Wan Hazli Wan Kadir

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).

Development of local atmospheric model for estimating solar irradiance in Peninsular Malaysia

Incoming solar irradiance covers a wide range of wavelengths with different intensities which drives almost every biological and physical cycle on earth at a selective wavelength. Estimation of the intensities of each wavelength for the solar irradiance on the earth surface provides a better way to understand and predict the radiance energy. It requires that the atmospheric and geometric input and the availability of atmospheric parameter is always the main concern in estimating solar irradiance. In this study, a local static atmospheric model for Peninsular Malaysia was built to provide the atmospheric parameters in the estimation of solar irradiance. Ten years of monthly Atmospheric Infrared Sounder (AIRS) average data (water vapor, temperature, humidity and pressure profile) of the Peninsular Malaysia was used for the building of the atmospheric model and the atmospheric model were assessed based on the measured meteorological data with RMSE of 4.7% and 0.7k for both humidity and temperature respectively. The atmospheric model were applied on a well-established radiative transfer model namely SMARTS2. Some modifications are required in order to include the atmospheric model into the radiative transfer model. The solar irradiance results were then assessed with measured irradiance data and the results show that both the radiative transfer model and atmospheric model were reliable with RMSE value of 0.5 Wm−2. The atmospheric model was further validated based on the measured meteorological data (temperature and humidity) provided by the Department of Meteorology, Malaysia and high coefficient of determination with R2 value of 0.99 (RMSE value = 4.7%) and 0.90 (RMSE value = 0.7k) were found for both temperature and humidity respectively.

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.