several forestries with varying vegetation properties. In Nevelskoye forestry, we achieved a mean absolute error (MAE) of 1.6m for height, 0.084 for basal area, and 47.8 m3/ha for timber stock. The results obtained demonstrate promise for further integrating artificial intelligencebased solutions into forestry decision-making processes and natural resources management.

This article explores the application of fractal representations in the image processing of remote sensing data for Earth observation. Fractals, with their self-similar properties and complex patterns, offer a powerful mathematical framework for analyzing the intricate structures found in natural landscapes. The study highlights the advantages of using fractal-based methods over traditional image processing techniques, particularly in capturing the multifaceted textures and irregularities of Earth’s surface features. By leveraging fractal geometry, enhanced accuracy in the classification and interpretation of remote sensing images is achieved. This approach facilitates better monitoring and understanding of environmental changes, land use patterns, and natural disasters. The findings underscore the potential of fractal representations to significantly improve the quality and efficacy of remote sensing image analysis, providing a robust tool for Earth science research and practical applications in environmental management.

Biological crusts (BC) are an organic complex, composed of bacteria, cyanobacteria, diatoms, green algae, tiny fungi, ground jackets, and moss plants, as well as other related organisms. BC is distributed about 40% in arid areas, which is one of the important surface landscapes in the intertwined area between oasis and desert. The relationship between BC and drought is close, which can be used to monitor desertification and water resources in remote sensing. However, the current monitoring of BC uses a simple BC index composed of multi-spectrum to extract its area distribution in remote sensing, which makes it difficult to accurately detect its area. This study considers the characteristic bands in the hyper-spectrum cure and designs a hyperspectral index of BS, i.e., HBCI. The HBCI is used, and we successfully estimated the area of biological soil crusts (Kappa=0.86) in the PRISMA image. This finding has help for the regional climate change research.

Coastal ecosystems are vital land-sea interfaces that are necessary to sustain environmental stability and biodiversity. In many places, the complex network of vegetation cover serves as an essential buffer, preventing erosion, sustaining a variety of habitats, and enhancing ecological resilience overall The study utilizes advanced technologies like GIS, remote sensing, and Landsat images to analyze vegetation changes in Sfiha and Swani's coastal regions over the past 18 years, aiming to understand the impact of land use, climate fluctuations, and human activities on the landscape, Sophisticated remote sensing technology, Landsat satellite images, and Geographic Information System (GIS) tools are among the materials employed. During the designated period, the proportions of changes in the plant cover in the research area are determined and interpreted with the help of these resources. The study reveals significant changes in vegetation cover in Sfiha and Swani, revealing patterns of 21% (2003), 25% (2010), 33% (2015), and 15% (2021), providing insight into the temporal evolution of the coastal ecosystem and influencing conservation strategies and sustainable management practices. This study underscores the broader relevance of employing advanced spatial technologies for the monitoring and management of coastal ecosystems.Introduction

This study explores the intricate interactions between demographic processes and spatial variables through the lens of cartographic modeling, leveraging remote sensing data for enhanced precision. Land cover classifications reveal the dominance of urban and agricultural landscapes, setting the stage for a nuanced examination of demographic dynamics. Spatial correlations highlight the interdependencies between demographic variables, while regression coefficients provide insights into their impacts on the overall cartographic model. Predictive accuracy assessments validate the model's robustness, and spatial autocorrelation analyses unveil geographic clustering of demographic patterns. The integration of remote sensing data proves instrumental in enhancing the granularity of our understanding, offering valuable insights for sustainable urban planning and resource allocation. While acknowledging limitations, this study contributes to the broader discourse on urban development, offering a comprehensive framework for policymakers and researchers to make informed decisions in the context of evolving demographic and spatial dynamics.

The emergence of strong desert vortices covering large areas, including the areas of large cities with dense population, the need to quickly obtain sufficient information on the state of desert ecosystems in large areas makes the application of remote sensing methods an urgent issue. At present, the method of measuring the desert aerosol using scattered solar radiation has been proposed, and the method of measuring the optical thickness of the dust aerosol has been substantiated. A new method of optimization of remote sensing systems and information-measurement systems in general is proposed in the article. A method of remote determination of aerosol optical thickness increase, which involves measuring the scattered radiation of the sun, has been proposed. The main goal of the study is to develop a remote sensing method that allows determining the dynamics and cause of the increase in aerosol optical thickness. The scientific novelty of the work is a new method that is invariant to the variations of the aerosol optical thickness under the influence of hydration and dehydration processes. By using the proposed three-wave biparametric Sun photometer, the effect of aerosol hygroscopic growth on the measurement results is eliminated.The given critical assessment of the state of atmospheric pollution monitoring and the consideration of the main shortcomings of the aerosol pollution monitoring carried out in the country will allow further improvement of such monitoring in the future. The proposed remote sensing method for determining the dynamics of aerosol optical thickness growth allows to determine the origin of aerosol optical thickness growth by measuring scattered solar radiation at two different wavelengths and in two optical air masses.

Water transparency (i.e., Secchi disk depth (SDD) reflects light transmission capacity of a water body and influences growth of aquatic plants, aquatic organisms, and primary productivity. In this study, using remote sensing reflectance (Landsat 8) and SDD datasets, we predicted the water transparency in Saguling Dam for the first time. The results indicated that the models in the visible (Band 1, Band 2, and Band 3) combined with near infrared (Band 4 and Band 5) are the most robust and reliable to estimate SDD for Saguling Dam. Subsequently, multiple linear regression model was built using 125 pairs of in situ SDD results and concurrent with Landsat images during the last decade (i.e., 2013-2022). The models were validated with an independent dataset of 33 SDD measurement. The in situ remote sensing model compared well with the in situ SDD measurement where determinant coefficient (adjusted R2) and root mean square error (RMSE) ranging from 0.35 - 0.94 and 0.3 - 1.4 m, respectively. Finally, the model was applied to Landsat 8 images acquired between 2013-2022 to elucidate the spatial distribution of SDD in Saguling Dam for each year (i.e. temporal variations) with water area around 33.7 km2. The estimation results indicated that water transparency values in the inlet and outlet zone have relatively low SDD, with SDD value ranging from 0.019 – 1.01 m and 0.24 – 1.04 m, respectively. Furthermore, based on the National Water Quality Standards (i.e., Indonesia Government Regulations No. 22 year 2021/ Peraturan Pemerintah No. 22 Tahun 2021), the estimation of spatial SDD results indicated that > 99% of Saguling Dam water bodies were classified as Class IV (i.e., could be only used for irrigation). This study provides the first comprehensive remote sensing model for Saguling Dam and can provide essential information for local water quality conservation.

Remote sensing research was carried out for a standard-gauge railbus used for passenger transport in the Wielkopolska voivodeship (Poland). Pollution concentrations were measured using a modular device for ecological assessment of moving vehicles - an emission gate. The basic tests were carried out using equipment from the PEMS group. The results from the remote sensing device were compared to them. Taking into account the results, it was concluded that the newly developed device allows for the assessment of pollutant concentrations in a non-invasive way, both for gaseous compounds and solid particles. The presented analyzes indicate that most of the concentrations reached values close to the minimum indications from the PEMS equipment.

Based on remote sensing data, it is possible to create a real-time database of agricultural sectors of the study area, in particular, types of crops, fisheries, arable land, and other sectors of agriculture. Remote sensing techniques can also be used to help determine crop yields, parasite spread, increased damage, and soil conditions using satellite imagery and aerial photography. In agricultural mapping, a classification algorithm is required that ensures the reliability and accuracy of the data extracted from the remote sensing data. Research and experiments have shown that increasing the accuracy of classification results requires not only the selection of a perfect algorithm but also a high level of knowledge and skills in the field in which the research is conducted. The mapping of agricultural sectors, in particular the classification of crops, also requires close acquaintance with the existing types of crops in the region, their dependence on natural and climatic conditions, and their development trends.

This study explores the spatio-temporal dynamics of the Normalized Difference Vegetation Index (NDVI) to detect early signs of land degradation. Utilizing high-resolution NDVI data from the Google Earth Engine, spanning from 2004 to 2023 with a 30-meter resolution, this research analyzes monthly variations. To illustrate these dynamics, the study focuses on Sabzevar County, located in northeastern Iran, which extends over 7,217 km²and is approximately 220 kilometers distant from Mashhad. Validation of the NDVI data was performed using field observations from strategically located vegetation plots. One square meter plots were systematically established along 100-meter transects (10 transects in total), where the vegetation coverage in each plot was quantitatively assessed by experts. Comprehensive statistical analysis incorporated Kendall’s tie test, alongside measurements of autocorrelation, coefficient of variation, and standard deviation, using R software to assess the trends and intensities of NDVI changes. The findings revealed a critical breakpoint in 2020, with increases in all three statistical indices— autocorrelation 0.82, coefficient of variation 0.65, and standard deviation 0.58—indicative of accelerating degradation prior to this year. Furthermore, the intensity of NDVI changes varied significantly across the study area, ranging from 0.05 in central and northern regions to 0.76 in the western parts. This research underscores the value of integrating field data with remote sensing technology to provide a robust analytical tool for early detection of land degradation. This method enables precise, timely assessment and proactive management of vulnerable ecosystems, particularly in arid regions.

Aquatic ecosystems are significant methane (CH4) emitters, potentially surpassing direct anthropogenic sources. Despite the advantages of satellite monitoring, its application for assessing methane content over freshwater bodies is not commonly encountered in scientific publications. Therefore, this study aims to assess methane levels in the atmosphere in and around the temperate reservoir area (Kuibyshev Reservoir, Russia) using Sentinel-5P/TROPOMI remote sensing data. The spatial distribution of CH4 content across the study area was heterogeneous and exhibited a latitudinal dependence, with concentrations decreasing from south to north. Seasonal variability in methane levels is observed, with the lowest values in spring and the highest in autumn. The average CH4 concentration over the period 2019–2023 was 1860±13 ppb. Additionally, a consistent trend of increasing annual methane background levels has been observed (up to 1878±11 ppb in 2023). Differences in methane levels are noted across different land cover types, with higher values typically observed above anthropogenically transformed landscapes, while minimal ones are found over extensive forested areas and the waters of the Kuibyshev Reservoir.

This study explores the dynamic response of vegetation productivity in semi-arid foothill rangelands of Uzbekistan, focusing on the Normalized Difference Vegetation Index (NDVI) dynamics and its correlation with climatic and environmental variables. In contrast to established relationships reported in previous studies, our findings reveal distinctive seasonal patterns in NDVI values, with higher values observed during spring (0.150-0.300) and lower values in summer (0.100-0.130). The chlorophyll and carotenoid content of A. diffusa exhibited variations in response to seasonal changes, emphasizing the importance of considering seasonal factors in rangeland health assessments. Chlorophyll and green aboveground biomass are identified as key indicators of vegetation productivity, crucial for maintaining ecosystem balance and contributing to climate regulation. Significant relationships are observed between NDVI and water resources, including precipitation and soil moisture (P ≤ 0.0001), highlighting the impact of climatic factors on vegetation cover. Positive correlations between NDVI and total green aboveground biomass underscore the relevance of NDVI as an indicator of vegetation health in foothill rangelands. This research project represents an initial step in investigating vegetation changes due to grazing in Uzbekistan, providing recommendations for sustainable rangeland utilization. Establishing an ecological monitoring system, incorporating GIS and Remote Sensing technologies, is proposed for comprehensive understanding and sustainable management of this complex ecosystem. Ongoing and future research endeavors aim to develop modern methods for rangeland vegetation monitoring, facilitating the identification of critical regions and offering solutions to prevent and mitigate desertification effects.

The article discusses the possibility of using artificial intelligence and machine learning methods to predict soil fertility based on remote sensing data. The research object was 70 soil samples taken in the village of Vshchizh, Bryansk region. Multispectral imaging of the earth's surface from the Sentinel-2 satellite was also used to assess the soil condition. The study in this article is based on the indicators of humus (%), P2O5 (mg/kg), and K2O (mg/kg). The prediction is done using a neural network model based on the Rosenblatt perceptron. Data analysis is conducted using the statistical software environment RStudio. The results of the model show the values of the total mean square error (MSE): MSE=0.178 for humus prediction, MSE=0.138 for P2O5 prediction, MSE=0.171 for K2O prediction. Additionally, the program calculated the correlation values between the predicted and calculated soil fertility. K(humus)=0.548, K(P2O5)=0.287, K(K2O)=0.244. Thus, the neural network most accurately predicted soil fertility based on the humus content.

Degradation presents a significant challenge, particularly exacerbated by current climate change. Our study focuses on the Lower Ziz watershed, located in southeastern Morocco, which is characterized by a semi-desert climate and is at high risk of land degradation due to water and wind erosion. Natural factors such as precipitation intensity and frequency, slope inclinations, low vegetation cover density, and soil characteristics, combined with anthropogenic activities, contribute to increasing the erosion risk within this watershed. The use of unsustainable agricultural practices has also contributed to this growing vulnerability. To investigate this phenomenon, we adopted the PAP/CAR method, a qualitative approach for mapping and assessing vulnerability to water erosion in the watershed. This method integrates essential physical data such as topography, lithology, soil types, and vegetation cover, facilitating a comprehensive analysis of erosion risk. Through the use of remote sensing tools and geographic information systems (GIS), we have generated detailed thematic maps, streamlined the mapping process, and improved data management. The findings of this research provide valuable insights for sustainable land and natural resource management in the Lower Ziz watershed and can aid in the formulation of prevention and mitigation strategies for water erosion in similar contexts globally. Our predictive mapping approach indicates that 37.34% of the watershed exhibits low to very low susceptibility to water erosion, while 55.96% experiences moderate erosion, with the remaining 6.7% being highly erodible. Additionally, our descriptive analysis highlights the prevalence of gully and surface gullying erosion, encompassing 49.32% of the area, as well as deep gully erosion, affecting 41.89%.”

Lithological mapping is a crucial component of geological analysis, providing valuable insights into a region's mineralization potential and aiding mineral prospecting efforts. Manual execution of this task, especially in remote and resource-intensive areas, poses significant challenges. The integration of artificial intelligence (AI) techniques with remotely sensed data offers a swift, cost-effective, and precise approach to lithological mapping. In this study, machine learning algorithms (SVM, RF, and ANN) and deep learning techniques (CNN) were employed to map lithological units in an area, half of which lacked any published geological map. The study area is situated in the Bab Boudir rural municipality within the Taza province, geologically located in the Meso-Cenozoic cover of the Tazzeka inlier and characterized by moderate vegetation. Furthermore, the study evaluated the effectiveness of two types of remote sensing data: multispectral data from Sentinel-2 and hyperspectral data from Hyperion. The results revealed that the SVM and CNN methods achieved the highest overall accuracy and kappa coefficient, followed by the RF classifier, while the ANN approach yielded lower accuracies.

The need to classify sentinel-2 satellite images to create land use /land cover (LULC) are essential to analysis the processes of environment problems and to improve living conditions. Hence, this research aims to assess of accuracy classification by Support Vector Machine (SVM) approach to create LULC maps from sentinel-2 satellite images using remote sensing and GIS. The selected study area for this research is Baghdad city because of it has a unique political stability and due to rapid urbanization that lead to rise additional request for natural resources and affected on LULC in Baghdad city. After preprocessing and processing of satellite images, thematic maps were created and classified into five main classes based on visual interpretation and visit the field of the study area containing: urban, vegetation, soil, asphalt roads, and water bodies. The results showed that classification accuracy assessment of SVM algorithm are acceptable because of overall accuracy and Kappa index equal (88%, 0.84) respectively.

Increased water runoff due to land use change phenomena has triggered flooding events. The objective is to identify flood potential in Simbang Sub-Watershed in Maros Regency using rational methods and remote sensing data. Potential flood hazards were analyzed using the weighting method with the parameters NDVI, MNDWI, NDSI, rainfall, and annual flow coefficient. The highest debit of runoff occurred in 2017, with a value of 113.36 m3/s, while the lowest occurred in 2019, with a value of 63.91 m3/s. The NDVI value is 0.37–1 with high vegetation covering an area of 3,089 ha, while the low-very low vegetation has value -0.03–0.25 with an area of 1,668 ha. The MNDWI value ranges from 0-0.33 with a moderate wetness level covering an area of 741 ha and an NDSI value ranging from -0.06–0.43 for bare land surrounding an area of 738 ha, which has an impact on reducing water catchment areas which can trigger an increase in surface water runoff discharge. The average rainfall is 2,965 mm/year, the area with low potential for flooding is 3,705 ha, and the area prone (moderate) to flooding is 1,450 ha. The rainfall factor is the main priority trigger for flood events with weight of 0.266, and the soil index is the lowest priority factor with weight of 0.145. Surface water runoff in the Simbang Sub-Watershed area makes a small contribution to the flood events that occurred in Maros Regency with an annual flow coefficient value of 0.23.

This study aim to determine motorcycle exhaust emissions and emission factors on urban roads using unintrusive roadside remote sensing equipment, specifically the ETC M752 type version 3.1 from 2020. The research gathered 1012 valid and identified vehicle samples from five locations, comprising four in Jakarta and one in Bandung. The results unveiled median motorcycle emissions: CO2 (13.90%), CO (1.42%), smoke (0%), HC (1499 ppm), and NOx (2447 ppm). These measurements were recorded at an average speed of 21.81 km/h with an average acceleration of 0.05 m/s². Meanwhile the emission factors determined from this study were: 46.50 g/km, 1.95 g/km, 0 g/km, 0.22 g/km, and 0.46 g/km for CO2, CO, soot, HC, and NOx, respectively. An increase in the production year of motorcycles led to a significant reduction in exhaust emissions and emission factors. Across all production year categories, CO and HC emissions remained within acceptable thresholds. CO and HC emission factors already meet Euro 3 standards, while the NOx emission factor still does not meet the standard. Enhancing engine cylinder capacity resulted in a slight uptick in motorcycle exhaust emissions and emission factors. Specifically, the CO and HC emissions on Yamaha motorcycles are lower compared to Honda, while the NOx emissions on Yamaha motorcycles are higher compared to Honda. However, both brands have met Euro 3 standards for CO and HC but not for NOx.

Currently, forest enterprises are characterized by depletion of forest resources and their significant remoteness. To solve a whole range of tasks related to forest management, monitoring of the environmental situation, control of the forest fire situation, remote sensing systems of the Earth have been developed and, to date, are successfully used. At the moment, such systems are located in almost every region, allowing dispatch centers to quickly perform their work. On the basis of the centers there are several operators who transmit information to the forestry and monitor a lot of video surveillance cameras in real time. Cameras help to detect fires and various smoke points much faster and more efficiently than satellite photography. However, the existing methods for detecting fires in forests have long been outdated and require the introduction of modern auxiliary engineering software solutions in this process. Research and development of systems that allow tracking fires that may be located far from human habitats, and their implementation in the work of forest protection enterprises is a top priority in the Ministries of Forestry.

Nitrate is an essential nutrient in phytoplankton's photosynthesis process. In addition, phytoplankton uses nitrate for their growth and reproduction. Nitrate abundance on the coast will affect primary productivity and biogeochemical cycles. The availability of nitrate observation data, especially around the Savu Sea coast, is minimal. In this study, the estimation of nitrate in the coastal area of the southern part of Sumba Island and the eastern part of Savu Island by using the generalized additive model (GAM). Seventy-one nitrate observation data were used to build the GAM model, and remote sensing data were used as input data for nitrate estimation. Sea Surface Temperature (SST) and chlorophyll-a data were obtained from Aqua-MODIS. Sea Surface Salinity (SSS) and Sea Surface Windspeed (SSW) data were obtained from a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) Soil Moisture-Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT), respectively. This study uses the Generalized Additive Model (GAM) approach to predict the distribution of nitrate concentrations and determine the main driving factors associated with nitrate. Based on the result, temperature is the dominant factor in nitrate estimation, while chlorophyll-a has a relatively small influence. The best model to predict nitrate distribution uses four parameters, namely SST, SSS, SSW, and chlorophyll-a. The validation results of the expected nitrate value obtained from the model with the observed nitrate value obtained results with the same value range of 0 - 0.35; the difference is the value of the distribution. From the comparison results, the R2 value is 0.357.

Forest fires in Turkiye are a frequently encountered natural disaster, especially in recent years. After a fire, identifying the plant species to be used in the area and determining the intensity of the fire in the region are important for assessing the area’s capacity for natural regeneration. In this process, geographic information systems and remote sensing methods and techniques are commonly used tools for assessing burned areas and fire intensities following forest fires. In this study, one of Turkiye’s devastating fires, the Marmaris fire, which began on June 21, 2022, is examined. Landsat satellite images, geometrically and radiometrically corrected, were utilized to determine the intensity of the Marmaris fire. As part of the method, the NBR (Normalized Burn Ratio), dNBR (Difference Normalized Burn Ratio), NDVI (Normalized Difference Vegetation Index), and dNDVI (Difference Normalized Difference Vegetation Index) indices were used for burned area detection. As a result of the study, information about the burned areas, fire intensities, and the regenerative capabilities of post-fire vegetation growth has been obtained. Furthermore, suitable pioneer plant species, considering the fire intensity, have been suggested for the restoration of burned landscape areas after the fire. This study is expected to serve as a guide for relevant public institutions and organizations, particularly the General Directorates of Forestry, enabling them to respond most effectively after fires and implement reforestation efforts considering the usage areas and growth capabilities of needle-leaved and broad-leaved plants.

Actual evapotranspiration (ETa) is essential data in developing water budgets and calculating irrigation water requirements in large-scale irrigation schemes. Nowadays, remote sensing (RS)-based surface energy balance models help us estimate ETa with high resolution when compared to direct methods that focus only on a single point in a field. This study aimed both at estimating ETa by Mapping EvapoTranspiration at high Resolution with Internal Calibration (METRIC) and Google Earth Engine Evapotranspiration Flux (EEFlux) platform and comparing reference evapotranspiration based on the Penman-Monteith equation (ETo) with ETa by the METRIC and EEFlux in a sub-catchment (A=9495 ha) under irrigation, located in the Lower Seyhan Plain (LSP), Turkiye in the 2020 hydrological year. For this purpose, 16 Landsat 7 and Landsat 8 images, local climatic data acquired from two meteorological stations in the catchment, and CFSv2 gridded global data were used. Results showed a good agreement between ETa-EEFlux with ETa-METRIC. Moreover, a strong correlation was found between ETo and ETa-METRIC (r=0.93 and slope close to 1 and RMSE value of 0.74 mm day-1) if compared to the relationship between ETo with ETa-EEFlux. The results show the potential of applying the METRIC model and EEFlux for mapping ETa over a large-scale irrigation scheme.

The dynamic development of modern cities requires new solutions to urban planning and management by local regional authorities. The paper focuses on ecological indicators based on Earth Remote Sensing Data (ERSD) of the snow cover with the purpose to evaluate the city and to plan ecological environment protection strategy. The paper deals with the method of using space images to assess the snow cover pollution of Chelyabinsk, a large Russian industrial city. The assessment of the snow cover of Chelyabinsk was carried out by comparing the heavy metals concentrations with the Landsat 8 data. The spectral indices were calculated for fourteen sites evenly distributed over the urban area of four types: courtyards, car parks, industrial zones and roads. We found a statistically significant difference between the Swirl/Green index and the site type and a correlation with the concentrations of dissolved and suspended forms of heavy metals in snow cover. Snow cover indices can be used as ecological indicators of urban environment.

This document is produced as part of a project of mapping, monitoring and prediction of urban sprawl in the city of Rabat, using algorithms of machine Learning, remote sensing and geographic information system. It therefore aims to determine and monitor the urban sprawl of the city of Rabat, using new Geo-spatial technologies, this socio-economic mechanism is theoretically based on the increase in the horizontal area of the city, and a decrease in population density. On the contrary, the city of Rabat is characterized by a negative growth rate between the years 2004-2014 at about -0.79% different from 1.2% at the national level, and defined by a higher population density of 255 inhabitants per km2, these statistics were directed to a hypothesis that indicates a horizontal confinement of the city of Rabat and reverse immigration to per-urban territories. In addition, this project is mainly aimed at the implementation of a computerized simulation model for the characterization and prediction of the urban expansion of the city of Rabat for 40 years (1990, 2000, 2010, 2020, 2030). To conclude, the city of Rabat has experienced a very slow urban sprawl, thanks to the green belt put in 1971, and the modernization strategy that has been adapted in recent years to eliminate informal housing, in addition to the development of roads and the new TGV railway line, and also to the lack of land use, of which many large villas of several hectares were built. Although this mechanism is moving very slowly, the city of Rabat has had negative consequences on the territory and the environment. So, local authorities need a creative, innovative and intelligent urban planning programme to both eliminate the effects of this mechanism, and build the pillars of the functional city (smart city).

Surface water plays a vital role in the living environment. The shrinking surface water area in urban areas is one of the main culprits causing serious environmental problems such as flooding, ecosystem imbalance, etc. In Vietnam, surface water area in the urban center is rapidly declining due to urbanization, especially in the capital - Hanoi, where the urbanization rate is highest. This study examines the mutation in surface water area in several central districts in Hanoi through the period of 1993-2020 using remote sensing images and GIS. The result shows that the surface water area in the research region decreased gradually over the years from 5881.2 hectares to 4328.7 hectares. This parameter dropped more steeply in recently established districts and those adjacencies namely Thanh Tri, Gia Lam, and Dong Anh than in central districts, Hoan Kiem and Hai Ba Trung for instance. Especially, In 2000 and 2007 the rate of reduction was highest. This finding is useful for urban planners, managers, and policymakers involved in Vietnam's sustainable urban development process.

The study describes a case study from an ecologically fragile region of Kerala, India, in which GIS and remote sensing techniques were used to identify groundwater potential zones (GWPZ). The study area has been adversely affected by pollution from various sources, and residents have become reliant on groundwater for their drinking water needs. Here, the Weighted Index Overlay Analysis method was used to identify the potential groundwater zones to assign ranking values to different classes of individual thematic maps based on their mediating and moderating influence on groundwater potentiality. Twelve thematic layers were used. The results of the GWPZ were validated by overlaying samples on the potential zones in the GIS environment. The study emphasizes the importance of GWPZ mapping for planning new abstraction well locations to meet increasing water demand and improve the awareness of groundwater availability for sustainable development.

The composition of melioration systems includes underground pipelines, drains, hydraulic structures that may fail and require repair, but it is impossible to detect a problem area without monitoring the area of the system by antediluvian methods or conducting survey work. The purpose of the work: to justify the use of soil moisture maps obtained by the DJI PHANTOM 4 MULTISPECTRA UAV to identify problem areas of the reclamation network or vice versa absence. Aerial photography was carried out in May 2021 on the treated section of the drainage and humidification network of the Yakhrom floodplain of the Moscow region. As a result of the survey, a digital terrain model, an operational humidity map based on the calculated NDWI water index were obtained. The flight altitude of the quadcopter was 180 m above the earth's surface, the pixel size of the image of the earth's surface to be 7.9 cm. According to the results of aerial photography on the selected site, the absolute marks of the earth's surface vary from 134.67 to 135.23. On average, the slope of the earth's surface of the selected site is 0.002 in the direction from northwest to southeast. There are no pronounced microforms of relief moisture in the surface layer of the soil obtained in the field corresponds to the variety of color areas of the NDWI water indices highlighted on the operational humidity map. In the soil conditions of the studied area, the moisture content of the surface soil layer is logarithmic with the normalized difference water index NDWI. The correlation coefficient is 0.9 ± 0.06, which indicates a very high relationship between the studied indicators. Such a map can be used to identify problem areas of the reclamation network.

The Temsamane massif is part of the external Rif of Morocco and is distinguished by more intense deformations, linked to its topographical, lithological and structural characteristics. Many natural hazard phenomena, and therefore damage and loss of resources and people, are present. The detailed work on these activities remains much localized using traditional techniques. The analysis using differential interferometry techniques of Synthetic Aperture Radar (SAR) images (DINSAR) made it possible to determine the instability and vulnerability to subsidence risks from two images from the Sentinel satellite of two different years (2018 and 2022). The results obtained show the presence of some areas of subsidence in the urban sector, the continuation of shrinkage and slight uplift in the relief areas, and generally instability in rural areas.

DEMNAS and Sentinel-1 are profoundly utilized for the small island morphometry analysis. This study explores the Satonda Island-West Nusa Tenggara landforms through the morphometry analysis using the DEM application and GIS analysis. As a volcanic island the Satonda Island coverage area is approximately about 2.600hectare dominated by the slopes with the steep slope (16o – 35o) at the northwest in a moderately steep (8° – 16°) at the southeast. Here, we uncover that the Satonda Island experiences five landforms carved by volcanic processes and other five are generated by the marine processes. As a typical of volcanic island, this island has a cone shape with the caldera founded in the middle with the radial flow pattern.

Banggai waters are a strategic area rich in fish resources and a source of livelihood for most of the local population. Mapping fish stocks is important to ensure the sustainability of fishing in the area. One of them uses hydroacoustic data and satellite imagery to provide more comprehensive results on fish stocks and more accurate predictions of potential fishing zones. This study will spatially estimate fish density in Banggai waters using hydroacoustic data and investigate the distribution of chlorophyll-a and sea surface temperature using Himawari-8 imagery. Hydroacoustic data processing will produce target strength (TS) and volume backscatter strength (Sv) values to determine the value and distribution of fish density. Meanwhile, ZPPI results are obtained from the overlay of chlorophyll-a and sea surface temperature. From the results of mapping the potential fishing zone, the high category has the highest percentage of 43%, low 28% and medium 29%. The ZPPI point distribution map then shows the concentration of ZPPI points in the southern Banggai waters. The fish density distribution map shows that the spatial distribution tends to be around the coast of Banggai Peninsula, dominated by small and very small fish categories. Correlation of fish density point distribution with ZPPI results and oceanographic parameters has a positive influence on fish density from GAM modelling.

Sundaland ecosystems are under threat from human activity and climate change such as logging, agricultural practices, overexploitation of wildlife and climatic change that have led to frequent forest fires and a decline in indigenous plant and animal species. This study investigates the risks to Sundaland's biodiversity as well as the management possibilities using GIS, RS, and AI. The goal was to find out how artificial intelligence (AI) can be applied to effectively manage biodiversity and expand on the body of knowledge already available about the useful roles that GIS and RS play in the area. In this systematic method, seven databases were used to gather data from 110 research publications, of which 101 were screened for scope and subject variable. 80% (81articles) of the examined studies collected data using GIS and RS. It is found that. AI in biodiversity management is poised to grow, offering new opportunities to address the intricate challenges facing our planet's diverse ecosystems. In conclusion, for efficient monitoring, well-informed policy creation, and decision-making to guarantee the long-term preservation of Sundaland's biodiversity, integration of GIS, RS, and AI is essential.

The watershed has many functions related to ecosystem protection. The existence of vegetation in the watershed can absorb some carbon from the atmosphere through photosynthesis. In fact, this carbon sequestration potential is declining due to vegetated land conversion phenomena. Therefore, monitoring carbon sequestration in this area needs to be done. However, it faces a problem due to the large size of the watershed area in a tropical region, so carbon sequestration cannot be measured by field-measurement. By combining RS and GIS, the carbon sequestration in a large watershed can be measured frequently to solve the problem. This research aims to analyze the dynamic change of carbon sequestration in Upper Bengawan Solo from 2000 until 2020. In this research, carbon sequestration was modelled using the Net Primary Productivity (NPP). NPP was measured by using the CASA method used Landsat Imagery and Meteorological Data from Meteorological Agency of Indonesia (BMKG). The results show that the carbon sequestration in Upper Bengawan Solo over 20 years decreased. The most significant decrease happens in the sub-urban area surrounding the urban area in Surakarta city. It indicates an environmental change in this watershed due to physical or human interference during this period. Meanwhile, by using RS and GIS, the spatial distribution of carbon sequestration change can be known, so it can be used to pinpoint the location which needs prior attention based on the higher level of these changes. This research implies that combining RS and GIS can help carbon sequestration monitoring be quicker and cost-limited.

The Moroccan coastline, in particular the Atlantic coast, presents a unique geographical and environmental context that requires special attention. Beaches in this region are subject to natural and anthropogenic forces that influence their dynamic evolution. This study focuses on the in-depth analysis and monitoring of Anza beach in Agadir, Morocco. The aim is to provide an in-depth understanding of the evolution of the Anza coastline. Shoreline analysis quantifies shoreline variations and changes over time using the Digital Shoreline Analysis System tool (DSAS). This analysis provides important information on the erosion and accumulation processes that influence the stability of Anza beach.

Methane (CH4) is the second most prevalent greenhouse gas after carbon dioxide. Its concentration in the atmosphere has been increasing at an accelerated rate in recent years, primarily attributed to anthropogenic activities. The article provides an assessment of methane concentrations over the Republic of Tatarstan (Russia) territory for a five-year period (2019–2023) using TROPOMI/Sentinel-5P data. Access to the data and key operations were conducted through the Google Earth Engine cloud platform. On average, the methane concentration was 1835±9 parts per billion (ppb) in 2019, 1854±12 ppb in 2020, 1863±9 ppb in 2021, 1868±10 ppb in 2022, and 1877±8 ppb in 2023, respectively. The average CH4 concentration for the study period (2019–2023) was 1865±7 ppb. There is a steady trend of increasing annual background methane levels.

The ecology and all of its components are suffering greatly as a result of the unchecked speed of development. At this rate, environmental degradation will have an impact on humanity and associated fields. In order to prevent the consequences of expansion from pushing the environment into a situation from which it is incapable of recovering, there should be ongoing, earnest efforts made towards sustainable development. The three pillars of ecodevelopment are the environment, humanity, and economy. A stable growth rate is necessary to attain a just balance between these pillars. Since agriculture employs the majority of the population, it also has an impact on the balance of the ecosystem. Because every unplanned step towards progress puts us back in the environmental front, we must thus be mindful of the boundaries of environmental challenges in order to achieve equitable economic growth. The hope for sustainable development lies in decreased deforestation, greater food security, conservative agricultural practices, the use of biopesticides, and prudent use of natural resources. To be effective, environmental policy probably needs to employ a variety of tools, each addressing a distinct aspect of the issue and attempting to minimise redundancies and pointless regulations. Appropriately pricing environmental inputs facilitates sustainable resource provision and use management. Long-term corporate investment in new technology and innovation is encouraged by a consistent and clear environmental policy, which also increases investment certainty. Environmental and economic success are interdependent. Economic activity and advancement depend on the natural environment because it provides the resources needed to produce goods and services and because it processes and absorbs waste and pollution, which are unwanted byproducts. This paper focuses on how environmental assets assist control risks associated with social and economic activities, flood risks, local climate regulation (temperature and air quality), and the availability of clean water and other resources.

Indonesia faced several challenges regarding water quality such as water exploitation and contamination caused by human activities. Comprehensive and sustainable water management is required to ensure its availability for the society. Ecosystem quality monitoring is needed to make sure the availability of water resource all year round by using modelling to assist. This paper presented application of Artificial Neural Network (ANN) utilizing multilayer perception model with a backpropagation algorithm to predict water clarity in Saguling Reservoir provided by PT Indonesia Power. ANN performance of predicting water clarity level were evaluated using regression analysis (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE). Based on the results, prediction data during rainy season shows better performance than dry season with R2 value of 0.94, MAE value of 0.035, and MSE value of 0.0032 meanwhile dry season data of R2, MAE, and MSE are 0.83, 0.041, and 0.0045 respectively. ANN prediction model demonstrated a relatively good prediction capability of water clarity and may be used as one of references in classifying the water quality level of Saguling Reservoir quantity and quality integrated maintenance.

The paper presents the results of application of the method of automatic generation of representative and unbiased set for in-season cotton crop mapping, based on crop simulation model, previously parameterized using ground truth and satellite data. The method provided confident mapping of cotton fields without using actual ground-truth information or apriori information about their in-season phenology. Overall mapping accuracy calculated using relevant ground truth data for cotton fields has reached 95.6 %. Consideration of time series of NDVI values as a model of phase characteristics allowed using relatively simple criteria to identify typical representatives of the selected crop on the basis of analysis of their seasonal phenology and made it possible to build a reference sample for modeling and further classification.

With the development of space science and technology, various resource monitoring environmental satellites provide multi-platform, multi-spectral, multi-temporal and wide-range real-time information for the study of surface dynamic changes, and remote sensing technology has become a powerful technical means for human to study the earth’s resources and environment, while high-resolution and hyperspectral remote sensing has become the main source for fruit tree growth monitoring and fruit quality detection acquisition. This paper has the following aspects to introduce the current situation of application of high-resolution and hyperspectral remote sensing data.

Remote sensing technology and geographic information systems allow the assessment of terrestrial carbon stocks in large areas. The article considers the procedure of remote monitoring organization in the forest and water parts of the Volga Carbon polygon (Republic of Tatarstan, Russia). The main purpose of the article is to present the structure of remote monitoring of the polygon, show the results of the first year of remote research, and discuss the need to use other remote sensing methods. This study reviews and highlights the advantages and limitations of various remote sensing methods and sensors, including optical, multispectral, radar, and lidar, which are widely used for above-ground biomass (AGB) and carbon stocks (CS) estimation. The first results of field studies by unmanned aerial vehicle (UAV) at the Volga Carbon polygon are presented: orthophotomaps, digital terrain and relief models, bathymetric map and multispectral image time-series. Different remote sensing methods from echolocation to the multispectral survey are used, and the most optimal remote sensing data and processing methods are determined. For the first time for the Republic of Tatarstan, an integrated carbon balance monitoring system has been developed.

The research conducted in Kelantan focused on analysing the distribution of oil palm plantations using remote sensing data and ArcGIS, a Geographic Information System (GIS) platform. The demand for accurate and up-to-date information on oil palm plantations has been increasing due to advancements in technology and the need for effective management of the environment. The study aimed to compare the distribution of oil palm plantations in 2016 and 2021 by using vegetation analysis techniques such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI). Remote sensing data from Landsat 8, specifically Bands 5, 4, and 2, were utilized to derive these vegetation indices. Ground-truthing data, obtained through GPS coordinates, were employed to increase the accuracy of the analysis. The expansion of oil palm plantations and non-oil palm areas was assessed using the Supervised Classification Maximum Likelihood method. The distribution data of oil palm plantations is highly sought after by oil palm plantation companies and serves public and private purposes, contributing to environmental monitoring and promoting sustainable practices.