bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Assessment of the Unsupervised Land Use Classification by Using Proprietary GIS and Open Source Software
Authors: Ismail Muhammad Azizol , Muhamad Ludin Ahmad Nazri , Hosni Nafisa 
Published: August 2020

Abstract
Mapping and investigating land use land cover (LULC) changes over a particular region is crucial for resource management, sustainability development, and holistic planning. An increasing rate of urban growth and urban sprawl could induce changes in land use as well as land transformation. However, accurate and up-to-date information about LULC is required for providing better understanding and assessing the environmental consequences of such changes. In this study, the 2017 image from the Sentinel-2A Satellite was utilized to demonstrate the land cover classification analysis in Iskandar Malaysia. Usually, land use classification analysis is conducted through proprietary GIS software. However, this decade shows the advancement in software development, thus the emerging of free/open source software in the geospatial world. Hence, to execute land cover analysis using the Unsupervised Classification technique, the proprietary GIS software (ArcGIS) and free/open source software (QGIS) were deployed. Then, the examination of accuracy assessment was carried out for the selected software. The sum of 250 random points was established for the assessment purpose. The results showed the overall accuracy for ArcGIS and QGIS were 82.80% and 80.40% respectively. The kappa coefficient for ArcGIS was 0.7395, while kappa coefficient for QGIS was 0.7094. Besides that, ArcGIS demonstrated better producer’s accuracy in the forest and agriculture land covers classification. Meanwhile, QGIS exhibited better producer’s accuracy in the built-up and water features classification. To summarize, ArcGIS and QGIS software are reliable to be used in the land cover classification.
Improved Land Cover Mapping Using Landsat 8 Thermal Imagery
Authors: Al-Doski Jwan , Mansor Shattri B. , San H’ng Paik , Khuzaimah Zailani 
Published: August 2020

Abstract
Detection of land cover (LC) changes allows policymakers to recognize the complexities of environmental modification and change to achieve sustainability of economic growth. As a result, recognition of LC features has appeared as an essential research dimension and, consequently, an appropriate and reliable methodology for classifying LC is occasionally required. In this research, Landsat 8 satellite data captured by Operational Land Imager (OLI) and Thermal Infrared Scanner (TIRS) were utilized for the LC classification using the Support Vector Machine (SVM) classifier algorithm. The aim of the study is to enhance classification accuracy by integrating the use of data from satellite thermal and spectral imaging. Land Surface Temperature (LST) is sensitive to the soil surface characteristics, therefore, it may be used to gather LC feature information. The classification accuracy was designed to enhance the integration of thermal information from Landsat 8’s thermal band TIRS and Landsat 8 OLI’s spectral data. In this study, Advanced Thermal Integrated Vegetation Index (ATLIVI) and Thermal Integrated Vegetation Index (TLIVI) established and revealed fairly strong correlations with the related surface temperature (Ts) by R2=0,7 and 0,65 respectively. The relationship between Ts and the other vegetation indices based on the empirical parameterization demonstrate that these two indices showed an improvement of almost 6% in the overall accuracy of the LC classification results compared to the Landsat 8 Standard False Colour Composite image as an input data using SVM algorithm.
Conceptual Framework for Walkability Assessment for Pedestrian Access to Rail Transit Services by using Spatial-MCDA
Authors: Naharudin Nabilah , Salleh Abdul Hakim , Halim Maisarah Abdul , Latif Zulkiflee Abd 
Published: August 2020

Abstract
Rail transit services are becoming an important aspect in enhancing mobility in a city. Its level of service is influenced by the journey from the very first to the last mile of using the transit service which refers to walking to access a transit station. Many studies had emerged in an attempt to measure walkability which most of them translated pedestrian’s preferences or perception on walking or used accessibility analysis. There could be drawbacks in these studies which are (a) only few of them had include indoor walking environment in measuring walkability, (b) there could be inconsistencies in making judgement for many criteria simultaneously, and (c) walkability is never a measure of accessibility only. Therefore, this study will attempt to (a) create a spatial model representing indoor walking environment that can be applied in measuring walkability, (b) evaluate the priorities for each of the walkability criteria by using Analytical Network Process (ANP) and (c) measure walkability of pedestrian routes and the existing rail transit stations by using GIS. The main finding of this study will be the walkability index for pedestrian routes to the existing rail transit stations in KL city centre.
Non-Destruction Method for Detecting Corroded Underground Pipe Using Ground Penetrating Radar
Authors: Shokri Nurhazimah Husna , Amin Zulkarnaini Mat , Seli Vanessa Ak 
Published: August 2020

Abstract
Ground-penetrating radar (GPR) is a geophysical method that can accurately map the spatial extent of near-surface objects or changes in soil. It also produce radargram of those features. The demand for underground utility mapping in Malaysia is increasing rapidly for construction and non-construction activities. However, implementation of GPR in detecting corroded underground pipe in Malaysia can considered unpopular due to less exposure and exploration. Over the time, underground utility pipe will undergo corrosion process. Thus, is it relevant to detect old corroded pipe using GPR? On this basis, the aim of this experimental study was to validate the condition of corroded pipe by comparing its radagram profiles and to evaluate the significant signal reflectivity which were tested on three different pipe condition, namely new, average and old corroded pipe. The result of GPR calibration and data on GPR were processed using Reflex 2D software. In this study, with the aid of Time-Domain Reflectometry (TDR) sensor was used to obtain the soil moisture as it is one of the biggest in fluent to corrosion activity. In the GPR method, radar antennas moved along the ground in transects (B-scan), and two-dimensional profiles of a large number of periodic reflections (A-scan) were obtained. As conclusion, these GPR datasets and soil moisture from TDR reading are evaluated and been analysed thoroughly. At the end of this paper, it is suggested that GPR is efficiently can be used to detect corroded pipe.
Assessing Hutan Simpan Ampang using GIS-based Potential Surface Analysis approach
Authors: Hussainzad Emal Ahmad , Mohd Yusof Mohd Johari , Gao Hangyu , Yu Yang Kevin Kor , Kueng Ming Norman Wong , Abdul Majid Mohammad Shafiq Bin , Xu Tengyun 
Published: August 2020

Abstract
This paper aims to determine potential spaces for sustainable future development at Hutan Simpan Ampang, Ulu Klang, Malaysia. The site is highly valuable due to strategic location and high availability of spaces. However, due to several landslide incidents happened in Ulu Klang over the past decades, the site is categorised as a landslide-prone area. Therefore, Potential Surface Analysis (PSA) is conducted to determine the potential areas within the site that are safe and suitable for future development. All the factors were processed in Geographic Information System (GIS) through the overlay mapping technique, combining spatial and attribute data to obtain the suitability map. The result found that the majority coverage of the site is not suitable for any future development. There are only a few coverage areas that are suitable for small scale development. However, a combination of the very high suitability area and the high suitability area expanded the opportunity for sustainable future development.
Predictive Model of Mangroves Carbon Stocks in Kedah, Malaysia using Remote Sensing
Authors: Hashim T M Z T , Suratman M N , Singh H R , Jaafar J , Bakar A N 
Published: August 2020

Abstract
Mangroves are recognized as an ecosystem that grow and dominate the coastal areas of tropical and sub-tropical regions across the world. They not only provide ecological and socio-economic support, but also play a pivotal role in offsetting an excess of carbon from the atmosphere. Despite the crucial roles provided by mangroves, the ecosystem has degraded at an alarming rate mainly due to anthropogenic activities. Remote sensing technology provides a new dimensional perspective in monitoring and estimating tree biomass and carbon stocks. Therefore, this study aimed at (1) estimating the carbon stocks of mangroves in Kedah, Malaysia, (2) investigating the relationships between mangrove stand parameters with spectral reflectance recorded from Landsat 8 Operational Land Imager (OLI) data, and (3) developing predictive models for estimating the carbon stocks of mangroves by combining the ground and Landsat 8 (OLI) data. For the purpose of this study, a total of 81 mangrove stand data sets measuring at 100 m × 100 m were collected throughout Kedah, Malaysia. Within the stand, seven randomly selected plots were established and all individual mangroves parameter (diameter at breast height (DBH) and height) were measured. The 81 stands were split into two independent data sets for developing and validating the models (56 and 25 stands, respectively). Multiple regression technique with least square approach was used in the model development process. From several good candidate models, a model consists of four predictive variables (bands 3 and 6, Normalized Difference Vegetation Index (NDVI) and simple ratio seems to be predicting reasonably well based on its simplicity and practicality (p≤0.001, R2 = 0.56). Validation of the model has resulted in Mallow’s prediction criterion (Cp) value of 4.28 and Root Mean Squared Error (RMSE) of 4.11 Mg/ha. The information from this study may provide useful input for future research and can be crucial tools for the government and stakeholders in future decision making for the sustainability of mangrove resources.
Lineament Assessment of Aynak Copper mine using Remote Sensing approach
Authors: Mohammadi Farid Ahmad , Amin Zulkarnaini Mat , Ahmad Anuar Bin 
Published: August 2020

Abstract
This research developed a new approach to extract the lineaments using Landsat, QuickBird and ALOS PALSAR satellite remotely sensed data in Aynak copper mine which is located in Kabul tectonic block and the Tethyan Copper Belt of Afghanistan. Lineaments and trench directions are not relatively studied in the study area which will bring obstacles in exploration and extraction of the mine. This research aims to extract the lineaments for studying the better trench direction in the study area. Lineaments such as faults and joints are extremely important structures that not only show the surface structure but also give us important knowledge about the subsurface of the earth. Afghanistan is a part of the Alpine-Himalayan orogenic belt which suffers from high tectonic movements in some areas, especially north-eastern and central parts which leads to the occurrence of different lineaments and unconformities. Lineaments can be surveyed and mapped using field geological survey or using satellite remotely sensed data. Satellite remote sensing data costs less and takes less time compare to field geological survey for lineament extraction. This research used Landsat 8 natural color with 2, 3, 4 band composition, QuickBird 1m resolution, and ALOS PALSAR 12.5 m satellite imagery to detect and map the lineaments in the Aynak copper mine area in Afghanistan. Geomatica software was used for lineament extraction using the Manual technique. Then the rose diagrams of the lineaments were plotted using Polar Plots extension of ArcGIS. Next, the rose diagrams derived from the two mentioned lineament extraction techniques were compared and evaluated. Global Mapper software was used for the extraction of lineaments using Visual technique and to map the rivers and ridges in the Aynak area of interest for validation of the results. Finally, the best orientation for trenches in Aynak copper mine was derived. This study found that faults and joints in the study area have a NE-SW trend and directions. This study will contribute to a better understanding of the trench direction and mineral exploration in Aynak copper mine.
Evaluation of allometries for estimating above-ground biomass using airborne LiDAR data in tropical montane forest of Northern Borneo
Authors: Loh Ho Yan , James Daniel , Fei Liew Jim Jun , Ioki Keiko , Phua Mui-How 
Published: August 2020

AbstractTropical forests play a crucial component of the terrestrial carbon pool and estimate of above-ground biomass (AGB) with high accuracy is important in quantifying tropical forest carbon stocks. There are several allometries available for estimating tropical forest tree AGB using field measurements, the choice of allometric equation is a decisive factor that can influence the AGB estimation accuracy. This study examined the use of allometric equations to accurately estimate AGB using airborne LiDAR data. The LiDAR data of Ulu Padas area was acquired using Optech Orion C200. 56 field plots were established to collect data on diameter at breast height, tree height and tree species. Field AGB was calculated from allometric equations of Yamakuraet al. (1986), Basukiet al. (2009), Chaveet al. (2005) and Chaveet al. (2014). All LiDAR-derived height metrics and variables were correlated with field AGB (R: 0.30-0.88). Based on stepwise multiple regression analysis, Chaveet al. (2014) allometry had highest model R2, explaining 81% of the variance of the field AGB. In short, allometry that includes wood density should be used in LiDAR applications on tropical forest AGB estimation.
Modelling Burglary Susceptibility – An Expert Judgement Approach
Authors: Azmy S N , Rahman M Z A , Amerudin S , Zainon O 
Published: August 2020

Abstract
Developing a model allow a better understanding of the nature of a complicated phenomenon. With advancement of tools and technology, model development has been applied widely to mimic the phenomena of interest, spatial or non-spatial wise, allowing a guided decision making to be made. In this paper, the phenomena of burglary vulnerability and susceptibility are modelled based on expert opinion input to create a model that imitates the expert profiling of burglary occurrences, which is dependent on individual expert wisdom and experience in handling the burglary investigation. Due to seriousness of burglary crime offences in Malaysia, especially the urban areas, a prediction model is needed to correlates the factor of crime and further estimates the spatial susceptibility to work hand in hand with other government initiatives in reducing crime. Eighteen (18) indicators and 63 sub-indicators has been identified to be significant in defining the susceptibility of burglary. Apart from input of rating and ranking of indicators and sub-indicators obtained from questionnaire distribution to expert in handling burglary, the geospatial based data were also incorporated into the model to add the element of spatial accuracy in susceptibility prediction. The geospatial data includes the distribution of burglary incidence from 2010 – 2016, the census data, the building footprint data and the demarcation area. For the collected questionnaire feedback, the procedure of Analytical Hierarchy Process (AHP) were adapted to determine the weight value considering the rating input of expert from the distributed questionnaire. The input of weight and scoring were applied to the corresponding spatial features and combined with the operation of weighted sum to yield the total burglary susceptibility of a place. The results of the model were validated with the real reported burglary frequency based on True Positive Rate correlation matrix. The model validation finds that the model have a sensitivity of 82% in classifying the burglary susceptibility of the building polygon inside the study area. However this model still requires some improvement as it is still lacking to perform the classification of incidence intensity correctly.
Implementing Sustainable Urbanization Development Using Geospatial Approach
Authors: Ismail Muhammad Azizol , Muhamad Ludin Ahmad Nazri , Hosni Nafisa 
Published: August 2020

Abstract
There are many factors that contribute to urbanization such as natural increase in population and migration from rural to urban areas. Failure to regulate sustainable urban growth management will arise significant issues for instances depletion of natural resources, urban sprawl, and environmental pollution. This paper is geared towards investigating and mapping land use land cover (LULC) changes in Iskandar Malaysia, specifically in Iskandar Puteri and western gate development. The satellite images from the Landsat Thematic Mapper (TM) and Sentinel 2A for the year of 2007 and 2017 were deployed in the process of quantification of changes detection by using the Supervised Classification technique. Then, the magnitude of the changes was determined. IDRISI Terrset software was used in order to predict the future expansion of the study area until 2030. Finally, this paper proposed the delineation of urban growth limit as a sustainable strategy to direct further geographical development. The modelling of urban growth limit was based on the socio-economic and bio-physical factors. The findings and analysis in this paper have proven how prominent geospatial technology can be deployed in implementing sustainable development. The generated results have important policy implication for urban planners, stakeholders, and industrial players.
Review of Spatial Model to Assess Suitability of Affordable Housing Based on Demand Criteria
Authors: Zamri Zur’ain , Tarmidi Zakri , Adi Maimun Nurul Hana 
Published: August 2020

Abstract
Affordable housing has become essential, especially in developing countries, to cater the people needs on house. Most of the affordable housing focus is on providing house at affordable prices, but the locations of these affordable housing often not suitable with demand of people. Several initiatives were conducted to identified the demand of people, and the suitability of their houses with their demand. These model has different purpose, and lack of spatial element. Some model defining the people preference in order to choose a suitable for their house, while other model focusing on goals that relate to the site suitability of house that involve several cities. This study focus on integrating spatial information and analysis to assessing the suitability level of affordable housing in Malaysia. The assessment model consists of four main indicators; suitable site of affordable house, demographic factor, suitability house according neighbourhood context, and commute distance. With proper assessment, a better location can be defined, or more suitable facilities can be implement in the future in surrounding area of these affordable housing.
The use of geospatial data from GIS in the quantitative analysis of landslides
Authors: Ibrahim Muhammad B , Harahap Inrda S H , Balogun Abdul-Lateef B , Usman Aliyu 
Published: August 2020

Abstract
This study was conducted in order to compare two advanced technique used in establishing landslides susceptibility maps. The study considers a method of landslides analysis using the analytical hierarchy process (AHP) to check the occurrence of landslides in the study area through the establishment of a landslides susceptibility map based on the causative factors of landslides in the area. To further check and validate the process, it was compared with a more recent approach that is the soft computing (machine learning) technique. After the comparison, the enhanced analytical hierarchy process performed wonderfully well but not better than the machine learning method of analysis. Using the AHP methods, it was able to identify rainfall precipitation to be the major trigger mechanisms while 12 other conditioning factors were also identified. From the results obtained, it was observed that a good portion of the study area can be said to be susceptible to landslides. The analysis suggested that though the slides were fully triggered by rainfall precipitation, other factors such the geological and hydrological conditions facilitate the rapid occurrences of the phenomenal landslides in the study area. Validation was carried out by comparison of obtained results with inventories.
A spatial analysis of the relationship between socio-demographic characteristics with burglar behaviours on burglary crime
Authors: Chiew Ling Sie , Amerudin Shahabuddin , Yusof Zainab Mohamed 
Published: August 2020

Abstract
In recent years, the criminal and violent behaviour has become a national crisis in Asia and many pieces of research have been implemented to determine the criminal phenomena and the pattern of a crime occurred. Among the occurrences of crime incidence, the burglary crime was the most regular type of crime that commonly occurred in the urban area after the motor vehicles theft in Malaysia. Normally, the crime incidences were affected by several coinciding variables such as the offender’s behaviour and motivation, the physical environment factor and the victim’s behaviour. Overall, there are seven fundamental socio-demographic variables were determined and applied to investigate the pattern and motivation of burglar commit a crime incidence. The results showed that the burglary crime incidences were affected by the socio-demographic variable and burglar behaviour by using the spatial analysis method.
Spatio-temporal assessment of Aerosol Optical Depth from Himawari-8 satellite data over Malaysia
Authors: Kamarul Zaman Nurul Amalin Fatihah , Kanniah Kasturi Devi 
Published: August 2020

Abstract
Aerosols, particulate matters and air pollution are significant atmospheric issues in the Southeast Asia (SEA) countries particularly Malaysia. Despite their importance, data to investigate their spatio-temporal variation, estimating their abundance and modelling the trend are not widely available due to financial constraints and technical expertise. Remote sensing data are alternative solution to the limited data available from ground stations. One of the latest remotely sensed Aerosol Optical depth (AOD) data available is from Himawari-8 satellite that provides high temporal resolution data (every 10 minutes) at moderate spatial resolution of 5 km. Nevertheless, the robustness of these data for the tropical region has not yet been validated yet. This study aimed at assessing the performance of Himawari-8 AOD L2 product by (i) comparing its absolute AOD values against AOD retrieved from AERONET stations and (ii) analysing the spatial and temporal patterns of the AOD data. The AERONET data were averaged for ±5 min of the Himawari-8 overpass and matched to each other using statistical measures i.e. Correlation, Root mean square error (RMSE), Relative bias (RB) and Mean Absolute Error (MAE). Preliminary results of the analysis provided a coefficient of determination (R2) value = 0.31, RMSE = 0.16, and MAE = 0.15. The Himawari-8 AOD product is found to be overestimated the AOD values by 24.19%. Nevertheless, Himawari-8 AOD L2 product is able to exhibit similar spatial and temporal patterns compared to other remotely sensed AOD data such as that of MODIS. The high temporal resolution of Himawari data will facilitate frequent monitoring of air pollution in this region.