Abstract. Affordable housing was developed in order to give equal opportunity for middle and low-incomers in owning a house, especially in Malaysia. To make sure that these people can have a quality house, the National Housing Policy (DRN) with Pelan Tindakan DRN has been introduced by the Malaysian Government to not only provide adequate housing, but also a comfortable, fun and affordable for the wellbeing of the people in Malaysia (KPKT, 2011). Therefore, sustainability for housing is important to achieve balance between economic development, social interactions and environmental impact by reducing the problems related to population growth, urbanisation, slums, poverty, climate change, lack of access to sustainable energy, and economic uncertainty. One of the goals in DRN and Pelan Tindakan Dasar Perumahan Negara (PTDRN) is to provide an affordable housing and ensure the people from low-income can own a house. However, there is an issue towards assessing the sustainability level of affordable housing, especially in social aspects. This study will discuss on sustainability of affordable housing in Malaysia focused on social aspects. Assessment of spatial indicators was conducted to assess the indicator's implementation of social aspect of the sustainability model. The indicators used in this study include public community facilities, health, safety, religion, and public transportation. These indicators will determine the level of sustainability of the affordable housing. From the results, most of the affordable housing in Malaysia is in intermediate level of sustainability in term of social aspects. These results can help/guide the Government in planning and development in the future, especially with collaboration from private agencies and non-government organization (NGO).
Digital image classification of satellite remotely sensed data for forest mapping have been a great challenge in tropical rainforest areas due to high density of forest species in per unit area. This resulted in mixed pixels (mixels) in the captured image. Even with the advent ultrafine spatial resolution of current satellite images, the pure pixels of specific species within captured image is very rare. In automated digital image classification, mixels must be unmixed for an optimum output. This paper reports the results of study undertaken to spectrally unmixing of ASTER satellite data for detecting and mapping composition of Chengal trees (Neobalanocarpus heimi) within the mixels. Mixture tuned matched filtering (MTMF), a specialized type of mixture analysis, was used in this study. Results, indicated that decomposed mixels have high correlation with in-situ verification (r 2 =0.9, n=33, p<0.005). It is therefore concluded that mixels of ASTER satellite data offers prominent application for mapping specific trees distribution and abundance map as exemplified with chengal trees.
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