Identification Of Mining Disturbance And Analysis Of Its Change Process For Mining Area In Steppe

There are huge reserves of mineral resources in China's steppe.Under the background of increasingly tense global energy situation,the exploitation of steppe mineral resources will provide energy support for China's national economic development.We refer to mining activity as disturbance,given the severe alterations caused by mining to the land's natural state.As well as affecting the land surface and vegetation,mining disturbances also have indirect effects beyond the mined areas due to influence on water resources,aquatic ecology,terrestrial wildlife and ecosystems,and air quality and human health.However,there is a lack of research on mining-disturbance identification and analysis of its change process for mining area in steppe,making it difficult to quantitatively acquire the location and time information of mining disturbance and evaluate the reclamation situation after mining.Thus it is not conducive to monitoring and renovation of ecological environment for mining area in steppe.Therefore,it is necessary to quantitatively and accurately identify mining disturbance and analyze the mining-reclamation changing process for mining area in steppe.With the continuous improvement of aerospace technology and the performance of earth observation sensors,remote sensing technology has been widely used in environmental monitoring.Traditionally,the research on environmental monitoring and mining disturbance identification for mining area is mainly based on land use classification.And the purpose of monitoring environmental changes for mining area is achieved by comparing the changes of land use types at different time.This method needs to classify remote sensing images,which is vulnerable to the image classification accuracy and difficult to reflect the mining-reclamation changing process.In recent years,the research on mining disturbance identification mainly take vegetation as the medium to identify mining disturbance by monitoring the process of vegetation change using the corresponding vegetation index derived form long time series remote sensing data.However,the existing research mainly focuses on forest region.There is almost no research on mining disturbance identification for mining area in steppe.Unlike forest region,mining areas in steppe of China are mostly located in arid and semi-arid regions,where the ecosystem is relatively fragile.Furthermore,there are also many other forms of disturbances in mining area.Therefore,identification of mining disturbances for mining area in steppe is relatively more difficult.Because of its fragile ecology and the urgency of environmental monitoring for mining areas,how to identify mining disturbances for mining areas in steppe and analyze the reclamation status has become an urgent problem to be solved.This paper takes Bal Rishi Le mining area as study area and take Landsat TM/ETM+/OLI image data from 1985 to 2015 as data source.The study is mainly support by the "Thirteen Five" national key research and development project"Technology and Demonstration of Ecological Restoration and Comprehensive Renovation of Large Coal and Electricity Bases in Eastern Steppe Area" with the NO.of 2016YFC0501101-4.Based on the existing research and the coal mining process in the study area,this paper makes full use of the related theoretical knowledge such as time series analysis,Surveying and mapping,mathematical statistics and geography.The vegetation coverage is inversed and the trend of vegetation change is analyzed for the study area.By analyzing the main difficulties in identifying mining disturbances for the study area,exploratiing of the characteristics of mining disturbances,and analyzing the characteristics of vegetation change in the process of mining-reclamation,a set of techniques which integrate remote sensing time series denoising,mining disturbance identification and evaluation of reclamation status is put forward.The research methods are evaluated from qualitative and quantitative perspectives.The main conclusions of this paper are as follows:(1)Based on the selected Landsat TM/ETM+/OLI images,the vegetation coverage of the study area is inverted by using the binary pixel model.And then the changing trend of vegetation coverage is analyzed using the linear regression method.The results show that the vegetation coverage of the study area is obviously degraded.The areas with significant degradation or extremely significant degradation accounted for 11.22%of the study area.In order to ascertain the proportion of mining disturbance in vegetation degraded area,it is necessary to identify mining disturbance for the mining area.(2)In order to remove the effect of climatic conditions and noise on the identification of mining disturbance for mining area in steppe,a method of removing the noise from Landsat inter-annual NDVI time series data,designated BISE-WT filter,is proposed.In order to evaluate the feasibility of the new filtering method,BISE(Best Index Slope Extraction)and S-G(Savitzky-Golay filter)are taken as the references.The performances of the reference methods and the new method are evaluated from three aspects:qualitative evaluation,quantitative evaluation and change detection analysis.The results show that the new method can retain the real information in time series data while removing noise.(3)On the basis of previous studies and actual circumstances of the study area,a method of mining disturbance identification for mining area in steppe is proposed,which combines the spectral characteristics of ground objects,the changing regular pattern of mining disturbance and the topographic relationship of objects.Then the method is applied to the study area and achieve an accuracy of 92.2%(Kappa coefficient=0.83),which is 10.2%higher than the spectral method.(4)The research divide the change process of mining disturbance into four stages:pre-mining,vegetation clear-cutting or occupation,mined and post-mining by analyzing the vegetation change process of the mining disturbance.And then parameterize each stage with the corresponding parameters,thresholds or formulas.These parameters,thresholds or formulas form the mining disturbance template.Then match the NDVI time series of each mining-disturbance pixel with the template to get the corresponding matching parameters.The matching parameters are used to determine the disturbance year and mining-reclamation trajectory type of the mining disturbance.The results show that the accuracy for identification the disturbance year of mining disturbance is 74.4%and the accuracy of identification for the Mining-Reclamation trajectory type is 85.7%.(5)According to the intensity,mining disturbance in the study area can be divided into two stages:the first stage is 1986-1998,during which sporadic mining disturbance occurred;the second stage is 1999-2015,during which large-scale coal mining was carried out.(6)Nearly 90%of the mining disturbance areas in the study area are in the status of no vegetation restoration,and very few mining disturbance areas reach to the vegetation level of pre-mining.In this paper,using Landsat long time series remote sensing image data,a set of techniques for time series data reconstruction,mining disturbance identification and reclamation status evaluation is proposed.The techniques have been applied to Bal Rishi Le mining area and acquired some good research results.However,there are still many problems to be solved.In the future,related research will be carried out mainly from different mining technologies,different image classification and recognition methods.