Applied Geomatics最新文献

Globally, vanilla annual consumption has increased, yet its production is almost constant across all areas. In Uganda, it faces significant challenges due to inadequate spatial knowledge of ideal soil conditions and climatic factors. Prior studies have centered on biophysical factors like soil texture, elevation, and land use in identifying suitable areas. In previous research, climate and soil chemical properties have not been included in the biophysical assessment of land. This study incorporates them in evaluating land areas suitable for vanilla growing, particularly in eastern Uganda with low vanilla production. Land suitability assessment (LSA) was made using climatic conditions, topography, and soil chemical and physical properties integrated with multicriteria decision-making (MCDM). Climatic data such as temperature was obtained from the climatic research unit (CRU) for a ten-year average period (2014–2023), and rainfall data for ten years (2014–2023) was obtained from climate hazards infrared precipitation (CHIRPS). Topographical data, such as the digital elevation model, was obtained from the United States Geological Survey (USGS). Soil's physical and chemical properties were obtained from Food and Agriculture Organization (FAO) data. Thematic maps for each parameter were developed using ArcGIS 10.8.2. Each parameter was reclassified; a weighted sum overlay was implemented to investigate the final land suitability assessment map. Ten composite soil samples were taken from randomly sampled areas of the case study and sent to the laboratory for standard soil tests on texture and pH to validate the suitability model. This showed a strong positive correlation between the model data and the observed sampled data. The highly suitable category covered 0.000425%, very suitable—77.487%, suitable—17.6159%, moderately suitable—4.5909%, marginally suitable—0.3161%, and not suitable—0%. Most of the land in the study area is in a very suitable category, which shows that the region can become a significant player in the vanilla industry. However, parts with moderately and marginally suitable categories need intensive land management activities to increase land quality for better vanilla yields. Therefore, LSA is recommended before a land utilization decision has to be made.

The purpose of this work is to evaluate the use of Fréchet Distance as a new method of analyzing positional accuracy in linear features. The Fréchet Distance considers the order of the vertices as well as line orientation, graphically demonstrating the behavior between them along the entire path. The graphic resource that makes this analysis possible is entitled free-space diagram, consisting of a system of two-dimensional coordinates that present the interactions throughout ellipses. The discrepancies obtained by the Fréchet Distance were compared with the values found by the Epsilon Band, Hausdorff Distance, Vertex Influence, Buffer, and Buffer Overlap Statistics methods. The experiment is composed of simulated data, which were created to investigate some effects, such as systematic displacements, positional differences in the initial and final vertices of the lines, presence of outliers and scale uncertainties, and also composed of real data. The results portrayed the sensitivity of the Fréchet Distance in relation to the outliers, in addition to proving the variations that occurred in the discrepancies when there was a change in the direction of the lines. At the end of this study, it was possible to propose a new methodology for applying the Fréchet Distance in the analysis of positional accuracy using linear features.

The primary goal of the study is to employ Synthetic Aperture Radar (SAR) data and efficacy data driven approaches in modeling Surface Soil Moisture (SSM) of cultivable marginal bare fields. Three experimental test fields were selected which are basically cultivable but due water deficiency the fields are left bare. Samples for surface soil moisture, soil surface roughness and bulk density are collected from test fields in grid sampling manner in parallel with SAR data pass over study area. Sentinel-1 A data is pre-processed and each field sampling grid backscattering energy values are obtained. Surface roughness, dielectric constant and backscattered energy were used as input features to model SSM using Random Forest Regression (RFR), Support Vector Regression (SVR) and Back Propagation Artificial Neural Network (BPANN).We observed that BPANN outperformed SVR and RF by accurately predicting soil moisture with RMSE = 0.077 m³m⁻³, bias = 0.013m³m⁻³, and R = 0.94.This study sheds light on small scale agricultural lands which are deficient of water to support crop growth.

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.