Comparing to optical remote sensor, Synthetic Aperture Radar (SAR) is an active sensor and radar signal can penetrate clouds for working in near all-weather/day-night. Therefore, SAR damage assessment methods enable useful disaster response in typhoon and heavy rainfall. This study first uses SAR images to detect change of surface water area in Zengwen Reservoir and limits surface water areas by HAND (Height Above the Nearest Drainage) mask and radar layover & shadow mask. The result’s F1-Measure increase from 66.6% to 92.0% after using mask data. Backscatter decreases due to totally flooding, therefore threshold values can be used for separating flooded area. The best detected result: F1-Measure 82.6% occurs when the threshold is 6.2dB. This study proposes a flood detection process by SAR image, and uses actual flood events as a case to estimate the impact of flooding range. The results of the analysis are quite helpful for providing the assessment and quick response after a hazardous event.

Both MODIS and Landsat 8 provide the land surface temperature (LST) images, but their images are slightly different both in the spatial resolution and in the calculation of LST. The MODIS retrieves LST from thermal infrared response at the spatial resolution of 1 kilometer, whereas the Landsat 8 images retrieve LST from band 10 and 11 thermal infrared response with the spatial resolution of 30 meters. These differences are the focus of this paper and are evaluated in the process of finding urban cool islands (UCI). Taipei City is selected as our research area. The heat generated from high-rise buildings and artificial impervious surfaces in Taipei results in the urban heat islands. Yet, there are also several urban parks with high-density vegetation coverage where can cool down the surface temperature of the city. Thus, we expect to acquire large-scale LST by multi-temporal MODIS and Landsat 8 images, considering the relationship between the green area in downtown and the spatial resolution of images, and assessing how large the green area may result in the UCI. Finally, we also use the temperature actually measured by the ground weather station for validation, analyzing whether both of two images can be used to evaluate the UCI effect.

Recent advancements in the geographic information systems and remote sensing technology have supported the development of geospatial-temporal modeling approaches for air pollution. Previous studies estimated the spatial-temporal variability of air pollutants using a single model, but only a few studies considered exposure assessment using multiple models and compared model performance. In this study, O3 data during 2015 to 2018 was collected from specific industrial monitoring stations provided by the Taiwan Environmental Protection Agency. Three geospatial-temporal modeling approaches including land-use regression (LUR), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR) were used to predict O3 for our comparison. The results showed that R2 obtained from all models were 0.51 to 0.64. Furthermore, the GTWR model has the greatest performances compared to LUR, and GWR models.

The first public bicycle system of Taipei City, YouBike, has the total rented over 106,895,634 times. Taipei is the city providing most well-developed public transportation systems and public bicycle systems in Taiwan. Currently, Taipei government builds new YouBike systems with 3 standards, but it still not enough to fit the need of YouBike riders. This study is based on the need of YouBike user, we extracted geographic data from OpenStreetMap(OSM) and the volunteered geographic information (VGI) such as roads, sidewalks, bicycle lanes, and popular destinations in Taipei city. These data were treated as the influence factors for location analysis of stations. The statistics is used to evaluate the efficiency of the circulation of bicycles, in order to find out the suggested location of the docking stations and improve the efficiency and stimulate more usage.