Applied Geomatics最新文献

{"title":"Predicting the spatiotemporal changes of an agriculturally vulnerable region of Bangladesh","authors":"Sayeda Laizu Aktar,&nbsp;Moon Islam,&nbsp;Afsana Haque","doi":"10.1007/s12518-024-00595-2","DOIUrl":"10.1007/s12518-024-00595-2","url":null,"abstract":"<div><p>Agricultural land, the primary factor of food production, is essential for ensuring food security. Land constraints have led policymakers to promote agricultural intensification to achieve higher production, which is no longer sustainable. In Bangladesh, the consistent decline of agricultural land at a regional scale is a rising concern for food security. This study intends to assess the spatiotemporal changes in agricultural lands concerning food security, including temporary cropland, permanent cropland, and fallow land. LANDSAT satellite imagery for 1995, 2010, and 2022 were categorized using a hybrid image classification method. However, the study limits to produce higher accuracy as compromised due to the spatial resolution of LANDSAT imagery. MLP-CA Markov chain model was used to predict the agricultural land for 2041 by employing driver variables. The study finds around 15% loss in agricultural land from 1995–2022 with significant losses (12%) between 2010–2022. The built-up area is doubled after each of the time periods. Temporary crop-producing lands are declining quickly and converted rapidly (around 30%) to built-up areas between 2010–2022. Notably, agricultural land near riverine zones rapidly converts to built-up areas, hinting at potential environmental consequences. The model predicts around 10% loss in agricultural land with a likely conversion around cities and riverine areas, driven by infrastructure development. Contradictory sectoral policies have driven such conversion without effective land use policy. Hence, the study implies formulating a physical plan and urbanization policy for growth control and management, as well as land zoning and master plan for protecting valuable agricultural land.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"1003 - 1023"},"PeriodicalIF":2.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new fuzzy location-based approach for fire station site selection in Tehran","authors":"Giti KhoshAmooz","doi":"10.1007/s12518-024-00597-0","DOIUrl":"10.1007/s12518-024-00597-0","url":null,"abstract":"<div><p>The growing population density would increase the demand for urban facilities. One of the most important kinds of these facilities is fire stations, whose duties include securing, preventing, and fighting fire. One of the most important problems with fire stations could be their inappropriate distribution and, therefore, the limitation of their service area. So, optimal site selection of fire stations is considered the main problem in this study. The goal of our study is to do site selection in Tehran, the capital of Iran, by considering seven criteria: farness from existing fire stations, closeness to main roads, closeness to high population density places, closeness to gas and fuel stations, closeness to historical, cultural and recreational sites (cinemas and museums), closeness to green spaces and closeness to evacuation places. These criteria maps were fuzzified with the help of a linear membership function. Then, they were overlaid with the Gamma fuzzy operation. Then the 5-minute service area of each station was computed and excluded from the decision space, and the places with high scores were determined as the best places to locate new fire stations. These places are located in five of Tehran’s regions. These regions are 14, 15, 16, 17 and 20.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"987 - 1001"},"PeriodicalIF":2.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical Hierarchy process and geospatial techniques for Delineation of Groundwater potential zones in Bundelkhand Craton Region, India","authors":"Mukesh Kumar,&nbsp;Pitam Singh,&nbsp;Priyamvada Singh","doi":"10.1007/s12518-024-00592-5","DOIUrl":"10.1007/s12518-024-00592-5","url":null,"abstract":"<div><p>Groundwater is considered to be the most reliable source of fresh water. Groundwater supplies are under grave danger due to a number of factors, including an increasing population, urbanization, and industry. Finding groundwater with reasonable precision is frequently a difficult task. In this work, the groundwater potential zones (GWPZs) in the Bundelkhand Craton region of India are mapped out with the help of analytical hierarchy processes (AHP) that are based on Geographic Information Systems (GIS) and remote sensing (RS) techniques. Various groundwater affecting elements has been generated with the remote sensing data in GIS environment. The AHP method was used to determine the weights that should be allocated to each affecting elements and their sub-features as well. In order to determine the GWPZs of the research region, each of these thematic layers was combined on top of the previous one after the appropriate weights were assigned. As a result, the GWPZs that were acquired were divided into five distinct classes, which were respectively designated as “very low”, “low”, “moderate”, “high”, and “very high” GWPZs. The findings of this study showed that \"very high\" GWPZ comprises 1.42% (380.55 km2), \"high\" GWPZ comprises 12.48% (3340.63 km2), \"moderate\" GWPZ comprises 67.83% (18152.1 km2), \"low\" GWPZ comprises 17.26 (4619.64 km2), and \"very low\" GWPZ comprises 1% (267.85 Km2) of the overall studied region. The result that was achieved is verified with the assistance of well discharge rate data. Overall, this research provides a technique to delineate groundwater potentiality, which will be very helpful for managing groundwater resources.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"973 - 986"},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landslide potential mapping applying maximum entropy to continuous change maps","authors":"Rocío Ramos-Bernal,&nbsp;René Vázquez-Jiménez,&nbsp;Wendy Romero Rojas","doi":"10.1007/s12518-024-00596-1","DOIUrl":"10.1007/s12518-024-00596-1","url":null,"abstract":"<div><p>Landslide mapping inventories are crucial for disaster prevention and risk mitigation. Remote sensing uses remote sensors that record data from the Earth’s surface encoded in digital images distributed in electromagnetic spectrum ranges, allowing us access to various types of information. This, in conjunction with appropriate spatial analysis and modeling techniques, allows us to monitor the phenomena, such as landslides, that put man-nature coupled systems at risk. This paper presents a practical alternative for integrating landslide inventories in the central area of the state of Guerrero in Mexico by using the maximum entropy model (MaxEnt), a machine learning algorithm oriented to the potential prediction of patterns using continuous change (CC) maps as input. These maps were obtained using the unsupervised change detection methods linear regression and difference applied to transformed images, the normalized difference vegetation index (NDVI), and principal component analysis (PCA). The selection of supplementary input data was made by using the jackknife test to assess the contribution of the main determinant factors of slope stability: lithology (L), angular slopes (AS), and terrain orientation (TO). Ground truth landslide samples were used for the algorithm training (2/3) and the accuracy assessment of the final inventory map (1/3). The landslide inventory map derived by combining the MaxEnt model, the thresholding by the secant method, and the discrimination of pixels with slope values less than 5° reveals a high accuracy and visual concordance with reality, reaching 3.0% and 3.5% in commission and omission errors, a Kappa concordance index of 93.37%, and an AUC of 0.75, indicating MaxEnt is a practical and efficient tool that allows for the rapid and accurate generation of reliable maps for the detection of landslides.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"951 - 971"},"PeriodicalIF":2.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensor fusion of robotic total station and inertial navigation system for 6DoF tracking applications","authors":"Tomas Thalmann,&nbsp;Hans Neuner","doi":"10.1007/s12518-024-00593-4","DOIUrl":"10.1007/s12518-024-00593-4","url":null,"abstract":"<div><p>This paper presents a novel approach for sensor fusion of robotic total station (RTS) and inertial navigation system (INS) to enable 6-degree-of-freedom (6DoF) pose estimation. Tight coupling of a spherical measurement model for RTS is developed, providing advantages over the traditional cartesian 3D-position measurement model, including supporting INS solution when distance measurements are unavailable and performing outlier detection in spherical observation space. Simulation studies demonstrate that replacing Global Navigation Satellite Systems (GNSS) with RTS for fusion with INS is beneficial in any environment (given line-of-sight (LOS) availability), even under ideal GNSS conditions. Furthermore, investigations on measurement models and failure identification over the entire range of RTS measurements reveal that the spherical model is advantageous over the cartesian model in certain regions. The developed methods are validated in a practical application for tilt compensation of an RTS pole, indicating a base 2D-RMSE of 3.8 mm for almost static and almost vertical poles, increasing with tilt and velocity.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"933 - 949"},"PeriodicalIF":2.3,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00593-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph theory applications for advanced geospatial modelling and decision-making","authors":"Surajit Ghosh,&nbsp;Archita Mallick,&nbsp;Anuva Chowdhury,&nbsp;Kounik De Sarkar,&nbsp;Jayesh Mukherjee","doi":"10.1007/s12518-024-00586-3","DOIUrl":"10.1007/s12518-024-00586-3","url":null,"abstract":"<div><p>Geospatial sciences (GS) include a wide range of applications, from environmental monitoring to infrastructure development, as well as location-based analysis and services. Notably, graph theory algorithms have emerged as indispensable tools in GS because of their capability to model and analyse spatial relationships efficiently. This article underscores the critical role of graph theory applications in addressing real-world geospatial challenges, emphasising their significance and potential for future innovations in advanced spatial analytics, including the digital twin concept. The analysis shows that researchers from 58 countries have contributed to exploring graph theory and its application over 37 years through more than 700 research articles. A comprehensive collection of case studies has been showcased to provide an overview of graph theory’s diverse and impactful applications in advanced geospatial research across various disciplines (transportation, urban planning, environmental management, ecology, disaster studies and many more) and their linkages to the United Nations Sustainable Development Goals (UN SDGs). Thus, the interdisciplinary nature of graph theory can foster an understanding of the association among different scientific domains for sustainable resource management and planning.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"799 - 812"},"PeriodicalIF":2.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precise position estimation methods for differential-IRNSS using iterative algorithm","authors":"Nageena Parveen Syed,&nbsp;Thaherbasha SK","doi":"10.1007/s12518-024-00588-1","DOIUrl":"10.1007/s12518-024-00588-1","url":null,"abstract":"<div><p>The Indian Regional Navigation Satellite System (IRNSS) is an elegant system for positioning of users in India. It also provides its services to an extended region. In the IRNSS, to find the unknown user location, the used pseudo range method gives only meter-level accuracy. In order to reduce the noise impacts on measurement, and to improve position accuracy (meter-level to centimeter-level) for IRNSS, in this paper, carrier phase (CP) measurement technique is employed. Here, CP-based differential techniques are proposed to find the unknown user/rover receiver position. The corresponding incremental position (positional error) is computed in both double difference (DD) and triple difference (TD) techniques (with and without filter). In this paper, the CP measurement-based differencing techniques (DD/TD) supported with recursive filter (Kalman) is analyzed. This type of methodology is mostly useful in surveying applications. With this approach, the errors are reduced greatly, and the obtained accuracy is in centimeters. The filter utilizes and projects the previous and current activity data to estimate the float response. In this work, an iterative algorithm is used to find the precise position of the rover. For a short baseline, the obtained positional errors are compared for DD and TD with/without recursive filter. To simulate the real-time data, MATLAB programming is used. The obtained root mean square error (RMSE) in position estimation in IRNSS is 2.3391 m and 0.6901 m with DD, and 0.1079 m and 0.0518 m with TD without and with filter respectively.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"923 - 931"},"PeriodicalIF":2.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban green space cover change analysis using GIS and remote sensing in two rapidly urbanized cities, Debre Berhan and Debre Markos, Ethiopia","authors":"Alemaw Kefale,&nbsp;Aramde Fetene,&nbsp;Hayal Desta","doi":"10.1007/s12518-024-00591-6","DOIUrl":"10.1007/s12518-024-00591-6","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"905 - 922"},"PeriodicalIF":2.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00591-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The spatial–temporal variability of chlorophyll-a across the eastern Indonesian seas region using sentinel-3 OLCI","authors":"Eko Yuli Handoko,&nbsp;Muhammad Aldila Syariz,&nbsp;Noorlaila Hayati,&nbsp;Megivareza Putri,&nbsp;Mukhammad Muryono,&nbsp;Chung-Yen Kuo","doi":"10.1007/s12518-024-00590-7","DOIUrl":"10.1007/s12518-024-00590-7","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"897 - 904"},"PeriodicalIF":2.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bibliometric analysis for remote sensing applications in bush encroachment mapping of grassland and savanna ecosystems","authors":"Siphokazi Ruth Gcayi,&nbsp;Samuel Adewale Adelabu,&nbsp;Lwandile Nduku,&nbsp;Johannes George Chirima","doi":"10.1007/s12518-024-00589-0","DOIUrl":"10.1007/s12518-024-00589-0","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"881 - 896"},"PeriodicalIF":2.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00589-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}