リモートセンシング技術を利用 した都市生態系における植物開花フェノロジー評価

首都大学東京, 2015-09-30, 博士（観光科学）, 甲第589号首都大学東

Low carbon industrial site planning for energy sustainability: drone databases on thermal heat loss (sub-project- MRUN)

In Malaysia, the main sources of green house gasses include Carbon Dioxide(CO2), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Particle Matters. The 21% was identified had released from the industrial sectors represent 3rd rank of contributors other than. Motor vehicles and the power stations are the main contributors, Meanwhile CO2 emissions are mainly caused by transportation activities (97.1%). 48% of total SO2 emissions are produced by power stations in and around the country. This amounted to a staggering 78,416 metric tonnes of SO2 released into the atmosphere

Assessing the suitability of affordable housing based on demand criteria

Affordable housing has become essential to provide housing with affordable prices, but most of the locations of affordable housing are often not suitable with the people's demand. To assess the suitability of affordable housing, several initiatives has been developed, to match the demand and location of the affordable housing, but most the models lacks of spatial element, other model focusing on the goal related to site suitability to cities, and people's preferences of housing. This study aims to integrating spatial information and analysis to assessing the suitability level of affordable housing in Malaysia. This assessment model consists of 3 main indicators; suitability house according to neighbourhood context, demographic factor and commute distance. The method use in this study is using multi-Criteria Analysis, using weighted scoring techniques. The results show that most of the affordable housing score more than 60% average, with the highest score are 84% and the lowest score are 57.9%. this shows that the suitability level of affordable housing in the study area is good. These indicators can be used for further investigation of other affordable housing, and also in finding the suitable site for affordable housing in the future

Assessing safety level of affordable housing based on safe city concepts

Safety of residential areas, especially affordable housing has become an important aspect, and has been listed as part of Sustainable Development Goal (SDG) initiatives by the United Nation (UN). Several initiatives on measuring the safety level have been proposed, including Safe City Index 2019, Safe City Program, Defensible Space Concept, crime prevention through environmental design (CPTED), Smart Sustainable Cities, etc. However, some of these initiatives focus only on crime, others focus on steps to improve safety and other models that are broader but not specific to safety. Besides that, these models place less emphasis on the aspects of spatial assessment, especially the safety level assessment based on affordable housing location in Malaysia. To handle these issues, this study's aim is to enhance current indicators to assess the safety level of affordable housing, using Kuala Lumpur, Malaysia as a case study area. This study identified 6 indicators; crime, safety and security, infrastructure security, accessibility, natural disaster, and health security. Spatial analysis was done based on the indicators, and from the results, it shows that almost all of the affordable housing score are more than 50%, with the highest score is 76.50%, and the lowest score is 44.7%. This indicator can be used as a basis to assess the safety level of affordable housing, especially in Kuala Lumpur, Malaysia

Mapping of utilities risk for sewerage system asset management

Sewerage system provide infrastructure that conveys sewage or surface runoff using sewer network. Sewerage system requires perfect infrastructure to ensure that sewage can be channelled to the sewage treatment plant safely. Meticulous asset management for sewerage system is highly recommended to avoid any risk to assets that deliver sewage. Risk assessment strategy for sewerage asset is basically will evaluate the risk factor that influences the asset to encounter the potential problem. Current risk assessment framework didn't include spatial analysis to show the locations of the assets with risk. This study focused on the production of risk map that aims to analyse potential risk for asset management of sewerage system in Majlis Perbandaran Johor Bahru Tengah (MPJBT) using spatial analysis. The spatial analysis used in this study is slope analysis and asset's analysis focusing on age sewerage assets parameter. Combination of both factors show the risks that require more attention from the sewerage system management. The results show there were 90.8% of the asset have low risk, 8.9% have medium risk and 0.3% asset have high risk. These results can be used by the sewerage assets authority to make better decision in preventions and manage risk that could happen to the sewerage assets

Estimating relative abundance of tree species in tropical rainforest using remotely sensed data

Mixed pixel occurrence in remote sensing imagery is a main source of problems in classifying ground features, especially when dealing with complex ecosystems such as tropical rainforest areas due to its high diversity of tree species. Pure pixel composed of a single species is very rare in most remote sensing imagery even in some advent ultrafine spatial resolution. In order to achieve an optimum output in classification of tree species in the forest, mixed pixel must be spectrally unmixed using sub-pixel approaches. This study was carried out in order to estimates the composition of tree species in Pasoh Forest Reserve by estimating the relative abundance of the tree species. The estimation of relative abundance was carried out using two types of spectral unmixing approaches which are Mixture Tuned Matched Filtering (MTMF) and modified Canopy Fractional Cover (mCFC). MTMF and mCFC were employed to Hyperion EO-1 satellite image with 30 meters spatial resolution. The relative abundance of Chengal trees was firstly estimated at a plot of 50 hectare. The correlation coefficients between the relative abundance obtained from MTMF and mCFC with the relative abundance of ground data in 50 hectare plot was 0.46 and 0.67, respectively. Therefore, mCFC was selected as it gives more encourage result in order to estimate relative abundance of Chengal trees at wider area such as compartment level. The model obtained from this study would be useful in forest monitoring and managemen

Al-Quran dan geologi

Dalam Al-Quran ada dinyatakan mengenai geologi dan juga geografi yang kedua-duanya memainkan peranan penting bagi mengekalkan keseimbangan bumi. Oleh itu, perbincangan dalam bab ini ditumpukan kepada definisi geologi dan geografi. Selain itu, dalam al-Quran ada juga disebutkan gunung sebagai pasak, ciri fizikal gunung, pergerakan gunung, pergerakan benua, pembentukan gunung berapi di bawah laut, gempa bumi, pengajaran di sebalik kejadian gempa bumi dan gunung berapi, dan kemenangan byzantine. Kesemuanya dibincangkan kerana mempunyai kaitan antara satu sama lain

Remote detection of flowering Somei Yoshino (Prunus x yedoensis) in an urban park using IKONOS imagery: comparison of hard and soft classifiers

Identification of flowering trees in urban areas is challenging due to weak spectral signals and the high heterogeneity of urban landscapes. We hypothesized that a soft classifier, such as mixture tuned matched filtering (MTMF), would be better able to identify pixels including blooming cherry trees than a hard classifier such as maximum likelihood (ML). To test this hypothesis, we compared the accuracy of MTMF and ML in classifying blossoms of Somei Yoshino cherry trees (Prunus × yedoensis) in an urban park in Tokyo using IKONOS imagery. An accuracy assessment demonstrated that the MTMF classifier (overall accuracy: 62.2%, kappa coefficient: 0.507, and user's accuracy of SY: 48.1%) performed better than ML in identifying flowering SY (overall accuracy 48.7% with kappa accuracy: 0.321 and user's accuracy of blooming SY: 38.9%). Our results suggest that both methods are able to classify cherry blossoms in an urban landscape, but MTMF is more accurate than ML. However, the producer's accuracy of MTMF (72.7%) was slightly lower than ML (77.7%), suggesting that the accuracy of MTMF could decrease due to the limited number of available bands (four for IKONOS) and the existence of endmembers, such as dry grass in this study, with stronger signals than flowers