Applied Geomatics最新文献

The upstream area of the watershed has high rainfall, resulting in large volumes of runoff and peak discharge. The runoff discharge causes soil erosion, transporting soil particles by the flow and eventually settling as sedimentation. This sedimentation leads to river siltation and narrowing. Additionally, high-flow discharge causes turbulence and flooding. This research aimed to predict sedimentation rates due to land erosion in the watershed using the GIS-based Modified Universal Soil Loss Equation (MUSLE). The study was conducted in the Krueng Peuto sub-watershed in Indonesia. The interphase modeling revealed that the highest land sedimentation rate in the Krueng Peuto watershed occurred in 2015, with the sediment of 40,503.10 Mg.y⁻¹, while the least was in 2013, with 2,006.52 Mg.y⁻¹ of sediment. The results indicate that surface runoff has the most significant influence on land sedimentation. The rate of soil loss is closely related to land conservation practices, with poorly vegetated land contributing the most to surface runoff. Water flow velocity and its destructive power erode the soil into tiny grains, transported and deposited as sedimentation in the river. The MUSLE’s capability in identifying erosion-prone areas and predicting sediment yield based on rainfall events is crucial for effective sediment management planning. Implementing long-term land conservation measures is essential to preserve land capacity effectively.

The research problem of the article is to devise a universal mathematical procedure for calculating point coordinates from typical planar surveying measurements. The proposed solution involves calculating the intersection points of two circles with radii equal to the measured distances (the distance-distance intersection problem). The author demonstrates a straightforward method for reducing every typical surveying problem to the distance-distance intersection form. The procedure also verifies the accuracy of the calculated coordinates. The derived equations were tested numerically using practical examples. The devised procedure will be integrated into an exhaustive numerical algorithm for diverse surveying problems regardless of the geometric approach during measurements.

A drainage analysis of Karanja River has been carried out using geospatial technique. These methods are considered effective for extracting river basin and their drainage networks. The drainage network extracted was categorized using Strahler’s classification system, revealing a dendritic drainage pattern in the basin. Consequently, the study concludes that remote sensing data, particularly SRTM–DEM data with a 90 m resolution, combined with geoprocessing techniques, serve as an effective tool for conducting morphometric analysis and evaluating linear, areal, relief, geometric, morpho-tectonics and social aspects of morphometric parameters. The Karanja basin covers an area of 2959 km², with the high surface rock permeability, low surface runoff, high infiltration rate, and low erodibility. The main stream length ratio of the basin is 36.29, suggesting that increasing trend between the highest and the lowest stream. The watercourses are elongated due to the shallow relief, resulting in a lower peak flow and a longer flow duration. The basin exhibits a gentle slope, minimal runoff potential, and mature stage of landform evolution. The asymmetric factor indicates the north-eastern shift of the channel. GIS-based analysis of all morphometric parameters, along with the erosional development of the area by the streams, indicates that the landscape has progressed well beyond maturity, with lithology playing a key role in shaping the drainage patterns. Advanced geospatial technology can be applied to geo-hydrological research systems in environmental management, watershed management and land system management, etc. with the future hydrological prospects of the area.

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.

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.

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.

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.