Journal of Advanced Geospatial Science & Technology
Journal of Advanced Geospatial Science & Technology (JAGST) welcomes quality research in the area of Advances on Geospatial science & technology) for the use of Natural science, space atmosphere and also empirical studies that involves: o Artificial intelligence, Machine learning, and Deep learning o IoT, sensors, Unmanned Aerial Vehicle (Drone), Light Detection and Ranging o (Lidar), Hyperspectral and High spatial resolution satellite data application, ground penetration radar (GPR), Laser scanning o Big geo data including geo cloud computing o Geovisual analytics and modelling o 3D and multi- dimensions Geographic Information System (GIS) o Advances in Geo-crowdsourcing and Volunteered Geographic Information o Advances in geodesy, geodynamics, and deformation monitoring o Ocean dynamics and tidal modeling o Advances in satellite geodesy, photogrammetry and hydrography Indexed by: MyJurnal, Google Scholar
Modelling of Submerged Targets Affected by Refraction Using Green-Wavelength Leica C10 Scanstation
This paper presents an extensive approach into the capability of terrestrial laser scanning (TLS) operating at the 532 nm green wavelength for modelling submerged targets under varying water depths with the effects of refraction acting upon the targets. In this research, a Leica 0 C10 TLS was deployed to scan a controlled rectangular pool containing a submerged cuboid target and check markers positioned equally at depths ranging from 0 to 15 cm incrementally. The Point cloud data were processed using Leica Cyclone Register and 3DSurvey software, while a MATLAB-based algorithm to solve the refraction correction based on Snell’s law was applied. Results demonstrated accountable horizontal and vertical displacements of submerged markers, which are consistent with theoretical refraction effects, averaging 2cm and 2mm for the horizontal and vertical displacement, respectively, with the presence of water depth at 3cm assessed by visual evaluation of the markers. The post-processing correction using MATLAB significantly improved the alignment precision and allowed 3D mesh models to approximate the true target dimensions more accurately, presenting the mean error vectors of 1.36m for 0, 0.34m for 0, and 0.41m for 0. This paper highlights the potential of 532 nm TLS for shallow-water 3D modelling applications and the significance of refraction correction.
Copyright (c) 2026 Journal of Advanced Geospatial Science & Technology
The Flood Inundation Mapping of Kampung Sungai Kertil in Kuala Muda, Kedah
This study focuses on using a geomatics-based flood modelling as a tool to visualise and communicate predicted flood levels in Kampung Sungai Kertil in Kuala Muda, Kedah. Flooding is a recurring problem in the area, causing significant damage and disruptions to human settlements, infrastructure, and the economy. This research aims to develop an effective flood model that enhances the accuracy of flood map preparation and facilitates a better understanding of flood risks. The research utilises a combination of hydrological modelling and geographic information systems (GIS) to analyse flood-prone areas in a case study region. In ArcGIS, the Hec-GeoRAS extension extracts geometric information from Digital Elevation Model (DEM) and imports data from HEC-RAS for flood inundation mapping. Data from the SRTM DEM is used in this case study. In order to accurately simulate floods and analyse water levels using the steady flow simulation approach, RAS layers that include crucial hydraulic parameters are created within HEC-RAS. The model results demonstrate that higher discharge scenarios (up to 150 m³/s) produce substantially larger inundation extents, identifying seven settlements within the predicted flood-prone area.The study advances a thorough knowledge of regions vulnerable to flooding by creating maps of flood potential, which supports preventative flood management measures. The results underscore the importance of flood modelling in improving the precision of flood map creation and bolstering efficient flood control strategies.
Plate Tectonic Calculator: Stand-Alone Application for Estimating Plate Tectonic Motion over the Earth’s Surface
Plate tectonic motion is the displacement of the plates over the earth’s surface. Significant displacements can be detected in regions with high seismic activity. Following Euler’s theorem, continental plates rotate about an axis that runs through the center of the earth’s sphere. Therefore, plate tectonic motion is emphasised, with the rate of movement calculated annually. Hence, this study aims to develop a stand-alone application to calculate plate tectonic motion concerning Euler’s theorem. A plate tectonic calculator (PTC) is designed using optimised parameters and formulations that can calculate motion in any region. Furthermore, the set of formulations from Euler’s theorem that have been revised into a comprehensive form is verified using manual calculation in MATLAB. The formulations are categorized into two stages: the inverse Euler pole problem in the Local Geodetic Coordinate System (LG CS) and the direct Euler pole problem in LG CS. After integrating the PTC in C# using Visual Studio, the sample coordinates and velocities of stations from four case datasets are used to test the PTC’s performance. Moreover, the effectiveness of the PTC is further assessed by comparing its outputs with those of the existing Euler Pole Calculator (EPC). The results show no significant differences in any parameter across all cases. This is because the differences are too small and within uncertainties, and also do not exceed the tolerance range of the standard deviation for each parameter involved. The graph of the velocity differences between PTC and EPC indicates the discrepancies are less than 1×10⁻⁶ meters per year, whereas the velocity residuals and standard deviation show the same discrepancies that are less than 1×10⁻⁹ meters per year. Consequently, it can be concluded that the PTC’s performance has been successfully verified. In addition, it can more effectively detect plate tectonic motion within the given region.
Assessment of Mangrove Channel Changes in the Langkawi River Using Geospatial Techniques
Channel alterations represent a primary concern within coastal mangrove ecosystems, which possess substantial ecological and socioeconomic value. These mangrove channels in the Langkawi River have encountered a variety of environmental challenges over time, such as urbanisation, pollution, and natural changes. This study aims to evaluate changes in the mangrove channels along the Langkawi River in Pulau Tuba between 2018 and 2023 using remote sensing techniques. The methodology involved a comprehensive assessment of channel changes in these locations through the comparison of satellite imagery data to identify changes occurring within the channels. Based on the initial findings, the primary objective was to classify the mangrove channels using supervised classification of Sentinel imagery. The study successfully identified and classified four distinct land cover types: forest, water bodies, mangroves, and developed open areas, using geospatial techniques in ERDAS IMAGINE 2015 and ArcMap. The second finding relates to the breadth of the mangrove channels, which indicates that the maximum rate of erosion was 65 metres, while the minimum rate was 0.09 metres. Furthermore, there are accretions that occur at a minimum rate of 0.85 and a maximum rate of 26.7 meters. The final findings are presented in the form of a risk analysis map of alterations to the mangrove channel in the Langkawi River. These findings could be valuable to organisations such as LADA, JUPEM, and NAHRIM, particularly for future monitoring and planning purposes.
Evaluation of 3D Reconstruction of Non-collaborative Surfaces using Neural Radiance Fields
This paper investigates the effectiveness of Neural Radiance Fields (NeRF) in reconstructing the three-dimensional models of non-collaborative surfaces, with a specific focus on transparent and reflective materials. These surfaces often pose significant challenges to traditional photogrammetric methods due to their optical complexity. The study compares the accuracy of NeRF with that of conventional photogrammetric techniques based on Multi-View Stereo (MVS). Two objects, a plastic and an aluminium water bottle, were photographed with a smartphone and processed in both Agisoft Metashape and NefStudio to generate 3D models. The reconstructed models were evaluated against reference point clouds obtained through laser scanning. Accuracy assessment was conducted using the M3C2 analysis in CloudCompare, in which NeRF achieved mean distance errors of 0.650 mm and 0.153 mm for transparent and reflective surfaces, respectively. These values were lower than those obtained from MVS, which recorded 0.833 mm and 0.088 mm, respectively. The results indicate that while photogrammetry yields reliable outcomes for textured surfaces, NeRF demonstrates improved performance in modelling complex reflective and transparent geometries. These findings support the potential of NeRF as a practical alternative or complementary approach to traditional photogrammetry in scenarios involving challenging surface characteristics.
Effect of Urban Heat Island on Meningitis: Insights from Remote Sensing Analysis
The interaction between urban heat islands (UHIs) and public health is an increasing concern, particularly in semi-arid regions such as Kano Metropolis. Although prior research has identified links between climatic factors and infectious diseases, the specific connection between UHI intensity and meningitis incidence remains insufficiently studied. This study utilized remote sensing methods to examine spatial and temporal UHI patterns through satellite imagery from 2015 to 2023. Epidemiological data on meningitis cases were combined with UHI maps to analyze correlations and identify high-risk zones. Statistical analyses, including ordinal logistic regression, were employed to evaluate the relationship between UHI intensity and disease prevalence. The results demonstrated a strong correlation between UHI intensity and meningitis incidence, with 73% of cases occurring in areas classified under the "Strongest" UHI category. Temporal analysis identified 2017 as the peak year, contributing 94% of the recorded cases. Regression analyses confirmed significant associations, with UHI intensity emerging as a key predictor of meningitis risk (p = 0.01). Spatial visualizations revealed clusters of cases in areas with high UHI intensity, underscoring the compounded health risks in densely urbanized regions. This study emphasizes the significant influence of UHIs on meningitis patterns in Kano Metropolis. The findings highlight the necessity of climate-responsive urban planning and focused public health strategies to reduce disease risks in rapidly urbanizing environments.
GIS-Based Site Suitability for 5G Telecommunication Towers Enhancing Mobile Network Coverage
Wireless communications require infrastructure for network access and coverage, which is provided by telecommunication towers. These towers are essential for 4G and 5G networks, necessary for wireless communications, mobile networking, and radio broadcasting. Considering the limited adoption of 5G technologies in Malaysia, this research focuses on a spatial and suitability analysis that will evaluate existing networks based on the existing towers in Kulai, Johor, while assessing site suitability based on several criteria. This study aims to propose new potential areas for 5G towers that will accelerate the expansion of the 5G coverage in Kulai, Johor. The objectives of this study include (1) to identify the parameters for site suitability analysis for proposing a new location for 5G telecommunication towers, (2) to evaluate the current 4G and 5G mobile towers based on the location of existing telecommunication towers, and (3) to propose a new area of 5G telecommunication towers in Kulai, Johor. The scope of this study focuses on 5G telecommunication towers in the Kulai District, Johor. The methodology of this study consists of 3 phases: research formulation, data collection and processing, and analysis. The analysis employed Spatial Multi-Criteria Analysis (SMCA), which comprises four parameters: the locations of existing towers, the signal radius coverage of the towers, terrain, and population density. The analysis reveals that 4G infrastructure currently provides coverage to 76.7% of the study area, whereas 5G coverage is limited to only 7.9%, primarily due to its shorter signal range necessitating a denser tower network. Population density assessment indicates that urban areas generally receive partial 5G access, while rural and low-density zones remain underserved, underscoring the need for prioritised deployment in these locations. Furthermore, elevation and site suitability analyses demonstrate that flat, low-lying areas offer the most favourable conditions for future 5G tower placement, thereby supporting efficient network expansion and enhanced performance. Furthermore, the results of this study will be crucial for the deployment of 5G infrastructure and strategic planning, ensuring enhanced network availability and connectivity.
Copyright (c) 2025 Journal of Advanced Geospatial Science & Technology
Assessing Vegetation Health Impacts of Air Pollution Using Medium-Resolution Satellite Data: A Case Study of Pasir Gudang, Malaysia
Air pollution has become a common phenomenon nowadays, and people are unaware that polluted air can affect the health of vegetation. As we know, vegetation plays a crucial role in producing oxygen in the air, which helps reduce air pollution. This study was conducted in response to air pollution incidents at the Kim-Kim River in Pasir Gudang, Johor, Malaysia, in March 2019. The illegal chemical dumping that produced chemical gas in the Kim-Kim River impacted the affected areas and vegetation health. Thus, using the medium-resolution remote sensing method, the researchers were able to detect the impact of air pollution on the health of vegetation growth. Four of the vegetation indices from medium resolution multispectral bands were extracted, namely the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index (ARVI). As a result, vegetation indices from Sentinel-2 and Landsat-8, respectively, successfully demonstrate the correlation trend between vegetation health and air pollution, generating trends in vegetation health affected by air pollution.
Land-Cover Change and Predictive Modeling of Urban Heat Dynamics in Kano, Nigeria
Urbanisation profoundly alters land-use and land-cover (LULC) patterns, often intensifying urban heat island (UHI) effects and threatening sustainable development. In rapidly growing semi-arid cities such as Kano Metropolis, Nigeria, these dynamics remain underexplored. This study investigates the relationship between LULC transitions and urban thermal dynamics over nearly four decades (1984 - 2023), while also forecasting future changes. Multi-temporal Landsat imagery was classified using the Classification and Regression Tree (CART) algorithm, achieving overall accuracies ranging from 92.1% to 98.5% and Kappa coefficients exceeding 0.85. Vegetation and built-up indices, including the Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI), were analysed alongside Land Surface Temperature (LST) to assess urban heat patterns. Predictive modelling employed the Cellular Automata Artificial Neural Network (CA-ANN) approach, which was validated with an overall accuracy of 92% and a Kappa coefficient of 0.86. Results show that built-up areas expanded from 43.06 km² (2.93%) in 1984 to 381.79 km² (25.95%) in 2023, an almost 800% increase, while bare land declined by 23.2%. Mean LST rose from 39.7°C in 1984 to 41.5°C in 2023, with peak values exceeding 52°C in 2010. A strong positive correlation was found between NDBI and LST (r = 0.57), while NDVI showed a negative correlation with LST (r = -0.32), highlighting the cooling effect of vegetation. Model simulations predict continued urban expansion through 2050, with built-up areas increasing by 6.5% and further intensification of UHI effects if unchecked. These findings emphasise the urgent need for sustainable urban planning, including the preservation of vegetation cover, the development of green infrastructure, and climate-sensitive construction practices. The study offers critical insights for policymakers and urban planners seeking to mitigate thermal stress and foster climate-resilient urban development in sub-Saharan Africa.
Urban Heat Island Dynamics in Johor Bahru, Malaysia: Influence of Vegetation and Urbanisation on Surface Temperature
Urban heat islands (UHIs) pose escalating challenges for tropical cities, intensifying thermal discomfort and exacerbating energy demands. This study examines the impact of vegetation cover and built-up intensity on land surface temperature (LST) in Johor Bahru, Malaysia, by combining remote sensing-derived indices, including the normalised difference vegetation index (NDVI) and the normalised difference built-up index (NDBI), with multiple regression modelling and spatial mapping. Landsat 8 imagery was processed to generate LST, NDVI, and NDBI layers, revealing heterogeneous spatial distributions characterised by pronounced thermal hotspots in densely built-up zones and cooler surfaces in areas of higher vegetation density. Unlike most prior NDVI–NDBI–LST studies that treat these indices separately, this study explicitly quantifies both their individual effects and their interaction. Simple linear regressions showed a moderate inverse relationship between NDVI and LST (adjusted R² = 0.253) and a positive association between NDBI and LST (adjusted R² = 0.568). Incorporating both indices into a multiple regression model explained approximately 57% of the variability in LST, with NDBI emerging as the dominant predictor. Introducing an interaction term (NDVI × NDBI) further improved model performance (adjusted R² = 0.579), highlighting that the cooling effect of vegetation is contingent upon surrounding built-up intensity. Spatial mapping underscored these findings, visually delineating areas where vegetation most effectively mitigates surface warming and identifying transitional zones that could benefit from targeted greening interventions. The results highlight the combined impact of vegetation and impervious surfaces on shaping urban thermal environments, providing critical insights for developing nuanced, context-specific urban heat mitigation strategies.
Validation Of Satellite-Derived Aerosol Optical Depth Using Long-Term Sunphotometer Data In Malaysia
Aerosol Optical Depth (AOD) is a dimensionless measure of the extinction of solar radiation by aerosols in the vertical column of the atmosphere and it is widely used as an indicator of air quality. It can be measured from space using satellite sensors such as MODIS, which observes the total atmospheric column from the top of the atmosphere to the surface, or from the ground using AERONET sunphotometers, which measure the attenuation of direct solar radiation from the ground to the atmospheric boundary layer. In Malaysia, satellite-derived AOD retrieval is often challenging due to frequent cloud cover, high humidity, surface heterogeneity, and spectral mismatch between MODIS wavelengths (470 nm and 550 nm) and AERONET wavelengths (440 nm and 500 nm). These differences in measurement time, spatial scale, and wavelength often cause MODIS to overestimate AOD, especially during high aerosol loading. AOD is closely linked to fine particulate matter (PM2.5) making it important for assessing air quality impacts on health and the environment. Globally, accurate AOD information supports climate and haze studies, while in Malaysia, it is essential for monitoring seasonal haze and urban pollution. This study applied spatial, temporal, and spectral correction techniques to minimise discrepancies between MODIS MAIAC AOD and AERONET Level 2.0 data from USM Penang and Kuching, Malaysia improving the reliability of satellite AOD for air quality monitoring in tropical conditions. Long-term MODIS MAIAC data (MCD19A2.061) from 2012 to 2024 for Penang and from 2012 to 2020 for Kuching were used, along with AERONET data from USM and Kuching stations. Spatial correction was applied using regression on a 3×3 MODIS pixel grid. Spectral correction used interpolation to estimate AOD at 470 nm and 550 nm. Temporal correction was performed by filtering AERONET data between 1000h until 1100h to match the MODIS Terra overpass. The corrected MODIS AOD values showed improved agreement with AERONET. In Penang, the 550 nm result showed R = 0.72 and RMSE = 0.08, while in Kuching, R = 0.97 and RMSE = 0.15. Seasonal and frequency analysis revealed that MODIS tends to overestimate AOD, especially during haze events. This study demonstrates that calibrated satellite AOD data using long-term observations can support more reliable air quality monitoring in urban Malaysian settings.
Sea Grape (Caulerpa lentillifera) Potential Cultivation Area Using GIS Approach in Sabah
The aim of this study is to apply a GIS-based approach to identify suitable areas for cultivating sea grapes (Caulerpa lentillifera) in Sabah. Specifically, the objectives are to assign weights to the factors influencing site suitability and to pinpoint potential cultivation zones. Key environmental variables considered include sea surface temperature, salinity, water depth, chlorophyll concentration, and UV light. Data for these criteria were sourced from NOAA, GEBCO, and other open-source databases. The Ranking Weighting method was used to establish the relative importance of each factor, followed by a Weighted Overlay Analysis (WOA) in GIS to produce a suitability map for sea grape cultivation in Semporna, Sabah. Results show that large areas around Semporna provide ideal conditions for sea grape farming, with high suitability indicated in green on the map. Among the factors assessed, salinity emerged as the most critical, while sea surface temperature had the least influence. The findings were further validated by expert input. The novelty of this study lies in it being the first comprehensive GIS-based assessment of sea grape cultivation potential in Sabah. While previous aquaculture research in the region has focused largely on other marine species or relied on isolated environmental parameters, this study integrates multiple environmental datasets within a spatially explicit framework to holistically evaluate site suitability. By combining remote sensing data, ranking-based weighting, and GIS overlay techniques, the research not only fills a critical knowledge gap but also establishes a replicable model for aquaculture site selection in Sabah’s tropical marine environments. Overall, the study demonstrates the effectiveness of GIS in identifying potential aquaculture sites for sea grape cultivation in Sabah. The resulting suitability map can serve as a strategic decision-support tool for the Sabah government and stakeholders in expanding sustainable sea grape cultivation.
Tool-Based Automation in 3D Point Cloud Processing – A Review
This paper provides a systematic review of tool-based automation in 3D point cloud processing, focusing on open-source platforms and their capabilities to facilitate spatial workflows. The irregular nature of point clouds and their increasing volume are increasingly utilized in geomatics, construction, agriculture, and heritage documentation, making manual processing impractical. Scalability, accuracy, and usability, therefore, require automation. It was a systematic review based on Scopus, IEEE Xplore, and Web of Science (2019-2025). A total of 36 articles were located using keywords, including automation, machine learning, open-source tools, and point cloud processing, which applied automation in a tool or platform. The results are organized into three domains: segmentation, classification, and reconstruction. In segmentation, voxel-based partitioning and transformer networks have been implemented in platforms such as CloudCompare and Open3D to enhance scalability and detail capture. In classification, tools are increasingly instruments that integrate machine learning with other forms of contextual reasoning, and forestry and UAV applications are examples of their potential. Reconstruction studies show an increasing relationship between BIM and heritage workflows, enabled by tools such as Cloud2BIM and Open3DGen. Although these methods reduce manual labor and increase efficiency, they still have limitations in terms of compatibility, benchmark standardization, and cross-domain applicability. The review, in general, synthesizes progress but also points out a lack of consistency in current advances. It also helps guide future point cloud research by clarifying the strengths and gaps in creating more compatible, benchmarked, and domain-flexible tool-based automation.
Twenty-Five Years of Satellite-Based Particulate Matter Estimation: A Bibliometric Analysis
Over the past two decades, the integration of remote sensing technologies for estimating particulate matter (PM) has increased rapidly, reflecting the global urgency of monitoring air pollution at large spatial scales. This study presents a bibliometric analysis of 4,155 journal articles published between 2000 and 2024, retrieved from the Scopus database. Only peer-reviewed English-language journal articles were included, while conference proceedings, reviews, and book chapters were excluded. By leveraging the Bibliometrix package in R, this analysis maps scientific trends in remote sensing applications for estimating particulate matter (PM2.5 and PM10). Results show that annual publications grew from fewer than 50 per year in the early 2000s to more than 500 by 2024, with China (n = 10,023), the United States (n = 6,501), and India (n = 1,325) emerging as leading contributors. Keyword co-occurrence and thematic clustering highlight the growing use of machine learning, data fusion, and health-linked applications. International collaboration networks reveal strong linkages among China, the USA, and Europe, while regions such as Africa and South America remain underrepresented. These findings suggest that future efforts should focus on targeted investments in the underrepresented areas and on integrating remote sensing, machine learning, and health data into air quality indices to enhance environmental monitoring and policy relevance.
Spatiotemporal Analysis of Urban Heat Island Effects in Nigerian Cities Using MODIS Data
This study examines the geographical and temporal dynamics of the Urban Heat Island (UHI) effect over ten major Nigerian cities: Lagos, Kano, Ibadan, Benin City, Port Harcourt, Aba, Jos, Ilorin, Abuja, and Kaduna, spanning the period from 2001 to 2023. Ultimately steering urban climate adaptation strategy, the main goal is to assess the intensity and mitigation potential of UHI events through high-resolution remote sensing and climatic indicators. The study is based on three fundamental objectives: (i) to evaluate long-term trends in Land Surface Temperature (LST) and vegetation cover using MODIS satellite data; (ii) to identify and classify UHI hotspots and their vegetation status; and (iii) to quantify the impact of vegetation, albedo, and evapotranspiration in reducing urban heat through the Heat Mitigation Index (HMI). Using MODIS MOD11A1.061 daily LST products at 1 km resolution, MCD43A3 albedo data, and MCD12Q1 land cover datasets, the methodology applied a multi-parameter geospatial framework. While trend analysis was conducted using the altered Mann-Kendall test and Sen’s slope estimator to identify statistically significant changes in both LST and NDVI, NDVI was created to measure plant health. The HMI model geographically mapped areas with high, moderate, and low heat mitigation capacity under both daytime and nighttime settings by combining plant cover, albedo, and crop coefficient (Kc) data. NDVI and LST trends were studied using Pearson correlation analysis. Results show consistent UHI intensification in urban centers, with daytime LST values hitting 34–38°C and corresponding nighttime values staying high in most cities. As demonstrated by negative NDVI trends and R² values over 0.5 in various towns, vegetation decrease, most acute in Lagos, Abuja, and Kano, substantially correlates with increasing surface temperatures. On the other hand, cities like Ibadan and Benin City showed significantly high HMI zones (48–52%), highlighting the cooling capacity of preserved vegetative cover. While Jos and Ibadan exhibited lower albedo (<0.120) in vegetated areas, the albedo study revealed high reflectivity in the urban cores of Lagos, Kano, Ilorin, and Abuja, with values ranging from 0.140 to 0.259, which corresponds to impervious surfaces. The spatial overlap of low vegetation, high albedo, and thermal hotspots highlights even more the combined effect of surface change on UHI intensity. Cities like Kano and Lagos also revealed more than 17% barren or low vegetative Kc areas, which are directly linked to environmental pressure and inadequate cooling capacity. This study emphasises the importance of green infrastructure in reducing urban heat and offers an evidence-based basis for climate-resilient urban development in sub-Saharan Africa. Particularly for fast urbanising areas prone to climate-induced heat stress, the results suggest including vegetation restoration, urban greening programs, and nature-based solutions into long-term UHI mitigation and adaptation plans.
Geospatial Analysis of NDVI-Rainfall Dynamics under High ENSO Influence in Peninsular Malaysia
Malaysia (PM) rainfall varies significantly due to El Niño-Southern Oscillation (ENSO), making it an important region to study the relationship between NDVI and rainfall. These connections are complex, spatially non-linear, non-stationary, and scale-dependent, challenging conventional regression models. To address this, a local modelling approach, Geographically Weighted Regression (GWR) was employed, accommodating spatial variability in relationships. This study utilizes the CMORPH gridded dataset to explore the NDVI-rainfall relationship in PM during very strong El Niño events of 2015/2016, as well as strong La Niña events of 2010/2011. During the El Niño 2015/16 event, the Gaussian weighting function achieved an AIC of 398.48 and a quasi-global R² of 0.63, outperforming the Bisquare function's AIC of 434.12 and quasi-global R² of 0.48. In the La Niña 2010/11 event, the Bisquare function excelled with an AIC of 442.01 and a quasi-global R² of 0.52, while the Gaussian recorded an AIC of 505.69 and a quasi-global R² of 0.10. The median local R² for El Niño (0.6 to 0.8) was higher than that for La Niña (some areas dropping below 0.4), highlighting the GWR model's superior performance in capturing spatial variation. In terms of predictive power, the metrics demonstrate superior model performance during El Niño, with a Mean Absolute Error (MAE) of 0.5, a Root Mean Square Error (RMSE) of 0.66, an R² value of 0.63 indicating significant variance explained, and Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) both at 0.63 and 0.6, respectively.
An Approach of Affine Projective Transformation for Cadastre Data Migration
Digital Cadastral Database (DCDB) is emerging as a modern and comprehensive system that offers enhanced accuracy, compatibility and functionality for registration and management in land surveying practices. Modernising land surveying practices has brought about a crucial need to upgrade the existing spatial database to the DCDB in the Geodetic Datum of Malaysia 2000 (GDM2000). This study aims to investigate the cadastre data migration process in Peninsular Malaysia. A case study focused on the state of Johor, and a few cadastre parcels were analysed during the study period. The study’s methodology consisted of data collection, which involved gathering Pangkalan Data Ukur Kadaster (PDUK) data, including land surveys, property boundaries, and related records, as well as acquiring DCDB data and transformation parameters. Next, the data was analysed by evaluating the quality of PDUK data, identifying the inconsistencies, errors, and gaps, and then analysing the existing migration process to the DCDB. Afterwards, an Affine transformation was implemented, and the migration of PDUK data to DCDB needed to be conducted by considering the geometric properties of the parcels, such as points, straight lines, and/or planes. This established the relationship between the old and new datums. Subsequently, the pre-migration and post-migration datasets were compared in terms of accuracy and consistency. The area different from pre-migration and post-migration obtained is below 50 cm2. From the comparison result, recommendations for improving the migration process, including adjustments to the fitting method, were obtained. The findings offer valuable insights into migrating PDUK data to DCDB for land surveying practices in the Malaysian Peninsular.
Evaluating the Influence of La Niña on Tropical Greening in Borneo Through Geographically Weighted Regression
This study evaluates the influence of the 2007 strong La Niña event on tropical greening in Borneo by using geographically weighted regression (GWR) to assess spatial variations in vegetation response based on remotely sensed NDVI data. Moran’s I value, ranging from 0.012 to 0.034, indicates low positive spatial autocorrelation and significant spatial clustering of rainfall across Borneo, underscoring the importance of incorporating spatial factors in the analysis. The ANOVA test shows that each monthly GWR model significantly outperforms the Ordinary Least Squares (OLS) model (F > 1, p < 0.05), with September, October, and December exhibiting the strongest fit (Quasi-global R²: 0.2744, 0.3125, 0.2899; RSS: 0.2626, 0.2539, 0.2785). During the Northeast Monsoon (NEM), the rainfall-NDVI relationship is strongest, with maximum R2 values peaking at 0.74 in December, followed by 0.54 in February and 0.27 in November. Central and southern Borneo show the highest correlations, indicating that rainfall is a key driver of vegetation growth. During the Southwest Monsoon (SWM), the rainfall-NDVI relationship weakens, with maximum R2 dropping to 0.36 in August before rising to 0.49 in September. The lowest R2 (0.00–0.04) in northern and eastern Borneo reflects reduced rainfall influence due to orographic rain shadow effects from the Crocker Range and East Kalimantan highlands. Western Borneo’s peatlands and riparian zones retain moisture and sustain vegetation, while degraded forests, mixed land use, and plantations in the north and east show more significant NDVI fluctuations due to lower soil moisture retention. The predicted NDVI values during the 2007 La Niña event ranged from 0.5 to 0.9, with the model effectively capturing seasonal and spatial variations across Borneo, particularly during peak rainfall. However, it missed localized fluctuations and smaller-scale variations in February and November due to elevation, soil and vegetation type, and extreme rainfall variability. These findings suggest that local factors mediate La Niña’s influence on tropical greening, emphasizing the importance of spatial analysis in understanding climate-vegetation interactions under extreme conditions.
Long-Term Coastal Erosion Vulnerability Assessment in Kuala Rompin Using Remote Sensing (2003-2023)
Coastal erosion is a severe environmental threat, impacting many coastal areas worldwide. The increasing rate and severity of erosion occurrences can be attributed to natural and human sources. Coastal erosion is becoming more prevalent through rising sea levels, climate change, and human activities, including urbanisation and unsustainable coastal development. This study examines the geographical and chronological patterns of coastal erosion along the shoreline of Kuala Rompin, Pahang, for a long-term period, specifically from 2003 to 2023. Over the past twenty years, coastline features and coastal erosion rates have been revealed by utilising Landsat satellite optical imagery. Additionally, with the desired satellite revisit timeframe, the combination of high-resolution multispectral data and advanced GIS spatial analysis tools enables comprehensive change detection to assess the coastal landscape’s dynamic pattern and vulnerability rate. Data analysis indicates an average erosion rate of -0.476 meters per year, with significant variability along the coastline. Besides that, this study also delved deeper into the relationship between environmental factors and the likelihood of erosion, offering valuable insights into the factors that caused changes in the coastal area. The utilisation of GIS and remote sensing images in this study provided a flexible and adaptable framework in the form of classified remote sensing images and GIS models complete with analysis for evaluating the susceptibility of coastal areas in different regions, promoting a wider range of potential applications for the research findings to aid other departments in mitigating and preventing coastal erosion in the area. The findings provide an enhanced comprehension of the dynamics of coastal erosion around Kuala Rompin, facilitating well-informed decision-making for implementing sustainable methods in coastal management. In summary, this research can serve as a significant reference for environmental scientists, policymakers, and coastal management practitioners involved in addressing the effects of erosion on susceptible coastal regions.
Spatial Distribution Patterns of Vehicle-Etching Cases in Johor Bahru, Malaysia
Geographic Information Systems (GIS) are increasingly recognized across industries, with growing demand for system development in various sectors such as logistics, transportation, utilities, and government, particularly in crime mapping. However, crime databases in Malaysia currently lack spatial integration, especially in forensic applications. This study analyses the spatial distribution patterns of vehicle-etching cases in Johor Bahru District, Malaysia. The study has three main objectives: (1) to identify challenges in managing databases and information related to vehicle etching cases, (2) to develop a geospatial database integrating spatial data with existing crime records, and (3) to analyse the spatial distribution of etching cases within Johor Bahru District. The research methodology is divided into three phases: formulating the research background, data pre-processing and database design, and spatial distribution analysis. This study focuses on vehicle-etching cases within the jurisdiction of the Johor State Police Headquarters (IPK Johor). The results consist of non-spatial and spatial analyses. The non-spatial analysis examines case distributions based on vehicle type, colour, model, and quarterly trends. The spatial analysis includes five key components: heatmap and hotspot analysis, mean centre analysis, and spatial distribution pattern analysis. Findings indicate that the Pandan-Larkin and Kota Masai areas are the main hotspots for vehicle-etching cases. At the same time, mean centre analysis highlights the Bakar Batu-Tebrau area as a key location. Nearest Neighbour Analysis and Spatial Autocorrelation reveal an overall clustered distribution pattern. However, when analysed by Parliamentary boundaries, distribution patterns vary, exhibiting random, clustered, and dispersed patterns. This study underscores the importance of integrating spatial data into crime databases for improved forensic analysis and crime prevention strategies.
The Benchmarking Native Multi-Output, Regressor Chain and TPOT-MTR Models
Native multi-output expands its capabilities to include multi-target regression using a genetic programming approach. It outperforms state-of-the-art methods on multi-target regression datasets, enhancing prediction accuracy and decision-making in multi-target regression domains. However, it relies heavily on a single target strategy, which may not capture complex interdependencies between multiple targets. Additionally, the systems lack understanding of how linked targets in multi-target regression are handled, making it difficult for practitioners to determine the real connections. The purpose of this study is to evaluate and assess the Native Multi-Output, Regressor Chain and TPOT-MTR model using six relevant public datasets. The methods used were Pearson Correlation and aRRMSE. The research focuses on multi-target regression using AutoML and TPOT, focusing on the TPOT multi-output regression technique. The proposed model is expected to improve correlation and provide customization options, contributing to the advancement of multi-target regression using AutoML techniques. It concludes with a comprehensive analysis of the algorithm’s performance in addressing difficulties in multiple target regression and evaluating its ability to identify and employ relevant features for improved predictive correlation. The results show that TPOT-MTR shows stronger correlation performance across most datasets, especially when target relationship capture is vital. However, its performance advantage is reduced in datasets with weakly connected or naturally well-structured targets. In conclusion, TPOT-MTR is a reliable tool for modelling target correlations, especially in multi-target learning tasks. Its performance varies depending on the dataset structure, with Regressor Chain and Native Multi-Output performing better in densely linked datasets. However, it may not always provide the lowest aRRMSE, making it a good substitute for simpler models. Further research can explore more intricate datasets and integrate neural networks.
Assessment of Shoreline Changes at Northern Selangor Coast, Malaysia using Digital Shoreline Analysis System (DSAS)
Coastal erosion and accretion take a long time and can cause changes in the shoreline. This is due to the natural phenomena causing the loss or displacement of land along the coast. This incident will have an impact on communities near the beach as well as coastal development in Peninsular Malaysia. Therefore, monitoring the shoreline is necessary to protect the environment along the coast of Selangor and deal with the problems that occur. This study aims to assess shoreline changes on a monsoon-dominated beach using a linear regression rate and forecast significant upcoming shoreline movement on the Selangor coast. This study monitors shoreline changes at Sabak Bernam using temporal data, Landsat 8 OLI/TIRS, and Landsat 4-5 TM satellite images. The DSAS executes statistical operations such as shoreline change envelope (SCE), net shoreline movement (NSM), end point rate (EPR), and linear regression rate (LRR). Bagan Sungai Burong, Sungai Nibong, and Bagan Nakhoda Omar experienced significant accretion rates, with values ranging from 223.076 to 177.145 meters per year, as assessed by the SCE model. Conversely, the NSM model identified substantial erosion at Sungai Nibong (-57.503 meters per year) and high accretion at Bagan Sungai Burong (177.113 meters per year). Other models showed no significant differences in erosion and accretion rates across the five zones. The LRR model, preferred for its precision, was employed to predict future shoreline positions for 2040 and 2060. The results suggest minimal changes in coastal dynamics, indicating relative stability in the region's shoreline. This research provides critical insights into the coastal processes of Sabak Bernam, aiding in effective coastal management and planning for future resilience.
Copyright (c) 2024 Journal of Advanced Geospatial Science & Technology
Harnessing Machine Learning and Ensemble Models for Tourism Potential Zone Prediction for the Assam State of India
Although several popular tourist destinations exist in Assam, India, its charm remains enigmatic. This research was aimed at predicting the tourism potential zone (TPZ) for the state of Assam using five machine learning models (i.e., Conditional Inference Tree, Bagged CART, Random Forest, Random Forest with Conditional Inference Tree, and Gradient Boosting models) and one ensemble model. A 5-step methodology was implemented to conduct this research. First, a tourism inventory database was prepared using Google Earth Imagery, and a rapid field investigation was performed using the global positioning system and nonparticipant observation technique. A total of 365 tourism points were present in the inventory, 70% (224 points) of which were used for the training set and 30% (124 points) for the validation set. Tourism conditioning factors, such as relief, aspect, viewshed, forest area, wetland, coefficient of variation of rainfall, reserve forest, population density, population growth rate, literacy rate, and road–railway density, were used as independent variables in the modeling process. The TPZ was predicted using the above machine learning models, and finally, a new TPZ ensemble model was proposed by combining all the models. The result showed that all machine learning models performed well in terms of prediction accuracy, and the ensemble model outperformed other models by achieving the highest area under the curve (97.6%), Kappa (0.82), and accuracy (0.93) values. The findings from this research using machine learning and ensemble methods can provide accurate and significant information for decision-makers to develop tourism in the region.
Ordered Weighted Average Parameterization for Flood Related Disaster based Edutourism
Disaster-based edutourism can be defined as the activities of visiting environmental disaster areas occurred for education purposes. Although Kelantan witnesses flood disasters almost every year, little does the community know that the negative flooding phenomenon could be turned into disaster-based edutourism. The primary constraint of disaster-based edutourism is the limited specific parameters and subparameters that tourists prefer for attraction selection, within which tour packages are typically established. Moreover, regarding the parameter, there are few touches on the geospatial aspect, especially in tourist spatio-temporal movement. This study aims to perform the parameterization for attraction site selections of disaster-based edutourism utilizing the Ordered Weighted Average (OWA) model. This study has three objectives: to identify the parameters of the attraction selection for disaster-based edutourism, to carry out the parameterization of the attraction selection to create the spatial-related model for disaster-based edutourism and to design tour packages including specific disaster attractions. In brief, parameterization defines an attraction’s characteristics to provide a good tour. Consequently, the study suggested including a geospatial-based proximity measure between different attractions as an added parameter. The model’s outcome has shown the relationship underpinning the score of each single parameter and sub-parameter. The second output is the ranking of the attractions derived from the multiple products of OWA with the combination of parameter and sub-parameter scores. The top six attractions are Kelantan State Museum, Kelantan Public Library, Sultan Muhammad IV Kelantan Stadium, Tambatan Diraja, KTM Dabong and Tugu Peringatan Banjir. Finally, this ranking was used to produce the two types of tour packages. The first type of Set A tour package includes all six attractions in the whole state of Kelantan. Meanwhile, the second type of Set A tour package includes only the top four attractions listed for the Kota Bharu-based tour package. Though the prominence of spatial-linked parameters is of medium significance, this study is critical because it can assist tourists in planning their holidays via the suggested tour package. Subsequently, this will spur tourism in Kelantan based on the added value of disaster-based tourism.
A Perspective on Topographic Correction Methods on Satellite Images
The presence of atmospheric and topographic effects in satellite images is inevitable, which may reduce image information content. A standard procedure for improving satellite images is topographic and atmospheric correction during preprocessing. The topographic effect on satellite images is not an error but a distortion caused by solar and surface geometries. Surfaces facing toward the Sun tend to be bright, whereas surfaces facing away from the Sun are usually dark. This effect is strongly related to the solar surface incident angle, and it is one of the main factors that increases the spectral variation in satellite images. The objective of this paper is to review the commonly available methods for topographic correction. The spectral variation may reduce the accuracy of processes, such as surface topographical classification, which can limit the capability of autonomous remote sensing applications. Many have tried to reduce the effect of topography and achieved great success; however, most methods are complicated and require many parameters. The topographic correction methods can be categorized into two groups: empirical and physical methods. In this paper, a total of six empirical methods were reviewed, including Cosine correction (CC), Statistical-Empirical (SE) correction, Minnaert (MIN) correction, Shepherd and Dymond’s (SD) Correction Method, Sun-canopy-sensor (SCS) Models and Path Length Correction (PLC) Method. The algorithms and models used in the physical topographical correction method were also discussed. Parameters related to the topographic correction algorithm were reviewed in detail. This paper reviewed a total of six common topographic correction methods and seven assessment methods for topographic correction.
Modelling XCO2 for Peninsular Malaysia using Satellite Data and Atmospheric Parameters
This study aims to develop an algorithm for calculating the column-averaged dry air mole fraction of carbon dioxide (XCO2) over peninsular Malaysia using statistical methods. Data from five atmospheric variables consisting of the aerosol asymmetry factor (AAF), aerosol optical thickness (AOT), temperature (temperature), water vapor (H2O vapor) and aerosol single scattering albedo (SSA) were utilized to develop a predictive XCO2 regression model using multiple linear regression (MLR) for examining the impacts of the atmospheric variables on the XCO2. The predictive XCO2 regression model highly correlates with atmospheric variables (R2 = 0.68 for Northeast Monsoon and R2 = 0.64 for Southwest Monsoon). The validation results show that XCO2 yielded a strong R2 for the Northeast Monsoon and Southwest Monsoon seasons, i.e., 0.84 and 0.83, respectively. The proposed regression model exhibited excellent agreement under different monsoon seasons in Peninsular Malaysia.
Simulation of Sea Level Rise at Major Town of East Coast of Peninsular Malaysia
Global warming is caused by greenhouse gases resulting from natural causes and human activities. This event causes the sea level rise (SLR). The effects of SLR are the submerging of an area, saltwater intrusion, erosion, and the destruction of infrastructure and marine ecosystems. The objectives of this study are to identify the year when the sea water level rises 1 m, 2 m, and 3 m and to estimate inundated areas that are affected by floods at different sea water level scenarios at major towns of East Coast Peninsular Malaysia. The major cities selected are Kota Bharu, Kuala Terengganu, Kuantan, and Kota Tinggi, with a study site area of 96 km2, 38 km2, 16 km2, and 10 km2, respectively. Study site selection is based on the high concentration of socioeconomic (quality of life, economic value, and infrastructure). The SLR of 1 m, 2 m, and 3 m was based on tidal data and IFSAR data for the SLR simulation. The data collected were analyzed using ERDAS 2011 and ArcGIS software. Based on the 1 m to 3 m SLR, the water level is predicted to occur from 2319 to 2374 for all study areas. Results indicate that Kota Bharu is the most affected by the 1, 2, and 3 m SLR, with an inundation area of 15%, 37%, and 60% of its total study area. For 1 m SLR, just 5% of the total study area will be inundated in Kuala Terengganu, but this will increase to 51% when the water level rises to 3 m. Kuantan and Kota Tinggi are located on higher ground, where an increase in seawater level to 3 m will affect about 40% of the study area. Other factors related to extreme events or/and human activities would be accelerating sea-level rise to severe impacts on low-lying over a short period. However, information on SLR projection at the local level is indeed part of the future coastal management and town development. Adaptation options and flood mitigation for the most affected area should be considered to minimize the impact of SLR.
Multi-temporal Image Analysis for Land Cover Classification and Change Detection of Kuching Division, Sarawak
Kuching and its surrounding area are vital in Sarawak’s economic growth. Over the years, the transformation of its land cover (LC) has brought significant ecological, physical, and socioeconomic consequences. Updated and precise LC maps are essential for urban planning, sustainable development, and environmental forest degradation monitoring. This study mainly focuses on LC classification using the Support Vector Machine Classifier (SVM) algorithm. The change has been identified for five general classes, which are Urban Land, Barren Land, Forest Land, Agriculture Land, and Water Bodies, for 35 years using Landsat 5 TM image dated 26 June 1988 and Landsat 9 OLI image dated 16 April 2023 in ArcGIS 10.6.1 software. Change detection analysis indicated that from 1988 to 2023, the LC patterns changed significantly. The most substantial changes were urban land, which increased significantly from 6,200.71 ha in 1988 to 22,144.76 ha in 2023, which represents a net increase of 15,944.05 ha or 3.88 per cent change (%), followed by agriculture land, barren land, forest land, and water bodies categories. The results show good classification performance because the user’s accuracy for every class is above 85%. LC change, which displays the spatial expansion of the urban land in Kuching, indicates the development of Sarawak is in line with the aspiration to be a developed state by 2030 (Post COVID-19 Development Strategy 2030).
Hotspot Analysis of COVID-19 using Spatial Statistic in Selangor Malaysia
The study investigates the spatial distribution of COVID-19 cases in Selangor, Malaysia, utilizing geospatial and geostatistical techniques to identify and analyze hotspots. Focusing on data from 54 districts between April and August 2021, the research employs Global Moran’s I and Getis-Ord Gi* models to detect spatial autocorrelation and clustering patterns. The study also incorporates additional parameters, including new cases, cumulative cases, deaths, clusters, and population density, to comprehensively analyze COVID-19 hotspots. The results reveal a significant clustering of COVID-19 cases, with specific districts like Petaling, Hulu Langat, and Klang identified as high-risk hotspots. The findings of this research emphasize the critical role of spatial analysis in understanding the spread of infectious diseases like COVID-19. By identifying and mapping out high-risk districts, this study provides valuable insights that can inform public health strategies and optimize resource allocation in response to the pandemic. Identifying hotspots within Selangor underscores the necessity for targeted interventions and deploying healthcare resources to areas most affected by the virus. Ultimately, this study contributes to a deeper understanding of the spatial dynamics of COVID-19 in Selangor, offering a framework for future research and public health planning in the context of epidemic management.
Integration of Different Density UAV Lidar and Single Beam Echo Sounder (SBES) for River and Riparian Area Digital Terrain Model (DTM) Construction
Rivers and riparian areas are vital components of ecosystems, but accurately modeling their terrain presents challenges, especially in detecting the river surface. This paper proposes an integrated approach that combines UAV LiDAR and Single Beam Echo Sounder (SBES) data to construct a Digital Terrain Model (DTM) of river and riparian areas. The objective is to overcome the limitations posed by water, which absorbs near-infrared laser energy, resulting in weak or absent LiDAR returns. Different UAV LiDAR densities were examined to determine the optimal configuration for capturing riparian areas. Evaluation of the results utilized various metrics, including root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean bias error (MBE), and correlation coefficient (CC). Three ground filtering methods were implemented and assessed: morphological filters, adaptive triangulated irregular network (ATIN) filtering, and above-ground level (AGL) filtering. Among the evaluated methods, the DTM constructed using ATIN with an 80-meter flight configuration yielded the most accurate results. It achieved an RMSE of 0.18m, an MSE of 0.03m, an MAE of 0.17m, an MBE of 9.08m, and a CC of 1.00. Comparatively, other methods exhibited higher error values and lower correlation coefficients. The findings highlight the efficacy of ATIN filtering in conjunction with an 80-meter UAV LiDAR flight for obtaining reliable DTMs of river and riparian areas. This approach demonstrates significant improvement in accuracy, particularly in terms of RMSE and MSE. The derived DTM can be a valuable tool for safeguarding and managing these critical ecosystems. In summary, this paper successfully addresses the challenge of modeling river and riparian terrains by integrating UAV LiDAR and SBES data. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a highly accurate DTM. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a precise, high DTM with minimal error. The developed model contributes to protecting and preserving river and riparian ecosystems.