Abd Wahid Rasib

Per-pixel and sub-pixel classifications of high-resolution satellite data for mangrove species mapping

High spatial resolution sensors such as IKONOS and QuickBird, are expected to classify mangrove species more accurately than coarse spatial resolution satellite images. Conventional per-pixel classification techniques could not improve the classification accuracy when such high-resolution images are applied. Such failure has encouraged the invention of more sophisticated and deterministic techniques i.e. subpixel classifications. In this study, the mangrove forest at Sungai Belungkor, Johor, Malaysia was classified using IKONOS data. Two classification approaches were applied, namely per-pixel and sub-pixel techniques. The conventional per-pixel classifiers used in this study were Maximum Likelihood (ML), Minimum Distance to Mean (MDM) and Contextual Logical Channel (CLC) while the Linear Mixture Model (LMM) was selected as the sub-pixel classification approach. The classification results revealed that the CLC classification with a contrast texture measure at window size 21 x 21 yielded the highest accuracy (82%) in comparison to the ML (68%) or MDM (64%). The spatial distribution of the classified mangrove species and classes coincided with the common mangrove zones in Malaysia. For the results of the LMM, the fraction of pixels measured from the satellite imagery and observed in the field gave a good correlation with an R2 value of 0.83 for Bakau minyak, a moderate correlation with an R2 of approximately 0.71 for Bakau kurap and an R2 of 0.75 for the ‘Others’ type of mangrove species. An error image was also created to compare the best fitting spectrum produced by the inversion of the LMM with the original observed spectrum, where the maximum RMS error was only 5%.

The use of AVHRR data to determine the concentration of visible and invisible tropospheric pollutants originating from a 1997 forest fire in Southeast Asia

A massive forest fire in Indonesia in 1997 affected the whole Asian region by producing a large smoke plume, with Malaysia bearing the brunt due to the wind direction and weather conditions and because of its proximity to the source. The five primary fire produced pollutants were carbon monoxide (CO), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and particulate matter less than 10 µm (PM10). The first four of these are, of course, invisible to conventional satellite-flown multispectral scanners operating in the visible and near infrared regions of the electromagnetic spectrum. The fifth, PM10, is present in the haze and therefore makes an observable contribution to the signal received by the Advanced Very High Resolution Radiometer (AVHRR). The haze in AVHRR channels 1 and 2 data for the fires of September 1997 has been used to study the concentration of PM10 directly. It has also been used to study the concentration indirectly--as a tracer or surrogate--for the four remaining materials, the gases CO, SO2, NO2 and O3. Data from ground observations have been used to calibrate the results and the distributions of the fire pollutants over Peninsular Malaysia have been plotted.

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).

Determination of rubber-tree clones leaf diseases spectral using Unmanned Aerial Vehicle compact sensor

Currently, one of the remote sensing platform that adequatly been used is Unmanned Aerial Vehicle (UAV) which suitable in monitoring and mapping for agriculture sector at large area payload by compact sensor. Thus, this study is deploy the UAV compact sensor to identify the characteristics of rubber tree clone leaf diseases based on two groups of spectral wavelength which is visible (RGB: 0.4 µm – 0.7 µm) and near infrared (NIR: 0.7µm – 2.0 µm), respectively. Spectral obtained using UAV platform is then to be validated with ground observation handheld spectroradiometer. Eight types of rubber tree clones leaf at three different conditions (healty, unhealty and severe) were randomly selected within the 9.4 hectare Experimental Rubber Plot, Rubber Research Institute of Malaysia (RRIM), Kota Tinggi, Johor whereby consist RRIM 2000 series, RRIM 3000 series and PB series, respectively. As a result, this study has found that the spectral trend based on UAV compact sensor from eight different types of rubber tree clones leaf shows the similarity to the general basic vegetation spectral in transition from blue to NIR spectral. However, referring to spectral obtained from handheld spectroradiometer there is no drastically changes of spectral in visible region but drastically increase in NIR region. Thus, this study has conclude that the spectral signature characteristics for healthy, unhealthy and severe for leaf diseases from every single rubber tree clones can be identified obviously in NIR region using UAV compact sensor.

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.