Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557039
Kemeng Hu, Jin Zhang, Donghai Yan, Dong Wei
Based on the DEM-estracted data of the Fenhe River system, combined with characteristics of the Fenhe River's own structure and data structure, the data of river segments are represented by a graph data structure, and the method of determining the direction of the river segment, building the river body, and determining the mainstream and tributaries are used. Completed the construction of tree structure in the Fenhe River system. Based on structural data of the Fenhe River, the abstract data model of the spatial structure of the river network was used to express the hierarchical relationship among the rivers in the Fenhe River system and determine the location of the river in the river structure.
{"title":"Structural Establishment and Relational Expression of the Fenhe River System","authors":"Kemeng Hu, Jin Zhang, Donghai Yan, Dong Wei","doi":"10.1109/GEOINFORMATICS.2018.8557039","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557039","url":null,"abstract":"Based on the DEM-estracted data of the Fenhe River system, combined with characteristics of the Fenhe River's own structure and data structure, the data of river segments are represented by a graph data structure, and the method of determining the direction of the river segment, building the river body, and determining the mainstream and tributaries are used. Completed the construction of tree structure in the Fenhe River system. Based on structural data of the Fenhe River, the abstract data model of the spatial structure of the river network was used to express the hierarchical relationship among the rivers in the Fenhe River system and determine the location of the river in the river structure.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115114193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557163
Tan Wang, Jingjing Dong, Sha Xu, Ziyu Tong
On the macro scale, the quantitative representation and analysis of the urban form is an important part of the study of urban morphology. This paper introduces the concept of the Local Climate Zone (LCZ) which is a basic classification framework that describes the characteristics of urban underlying surface. This study uses WUDAPT method to generate LCZ maps. Through comparative analysis of LCZ maps of three Chinese cities with distinctive urban morphological feature, the quantitative representation and analysis of urban morphology on the macro scale could be completed. From the spatial distribution and numerical statistics of LCZ the cites' maps, the research indicates: The high-density and high-rise LCZ are main LCZ of Shanghai and Nanjing; Hefei has the highest industrial type LCZ10; in contrast, the cities' greening LCZ are consistent. And it can be seen from the comparison of IODs, three cities' LCZ spatial distribution are different. To summary, the establishment of the Local Climate Zone model for the city is feasible and effective for the quantitative representation and analysis of the urban morphology on the macro scale.
{"title":"Comparative Study of Urban Forms on Macro Scale","authors":"Tan Wang, Jingjing Dong, Sha Xu, Ziyu Tong","doi":"10.1109/GEOINFORMATICS.2018.8557163","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557163","url":null,"abstract":"On the macro scale, the quantitative representation and analysis of the urban form is an important part of the study of urban morphology. This paper introduces the concept of the Local Climate Zone (LCZ) which is a basic classification framework that describes the characteristics of urban underlying surface. This study uses WUDAPT method to generate LCZ maps. Through comparative analysis of LCZ maps of three Chinese cities with distinctive urban morphological feature, the quantitative representation and analysis of urban morphology on the macro scale could be completed. From the spatial distribution and numerical statistics of LCZ the cites' maps, the research indicates: The high-density and high-rise LCZ are main LCZ of Shanghai and Nanjing; Hefei has the highest industrial type LCZ10; in contrast, the cities' greening LCZ are consistent. And it can be seen from the comparison of IODs, three cities' LCZ spatial distribution are different. To summary, the establishment of the Local Climate Zone model for the city is feasible and effective for the quantitative representation and analysis of the urban morphology on the macro scale.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115141026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557154
Chaojun Wang, Hongrui Zhao, Ying Zhou
Ecological regime shifts occur when resilience is exceeded, and then ecosystem enters a new local equilibrium which differs in both structure and functioning from the previous state. Recently, interest in the influence of spatial characteristics of landscapes on resilience has increased. In this research, we both apply the concept of, and present a way to quantify spatial resilience in Yanhe watershed of China to foster regional sustainability. From the general assumption that when considering the spatial aspects of system resilience, it is important to grasp which system configuration and determine what the key challenges and uncertainties in the study area are. Spatial resilience in this situation focuses on the importance of ecological sensitivity and vegetation cover as Yanhe watershed is a fragile ecosystem and a typical agro-pastoral transitional zone. We then consider two criteria of ecological management in the study area, protection and recovery. Spatial resilience is then quantified through an indicator-based system, multi-criteria evaluation method, and spatial visualization based on a geographic information system (GIS). Then, ecological functioning zones, e.g., protection or recovery, are created according to the different degrees of spatial resilience. We believe that the results can be used by local governments to foster regional sustainable development through advancing protection and recovery activities, for instance, Grain for Green, in Yanhe watershed.
{"title":"Quantifying Spatial Resilience of Yanhe Watershed to Foster Ecosystem Sustainability","authors":"Chaojun Wang, Hongrui Zhao, Ying Zhou","doi":"10.1109/GEOINFORMATICS.2018.8557154","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557154","url":null,"abstract":"Ecological regime shifts occur when resilience is exceeded, and then ecosystem enters a new local equilibrium which differs in both structure and functioning from the previous state. Recently, interest in the influence of spatial characteristics of landscapes on resilience has increased. In this research, we both apply the concept of, and present a way to quantify spatial resilience in Yanhe watershed of China to foster regional sustainability. From the general assumption that when considering the spatial aspects of system resilience, it is important to grasp which system configuration and determine what the key challenges and uncertainties in the study area are. Spatial resilience in this situation focuses on the importance of ecological sensitivity and vegetation cover as Yanhe watershed is a fragile ecosystem and a typical agro-pastoral transitional zone. We then consider two criteria of ecological management in the study area, protection and recovery. Spatial resilience is then quantified through an indicator-based system, multi-criteria evaluation method, and spatial visualization based on a geographic information system (GIS). Then, ecological functioning zones, e.g., protection or recovery, are created according to the different degrees of spatial resilience. We believe that the results can be used by local governments to foster regional sustainable development through advancing protection and recovery activities, for instance, Grain for Green, in Yanhe watershed.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124226035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557149
Bijun Wang, Yulong Wang, K. Qin, Qizhi Xia
Human travel behavior can be obtained from the trajectory data generated by GPS devices, which can be reflected in different transportation modes and provide useful information for trajectory prediction, urban planning and traffic monitoring. In this article, we proposed transportation modes classification method based on Light Gradient Boosting Machine (LightGBM) to discover seven kinds of transportation modes from GPS trajectory data, including walking, cycling, taking a bus, taking a taxi, driving a car, taking the subway and taking a train. First, the original trajectories must be divided into some sub trajectories. There is only one transportation mode label in each sub trajectory. Second, the feature vector of sub trajectory is computed including eight basic and three advanced features. These basic features are distance feature, five velocity-related features and two acceleration-related features. Three advanced features are heading change rate (hcr), stop rate (sr) and velocity change rate (vcr), Final, the LightGBM classifier is used to detect the transportation modes automatically. The eXtreme Gradient Boosting (XGBoost) and decision tree are also used to verify the efficiency of our method. The experiment data are Geolife provided by Microsoft Research Asia. The results show that the LightGBM and XGBoost methods are more accurate than decision tree method and the LightGBM is better than XGBoost at the classification of car, subway and train.
{"title":"Detecting Transportation Modes Based on LightGBM Classifier from GPS Trajectory Data","authors":"Bijun Wang, Yulong Wang, K. Qin, Qizhi Xia","doi":"10.1109/GEOINFORMATICS.2018.8557149","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557149","url":null,"abstract":"Human travel behavior can be obtained from the trajectory data generated by GPS devices, which can be reflected in different transportation modes and provide useful information for trajectory prediction, urban planning and traffic monitoring. In this article, we proposed transportation modes classification method based on Light Gradient Boosting Machine (LightGBM) to discover seven kinds of transportation modes from GPS trajectory data, including walking, cycling, taking a bus, taking a taxi, driving a car, taking the subway and taking a train. First, the original trajectories must be divided into some sub trajectories. There is only one transportation mode label in each sub trajectory. Second, the feature vector of sub trajectory is computed including eight basic and three advanced features. These basic features are distance feature, five velocity-related features and two acceleration-related features. Three advanced features are heading change rate (hcr), stop rate (sr) and velocity change rate (vcr), Final, the LightGBM classifier is used to detect the transportation modes automatically. The eXtreme Gradient Boosting (XGBoost) and decision tree are also used to verify the efficiency of our method. The experiment data are Geolife provided by Microsoft Research Asia. The results show that the LightGBM and XGBoost methods are more accurate than decision tree method and the LightGBM is better than XGBoost at the classification of car, subway and train.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124275772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557054
Wei Yang, Wei Lu, T. Ai, T. Zhang
Crowdsourcing trace data mining plays an important role in behavior pattern mining, place sensing, etc. This paper proposes a new method to automatically detect cycle periodic behavior and extract outdoor sports place from running tracks. First, the cycle periodic behavior is modeled using movement parameters. Second, based on the features of cycle periodic pattern, the trajectory distance matrix search algorithm is presented to detect periodic behavior and extract periodic tracks. Last, the sports place information is extracted by Delaunay triangulation and reverse geocoding method from collective cycle periodic tracks. Experiments were conducted using one month smartphone app running traces in Beijing, and the results show that the proposed method can more effectively identify cycle periodic pattern compared to the Apriori method, and it can efficiently extract sports place information.
{"title":"Cycle Periodic Behavior Detection and Sports Place Extraction Using Crowdsourced Running Trace Data","authors":"Wei Yang, Wei Lu, T. Ai, T. Zhang","doi":"10.1109/GEOINFORMATICS.2018.8557054","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557054","url":null,"abstract":"Crowdsourcing trace data mining plays an important role in behavior pattern mining, place sensing, etc. This paper proposes a new method to automatically detect cycle periodic behavior and extract outdoor sports place from running tracks. First, the cycle periodic behavior is modeled using movement parameters. Second, based on the features of cycle periodic pattern, the trajectory distance matrix search algorithm is presented to detect periodic behavior and extract periodic tracks. Last, the sports place information is extracted by Delaunay triangulation and reverse geocoding method from collective cycle periodic tracks. Experiments were conducted using one month smartphone app running traces in Beijing, and the results show that the proposed method can more effectively identify cycle periodic pattern compared to the Apriori method, and it can efficiently extract sports place information.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115944731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557081
He Li, Yingmei Wu, Rongfeng Yang, L. Hong
Liupanshui city is an important karst area where the situation of rocky desertification is very serious in Guizhou Province, southern china. Vegetation coverage is an important ecological parameter for evaluating rocky desertification degree. This study investigates different characteristics of the spatial-temporal changes of vegetation cover and associated driving forces of vegetation coverage change were also analyzed in Liupanshui city of western Guizhou Province using the data set of Landsat (2002–2015) at spatial resolution of 30m and temporal resolution of 16-day. The results show that: 1) the spatial distribution pattern of vegetation cover in Liupanshui city is high in the east whereas low in the west. The average annual vegetation coverage in the south of Liupanshui city is higher than in the north. The average annual vegetation coverage in Panxian County, Shuicheng County, Zhongshan District, and Liuzhi District are 0.6047, 0.5949, 0.4432, and 0.6140 respectively. 2) Average annual vegetation coverage improved obviously in the past 13 years. The growth rate of average annual vegetation coverage is 0.028/10yr and the average vegetation coverage is 0.5957. 3) The proportion of vegetation cover with extremely significant increase and significantly increase is far higher than that of vegetation cover with extremely significant reduced and significantly reduced in the city. In other words, the improvement in vegetation cover may expand to most parts of the study area.
{"title":"Spatial-Temporal Change of Vegetation Coverage Based on NDVI in Liupanshui City","authors":"He Li, Yingmei Wu, Rongfeng Yang, L. Hong","doi":"10.1109/GEOINFORMATICS.2018.8557081","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557081","url":null,"abstract":"Liupanshui city is an important karst area where the situation of rocky desertification is very serious in Guizhou Province, southern china. Vegetation coverage is an important ecological parameter for evaluating rocky desertification degree. This study investigates different characteristics of the spatial-temporal changes of vegetation cover and associated driving forces of vegetation coverage change were also analyzed in Liupanshui city of western Guizhou Province using the data set of Landsat (2002–2015) at spatial resolution of 30m and temporal resolution of 16-day. The results show that: 1) the spatial distribution pattern of vegetation cover in Liupanshui city is high in the east whereas low in the west. The average annual vegetation coverage in the south of Liupanshui city is higher than in the north. The average annual vegetation coverage in Panxian County, Shuicheng County, Zhongshan District, and Liuzhi District are 0.6047, 0.5949, 0.4432, and 0.6140 respectively. 2) Average annual vegetation coverage improved obviously in the past 13 years. The growth rate of average annual vegetation coverage is 0.028/10yr and the average vegetation coverage is 0.5957. 3) The proportion of vegetation cover with extremely significant increase and significantly increase is far higher than that of vegetation cover with extremely significant reduced and significantly reduced in the city. In other words, the improvement in vegetation cover may expand to most parts of the study area.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"22 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116494285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557167
Yulian Yang, Qiuli Tian, Kun Yang, Chao Meng, Yi Luo
Based on the observed PM2.5 concentration data in 2016, ordinary kriging (OK) and sequential Gaussian simulation (SGS) were used to map spatial distribution of PM2.5 in China, and SGS can model not only single, but also multi-location uncertainties, which assess the uncertainty of the PM2.5 spatial distribution. A smoothing effect was produced when using OK technique in mapping of PM2.5, however relatively discrete and fluctuant map was obtained by the SGS. Their results of spatial distribution show that east and west regions have higher PM2.5 concentration, middle regions have lower concentration in China. Based on the SGS realization, the probability that PM2.5 concentration at single location was higher than the defined threshold (10μg/m3) was big for the whole study area. The minimum value was 0.77. When the defined threshold changed to 35 μg/m3, the extent of higher probability was shrunk, the bigger value (0.8-1) existed in Xinjiang and North China. The probability which PM2.5 concentrations were higher than the defined threshold in several locations at the same time was also called joint probability. Given the critical probabilities (pm=1 and> 0.98), joint probability of PM2.5 in area a being higher than 10μg/m3 respectively is 0.85 and 0.5; while joint probability of PM2.5in area a being higher than 35μg/m3 respectively is 0. 65 and 0.14. The probability map can be very helpful for controlling and making environmental management decision of PM2.5 pollution.
{"title":"Using Sequential Gaussian Simulation to Assess the Spatial Uncertainty of PM2.5 in China","authors":"Yulian Yang, Qiuli Tian, Kun Yang, Chao Meng, Yi Luo","doi":"10.1109/GEOINFORMATICS.2018.8557167","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557167","url":null,"abstract":"Based on the observed PM2.5 concentration data in 2016, ordinary kriging (OK) and sequential Gaussian simulation (SGS) were used to map spatial distribution of PM2.5 in China, and SGS can model not only single, but also multi-location uncertainties, which assess the uncertainty of the PM2.5 spatial distribution. A smoothing effect was produced when using OK technique in mapping of PM2.5, however relatively discrete and fluctuant map was obtained by the SGS. Their results of spatial distribution show that east and west regions have higher PM2.5 concentration, middle regions have lower concentration in China. Based on the SGS realization, the probability that PM2.5 concentration at single location was higher than the defined threshold (10μg/m3) was big for the whole study area. The minimum value was 0.77. When the defined threshold changed to 35 μg/m3, the extent of higher probability was shrunk, the bigger value (0.8-1) existed in Xinjiang and North China. The probability which PM2.5 concentrations were higher than the defined threshold in several locations at the same time was also called joint probability. Given the critical probabilities (pm=1 and> 0.98), joint probability of PM2.5 in area a being higher than 10μg/m3 respectively is 0.85 and 0.5; while joint probability of PM2.5in area a being higher than 35μg/m3 respectively is 0. 65 and 0.14. The probability map can be very helpful for controlling and making environmental management decision of PM2.5 pollution.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125107529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557191
Dongsheng Bi, Yanhui Zhu
We propose a new non-rigid image registration method with spatial structure Preservation. We added two spatial constraints in the regularization term to maintain the spatial structure in iterations. We tested the proposed method's performance in remote sensing images and low-altitude aerial images, comparing it with three state-of-the-art methods. The results of the experiment show that our method shows the better performance in most cases.
{"title":"Non-Rigid Image Registration with Spatial Structure Preservation","authors":"Dongsheng Bi, Yanhui Zhu","doi":"10.1109/GEOINFORMATICS.2018.8557191","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557191","url":null,"abstract":"We propose a new non-rigid image registration method with spatial structure Preservation. We added two spatial constraints in the regularization term to maintain the spatial structure in iterations. We tested the proposed method's performance in remote sensing images and low-altitude aerial images, comparing it with three state-of-the-art methods. The results of the experiment show that our method shows the better performance in most cases.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125213665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557143
Danning Zhang, Meng Zhang, Bo Zhang
PM2.5 refers to a kind of particulate matter whose diameter is equal to or less than 2.5 micrometers in the atmosphere. Due to its characteristics of small particle size, easy-adsorption for toxic substances, long-time suspension in atmosphere and far-distance transportation, PM2.5 can enter the human lung and blood through breath, then cause respiratory diseases and central nervous system diseases. Therefore, people are paying more and more attention to PM2.5. This research is dedicated to identifying the main factors and the significant geographical elements of PM2.5 pollution based on the tools of ArcGIS, SPSS and Canoco, where ArcGIS is used to perform spatial interpolation and extract information while SPSS and Canoco have been implemented to conduct correlation analysis. The results are as follows: (a) The generally distribution of Xian's PM2.5 is the eastern part is higher than the western part; (b) PM2.5 is positively correlated with DEM, RDLS (relief degree of land surface), Aspect. In the process of increasing the buffer radius from 1 kilometer to 5 kilometers, it maintains a strong and significant positive correlation between PM2.5 and each geographical element; and (c) RDLS is the primary geographic factor and has significant influence on the diffusion and distribution of PM2.5 under different buffer radius from 1 kilometer to 5 kilometers.
{"title":"Study on the Response of PM2.5 Pollution to Different Geographical Factors","authors":"Danning Zhang, Meng Zhang, Bo Zhang","doi":"10.1109/GEOINFORMATICS.2018.8557143","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557143","url":null,"abstract":"PM2.5 refers to a kind of particulate matter whose diameter is equal to or less than 2.5 micrometers in the atmosphere. Due to its characteristics of small particle size, easy-adsorption for toxic substances, long-time suspension in atmosphere and far-distance transportation, PM2.5 can enter the human lung and blood through breath, then cause respiratory diseases and central nervous system diseases. Therefore, people are paying more and more attention to PM2.5. This research is dedicated to identifying the main factors and the significant geographical elements of PM2.5 pollution based on the tools of ArcGIS, SPSS and Canoco, where ArcGIS is used to perform spatial interpolation and extract information while SPSS and Canoco have been implemented to conduct correlation analysis. The results are as follows: (a) The generally distribution of Xian's PM2.5 is the eastern part is higher than the western part; (b) PM2.5 is positively correlated with DEM, RDLS (relief degree of land surface), Aspect. In the process of increasing the buffer radius from 1 kilometer to 5 kilometers, it maintains a strong and significant positive correlation between PM2.5 and each geographical element; and (c) RDLS is the primary geographic factor and has significant influence on the diffusion and distribution of PM2.5 under different buffer radius from 1 kilometer to 5 kilometers.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125841954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557200
Xiaoju Xiong, Chungui Liao, Baoqing Hu
To explore the Guangxi Xijiang River Basin changes rule and influence factors of autumn drought, enhanced vegetation index (EVI) and land surface temperature (LST) data were used to establish the EVI-LST feature spaces, using a linear regression method to simulate the temperature vegetation dryness index trend. The result showed that: the autumn drought in the study area increase gradually from September to November. The main area of heavy drought in the northeast, the drought distribution of the north to the south of diminishing trend. Temperature vegetation dryness index change trend of average was 0.33%; high value area distribution in of northwest and southeast, central is low. The proportion of karst area drought for 76.66%, the proportion of semi-karst area drought for 71.9%, and the proportion of non-karst area drought for 68.1 %, Karst area drought ratio is greater than the non-karst area. Drought distribution of Guangxi Xijiang River Basin significantly affected by altitude. With the rise of altitude, drought area ratio showed an increasing trend. Temperature vegetation dryness index can be greatly used in drought early warning and monitoring of Guangxi Xijiang River Basin, this method can be used in long-term and large-scale drought monitor and forecast in real-time.
{"title":"Application Study of Temperature Vegetation Drought Index in Guangxi Xijiang River Basin Autumn Drought Monitoring","authors":"Xiaoju Xiong, Chungui Liao, Baoqing Hu","doi":"10.1109/GEOINFORMATICS.2018.8557200","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557200","url":null,"abstract":"To explore the Guangxi Xijiang River Basin changes rule and influence factors of autumn drought, enhanced vegetation index (EVI) and land surface temperature (LST) data were used to establish the EVI-LST feature spaces, using a linear regression method to simulate the temperature vegetation dryness index trend. The result showed that: the autumn drought in the study area increase gradually from September to November. The main area of heavy drought in the northeast, the drought distribution of the north to the south of diminishing trend. Temperature vegetation dryness index change trend of average was 0.33%; high value area distribution in of northwest and southeast, central is low. The proportion of karst area drought for 76.66%, the proportion of semi-karst area drought for 71.9%, and the proportion of non-karst area drought for 68.1 %, Karst area drought ratio is greater than the non-karst area. Drought distribution of Guangxi Xijiang River Basin significantly affected by altitude. With the rise of altitude, drought area ratio showed an increasing trend. Temperature vegetation dryness index can be greatly used in drought early warning and monitoring of Guangxi Xijiang River Basin, this method can be used in long-term and large-scale drought monitor and forecast in real-time.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126787395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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