Fine particulate matter has become the premier air pollutant of Beijing in recent years, enormously impacting the environmental quality of the city and the health of the residents. Fine particles with aerodynamic diameters of 0~0.3 μm, 0.3~0.5 μm, and 0.5~1.0 μm, from the yeasr 2007 to 2012, were monitored, and the hospital data about respiratory diseases during the same period was gathered and calculated. Then the correlation between respiratory health and fine particles was studied by spatial analysis and grey correlation analysis. The results showed that the aerial fine particulate matter pollution was mainly distributed in the Zizhuyuan sub-district office. There was a certain association between respiratory health and fine particles. Outpatients with respiratory system disease in this study area were mostly located in the southeastern regions (Balizhuang sub-district office, Ganjiakou sub-district office, Wanshoulu sub-district office, and Yongdinglu sub-district office) and east-central regions (Zizhuyuan sub-district office and Shuangyushu sub-district office) of the study area. Correspondingly, PM₁ (particulate matter with aerodynamic diameter smaller than 1.0 um) concentrations in these regions were higher than those in any other regions. Grey correlation analysis results showed that the correlation degree of the fine particle concentration with the number of outpatients is high, and the smaller fine particles had more obvious effects on respiratory system disease than larger particles.

A new method applying close range laser scan (Lidar) survey in capturing 3D objects was presented in this study. One linear laser source Lidar was used for the survey of the objects. This approach is particularly essential for 3D modelling of urban buildings in a geospatial database. Data calibration and merging were conducted for combing point cluster datasets surveyed from different locations and angles of one object in order to generate a 3D data model for the entire object. The point cluster data were transformed and converted into a triangulated irregular network (TIN) mesh model in a 3D geospatial database. The TIN mesh model was compressed and simplified by applying a computing script of surface variability priority parameters. The digital surface images and textures of the building object were draped onto the 3D TIN model according to their coordinate locations. This research provides a direct and fast means of capturing 3D objects on the Earth's surface in a geospatial database.

Point-based and object-based building extractions were conducted in airborne LiDAR data in a sample area of Buffalo, New York. First, the earth surface points were filtered from the entire laser scan data set using a new filtering algorithm, which combines the TIN slope modelling and statistical analysis. The off-ground points were extracted for buildings in the study area using both point cluster analysis and object-oriented classifications. The accuracies of both approaches were tested using the digitised ground truth. The outcomes of accuracy testing of the point-based method are correctness: 88.74%, completeness: 92.67% and quality: 83.50%. The results of the accuracy of object-based building extraction are correctness: 87.21%, completeness: 60.14%, and quality: 55.26%. Reconstructions of 3D building models based on the extracted building points were performed. This study contributes scientific and technological knowledge for researchers in developing more effective methods in converting the LiDAR survey to a 3D GIS database.

This research diagnoses the exposure of the residential population and the vulnerable groups of children and elderly people to air particle pollution in urban Beijing. We surveyed the air particle pollutant concentrations in the field. We used a universal kriging model in a geographic information system to interpolate the spatial distributions of each pollutant. Spatial patterns of air particle pollution were overlaid to community-level population distributions to identify the community exposures to high air particle pollution. Spatial and statistic modeling reveals that high concentration of ultra-fine air particles of particulate matter (PM) 0.3 μm is strongly associated with high-population urban communities in the southwest and central western areas in the winter season. By contrast, all the other particle sizes surveyed (PMs of 0.5, 1.0, 3.0, and 5.0 μm) indicate that high concentrations in the summer are associated with high-population communities. Reversed spatial and temporal patterns between PM 0.3 μm and other particle sizes suggest that PM 0.3 μm may have different sources of origin.

Drone-based RGB and thermal infrared remote sensing are rapidly becoming useful tools for monitoring crop and soil status for precision farming. The objectives of this study are: 1) flying UAV or drone to collect large spatial scale RGB and infrared aerial images during the growing season; and 2) producing high spatial-temporal resolution digital surface models (DSMs) and imagery mosaics to help farmers monitor surface moisture status in the study vineyard. A total of four field aerial surveys were conducted during the growing season of 2018 from May to August. A surface moisture mapping index (SMMI) model was proposed based on modified normalized difference water index (NDWI) and topographic wetness index (TWI). This model combines the factors of both radiant reflection properties by moisture bearing surfaces of a farming field, and the slope gradient and micro-topographic positions in the field. The results indicate that the spatial pattern of SMMI, or the relative quantities of surface moisture contents, across the vineyard was maintained the same during the entire growing season. This might be impacted by localized factors of surface slope gradient and topographic positions. Meanwhile, temporal variations of surface moisture status are mainly influenced by frequency and intensity of the precipitations.

Space and time are fundamental concepts in ontological studies. This research applies analytical methods to recognize time-scale of sensor applications in analyzing geographic data. Representations of time in this research are classified into 1) Time-interval, 2) Time-duration, 3) Time-point, and 4) Time-dimension. Time-scale refers the size of temporal intervals either built subjectively or objectively for measuring geographic processes or events. Three types of time scales were identified in sensor detections: 1) existence duration, 2) sensor observation duration, and 3) validity duration. The existence duration in time scale system represents the original factor of a geographic event, sensor observation duration designed by researchers is a ‘tool’ to search the validity duration, and validity duration quantifies a theory can be hold true only during a certain period and at a certain time scale interval. Two experimental researches of time scale sensitive sensor deployment were conducted; 1) Spatial and Temporal Changes of Parking Lot Land Use at SUNY – Buffalo State Applying a UAV Remote Sensor, and 2) Spatial Analysis of Distributions and Habitat Conditions of Fallopia japonica Invasive Species Applying UAV Remote Sensing. The major contribution of this research is to demonstrate geographic features and events occur and distribute at certain time durations.