Abdul Rashid Mohamed Shariff

GIS-based modeling for the spatial measurement and evaluation of mixed land use development for a compact city

Compact cities are recognized as sustainable urban forms rather than sprawl developments. Such cities are characterized by high density, land use diversity, accessibility, and efficient public transportation. However, few studies investigate how and how much these parameters affect and relate to compact cities. For instance, although mixed land use is the main key planning principle of compact development, no standard method exists for quantifying, measuring, and evaluating this parameter. This study performs a compact development analysis of Kajang City (Malaysia) with emphasis on evaluating and discussing the importance of mixed land use development. First, the land use diversity of Kajang City was measured. Second, the probability map of mixed land use developments was predicted using a weights-of-evidence (WoE) model. Finally, the importance of mixed land use for compact cities was evaluated using multicriteria decision analysis (MCDA). The created mixed land use probability map was validated using the receiver operating characteristic (ROC) technique. In addition, the 75% similarity between mixed land use and compact development suitability maps highlighted the importance of mixed land use development for compact cities. Results can be used as preliminary guidelines for local governments and planners regarding compact development and management to achieve sustainable urban forms.

Maximal service area problem for optimal siting of emergency facilities

Geographic information systems (GIS) have been integrated to many applications in facility location problems today. However, there are still some GIS capabilities yet to be explored thoroughly. This study utilizes the capability of GIS to generate service areas as the travel time zones in a facility location model called the maximal service area problem (MSAP). The model is addressed to emergency facilities for which accessibility is an important requirement. The objective of the MSAP is to maximize the total service area of a specified number of facilities. In the MSAP, continuous space is deemed as the demand area, thus the optimality was measured by how large the area could be served by a set of facilities. Fire stations in South Jakarta, Indonesia, were chosen as a case study. Three heuristics, genetic algorithm (GA), tabu search (TS) and simulated annealing (SA), were applied to solve the optimization problem of the MSAP. The final output of the study shows that the three heuristics managed to provide better coverage than the existing coverage with the same number of fire stations within the same travel time. GA reached 82.95% coverage in 50.60 min, TS did 83.20% in 3.73 min, and SA did 80.17% in 52.42 min, while the existing coverage only reaches 73.82%.

Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember

Discrimination of tree species with different ages is performed in three classifications using hyperspectral data. The first classification is between Broadleaves and pines; the second classification is between Broadleaves, Corsican Pines, and Scots Pines, and the third classification is between six tree species including different ages of Corsican and Scots Pines. These three classifications are performed by having single- and multiple-endmember and considering five different spectral measure techniques (SMTs) in combination with reflectance spectra (ReflS), first and second derivative spectra. The result shows that using single-endmember, derivative spectra are not useful for a more challenging classification. This is further emphasized in multiple-endmember classification, where all SMTs perform better in ReflS rather than derivative in all classifications. Furthermore, using derivative spectra, discrimination accuracy become more dependent on the type of SMTs, especially in single-endmember. By employing multiple-endmember, the within-species variation is significantly reduced, thereby, the remaining challenge in discriminating tree species with different ages is only due to the between-species similarity. Overall, discrimination accuracies around 92.4, 76.8, and 71.5% are obtained using original reflectance and multiple-endmember for the first, second, and third classification, which is around 14.3, 17, and 8.3% higher than what were obtained in single-endmember classifications, respectively. Also, amongst the five SMTs, Euclidean distance (in both single- and multiple-endmember) and Jeffreys–Matusita distance (in single-endmember and derivative spectra) provided the highest discrimination accuracies in different classifications. Furthermore, when discrimination become more challenging from the first to second and third classification, the performance difference between different SMTs is increased from 1.4 to 3.8 and 7.3%, respectively. The study shows high potential of multiple-endmember to be employed in remote sensing applications in the future for improving tree species discrimination accuracy.

Digital Image Processing of Palm Oil Fruits

The oil content of the flesh of mesocarp has direct relationship with color bands red, green and blue. By running intensive experiments, it was found that oil content correlated with the red color band, with a regression value of 0.86. The finding of this study may be useful for determining the ripeness of oil palm for harvesting and for the use in the operation and control of continuous steriliser in palm oil mill.

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EFG) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EFG techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EFG technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.

A review on 3D terrain visualization of GIS data: techniques and software

3D terrain visualization of geographic information systems (GIS) data has become an important issue in recent years. This is due to the emergence of new geo-browsers such as Google Earth, widely popular among users. The availability of 3D representation tools has increased the demand for 3D terrain visualization. The aim of this paper is to review the literature related to the 3D terrain visualization of GIS data from the first map produced until the online mapping era. The reviews are divided into four different sections: manual visualization of 3D terrain, automated visualization of 3D terrain, online visualization of 3D terrain, and software for visualizing 3D terrain. Then, the paper compares between the different types of systems developed by various authors based on the capabilities and the limitations of the system. Some of the techniques have their own strengths and limitations which solve the problem in 3D terrain visualization. However, the research on improving 3D terrain visualization is still ongoing. This is due to the popularity of online environments and mobile devices that render 3D terrain. This review paper will help interested users understand the current state of 3D terrain visualization of GIS data in a better way.

A GIS-based model to analyze the spatial and temporal development of oil palm land use in Kuala Langat district, Malaysia

In Malaysia, areas under oil palm plantations have increased dramatically since the early twentieth century and have resulted in multiple conversions of land change. This paper presents a spatial and temporal model for simulation of oil palm expansion in the Kuala Langat district, Malaysia. The model is an integration of cellular automata (CA), multi-criteria evaluation (MCE), and Markov chain (MC) analysis while MCE provides transition rules of CA iterations and MC analysis assigns a transition probability to each single pixel at the time steps. Evaluation criteria consist of constraints and nine suitability factors indicating environmental and socio-economic issues of oil palm development. In the first simulation, changes of six land-cover classes were projected to the year 2008 based on transitions between 1997 and 2002. Two measures of quantity disagreement and allocation disagreement were adopted to validate model outcome. The simulation of land-cover change of the year 2020 was done based on the transition observed between 1997 and 2002 regarding the satisfactory agreement of the projection and the reference data at the first simulation. The results, based on five landscape metrics, indicated continuous spatial patterns of oil palm plantations but more fragmented spatial patterns of other land classes by the year 2020.

A refined classification approach by integrating Landsat Operational Land Imager OLI and RADARSAT-2 imagery for land-use and land-cover mapping in a tropical area

ABSTRACT Producing accurate land-use and land-cover (LULC) mapping is a long-standing challenge using solely optical remote-sensing data, especially in tropical regions due to the presence of clouds. To supplement this, RADARSAT images can be useful in assisting LULC mapping. The fusion of optical and active remote-sensing data is important for accurate LULC mapping because the data from different parts of the spectrum provide complementary information and often lead to increased classification accuracy. Also, the timeliness of using synthetic aperture radar (SAR) fills information gaps during overcast or hazy periods. Therefore, this research designed a refined classification procedure for LULC mapping for tropical regions. Determining the best method for mapping with a specific data source and study area is a major challenge because of the wide range of classification algorithms and methodologies available. In this study, different combinations and the potential of Landsat Operational Land Imager (OLI) and RADARSAT-2 SAR data were evaluated to select the best procedure for LULC classification. Results showed that the best filter for SAR speckle reduction is the 5 × 5 enhanced Lee. Furthermore, image-sharpening algorithms were employed to fuse Landsat multispectral and panchromatic bands and subsequently these algorithms were analysed in detail. The findings also confirmed that Gram–Schmidt (GS) performed better than the other techniques employed. Fused Landsat data and SAR images were then integrated to produce the LULC map. Different classification algorithms were adopted to classify the integrated Landsat and SAR data, and the maximum likelihood classifier (MLC) was considered the best approach. Finally, a suitable classification procedure was designed and proposed for LULC as mapping in tropical regions based on the results obtained. An overall accuracy of 98.62% was achieved from the proposed methodology. The proposed methodology is a useful tool in industry for mapping purposes. Additionally, it is also useful for researchers, who could extend the method for different data sources and regions.

Online 3D Terrain Visualization: A Comparison of Three Different GIS Software

In recent times, the online 3D terrain visualization has created much interest in many applications such as land information system, military, environmental, tourism and mapping science. Many systems such as CityGML, Terraserver, Google Earth and Spaceye3D have successfully adopted this technology in their system for different applications. The aim of this paper is to compare the effectiveness of developing online 3D terrain visualization by using three popular GIS Software; R2V, Arc View 3.2 and Arc GIS 9.2. In this paper, we implement the system based on the Digital Elevation Model (DEM) and Triangulated Irregular Network (TIN) for the terrain data and then overlay with satellite image. Finally, the effectiveness of online 3D terrain rendering in terms of terrain visualization quality, file size, and loading time are made to compare the quality of the systems. The results help the developers on finding the right software to be used for their online 3D terrain visualization.

Airborne hyperspectral discrimination of tree species with different ages using discrete wavelet transform

In this article, the capability of discrete wavelet transform (DWT) to discriminate tree species with different ages using airborne hyperspectral remote sensing is investigated. The performance of DWT is compared against commonly used traditional methods, i.e. original reflectance and first and second derivatives. The hyperspectral data are obtained from Thetford forest of the UK, which contains Corsican and Scots pines with different ages and broadleaved tree species. The discrimination is performed by employing three different spectral measurement techniques (SMTs) including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and a combination of SAM and SID. Five different mother wavelets with a total of 50 different orders are tested. The wavelet detail coefficient (CD) from each decomposition level and combination of all CDs plus the approximation coefficient from the final decomposition level (C-All) are extracted from each mother wavelet. The results show the superiority of DWT against the reflectance and derivatives for all the three SMTs. In DWT, C-All provided the highest discrimination accuracy compared to other coefficients. An overall accuracy difference of about 20–30% is observed between the finest coefficient and C-All. Amongst the SMTs, SID provided the highest accuracy, while SAM showed the lowest accuracy. Using DWT in combination with SID, an overall accuracy up to around 71.4% is obtained, which is around 13.5%, 14.7%, and 27% higher than the accuracies achieved with reflectance and first and second derivatives, respectively.