Applied Geomatics最新文献

Haridwar is an expanding urban centre in the Indian state of Uttarakhand. For an emerging urban centre like Haridwar, road traffic accident (RTA) studies are crucial to address increasing traffic challenges, identify accident-prone areas, and implement targeted safety measures. This research investigates the spatio–temporal patterns of RTA blackspots in the Haridwar district of India, considering both the presence and absence of a Crash Severity Index (CSI). The study uses Kernel Density Estimation (KDE) to identify blackspots, and the comap approach to examine spatio–temporal patterns across different times of day and seasons. The methodology involves collecting and preprocessing crash data from 2016 to 2019, applying the comap technique, incorporating the severity index (SI), using KDE, and finally, investigating the blackspots. The study found that the inclusion of CSI significantly impacts the ranking of blackspots, with high severity crashes often occurring during the summer season and between 12.00 h—17:59 h, and 0:00 h—5:59 h. The research aims to provide a more nuanced approach to identifying hazardous locations by weighting crashes based on their severity and to explore how these locations change over time and across different seasons. The findings of this research indicate that blackspots are not consistent across time or seasons, with specific locations showing higher concentrations of severe crashes during certain periods. The study identified key blackspots such as the Deoband Y-intersection and Jhabrera T-intersection along NH-334, and a curve section along NH-34. These locations are characterized by heterogeneous traffic, illegal crossings, narrow roads, and inadequate infrastructure. The research suggests implementing measures such as pedestrian walkways, road widening, improved signage, and better lighting to mitigate these issues. This study is the first spatio–temporal investigation of RTA blackspots in India and can help highway authorities in Haridwar and other cities to implement targeted safety measures.

The Leaf Area Index (LAI) is a measure of photosynthesis and transpiration, and it has become the common currency for agro-climatic researchers. The non-destructive technique of LAI estimation using remote sensing has immense potential. The challenge lies in estimating LAI at the field scale for implementing research results for crop management using Google Earth Engine (GEE) integrated with Python. Sentinel-2A datasets empowered by high spatial, spectral, and temporal resolution over an arid region of southwest Punjab, India were used to estimate LAI at field and district level. Wheat LAI was estimated for two consecutive years, 2016–2017 and 2017–2018. The comprehensive data analysis approach comprised of processing and estimation of LAI, designed for four significant phenological stages followed by validation with in situ field observed LAI collected from the experimental plots as well as with the Moderate Resolution Imaging Spectroradiometer (MODIS)’s LAI data products. The results showed a strong positive co-relationship between observed field LAI and Sentinel-2A estimated LAI as 0.64 and 0.47, with MODIS dataset as 0.24 and 0.19 for both years. Therefore, it can be concluded that field-level LAI can be estimated from Sentinal-2A satellite images with moderate accuracy by agricultural specialists and practitioners. 

Soft set theory has been well-known as a technique for tackling uncertainty-related problems and modeling uncertainty since it was proposed by Molodtsov in 1999. It has been applied to a number of theoretical and real-world problems. The core concept of the theory-soft set operations-has piqued the interest of academics ever since it was introduced. A number of restricted and extended operations have been defined, and their characteristics have been examined up to now. Our proposed restricted star and extended star operations are novel restricted and extended soft set operations, and we thoroughly analyze their fundamental algebraic properties. We also look into the distributions of this operation over other types of soft set operations. By considering the algebraic properties of the extended star operation and its distribution rules, we show that when combined with other types of soft set operations, it forms several important algebraic structures, like semirings in the collection of soft sets over the universe. Since the operations of soft sets provide the basis for many applications, such as cryptology, and decision-making processes, this theoretical study is highly significant from both a theoretical and practical standpoint.

Geomorphic change detection (GCD), geotechnical engineering, and hazard mapping are analyses that require the lowest possible absolute total error in digital elevation models (DEMs). One of the most common GCD analyses is the quantification of soil erosion. NRTK (Network Real-Time Kinematic) is one of the three primary modes within the broader method of direct georeferencing (DG). NRTK uses a network of multiple GNSS reference stations to provide real-time correction data to a UAV, enabling centimeter-level positioning accuracy. This approach eliminates the need for ground control points (GCPs), reducing both costs and survey time. However, its application in multi-temporal soil erosion analysis remains insufficiently researched. In this paper the NRTK approach in GCD analysis is evaluated using Matrice 210 RTK V2 at two case studies. In addition, the absolute accuracy of the NRTK was tested on three other sites. Although achieved results can be regarded as promising, especially at lower altitudes, this research highlights drawbacks when employing the NRTK in analysing soil erosion measurement. Namely, the use of the DG-based models in GCD analysis generated unreliable results when compared with the reference model derived using the SfM photogrammetry with GCP. In both study sites, the NRTK approach significantly overestimated the amount of accumulated sediment, affected the total net sediment difference, and eliminated a substantial amount of change. Although the NRTK approach shows limitations in reliably quantifying volumetric changes in soil erosion measurements, results indicate that NRTK can be applied for analyzing linear gully headcut retreat rates. For applications where achieving the lowest absolute total error is not a priority, NRTK can be a relatively reliable solution. However, researchers should exercise caution when using it to analyze soil erosion over different time scales, particularly if the rate of morphological change is low.

The largest urban agglomeration in North Bengal is in Siliguri, which has experienced fast peri-urban expansion and a steady loss of natural environment due to the city’s rapid urbanization and migrant influx over the past three decades. This study examines the relation between built-up area and population in the Siliguri Urban Agglomeration as well as the spatiotemporal dynamics of landuse/landcover changes during for 2031, 2041, 2051, and 2100 using a number of driving variables. Additionally, to illustrate urban sprawl, Shannon’s entropy approach has been used to estimate built-up expansion, and the integrated cellular automata (CA)-Markov model was used to predict future land use/land cover scenarios. The results indicate that vegetation and agricultural land would decrease as a result from 1991 to 2100, from 9.19 km2 in 1991 to 73.62 km2 in 2100. Over the next 110 years, a large increase is anticipated in the built-up area, from 7.43% of the total land use area to 61.62%. In addition, the results suggest that between 1991 and 2021, the built-up area significantly increased from 9.63 km2 to 49.25 km2, while agricultural land and vegetation cover decreased by 13% and 50%, respectively. The percentage growth of intra-district in-migration to the Siliguri urban agglomeration was also observed to increase by 170% from 31% between 1991 and 2021, which put a lot of pressure on urban land and transformed agricultural and vegetated land into built-up areas. According to the gain and loss technique, agriculture and vegetation suffered the greatest losses, at 36.16 km2 and 29.79 km2, respectively. The population in 1991 was estimated to be 364,000 according to census statistics and Landsat pictures, although the built-up area was just 9.19 km2. The predicted population increase within 30 years was 1,050,000, and the built-up area was 49.25 km. As a result, in the next days, this city may turn into an urban heat island. The results of this study will assist policymakers in creating management strategies for environmentally friendly and orderly urban expansion.

Ground-based augmentation systems (GBAS) enhance precision approach procedures by providing differential corrections from ground reference receivers, improving airborne accuracy and transmitting integrity data. This allows aircraft to calculate protection levels (PL) and ensure position error (PE) remains within acceptable bounds. However, ionospheric irregularities, particularly in low-latitude regions like Brazil, challenge GBAS efficiency, affecting availability and continuity during critical flight phases. To mitigate these disturbances, GBAS employs monitoring systems that assess integrity by tracking ionospheric conditions and other potential anomalies, ensuring computed PLs reflect the system’s ability to maintain safe operations under adverse environments. In this context, this study evaluates a simulated GBAS facility in Presidente Prudente, Brazil, using EUROCONTROL PEGASUS software to analyze the performance of GBAS approach service types C (GAST-C) and D (GAST-D) under quiet and disturbed ionospheric conditions. Results show that during periods of intense ionospheric activity, availability fell below the International Civil Aviation Organization (ICAO) threshold of 99%, with GAST-C and GAST-D achieving 94.3% and 93.5%, respectively. The study also investigated the effects of inflating the standard deviation of the vertical ionospheric gradient ((:{sigma:}_{vig})) to improve integrity, finding reduced occurrences of misleading information (MI) and no instances of hazardously misleading information (HMI). Availability, nevertheless, was further impacted, notably during disturbed periods. Satellite geometry and ionospheric scintillation were identified as significant factors in degrading positioning accuracy and protection levels. These findings highlight the importance of robust monitoring systems to ensure reliable GBAS operations in low-latitude regions and provide key insights for future deployment in Brazil.

With increased frequency and intensity of extreme climate events, unprecedented volumes of debris are created. Disaster debris can often be hazardous, and it obstructs relief activities by blocking the roads and preventing access to disaster sites. This highlights the importance of timely debris identification and removal efforts for effective relief and preliminary damage assessments. This work aims to automatically extract post-disaster debris from UAV footage using instance segmentation with YOLOv8-seg model. Automatic detection of debris, its type and geographical distribution allows efficient allocation of resources, prioritization of relief efforts and significant reduction in the time taken for disaster recovery. We use UAV images of Hurricane IAN, specifically of Julies Island along the coast of Florida. We trained and compared YOLOv8n (nano), YOLOv8m (medium), and YOLOv8x (extra-large) model architectures, to select the suitable model for post-disaster debris detection. Since debris clearance efforts typically depend on debris type, we trained and built specialized models for vegetation and non-vegetation debris separately. The YOLOv8x model exhibited the highest accuracy—83% accuracy for vegetation debris and 85% for non-vegetation debris, with corresponding mAP values of 62.2 and 66.1, respectively. The model detected non-vegetative debris as small as 0.13 square meters. Furthermore, we used YOLOv8 model to detect and track damaged, hazardous and non-hazardous assets on the street from UAV videos. We developed an algorithm to automatically produce georeferenced results from UAV images, enhancing the model's usability in real-world applications. The developed model automatically outputs precise location, size and area of debris, aiding post-disaster planning.

Borneo Island is home to the world’s largest peatland with relatively straightforward tectonics movement. Those nature would be beneficial to illuminate the hydrological cycle of the Earth-surface processes. We analyzed the total water storage changes deduced from the Gravity Recovery and Climate Experiment (GRACE) satellite where the Global Navigation Satellite System (GNSS) network may detect its deformation. High-frequency data in GNSS observation might benefit the hydrological cycle real-time monitoring. We investigate the spatial correlation of GRACE-based total water storage changes and the GNSS-derived horizontal dilatational strain rate. We found that the dilatational strain rate based on GNSS data negatively correlates to the Equivalent Water Height (EWH) change observed from GRACE data. Hence, the extensional dilatation rate (> 20 nanostrain/yr) is observed exactly in the decreased EWH region ( <-13 mm), while the compressional dilatational region (< -20 nanostrain/yr) is observed in the increased EWH (> 20 mm) trend. Finally, we highlighted that GNSS observation’s precise horizontal dilatation rate in Borneo Island is reliable for detecting seasonal total water storage changes.

This study investigates groundwater potential zones (GWPZ) in the southern coastal regions of India, specifically from Chettikulam to Kolachal, using remote sensing (RS) and geographic information system (GIS) techniques. The primary objective is to identify suitable locations for sustainable groundwater storage and select subsurface basins for household and irrigation use. The research incorporates multiple thematic layers, including geomorphology, lithology, land use and land cover (LULC), lineament density, drainage density, slope, Digital Elevation Model (DEM), and rainfall, to provide a comprehensive analysis of groundwater availability and distribution. The Analytical Hierarchy Process (AHP) and GIS were employed to normalize and weight each criterion, enabling a weighted index overlay analysis of the eight thematic layers. Groundwater recharge zones were classified into low, medium, and high potential based on cumulative weighted values. The results indicate that the majority of the study area lies in the low to moderate groundwater potential zones, with low-potential zones occupying 46.72%, medium-potential zones accounting for 49.14%, and high and extremely high potential zones representing 2.82% and 1.32%, respectively. These findings highlight the fact that medium- to high-prospective areas have greater potential for groundwater extraction. The study underscores the significance of understanding geological and hydrological complexities such as lithological variations, land use changes, and drainage patterns to support sustainable urban development and groundwater management.

Coordinate transformation facilitates the integration of geodetic coordinates of points obtained from different sources into a common geodetic reference frame. In existing studies, mathematical transformation models such as Bursa-Wolf, Molodensky-Badekas, Veis, the affine transformation models and others have been applied. These models can lead to low accuracy, due to various factors, such as lack of understanding of the distortions and inconsistencies of the local datum and geodetic network distribution. Recently, Artificial Neural Networks (ANN) techniques for coordinate transformation have been evaluated in several countries and have been found to achieve better results compared to similarity models. In Kenya, there is little literature on the evaluation of these techniques for improving coordinate transformation. Therefore, this study aims to optimise the affine six-parameter 2-dimension coordinate transformation using ANN techniques. The methodology involves acquisition and processing of geodetic control datasets with common points in two coordinate systems: UTM and Cassini Arc 1960 for part of the Nyeri-Kirinyaga geodetic network, in Central region of Kenya. The Affine-6 transformation parameters are determined, applied for coordinate transformation and the distortions modelled. The transformation resulted in relatively low accuracy, possibly due to the limited ability of the model to map nonlinear patterns in the datum. This study proposed application of nonlinear ANN models; Multi-Layer Perceptron (MLP), and Radial Basis Functions Neural Network (RBFNN) to map the non-linear patterns and adjust the transformed coordinates, hence optimizing the Affine-6 model. A comparative evaluation was performed to determine the improvement in performance and compare the models. It was found that the ANN techniques improved the Affine-6 transformation by 92.55% and 92.27% in RMSE and 99.35%, 98.06% in horizontal error for MLP and RBFNN respectively.