Applied Geomatics最新文献

Green spaces improve societal well-being, foster connectivity to nature, and attenuate climate change. Despite Rwanda and other developing countries increasingly pursuing green economies, urban greening efforts still need multi-conceptual models that comprehensively address socio-economic and environmental requirements. This study employs a GIS-based Multi-Criteria Analysis (MCA) constructed on an Analytical Hierarchy Process (AHP) to predict green space intervention suitability across Kigali City, Rwanda. The study was based on nine factors namely: population density, slope, land cover types, proximity to roads, Normalised Difference Vegetation Index (NDVI), proximity to existing green spaces, proximity to water bodies, nitrogen dioxide concentrations, and elevation, to be used as criteria for the MCA. The findings indicate that 2.49% (1,816.19 ha) of Kigali City is highly suitable while 12% (8,744.68 ha) is unsuitable for green space interventions. Population density emerged as the most influential factor, with the city’s densely populated west-central areas exhibiting high suitability for green space initiatives. Strategically placing green spaces near population centres enhances their contribution to societal well-being, reduces transport costs, and encourages frequent use. By integrating GIS-based MCA with AHP, this study offers a robust framework for addressing green space accessibility challenges in Kigali, while simultaneously advancing climate-resilient urban development. We recommend planners prioritise Kigali City’s west-central areas for green space interventions, researchers leverage the GIS-MCA-AHP methodology for climate-resilient urban studies, and practitioners replicate this framework to advance socio-economically inclusive greening strategies.

Riverbank erosion is one of the most frequent natural hazards worldwide. Bangladesh is highly affected by this natural hazard every year. The lower segment of the Meghna River is highly vulnerable to this phenomenon. While previous studies have primarily focused on socio-economic impacts in study area or erosion-accretion detection in other major rivers, this study aimed to investigate the spatiotemporal dynamics of riverbank erosion, bank line shifting, and morphological changes in the Meghna River at Haimchar Upazila, Chandpur. Additionally, the study explored the factors driving erosion and potential mitigation strategies. A combination of primary and secondary data was used, including field surveys and satellite image analysis. Normalized Difference Water Index (NDWI) and unsupervised classification techniques were employed to analyze Landsat images from 1980, 1988, 2000, 2010, and 2021. Morphometric parameters such as river width, sinuosity index, and braided index were quantified to assess morphological changes using cross-sections and equations. Results indicate that the highest erosion (4219 ha) occurred between 1988 and 2000, while the lowest (2218 ha) was recorded from 2010 to 2021. Accretion peaked (4215 ha) between 2000 and 2010 and declined thereafter. Over the 42-year study period, the average annual rates of erosion and accretion were 85 ha/yr and 87.8 ha/yr, respectively. Variations in morphological parameters reflect dynamic channel changes, including the formation of bars and islands. Field surveys identified key erosion drivers and highlighted mitigation strategies relevant to the region. The findings underscore the need for integrated river management and adaptive planning to mitigate the adverse effects of riverbank erosion on local livelihoods. Incorporating social factors into future erosion management frameworks could enhance the effectiveness of mitigation measures. This study provides a foundation for developing targeted interventions and sustainable river management practices.

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. 

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.

In an era of rapid technological advances, artificial intelligence (AI) is becoming increasingly important in higher education. The use of AI tools presents both great potential and significant challenges, especially with regard to examinations and performance assessments. This paper provides a compact insight into the current state of development on AI in university education. Experiences are shared including questions and concerns about AI in higher education teaching. The role of AI in higher education is explored, focusing on its potential benefits and challenges, particularly in examinations and performance assessments. It provides insights into the current state of AI development in universities, highlighting its diverse applications and the associated ethical and practical considerations. Experiences and perspectives from educators are shared, reflecting both optimism about AI’s ability to enhance learning outcomes and concerns regarding fairness, transparency, and privacy. The paper emphasizes the need for responsible integration of AI in higher education in engineering disciplines, such as geomatics, balancing innovation with ethical considerations to promote equity and excellence in teaching and learning.

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.