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Hanoi City of Vietnam changes quickly, especially after its state implemented its Master Plan 2030 for the city’s sustainable development in 2011. Then, a number of environmental issues are brought up in response to the master plan’s implementation. Among the issues, the Urban Heat Island (UHI) effect that tends to cause negative impacts on people’s heath becomes one major problem for exploitation to seek for mitigation solutions. In this paper, we investigate the land surface thermal signatures among different land-use types in Hanoi. The surface UHI (SUHI) that characterizes the consequences of the UHI effect is also studied and quantified. Note that our SUHI is defined as the magnitude of temperature differentials between any two land-use types (a more general way than that typically proposed in the literature), including urban and suburban. Relationships between main land-use types in terms of composition, percentage coverage, surface temperature, and SUHI in inner Hanoi in the recent two years 2016 and 2017, were proposed and examined. High correlations were found between the percentage coverage of the land-use types and the land surface temperature (LST). Then, a regression model for estimating the intensity of SUHI from the Landsat 8 imagery was derived, through analyzing the correlation between land-use composition and LST for the year 2017. The model was validated successfully for the prediction of the SUHI for another hot day in 2016. For example, the transformation of a chosen area of 161 ha (1.61 km2) from vegetation to built-up between two years, 2016 and 2017, can result in enhanced thermal contrast by 3.3 °C. The function of the vegetation to lower the LST in a hot environment is evident. The results of this study suggest that the newly developed model provides an opportunity for urban planners and designers to develop measures for adjusting the LST, and for mitigating the consequent effects of UHIs by managing the land use composition and percentage coverage of the individual land-use type.
Nguyen Thanh Hoan; Yuei-An Liou; Kim-Anh Nguyen; Ram C. Sharma; Duy-Phien Tran; Chia-Ling Liou; Dao Dinh Cham. Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City. Remote Sensing 2018, 10, 1965 .
AMA StyleNguyen Thanh Hoan, Yuei-An Liou, Kim-Anh Nguyen, Ram C. Sharma, Duy-Phien Tran, Chia-Ling Liou, Dao Dinh Cham. Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City. Remote Sensing. 2018; 10 (12):1965.
Chicago/Turabian StyleNguyen Thanh Hoan; Yuei-An Liou; Kim-Anh Nguyen; Ram C. Sharma; Duy-Phien Tran; Chia-Ling Liou; Dao Dinh Cham. 2018. "Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City." Remote Sensing 10, no. 12: 1965.
The damage of buildings and manmade structures, where most of human activities occur, is the major cause of casualties of from earthquakes. In this paper, an improved technique, Earthquake Damage Visualization (EDV) is presented for the rapid detection of earthquake damage using the Synthetic Aperture Radar (SAR) data. The EDV is based on the pre-seismic and co-seismic coherence change method. The normalized difference between the pre-seismic and co-seismic coherences, and vice versa, are used to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, respectively. The backward change parameter is added to visualize the retrospective changes caused by factors other than the earthquake. The third change-free parameter uses the average values of the pre-seismic and co-seismic coherence maps. These three change parameters were ultimately merged into the EDV as an RGB (Red, Green, and Blue) composite imagery. The EDV could visualize the earthquake damage efficiently using Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2). Its performance was evaluated in the Kathmandu Valley, which was hit severely by the 2015 Nepal Earthquake. The cross-validation results showed that the EDV is more sensitive to the damaged buildings than the existing method. The EDV could be used for building damage detection in other earthquakes as well.
Ram C. Sharma; Ryutaro Tateishi; Keitarou Hara; Hoan Thanh Nguyen; Saeid Gharechelou; Luong Viet Nguyen. Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data. Sensors 2017, 17, 235 .
AMA StyleRam C. Sharma, Ryutaro Tateishi, Keitarou Hara, Hoan Thanh Nguyen, Saeid Gharechelou, Luong Viet Nguyen. Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data. Sensors. 2017; 17 (2):235.
Chicago/Turabian StyleRam C. Sharma; Ryutaro Tateishi; Keitarou Hara; Hoan Thanh Nguyen; Saeid Gharechelou; Luong Viet Nguyen. 2017. "Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data." Sensors 17, no. 2: 235.
Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Smoothing-Filter-Based-Intensity Modulation (SFIM) algorithms were used to pansharpen GeoEye-1 imagery. The pansharpened images were then segmented in Berkeley Image Seg using a wide range of segmentation parameters, and the spatial and spectral accuracy of image segments was measured. We found that pansharpening algorithms that preserve more of the spatial information of the higher resolution panchromatic image band (i.e., IHS and BT) led to more spatially-accurate segmentations, while pansharpening algorithms that minimize the distortion of spectral information of the lower resolution multispectral image bands (i.e., SFIM) led to more spectrally-accurate image segments. Based on these findings, we developed a new IHS-SFIM combination approach, specifically for object-based image analysis (OBIA), which combined the better spatial information of IHS and the more accurate spectral information of SFIM to produce image segments with very high spatial and spectral accuracy.
Brian Alan Johnson; Ryutaro Tateishi; Nguyen Thanh Hoan. Satellite Image Pansharpening Using a Hybrid Approach for Object-Based Image Analysis. ISPRS International Journal of Geo-Information 2012, 1, 228 -241.
AMA StyleBrian Alan Johnson, Ryutaro Tateishi, Nguyen Thanh Hoan. Satellite Image Pansharpening Using a Hybrid Approach for Object-Based Image Analysis. ISPRS International Journal of Geo-Information. 2012; 1 (3):228-241.
Chicago/Turabian StyleBrian Alan Johnson; Ryutaro Tateishi; Nguyen Thanh Hoan. 2012. "Satellite Image Pansharpening Using a Hybrid Approach for Object-Based Image Analysis." ISPRS International Journal of Geo-Information 1, no. 3: 228-241.
It is difficult to monitor forests in tropical regions with frequent cloud cover using optical remote-sensing data. Adequate multi-temporal, high-resolution imagery is often not available. Microwave imagery is able to penetrate cloud cover, enabling imagery of the land surface to be recorded more frequently. This study seeks to improve tropical forest mapping by combining optical and microwave imagery, with one of the main objectives being the discrimination of planted and natural forests. First, multi-spectral Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images were used to create a forest and land-cover classification of the study area. Subsequently, ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) single-polarized and dual-polarized microwave images were used to generate forest and land-cover masks to be used in combination with the ALOS/AVNIR-2 classification. The overall accuracy of the ALOS/AVNIR-2 classification was 77%. When the ALOS/PALSAR masks were used in combination with the ALOS/AVNIR-2 classification, the overall accuracy increased to 88% with higher than 90% accuracy for the main forest classes.
Nguyen Thanh Hoan; Ryutaro Tateishi; Bayan Alsaaideh; Thomas Ngigi; Ilham Alimuddin; Brian Johnson. Tropical forest mapping using a combination of optical and microwave data of ALOS. International Journal of Remote Sensing 2012, 34, 139 -153.
AMA StyleNguyen Thanh Hoan, Ryutaro Tateishi, Bayan Alsaaideh, Thomas Ngigi, Ilham Alimuddin, Brian Johnson. Tropical forest mapping using a combination of optical and microwave data of ALOS. International Journal of Remote Sensing. 2012; 34 (1):139-153.
Chicago/Turabian StyleNguyen Thanh Hoan; Ryutaro Tateishi; Bayan Alsaaideh; Thomas Ngigi; Ilham Alimuddin; Brian Johnson. 2012. "Tropical forest mapping using a combination of optical and microwave data of ALOS." International Journal of Remote Sensing 34, no. 1: 139-153.