Extraction And Spatial-temporal Distribution Of Color Steel Sheds Based On Remote Sensing Taking Anning District Of Lanzhou As An Example

With the implementation of major national strategies such as " the belt and road initiative" and "western development",Lanzhou,as a key city in the northwest,is an important node in the transformation and upgrading of cities and the rapid development of urbanization.The large-scale westward migration of enterprises in the East and the acceleration of urbanization have jointly promoted the emergence of a large number of phased products in the city,namely,various kinds of color steel sheet buildings(color steel shed),and shaped a more special urban "landform".Color steel shed is widely distributed in urban villages,urban fringes,urban-rural junctions and economic-technological development areas because of its light material,low cost,beautiful appearance and easy to build quickly.However,the particularity of the color steel shed leads to the prominent problems of fire and illegal construction.,which seriously disturbs the safety of life and property of the local residents,and also brings great resistance to the construction of civilized cities.Therefore,the research on the spatial and temporal distribution of color steel shed is helpful to provide data and means for the governance of related problems and the study of urban morphology.At present,the research on the urban color steel shed mainly focuses on fire and illegal construction,while the research on its spatial and temporal distribution characteristics and its relationship with the evolution of urban spatial morphology is very few.Only my team is doing preliminary exploratory research.Therefore,this paper takes the color steel shed in Anning District of Lanzhou City as the research object,and analyses its spatial and temporal distribution and evolution law in the city.In this paper,remote sensing images in 2005,2008 and 2017 are used as data sources to construct color steel shed extraction model and extract color steel shed for follow-up research.Spatial analysis,correlation analysis,regression analysis and other methods are used to analyze the spatial and temporal distribution,evolution law and the impact of key factors on the scale of color steel shed in Anning District.The main research results are as follows:(1)Based on the characteristics of spectrum,texture and geometry of the color steel shed,a remote sensing extraction model of the color steel shed is constructed.The model effectively fuses the three kinds of features to enhance the information of the color steel shed,and effectively improves the accuracy of the extraction of the color steel shed.(2)In this paper,taking Anning District of Lanzhou City as the research area,the spatial and temporal distribution characteristics of color steel shed are analyzed.The results show that the center of gravity of the color steel shed is moving northwest to the middle of the Anning District,next to Yingpanshui-Lanzhou Highway.With the passage of time,the clustering characteristics of the color steel shed become more and more obvious,and high-value clustering and low-value clustering appear.Secondly,using standard deviation ellipse and G_i^* index,the spatial and temporal evolution characteristics of the color steel shed are analyzed.The research shows that the extension direction of the color steel shed in Anning District of Lanzhou City is southeast-northwest direction,and the strip-like characteristics are obvious,both in quantity and area,there is a large increase.(3)In this paper,choosing the key factors and the correlation between the key factors and the color steel shed is studied.The results show that the influence of the key factors from 2005 to 2017 is population density,per capita GDP,per capita housing construction area,real estate investment,industrial economic benefit comprehensive index and industrial enterprise unit number in turn from big to small.From 2005 to 2017,the scale of color steel shed is negatively affected by per capita GDP,per capita housing construction area and real estate investment,and positively affected by population density,industrial economic efficiency index and the number of industrial enterprises.