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Ecological Environment Monitoring And Driving Factors Analysis Of Fengfeng Mining Area Based On Improved Remote Sensing Ecological Index

Posted on:2023-03-03Degree:MasterType:Thesis
Country:ChinaCandidate:P F MaFull Text:PDF
GTID:2531307055459734Subject:Master of Resources and Environment (Professional Degree)
Abstract/Summary:PDF Full Text Request
As is known to all,long-term large-scale coal mining and ultra-strength exploitation have caused regional ecological problems such as coal dust pollution,land subsidence,air pollution and soil pollution,etc.As one of the world’s major coal producing countries,it is particularly important to increase the ecological environment assessment of coal mining areas,and quickly and accurately monitor environmental pollution and research on corresponding technologies.With the development of science and technology,in recent years,remote sensing technology has become more and more widely used as a new technology and means to quickly monitor the ecological environment of coal mining areas and evaluate the quality of regional ecological environment.Fengfeng mining area is located in Handan City,Hebei Province,rich in coal resources,long-term mining makes the ecological and environmental problems prominent,considering that the air pollution in the affected area is more serious,with reference to the existing results,based on the Remote Sensing Ecological Index(RSEI),in the existing Normalized Difference Vegetation Index(NDVI),Humidity(WET),Normalized Differential Building Soil Index(NDBSI)and Land Surface Temperature(LST)indicators,the PM2.5 concentration index(DI)is added,and a new remote sensing ecological index(RSEInew)is constructed by using Landsat remote sensing images to analyze and evaluate the ecological environment of mining areas.The driving forces of ecological environment change in mining areas were studied.The main conclusions are as follows:(1)The contribution rate of the first principal component characteristic value of the data in each period was higher than 70%,which could better concentrate most of the attribute characteristics of the five indicators.The results of each index showed that the greenness and humidity indicators were opposite to the indicators of dryness,heat and PM2.5 concentration,which conformed to the general law of ecology and could more truly reflect the ecological environment status of the Fengfeng mining area.Taking five indexes as independent variable modeling analysis,it is concluded that the contribution to the ecological environment is NDBSI>NDVI>DI>WET>LST,the coefficients of NDBSI,DI and LST are-0.48,-0.31 and-0.22,and the coefficients of NDVI and WET are 0.34 and 0.22,the symbols are also opposite.All indexes are not eliminated,which proves the validity and applicability of the model.(2)In the past 20 years,the RSEInew grade of Fengfeng mining area has been mainly medium and excellent,and the ecological environment is generally good.Spatially,the areas with poor ecological environment are concentrated in the central and eastern urban areas and sporadic townships and mining areas of the Fengfeng mining area.The severely degraded and slightly degraded areas account for more in the central and eastern regions,which are urban expansion areas.From 2001 to 2012,the ecological environment fluctuated and changed in the trend of deterioration first,from 2001 to 2012,the ecological environment fluctuated and changed with the trend of deterioration first,and from 2012 to 2020,the ecological environment fluctuated and changed in a small range,and showed a trend of fluctuation improvement.(3)The changes of the ecological environment of each coal mine were analyzed from the scope of coal mine shaft field and gangue mountain and its surrounding range.In terms of the scope of the wellfield,after the closure of the coal mine,the RSEInewlevel of most areas has not changed in the past 20 years,the area of environmental improvement area is much larger than the degraded area,and the ecological environment has developed better.In the 20 years of continuous mining of coal mines,the RSEInew level of most areas has not changed,but the area of the area of the area of the area with environmental deterioration is greater than the area of the area of the area of the better area,and the ecological environment has deteriorated.In terms of coal gangue mountain and its surrounding area,most of the 12 coal mines studied from2001 to 2016 showed degradation in gangue mountains and surrounding environments,and most of the change trends from 2016 to 2020 were improved,and the surrounding environment of gangue mountains gradually improved in recent years,which also benefited from the guidance and implementation of government policies in recent years.(4)The change of ecological environment quality in the study area was affected by the combined influence of driving factors such as temperature,precipitation,temperature,humidity,sunshine,slope,soil erosion and land use change.Temperature changes change in the opposite direction to the RSEInew mean trend.Increased precipitation will promote the improvement of the regional ecological environment.The average monthly humidity and average monthly sunshine hours were moderately correlated and uncorrelated with the mean RSEInew,with coefficients of 0.55 and-0.12,respectively.In the past 20 years,the ecological environment in low slope areas deteriorated and the ecological environment in high slope areas improved.Soil erosion in the study area was mainly micro erosion and light erosion,and the area with low soil erosion intensity increased from 2000 to 2020,the soil erosion intensity in local areas intensified,and the overall soil erosion intensity weakened.In the past 20 years,the urban construction land has increased by 3.51%in the first ten years and 30.00%in the second decade,but the ecological environment has not declined sharply,mainly because the issuance and implementation of government policies have curbed the destruction of the ecological environment.
Keywords/Search Tags:new remote sensing ecological index, ecological environment, mine, spatiotemporal distribution, driving factors
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