Azman Ariffin

Extraction of Digital Terrain Model (DTM) over vegetated area in tropical rainforest using LiDAR

This paper presents investigations on the combination effect of landcover types, ground filtering approach and interpolation methods on Digital Terrain Model (DTM) generated from airborne LiDAR over vegetated area in tropical environment. The study area is separated into three landcover types i.e. oil palm, mangrove and mixed forest. The LiDAR data is filtered based on: 1) Adaptive TIN (ATIN), 2) Progressive morphology (Morph), and 3) Elevation Threshold with Expand Window (ETEW). The DTMs are generated by interpolating the ground points using Ordinary Kriging and Inverse Distance Weighted (IDW) methods. The quality of DTMs is evaluated based on the combination of quantitative and qualitative approaches. The results show that combination of ATIN and Ordinary Kriging has produced DTMs with higher quality compared to other combination of filtering and interpolation technique. The smallest value of RMSE obtained for terrain covered by oil palm (0.21m) followed by mixed forest (0.25m) and mangrove (0.32m).

Spatial-based sustainability assessment of urban neighbourhoods: a case study of Johor Bahru City Council, Malaysia

Rapid population growth has caused expansion of many major cities. Cities begin to expand into new areas as the demand for housing increases, thus, contributing to demand for a variety of natural and man-made resources for urban communities. However, it is our responsibility to sustain these resources so that their usage can be prolonged to the next generation. With sustainability as a goal, the use of indicators for urban monitoring and regulation is becoming more in demand. There are many non-spatial indicators in the form of words and statistics developed by local authorities for assessing urban development sustainability. This chapter proposes the use of spatial indicators for the same purpose. The indicators are derived from the Malaysian Urban Indicators Network (MurniNet) and are then developed using Analytical Hierarchy Process (AHP) comprising spatial elements of points, lines, and polygons. The AHP is used to determine the ranking of sustainability of urban areas. This study selects Johor Bahru City Council (JBCC) administrative area as a case. The result shows that spatial indicators can contribute to a better visualisation of sustainability via the production of sustainability map.

Integration of high density airborne LiDAR and high spatial resolution image for landcover classification

This paper discusses landcover classification using high density airborne LiDAR data and multispectral imagery. The study area is located at the Duursche Waarden floodplain, the Netherlands. The density of the FLI-MAP 400 LiDAR system is between 50 and 100 points per m2. Other than height and intensity, the LiDAR system also measures spectral information (Red, Green, and Blue). Several features are created for height, intensity, Red, Green, and Blue. The landcover classification process is divided into Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers. Each classifier is used on three different datasets: 1) FLI-MAP 400-generated multispectral images, 2) LiDAR-derived features, and 3) a combination of the multispectral images and the LiDAR-derived features. The results show that the SVM method produces better classification results than the ML method. Landcover classification based on the combination of LiDAR-derived features and multispectral images produces better results than classification based on either dataset only.