Zailani Khuzaimah

Land Use Change Effects on Extreme Flood in the Kelantan Basin Using Hydrological Model

Land use and land cover (LULC) change results in increased of flood frequency and severity. The increase of annual runoff which is caused by urban development, heavy deforestation, or other anthropogenic activities occurs within the catchment areas. Therefore, accurate and continuous LULC change information is vital in quantifying flood hydrograph for any given time. Many studies showed the effect of land use change on flood based on hydrological response (i.e., peak discharge and runoff volume). In this study, a distributed hydrological modeling and GIS approach were applied for the assessment of land use impact in the Kelantan Basin. The assessment focuses on the runoff contributions from different land use classes and the potential impact of land use changes on runoff generation. The results showed that the direct runoff from developmental area, agricultural area, and grassland region is dominant for a flood event compared with runoff from other land-covered areas in the study area. The urban areas or lower planting density areas tend to increase for runoff and for the monsoon season floods, whereas the inter-flow from forested and secondary jungle areas contributes to the normal flow.

Mangrove Changes Analysis by Remote Sensing and Evaluation of Ecosystem Service Value in Sungai Merbok’s Mangrove Forest Reserve, Peninsular Malaysia

Mangrove forests are an important ecosystem which provides socioeconomic value to humankind. Despite their great value, mangroves have one of the highest rates of degradation of any global habitat, which is about 1% of the existing area per year. In fact, the socioeconomic value and ecosystem services of mangroves as a natural product are underestimated. The ecosystem services provided by mangroves are often ignored by the ongoing process of mangrove conversion. This is a major reason why conservation of this ecosystem is not a popular alternative. Thus, the main objective of this study is to evaluate the changes in mangrove forests and valuation of their ecosystem services. SPOT 5 imageries of years 2000 and 2010 have been used for change detection analysis. The vegetation index such as NDVI and AVI and unsupervised classification technique were employed in image processing. In order to obtain the value of socioeconomic impact from the mangrove changes and biodiversity disturbances, the ecosystem service valuation (ESV) model was applied. Results show that the total value of the existing mangrove forest ecosystem service was RM1,901,859.84. The value per unit area is about RM 1,650.92 /ha. The total values of others were RM161, 33.2 (crop land) and RM3,107,500 (water bodies), respectively. It is evident that Sungai Merbok’s Mangrove Forest Reserve is very important for coastal ecology, where the orientation of mangrove ecosystem is huge and serves to provide essential services for the community. It also plays a crucial role in providing ecological balance to the coastal environment.

Impact of land cover change on the environmental hydrology characteristics in Kelantan river basin, Malaysia

Changing the land cover/ land use has serious environmental impacts affecting the ecosystem in Malaysia. The impact of land cover changes on the environmental functions such as surface water, loss water, and soil moisture is considered in this paper on the Kelantan river basin. The study area at the east coast of the peninsular Malaysia has suffered significant land cover changes in the recent years. The current research tried to assess the impact of land cover changes in the study area focused on the surface water, loss water, and soil moisture from different land use classes and the potential impact of land cover changes on the ecosystem of Kelantan river basin. To simulate the impact of land cover changes on the environmental hydrology characteristics, a deterministic regional modeling were employed in this study based on five approaches, i.e. (1) Land cover classification based on Landsat images; (2) assessment of land cover changes during last three decades; (3) Calculation the rate of water Loss/ Infiltration; (4) Assessment of hydrological and mechanical effects of the land cover changes on the surface water; and (5) evaluation the impact of land cover changes on the ecosystem of the study area. Assessment of land cover impact on the environmental hydrology was computed with the improved transient rainfall infiltration and grid based regional model (Improved-TRIGRS) based on the transient infiltration, and subsequently changes in the surface water, due to precipitation events. The results showed the direct increased in surface water from development area, agricultural area, and grassland regions compared with surface water from other land covered areas in the study area. The urban areas or lower planting density areas tend to increase for surface water during the monsoon seasons, whereas the inter flow from forested and secondary jungle areas contributes to the normal surface water.

Landslide Susceptibility Analysis Using An Artificial Neural Network Model

This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.