Analysis On Landscape Dynamics And Driving Forces Of Huaibei City Based On 3S

With the development of "3S" technology which includes geographic information system (GIS), remote sensing (RS), Global Positioning System (GPS), the scope of application of "3S" technology has been expanded, the dynamic analysis of landscape based on 3S technology has become more and more mature. The management and development of investigative resources is one of the earliest and most intensive areas of "3S" Technology, and with the continuous progress of coal mining industry, coal mining subsidence area landscape receives more and more concern, coal mining subsidence area harms animals and plants, destroy the ecological balance, so that research of landscape dynamic change of mine subsidence area has an important significance for governance policy and sustainable development.In order to improve the pollution control in coal mining subsidence and the ecological construction of mining area, this paper used Huaibei City Linhuan mine subsidence area as the study area to finish the following items: (1)Preprocessing and interpretation of remote sensing images in study areaAfter pre-processing the images of IKONOS in 2004 and WorldView-3 in 2014 by geometric correction, atmospheric correction and image enhancement, the pre-processed images were segmented and supervised classified by ENVI and eCongnition, combined with RS, GIS, GPS technology and field investigation. Finally, this paper got the following classification results and accuracy.a)Comparing the total classification accuracy with the Kappa index:The total classification accuracy of eCognition is 88% in 2014, Kappa index is 0.905, and the total classification accuracy is 81% in 2004, Kappa index is 0.823, which all are higher than ENVI's total classification accuracy and Kappa index.b)Comparing the classification accuracy of each landscape type:According to the data of ENVI, the classification accuracy of greenbelt, road and residential area is higher than other types', while the eCongnition performs well in classification accuracy of cultivated land, subsidence area, fly ash and gangue hill. The eCongnition software shows that the classification accuracy of the subsidence area is 83.47% in 2004 and 87.34% in 2014, while according to the ENVI software the classification accuracy in 2004 and 2014 is only 70.37% and 81.42%. It can be seen that ENVI performs excellently in classification of city-related types like cities and greenbelt. However, eCongnition adopts the method of first segmentation and second classification, which is more accurate in classification of mining land use, and is more suitable for the classification of landscape types in the study area. This paper got the landscape type dynamic change map and landscape type transition matrix by comparing the image interpretation results of eCognition software.(2)Establishment of landscape classification index system in study areaAccording to the "Land Use Dynamic Remote Sensing Monitoring Regulation" and China's current "Land Use Classification" (released in September 2007), combined with the landscape pattern characteristics of Linhuan mining area and the classification of mining landscape types in the existing literature, the study area is divided into 8 first-class and 16 second-class landscape types, which are merged into 10 landscape types with characteristics of Linhuan mining area:green land, farmland, mining land, fly ash, gangue hill, residential area, road, river, subsidence area, bare land. In this paper, the difference between the classification method and the previous research is that the subsidence area, fly ash and gangue hill are classified and contrasted separately, which has an intuitive understanding of the landscape dynamic change of Linhuan mining area and subsidence area. Due to the uneven land in subsidence area, the image shows some irregular region. In addition, the hill piled up by the light gray fly ash and the white gangue hill are very easy to distinguish in the image, all these characteristics arc convenient for classification processing of subsidence area, the fly ash and the gangue hill.(3)Analysis of landscape dynamic change in study areaThe conversion of different landscape types in Linhuan mine subsidence area from 2004 to 2014 is mainly the conversion of cultivated land area into subsidence area and the area of residential construction land, coal gangue and fly ash also increase significantly. The change of cultivated land, residential area and mining land landscape system mainly occurs in central, southwest and northeast, which is closely related to the intensity of human activities and the increase of mining intensity. The change of the area of the subsidence area is mainly centered on the 104 hm2 subsidence area in the central area, and the surrounding area is evenly collapsed, and the northern and eastern parts are more serious, which is closely related to the geographical position of the Linhuan coal mine.(4)Analysis of landscape index change in study areaBased on the theories of landscape science and ecology, this paper selects 17 evaluation analysis indexes and 2 kinds of relational relations to be divided,a) Plaque type level index (class level):number of plaques (NP), largest patch index (LPI), patch density (PD), landscape type area (CA), landscape type percentage (PLAND), landscape shape index (LSI), plate joint index (COHESION).b) Landscape level index:landscape shape index (LSI), aggregation index (AI), number of plaques (NP), largest plaque index (LPI), mean patch fractal dimension (FRACMN), Shannon diversity index (SHDI), contagion index (CONTAG), plaque polymerization degree index (AI), interspersion juxtaposition index (IJI), Shannon evenness index (SHEI).Landscape index analysis showed that the number of plots (NP) has increased hugely, it is precisely because of the continuous development of coal mines. In 2004, a large number of rural residents was developed, arable land decreased and construction land increased. Therefore the landscape shows different degrees of fragmentation. According to the perspective of patch density, the density of cultivated land, green land, residential area has increased, which is related to the sporadic distribution of cultivated land and bare land caused by collapse of subsidence area and expansion of construction land. While the number of patches and the density of the roads and rivers remain basically unchanged over a short span of 10 years. The Shannon evenness index (SHEI) and Shannon diversity index (SHDI) all show a significant increase trend, which indicates that the proportion of landscape types in Linhuan mine in this decade also increases significantly. After the increase of heterogeneity, the mining area shows a more balanced development trend. The changes of other indices also reflect the changes of landscape and reasons of change.(5) Analysis of environmental impact factors in the study areaAfter analyzing the influence of three natural factors on the dynamic changes of landscape in the past 10 years, this paper selects eight influencing factors of human environment to calculate the first and second principal components which are 5.587 and 1.076, and the cumulative contribution rate which is 83.3%. The result indicates that these two principal components could explain the driving forces of landscape pattern change in Linhuan mining area. The first principal component is positively correlated with house area, per capita GDP, total population, annual coal output and investment in fixed assets, and the minimum load is 0.804. Besides, it is negatively related to green area and grain yield, and the load is not less than 0.552. The second principal component is positively correlated with green area and grain yield per mu, and the load is 0.515. Also it is negative correlation with cultivated land area, and theAfter analyzing the influence of three natural factors on the dynamic changes of landscape in the past 10 years, this paper selects eight influencing factors of human environment to calculate the first and second principal components which are 5.587 and 1.076, and the cumulative contribution rate which is 83.3%. The result indicates that these two principal components could explain the driving forces of landscape pattern change in Linhuan mining area. The first principal component is positively correlated with house area, per capita GDP, total population, annual coal output and investment in fixed assets, and the minimum load is 0.804. Besides, it is negatively related to green area and grain yield, and the load is not less than 0.552. The second principal component is positively correlated with green area and grain yield per mu, and the load is 0.515. Also it is negative correlation with cultivated land area, and the load is 0.188.In summary, the first principal component is cultivated land area, which mainly reflects the impact of coal mining development, agricultural development and urbanization. The second principal component is the area of residential construction land, which mainly reflects the impact of policy adjustments. Therefore, the driving force of landscape dynamic change of Linhuan mine can be summarized to be coal mining development, agricultural development and urbanization and policy factors.