Applied Geomatics最新文献

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.

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.

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.

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.

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.