Applied Geomatics最新文献

Panorama photogrammetry, the process of analyzing panoramic images, has gained popularity in close-range photogrammetry for 3D reconstruction over the past decade. Initially, researchers utilized cylindrical or spherical panoramic images created through specialized cameras or conventional ones with rectilinear lenses. However, these methods were hindered by the high cost of panorama equipment and the need for manual reconstruction. Consequently, there's a growing demand for automated algorithms capable of reconstructing 3D point clouds from stitched panorama images. This study aims to provide a cost-effective solution for automatic 3D point cloud reconstruction from panoramas. The study is divided into two parts; it first outlines an image acquisition strategy for capturing overlapping perspective images to facilitate accurate panorama generation. Subsequently, it introduces an automated algorithm for 3D point cloud reconstruction from panorama images. The process utilizes the KAZE feature detector for feature detection and introduces a novel feature matching approach for matching panorama images. Accuracy assessment of the reconstructed 3D point clouds was done using three methods: Line Segment Based approach, yielding RMSE errors of 34.2mm and 39mm for dataset-1 and dataset-2 respectively, No-Reference 3D Point Cloud Quality Assessment, resulting in quality scores of 8.5939 and 7.4535 for dataset-1 and dataset-2 respectively, and M3C2 distance method computed value of 0.091059 and 0.165179 respectively, indicating high quality of the generated point clouds.

Soil erosion is a severe issue posing a number of adverse effects on the environment. It is a prominent hazard damaging the fertile agricultural land. Therefore, in this study, a spatio-temporal assessment of soil erosion was carried out in the Swat River Basin, Pakistan by employing the Revised Universal Soil Loss Equation (RUSLE). The parameters of the RUSLE model are rainfall erosivity, soil erodibility, slope length and steepness, land management and support practice. These factors were developed from monthly mean rainfall data obtained from the Regional Metrology Department Peshawar, FAO soil database, land use data prepared from Landsat-5 and 8 satellite imageries, topographic data obtained from the ALOS PALSAR Digital Elevation Model (DEM). The analysis discovered that 13% of the study area experienced severe erosion. Results of the spatial distribution and vulnerability to erosion within the Swat River Basin have been categorized into different zones such as very low (59.7%), low (19.5%), moderate (5.37%), high (6.86%), and very high (5.96%). These results accentuate the necessity for mitigation measures in the study area to mitigate the loss of valuable topsoil. This research possesses the potential to offer valuable insights into decision-making and planning to reduce the risk of erosion.

Land consolidation projects are fundamental tools that enable the reorganization of agricultural space to enhance agricultural productivity and improve quality of life in rural areas. However, the high costs associated with such projects necessitate ongoing refinement of their technical aspects, including cost reduction and shortened implementation time while maintaining the required accuracy parameters. This study aimed to assess the accuracy of digital orthomosaic creation obtained using UAVs from the perspective of the implementation of land consolidation projects. The research area is located in southern Poland (Przeginia village), and the data used for the study were obtained during the ongoing land consolidation project. The processing of the resulting images was performed with Structure from Motion algorithms using 103 adjustment points with known coordinates. An analysis performed using a set of 87 control points showed an average error in the position of points on a surface of 0.08 m in relation to control results carried out using the GNSS RTK technique. The observed maximum error value was 0.29 m. The analysis of the causes of the high value of observed errors indicates that they were the result of an incorrectly planned, too low number of control points and their uneven distribution across the study area.

Identification of potential lands on the basis of their environmental benefits and constraints can greatly contribute to the stability of canola production in different parts of the world including Sistan-Baluchestan province, Iran. Accordingly, using GIS, the scores of environmental factors affecting canola production including temperature, rainfall, slope, altitude, organic matter, soil salinity, pH and soil nutrients (N, P, K, Fe and Zn) were integrated along with the weights of analytical hierarchical process (AHP) for the production of canola suitability maps. The zoning maps of climate, topography, and soil as well as the canola suitability maps and the current production maps of canola were prepared. According to the AHP results canola cultivation was affected the most by climate (rainfall and water sources) compared with topography and soil. ArcGIS results indicated southern lands of Zahedan had the highest organic matter, and excluding Hirmand, other parts of the area had appropriate salinity for canola production. The most appropriate areas in terms of acidity for canola production are Hirmand and the central part of Zahedan. In arable soil, the nitrogen level was not maximum in the region, and Nimrooz and Zabol had the highest phosphorus. Potassium was average in the research area, and Zabol, Zahak, Nimrooz and Hamoon had the highest Fe. The output maps obtained from the combination of various ecological factors indicated that the moderate and non-suitable classes of land for canola cultivation are located in the northern parts of Zahak, Hamon, Nimzoz and the total lands of Hirmand and Zabol.

This work demonstrates the value of combining remote sensing, regression models, random forest (RF) algorithms, and artificial neural networks (ANN) to provide crucial information for landfill management in Jordan. The process of predicting land surface temperature (LST) using linear and nonlinear regression models, ANN, and RF depended on past LST time series retrieved from Landsat images for the years 2000 to 2018. Additionally, the study utilized the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), as well as data on humidity, wind velocity, and ambient air temperature. The deployed ANN model exhibited a coefficient of determination of 0.87 and a mean squared error of 6.40*10^-8. Similarly, the RF model accurately identified 93.88% of the LST values. The findings revealed that the LST at landfills was consistently higher than the summer air temperature, and that the LSTs of open landfill cells exceeded those of closed cells. Moreover, the predicted LST values from ANN and RF models surpassed those from linear and nonlinear regression models. Notably, the R^2 value of 0.81 indicates a strong correlation between ANN and RF findings.

The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation.

The objective of this study was to use dual-polarimetric radar data to create an hourly hail product, which would then be analyzed using Google Earth Engine (GEE), a cloud computing platform. We used ground-based weather radar from the Thailand Meteorological Department’s Chiang Rai station in the north of Thailand. Dual-polarimetric weather radar data were analyzed at 15-minute intervals with Python-based open-source radar libraries such as PyArt and Wradlib. Hydrometeor classification was conducted using simulated atmospheric sounding data obtained from ERA5 reanalysis data, which had been classified into ten classes between 17.00 and 20.00 Local Time. At a 2-kilometer altitude grid, similar hydrometeor types with comparable solid-state characteristics were collected and presented in CAPPI format. Furthermore, we used JavaScript programming to conduct a localized impact study of the hailstorm in GEE in order to prove the preliminary damage assessment concept by comprising sophisticated spatial overlays with land use data, urban regions, farmland, population data, and counts of roofed homes. The analysis results in GEE reveal the potential damaging area prone to hailstorm passage. This is the first attempt in Thailand to create an hourly hailstorm product and integrate it into the Geographic Information System (GIS) using GEE’s cloud-based platform. This invention can annually support local organizations in disaster monitoring, impact assessment, and adaptation to hail-related events.

A levelling network was readjusted and a new geoid model compiled within the framework of geodetic vertical datum modernization at the Hong Kong territories. To accomplish all project objectives, the quasigeoid model has to be determined too. A quasigeoid model can be obtained from existing geoid model by applying the geoid-to-quasigeoid separation. The geoid-to-quasigeoid separation was traditionally computed as a function of the simple planar Bouguer gravity anomaly, while disregarding terrain geometry, topographic density variations, and vertical gravity changes due to mass density heterogeneities below the geoid surface. We applied this approximate method because orthometric heights of levelling benchmarks in Hong Kong were determined only approximately according to Helmert’s theory of orthometric heights. Considering a further improvement of the accuracy of orthometric heights by applying advanced numerical procedures, we determined the geoid-to-quasigeoid separation by applying an accurate method. The comparison of the accurately and approximately computed values of the geoid-to-quasigeoid separation revealed significant differences between them. The approximate values are all negative and reach -2.8 cm, whereas values from the accurate method vary between -4.1 and + 0.2 cm. In addition, we assessed the effect of anomalous topographic density on the geoid-to-quasigeoid separation by employing a newly developed digital rock density model. According to our estimates the effect of anomalous topographic density reaches a maximum value of 1.6 cm, reflecting a predominant presence of light volcanic rocks and sedimentary deposits at the Hong Kong territories. Our numerical findings indicate that the conversion between geoid and quasigeoid models should be done accurately, even in regions with a moderately elevated topography.

The current study aims at GIS-based multicriteria decision analysis to generate a landslide-susceptible map from Baramulla to Uri Road segment along NH-1, Kashmir Himalaya, India. The landslide causative factors examined to generate our AHP matrix are slope gradient, elevation, slope aspect, curvature, distance to drainage, distance to roads, distance to lineaments, geology, land use/land cover, and Rainfall. The study mapped and identified the active landslides along NH-1 through extensive field investigations and other secondary data sources. The landslide events were dominated by rockfall and debris slides. Based on their importance in landslide occurrences, the thematic layers were given relative relevance scores using Saaty's scale. Besides, the Analytic Hierarchy Process was employed to normalize the relative weights and attributes of the various thematic layers. In addition, all thematic data layers were combined using a weighted linear approach to generate the landslide susceptibility map. Furthermore, the resultant landslide susceptibility map was classed into five categories viz., very high (24.18%), high (30.24%), medium (28.61%), low (15.28%), and very low (1.69%). The study reveals that 54.42% of the area falls under the high and very high susceptible zones. Likewise, 78.9% of overall model accuracy of final landslide susceptible zonation map was computed using the area under curve method. Moreover, this study would aid infrastructural, geo-environmental, and landslide hazard planning in the studied region.

The implementation of a Location Service for Emergency Medical Services system (LS4EMSs) is the goal of this study. by integration of pgRouting algorithm, Web Map Application, and Geo-IoT devices. The study is divided into 2 parts: (1) design of a security emergency incident location alarm system that can be used to track security emergencies in real time using Geo-IoT and (2) development of Emergency Routing Service (ERS) system based on web map application. NodeMCU ESP8266 and U-blox Neo-6 m GPS module were used for implementing Geo-IoT which can connect to Wi-Fi and give information including the location of the push button triggered by an individual in an emergency. ERS can determine the best route to take from the hospital or closest ambulance to the location where the Geo-IoT device is located. Free and Open-Source Software for Geospatial (FOSS4G) stack was used in the system’s development, since it is easily adaptable to cover different purposes, including fire, flood, or other transport movements. The Integration of Geo-IoT and Web Routing Service for LS4EMSs improves utility as it is a near real-time ERS system.