Applied Geomatics最新文献

Monitoring the amount of green space in urban areas is important for ensuring sustainable development and proper management. The study analyzed changes in urban green space coverage over the past 20 years in two rapidly urbanizing cities in Ethiopia, Debre Berhan and Debre Markos. The researchers used Landsat 5 and 8 data with a spatial resolution of 30 m to determine different land use and land cover classes, including urban green spaces, barren and croplands, built-up areas, and water bodies. The classification accuracy ranged between 90% and 91.4%, with a Kappa Statistic of 0.85 to 0.88. The results showed that both cities experienced significant decreases in vegetation cover in their urban cores between 2000 and 2020, with radical changes observed from green spaces and croplands to built-up areas. In Debre Berhan, barren and croplands decreased by 32.96%, while built-up and green spaces increased by 357.9% and 37.4%, respectively, in 2020. In Debre Markos, built-up areas increased by 224.2%, while green spaces and barren and croplands decreased by 41% and 5.71%, respectively. Overall, rapid urbanization threatens green spaces and agricultural areas, highlighting the need for ecological-based spatial planning in rapidly urbanizing cities.

The Eastern Indonesian Seas are among the most biodiverse maritime habitats. Changing chlorophyll-a concentrations affects primary productivity, and ecological changes. Monitoring chlorophyll levels is crucial for ocean health and nutrient availability. High-resolution ocean color data from the Sentinel-3 Ocean and Land Color Instrument allows for global chlorophyll monitoring. This study analyzes how monsoon activity affects chlorophyll distribution in eastern Indonesian oceans. Monthly Chlorophyll-a Concentration Retrieval with Sentinel-3 Ocean and Land Color Instrument Imageries was utilized to study the Eastern Indonesian Seas region from 2016–2021. The Case-2 Regional Coast Color processor, a neural network-based algorithm, was applied to all images for atmospheric correction processing and for ocean color products’ extraction. The distribution of chlorophyll-a in the eastern region of Indonesia varies significantly, with average concentrations ranging from 0.09 to 0.45 mg/m3 in the Banda Sea, Arafura Sea, Flores Sea, and Timor Sea. The Asian-Australian Monsoon System significantly impacts these patterns, with chlorophyll-a levels increasing during the Southeast Monsoon and decreasing during the Northwest Monsoon, particularly in areas with annual upwelling events.

Grasslands and savannas are experiencing transformation and degradation due to bush encroachment (BE). BE has been monitored using restrictive traditional techniques that include field surveys and manual long-term observations. Owing to the limitations of traditional techniques, remote sensing (RS) is an attractive alternative to assess BE because of its generally high precision and return interval, cost-effectiveness, and availability of historical data archives. Furthermore, RS has an added advantage in its ability of acquiring global coherent data in near-real time compared to the snapshot acquisition mode with traditional surveying techniques. Despite its extensive application and vast possibilities, a critical synthesis for RS successes, shortcomings, and best practices in mapping BE in savannas and grasslands is lacking. Thus, broadly, the direction, which this type of investigation has taken over the years is largely unknown. This study sought to connect and measure the progress RS has made in mapping BE in grassland and savanna ecosystems through bibliometric analysis. One hundred and twenty-three peer-reviewed English written documents from the Web of Science and Scopus databases were evaluated. The study revealed 13.05% average annual publication growth, indicating that RS and BE mapping research in grasslands and savannas has been increasing over the survey period. Most published studies came from the USA, while the rest came from South Africa, China, and Australia. The results indicate that BE has been extensively mapped in grasslands and savannas using coarse to medium resolution data. As a result, there is a weak relationship (r² = 0.324) between the dependent variable (aerial images) and the independent variable (percentage of woody cover). This connotes the need to improve BE assessments in grasslands and savannas by integrating recent high-resolution data, machine learning algorithms and artificial intelligence.

The watermelon cultivation industry in Morocco's arid desert regions has experienced swift expansion due to increasing demand both nationally and globally. Nevertheless, this growth has led to the depletion of the already scarce groundwater resources, necessitating a paradigm shift in water resource management. This study adopts an integrated approach, leveraging field measurements, laser diffraction for soil particle size analysis, GIS mapping, and remote sensing, to pinpoint optimal sites for rainwater harvesting (RWH). A comprehensive methodology involving Soil Conservation Service Curve Number (SCS CN), and various conditioning criteria layers (Rainfall, Land Use and Land Cover, Geomorphology, Slope, Topographic Wetness Index, Infiltration number, and Aspect) was applied. The Analytic Hierarchy Process (AHP) assigned weights to criteria, and a Weighted Linear Combination (WLC) approach in GIS produced an RWH suitability map. The map, classified into four zones (unsuitable, low, moderate, and high cover), showed promising potential for 5.24% of the study area. Field data validation after significant rain events confirmed an 86 percent overall map accuracy. Eight recommended RWH sites, including GPS coordinates, are proposed for decision-makers to facilitate strategic implementation, ensuring sustainable water availability for both drinking and irrigation in this arid region.

Travelling from a source to destination is always time-consuming but with the advent of remote sensing and Geographical Information Systems (GIS), it has turned to be quite beneficial to the commutators. Location based services gives the various aspects of the geospatial data. This includes dynamic maps during navigation, finding optimum path, network analysis, etc. The tourists should have thorough information of the tourist places and the available routes for the journey. With shortest path algorithm, the time and fuel can be saved for that vehicle. The proposed methodology focuses on route planning for the holy city, Haridwar and further journey. The cost attribute is considered in terms of time and distance to determine the optimum path between the tourist places. The results predicts that optimum route will save time and distance and will cover maximum tourist places in a single day. The analysis will be beneficial for the tourist planning to visit Haridwar and further journey.

The Himalayan glaciers are extremely susceptible to global climate change, leading to substantial glacial retreat, the creation and expansion of glacial lakes, and a rise in GLOFs. These alterations have changed river flow patterns and moved glaciers' borders, resulting in significant socioeconomic damage. Accurately monitoring glacial lakes is essential for managing GLOF events and evaluating the effects of climate change on the cryosphere. This study utilizes a Deep Learning-based U-net technique to extract glacial lakes from Landsat-8 satellite imagery by propagating characteristics and minimizing information loss. The method improves the importance given to glacial lakes, reduces the influence of low contrast, and handles different pixel categories. We applied this methodology to the Chandra-Bhaga basin, Himachal Pradesh, located in NW Indian Himalaya, and successfully extracted 107 glacial lakes. The U-net model attains an accuracy of 97.32%, precision of 95.98%, recall of 95.23%, MSE 0.0043, Kappa Coefficient 97.43% and an IoU of 97.45% during validation with high-resolution photos from Google Earth and a digital elevation model. The suggested approach could be beneficial for precise and effective monitoring of glacial lakes in different areas, assisting in the management of natural disasters and offering vital information on the effects of climate change on the cryosphere.

Jordan is severely affected by climate change, it suffers from significance fluctuation and decrease in the amounts of the annual precipitation basically during the last decade which had dire consequences for farmers and the provision of fresh water. In this study, the impact of climate change on the Al-Wala basin was analyzed during the period 2013 to 2024 using Geomatics techniques, Google Earth Engine (GEE) and machine learning codes. Soil and Water Assessment Tool (SWAT) model was used to simulate the hydrological process up to year 2064. Moreover, the Meteorological Research Institute Earth System Model (MRI-ESM2-0) was used to predict the change of water surface area of the Al-Wala dam lake in the future. Annual satellite images: Lanadsat and sentinel, covering the period of the study area were downloaded and enhanced. They permit to provide the necessary information to carry out this study. As result, an important fluctuation of the amount of annual rainfall quantity was observed as well as, the amounts of annual rainfall expected to increase and decrease wobbly for several years in the future. Overall the average annual runoff will increase by 10% compared to the baseline scenario. The minimum temperature is expected to be higher than their rates throughout the year by 0.09°- 0.11^o C, this will increase the evaporation rates with about 0.03%. The analysis of the sensitivity using the SWAT model was identified by 6 parameters out of 17. The regression coefficient (R²), Nash and Sutcliffe efficiency (NSE), on monthly basis, were above 0.60 for both of them which indicates satisfactory model results.

In recent years, statistical methods have been developed that include spatial considerations, for example, those that incorporate data with georeferencing. The descriptive part of geographical information systems currently provides many visualization and analysis tools; however, in terms of analysis, these systems are still quite limited, therefore, ignorance of these limitations may result in data with spatial effects being treated with conventional statistical methods for non-spatial use, which can certainly invalidate the excellent work of data capture with advanced tools such as those that are used daily in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case, it is associated with the lag or spatial overlap. The methodology is easy to apply in other designs with the development of the design matrix, its reparameterization and the choice of the respective weight matrix. This may cause users to reconsider the traditional method of analysis and incorporate some appropriate analysis methodology to address spatial effects present in data or in outputs from the modeling process.

Floods in Iran lead to significant human and financial losses annually. Identifying flood-prone regions is imperative to minimize these damages. This study aims to pinpoint flood-susceptible areas in the Great Karun Plain using remote sensing data, Google Earth Engine (GEE), and machine learning techniques. For the analysis, Landsat 8 data from April 8, 2019, and multiple variables including actual evapotranspiration, aspect, soil bulk density, clay content, climate water deficit, elevation, NDVI, land cover, Palmer Drought Severity Index, reference evapotranspiration, precipitation accumulation, sand content, soil moisture, minimum temperature, and maximum temperature were employed. These variables were utilized in the machine learning process to establish flood susceptibility zones. During the machine learning process, the base flow data of the Karun River was extracted from the Landsat image. A total of 19,335 samples were incorporated into the machine learning procedure using techniques such as MARS, CART, TreeNet, and RF. The model assessment criteria encompassed ROC, sensitivity, specificity, overall accuracy, F₁score and mean sensitivity. Results indicated that the TreeNet technique yielded the most promising outcomes among the machine learning algorithms with ROC values of 0.965 for test data. The characteristic criterion reached 91.2%, while the overall accuracy criterion stood at 91.12%. The model’s average sensitivity was 90.81%, and F1score was 63.51% for this technique. Additionally, analysis of the relative importance of independent variables highlighted that factors like vegetation cover (0.37), cumulative precipitation (0.23), soil water deficit (0.12), drought intensity (0.12), and landscapes (0.1) exerted a more pronounced influence on flooded areas compared to other variables.