GIS and Multi-criteria Analysis for School Site Selection (Study Case: Malacca Historical City)

Abstract: A set of school suitability map would be very useful for education planners when making a complex decision within a short period of time. This study will utilize both spatial and non-spatial parameters to establish a systematic site selection process for primary schools in Melaka Tengah District. It was carried out by using Geographic Information System (GIS) and Multi-criteria Decision Analysis (MCDA). Three analysis namely demographic, safety and constrain analysis were used to identify the potential sites. Then accessibility analysis, using expertise and public opinion were used to further analyze the potential site. The resulted map showed 54.2% of the total area is highly not suitable, leaving 46% suitable for school sitting. The final safety model output was compared with field verification data from State Education Department (JPN Melaka) and Malacca Historical City Council (MBMBB).

Rubber-Tree Leaf Diseases Mapping Using Close Range Remote Sensing Images

 Currently, close-range remote sensing method using drone-based platform which payload compact sensor has been used for monitoring and mapping in the agriculture sector at large area. Thus, this study is deployed drone with a compact sensor to identify the rubber tree leaf diseases based on two groups of a spectral wavelength which are visible (RGB: 0.4 µm – 0.7 µm) and near infrared (NIR: 0.7µm – 2.0 µm), respectively. Spectral obtained from drone-based platform will be validated using ground observation handheld spectroradiometer. Eight types of rubber tree clones leaf at three different conditions (healthy, unhealthy 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. Based on the result, quantitative analysis shows that the f-value is smaller than Critical-one tail for healthy, unhealthy while for severe the f-value is larger than Critical-one tail. The f-value is 2.887 < 4.283 (healthy), 0.002 < 0.264 (unhealthy) and 1.008 > 0.0526, respectively. Thus, this can be concluded that spectral and estimate is equal at the 0.05 significant levels. For qualitative analysis, it shows that each rubber clone tree diseases can be distinguished at the near infrared band for healthy, unhealthy and severe respectively

Rubber-Tree Leaf Diseases Mapping Using Close Range Remote Sensing Images

 Currently, close-range remote sensing method using drone-based platform which payload compact sensor has been used for monitoring and mapping in the agriculture sector at large area. Thus, this study is deployed drone with a compact sensor to identify the rubber tree leaf diseases based on two groups of a spectral wavelength which are visible (RGB: 0.4 µm – 0.7 µm) and near infrared (NIR: 0.7µm – 2.0 µm), respectively. Spectral obtained from drone-based platform will be validated using ground observation handheld spectroradiometer. Eight types of rubber tree clones leaf at three different conditions (healthy, unhealthy 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. Based on the result, quantitative analysis shows that the f-value is smaller than Critical-one tail for healthy, unhealthy while for severe the f-value is larger than Critical-one tail. The f-value is 2.887 < 4.283 (healthy), 0.002 < 0.264 (unhealthy) and 1.008 > 0.0526, respectively. Thus, this can be concluded that spectral and estimate is equal at the 0.05 significant levels. For qualitative analysis, it shows that each rubber clone tree diseases can be distinguished at the near infrared band for healthy, unhealthy and severe respectively