Based on the development of classification algorithm applied in monitoring spatio-temporal dynamic changes of coal-- mining areas, several improvements were made on feature space and classification model in this paper. There were two innovations in our study: 1) During building the feature spaces, a new index for extracting information about mining area was created, which can classify mining area and settlements efficiently; 2) a special ticket-voting SVM algorithm with wavelet kernel function was proposed, which provides higher classification accuracy than other traditional classifiers via the secondary classification. Here we took the northeast plain of Pei county in Xuzhou city as a studying region, applying the proposed method to implement the classification by using the image of multi-temporal TM/ETM from the year of 1987 to 2013. How to carry on deep analysis combined with various non-spatial data is much more significant. Then we studied the rules of dynamic changes of land use/cover and further analyzed their driving factors by combining RS interpretation with GIS spatial analysis techniques. In this study, image recognition technology was applied to the problems of environmental change in coal mining area. These explanations provide some valuable supports for human to recognize and deal with the conflicts between economic development and environmental protection in coal mining areas.
Accurate classification for land use/cover with remote sensing image is the premise of monitoring land use/cover change, as well as temporal and spatial change analysis, which can distinguish the number, location and type of changed land during monitoring years. This paper presents a decision tree classification method based on expert knowledge for temporal and spatial variation analysis of land use/cover, which takes advantage of new water index (NWI), normalization construction index (NDBI) and transformational vegetation index (TNDVI). It takes Qingpu District in Shanghai for example. Effective classification and information extraction are realized by using multi-temporal Landsat5 TM images from the year 1987 to 2007. It combines available ancillary geographical data, field survey data and statistical yearbook data to verify the analysis results, and then analyzes profoundly area variation trend of each classification and relative proportion during these years. Meanwhile, multi-temporal change monitoring results are used for the temporal and spatial variation analysis of water resources information, combining with existing relevant data of water quality. On the basis of buffer analysis for water quality and land use/cover in city-level water quality monitoring stations of Qingpu District, a correlation coefficient matrix has been calculated to analyze the relevance between land resources changes and water resources. In conclusion, the scale of urban area and water area are the primary driving force factors of LUCC, which also have an effect on water resource change of Qingpu District. This research represents the application of image recognition technology in the spatio-temporal changes of environment monitoring, and how to carry on deep analysis combined with various non-spatial data. It also provides objective reference about how to protect and improve water quality by controlling land use allocation in water source protection zones.