| Poverty is one of the most severe challenges facing the world today.Poverty eradication has always been an important topic discussed by the international community,which has made active efforts to eradicate poverty.In recent years,China has gained worldwide attention on the issue of poverty eradication.By the end of 2020,China have completed the building of a moderately prosperous society in all respects.However,poverty alleviation is not a permanent thing.Poverty alleviation counties that have just been lifted out of absolute poverty are still at greater risk of falling back into poverty due to factors such as resource conditions,the size and distribution of poverty alleviation industries,and the surrounding population base,as well as various economic conditions,natural environment and other external conditions.Therefore,how to use remote sensing technology and related spatial data processing to obtain natural resources,industrial spatial layout and other background conditions that affect county economic and industrial development,and promote rural revitalization and sustainable development through modeling analysis is a problem worthy of further study.This paper first analyzed the population,natural resources,geographical environment,poverty alleviation industry and their influencing ways that affect the sustainable development of the counties.Then,analyzed their data types,data acquisition methods and spatial characteristics,and study remote sensing information extraction technology or GIS data processing scheme.Based on the idea of detectability and quantification of evaluation indicators and the mechanism of "society-economyresource-environment",the remote sensing evaluation model and index system of county sustainable development were constructed,and the ways of obtaining and presenting each index were given.The objective weighting entropy weighting method was used to assign weights to the evaluation indicators.Finally,taking the poverty alleviation counties in Baicheng City of Jilin Province as the evaluation and verification region for sustainable development,the evaluation system of sustainable development characteristics in Baicheng counties was obtained,which verified the feasibility and operability of the method in this paper.Through the above work,the following achievements and understandings have been obtained:(1)Landsat data,Sentinel-2 data and Gaofen-6 data and other images can be used to extract county resources and remote sensing detectable industrial elements and other spatial information.Night light data can be extracted from Luojia-1 data to reflect regional population distribution and economic basis.Through processing and analysis of meteorological station data,POI interest points and other data,we can obtain meteorological environment and other factors that affect the sustainable development of county.Remote sensing and integrated data processing technology can provide technical support of spatial data extraction for the assessment of the stable development of the poverty alleviation counties,and provide objective and spatial data sets for the sustainable development model.(2)The sustainable development of poverty alleviation counties are restricted by many factors.Therefore,the impacts of "society-economy-resource-environment" should be comprehensively considered.Based on the idea that the evaluation index can be detected and quantified,the remote sensing evaluation model and index system of county sustainable development were constructed.The indicators in this study were composed of six first-level indicators,namely,spatial population index,resource condition index,geographical condition index,environmental impact index,transportation convenience index and industrial comprehensive index,and 18 secondlevel indicators were set below.Some of these indicators can be obtained by remote sensing information extraction and data mining technology,and some existing data can be obtained by spatial interpolation.Data with strong social attributes can be processed by data spatial transformation.(3)The entropy weight method was used to weight the first-level evaluation index,and the weights of each index were obtained.The order from high to low was spatial population index,industrial comprehensive index,transportation convenience index,geographical condition index,resource condition index,environmental impact index.The spatial population index,transportation convenience index and industrial comprehensive index were classified as social indicators,and the geographical condition index,resource condition index and environmental impact index were classified as natural indicators.The weight ratio of social index and natural index is about 7:1.Social factor is the absolute dominant factor for regional sustainable development,but it also needs the strong support of resource background conditions.Remote sensing data monitoring system can evaluate regional sustainable development objectively and comprehensively.(4)From the data analysis and evaluation results,in the research area,cropland and grassland are interleaved,while wetland is more concentrated.Wind power industry is widely distributed in Haituo Township,Xinpingan Township,Dagangzi Township,Fengshuishan Ranch Township.The sustainable development status of Zhenlai Township,Jianping Township in Zhenlai County,Da ’an Economic Development Zone,Dalai Township,Sikeshu Township,Lianhe Township,Anguang Township,Honggangzi Township in Da ’an County,Kaitong Township in Tongyu County are better.From the angle of balanced development,this paper analyzed the current situation of the research area and put forward the priority development pattern.It was suggested to focus on the development of Sifangtuozi Farm,Momoge Field,Hatuqi Mongolian Township in Zhenlai County,Longzhao Township in Da ’an County,Bamian Township,Shuangganglu Farm,Shihualu Township in Tongyu County.Practice shows that the use of remote sensing technology and other quantifiable spatial data can objectively and comprehensively reflect the sustainable development of the evaluation region,and provide solutions for remote sensing technology to help rural revitalization. |
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