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Introduction of Geoinformatic Researches at Yunnan University of Finance and Economics (China)

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Introduction of Geoinformatic Researches at Yunnan University of Finance and Economics (China) 69 Research Introduction Introduction of Geoinformatic researches at Yunnan University of Finance and Economics (China) Chunxue LIU School of Urban and Environment, Yunnan University of Finance and Economics, China 1. Introduction of Yunnan University of Finance 2. Geoinformatics Researches at School of and Economics Urban and Environment Yunnan University of Finance and Economics (YUFE) was Researches of Geoinformatics at YUFE are carried out in founded in 1951, and designated as one of the key provincial several schools, covering many fields such as engineering, institutions of higher education by Yunnan Provincial management and economics. This article focuses only on the Government in 1995. YUFE is a multi-disciplinary university main researches at School of Urban and Environment (SUE). which excels in economics and management and also, offers SUE, organized in 2007, is originated from the School of high-quality programs in philosophy, law, liberal arts, natural Statistics and Information that was established in 1999. In 2018, science and engineering. YUFE is located in the north of the teaching team of SUE was composed of over 70 faculty Kunming, the province capital of Yunnan, China. Currently, members and the total number of undergraduate and graduate YUFE has established 19 schools and enrolls about 27,000 students enrolled at SUE was around 1,000. students in 61 bachelor’s programs, 10 master degrees in first- SUE has built virous cooperation with many research class disciplines, 10 doctor degrees in first-class disciplines, and institutions, universities and enterprises for faculty training and 1 postdoctoral research station (Fig.1) (http://en.ynufe.edu.cn/ researches. SUE also keeps close research connections with index.htm). aboard institutions in the US, Japan, France, Britain and so on. These years, SUE has obtained research supports from National Natural Science Foundation of China (NSFC), local government and enterprises and accomplished many Geoinformatic researches on natural resources including mineral, water and land, environments and disasters. Mineral resource, in particular nonferrous mineral resource, roles importantly in the regional economic development. Comprehensive geoinformation analyses and predications have then developed significant economical mineral exploration methods. For improving the prediction accuracy, spatial distributions of ore bodies, lithology and magmatic rocks have been regarded as comprehensive information sources for the analyse. Because ore-controlling factors are different in various mines, Geoinformatic researches should be different depending on features of mine geology. For example, by considering spatial changes in semivariogram of ore grade, high accuracy modelling of ore grade was accomplished for the Yuanjiang gold mine and Dachang tin mine (Liu et al., 1999; Qin et al., 2001). Another example is 3D fracture distributions using GEOFRAC that consists of ordinary kriging, sequential Gaussian simulation, and principal component analysis to incorporate spatial correlation structures of locations and directions of sample factures (Koike et al., 2012, 2015). GEOFRAC was used in the Gejiu tin Figure 1. Photos of the main campus of Yunnan University of mine, situated in the southeast of Yunnan province (Liu et al., Finance and Economics (YUFE) in Kunming City. 2013; Liu et al., 2019: see Figs. 2, 3 and 4). Comprehensive 70 information analysis and prediction were mainly based on modeling of semivariograms and cross-semivariograms for information entropy theory to integrate orebody indicator quantifying correlation structures among multivariate and an information and applied to the Gejiu tin mine (Liu et al., 2003). extended standardized ordinary cokriging. In addition, a tensor product cubic smoothing surface method was used for space-time semivariogram modeling. SPMOK was applied to model the water quality space-time distribution in Lake Dianchi, situated in the southwest of Kunming City (Tan et al., 2012; Fig. 5). Another research example is surface water storage modeling Figure 2. Bird view map (A) and stereogram (B) of simulated fracture networks in the Gaosong field, Gejiu tin mine in the southeast of Yunnan province, China (Liu et al., 2019). Figure 5. Distribution map of water quality indicators (COD, TP and TN) in Jan. 2010 with circles for sample location and ☆ for town. Figure 3. Relationship between the geometry of tin orebodies and simulated continuous fractures that are composed of 160 facets or more (roughly longer than 2 km), coded as SF1, SF2, SF3, SF4, SF5 and SF6. OX represents the tin orebody. Dotted lines guide the control of steep fractures on the vertical displacements of the orebody. Figure 6. Temporal change in distribution map of precipitation (mm/ year) in the Cangshan basin, Dali City. Figure 4. Digital elevation model (left), large change portions of slope gradient (middle) and interpreted fractures (right) in the Gejiu tin mine. Water resource is indispensable for economic and social developments. Utilization, management and maintenance of water resource affects sustainability of the developments. With large change in water volume in time and space, water quality is characterized by many factors. Then, Geoinformatic researches on water resources have focused on space-time multivariate Figure 7. Temporal change in normalized difference vegetation ordinary cokriging (SPMOK). SPMOK consists of suitable index (NDVI) distribution in the Cangshan basin. 71 using SWAT (Soil and Water Assessment Tool) combined with was used to evaluate land ecological security in the Dali lake geostatistics and remote sensing. SWAT was applied with the for 16 natural, environmental and landscape indicators (Zhang data of precipitation, vegetation interception and soil infiltration et al., 2017). In addition, satellite images of ALOS, SPOT in the Cangshan basin in Dali City, located in the west of and Quickbird were applied to evaluate China’s alternative Yunnan province (Liu et al., 2014: Figs. 6 and 7). cultivation policies for replacing poppy to anti-drug, substitution LULCC (land-use/land-cover change) greatly affects regional planting of opium poppy in the north of Laos (Liu et al., 2010). ecosystem and environment. Modeling and prediction of LULCC Natural disasters have widely occurred and destroyed the can support land use policy and coordinate sustainable urban economic and society developments in China. Geoinformatic development and ecosystem protection, especially in plateau researches at SUE have also focused on monitoring and lake basins. Remote sensing and GIS techniques have been used predicting the disaster occurrence and reducing the influences. to monitor the dynamics of land use patterns through fractal RA (Rock Analyst) based dynamics-kinematic analysis under dimension from 1974 to 2008 in the Dianchi lake watershed the ArcGIS platform was built and applied to detect dangerous (Zhang et al., 2013: Fig. 8). SLEUTH (Slope, Land use, zones in the Wenchuan earthquake area, Sichuan Province (Liu Excluded, Urban, Transportation, and Hillshade) method was et al., 2012: Fig. 10). Mountain hazard extraction model has used to forecast the LULCC pattern changes under six policies been constructed based on remote sensing data to predict the for the urban development based on the LULCC data derived hazard areas and applied in Yunnan province (Xu and Liu, 2018) from remote sensing images in the Dianchi lake watershed (Zhao et al., 2010). LULCC was also simulated using a GIS technique from 1990 to 2010 in the Dongchuan district in the north of Kunming City (Li et al., 2017: Fig. 9). IDRISI software Figure 8. Land use classification and its temporal change in the Dianchi basin in the southwest part of Kunming City. Targeted years are 1974, 1988, 1998 and 2008 from left to right showing extension of urban area coloured by red. Figure 10. Rockfall simulation result for the Dujiangyan- Wenchuan highway, Sichuan province. Figure 11. Landsat ETM+ original image (left), density slice of Figure 9. Temporal change of land use in Dongchuan district in digital number (DN, middle) and extracted debris flow the north part of Kunming City. Targeted years are 1990, gullies (right) in Dongchuan district in the north part of 2000 and 2010 from left to right. Kunming City. 72 and the Wenchuan earthquake area (Su et al., 2008: Fig. 11). University of Science and Technology (Natural Science Those introduced Geoinformatic researches are mainly Edition), vol. 24, no.1, pp.171-176. focused on modeling space-time distribution changes in natural Liu, C. X., Qin, D. X., Dang, Y. T., and Tan, S. C. (2003) resources and influences from human activities. Since YUFE is Synthesis information based mineral resource prediction of a university dominant at economics and management, all these Gaosong field in Gejiu tin deposit. Advance in Earth Sciences, studies are aimed to enhance the space-time economics and vo.18, no. 6, pp.921-927. management researches and to support the natural resources Liu, C. X., Wei, Z. H., and Lv, X. J. (2014) Research on the policy decisions and supervisions. water resource utilization in Cangshan basin, Kunming. Yunnan Science & Technology Press Co., Ltd. 3. Concluding Remarks Liu, H. J., Lan, H. X., Zhang, J., Liu, J., and Yang J. (2010) Under the supports form cooperation institutions, Evaluation and analysis for the substitution planting for Geoinformatic researches at YUFE-SUE have concentrated to opium poppy in the north of Laos based on remote sensing. simulate the
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