Monitoring And Evaluation Of Mine Environment Based On Remote Sensing And GIS

The environmental degradation in mining area is the modern challenge for both the mining companies and MRL. Large-scale mining activity can disturb balance of our nature. Mining pollution can damage human living environment and endanger lives and properties.Dexing copper mine is one of the leading mining companies in our country. The 4# tailing reservoir of Dexing copper mine has biggest capacity in Asia. A study of environment of Dexing copper mine was carried out in project "effect and renovation of mineral resource exploitation on environment" from year 2002 to 2005. A series of space-based environment monitoring methods were employed based on fieldwork, including water pollution monitoring, LUCC monitoring, vegetation pollution monitoring, mining geological hazard assessment. Furthermore evaluation on mining environment has been fulfilled. They are faster, cheaper, bigger scale over regular monitoring methods. Based on satellite data such as TM, SPOT, SRTM radar survey data author enhanced satellite information effect. Author extracted indicator of geological environment, geography, ecosystem based on human-machine alternative interpretation on satellite data, analysis process of mining environment pollution, produced environmental evaluation map. In addition environmental changing trend was analyzed through satellite data in different period based on field check.Results(1) Based on water spectrum properties in different pollution level, the water pollution assessment model was carried out. Water pollution indicator was extracted from four TM satellite data. The spectrum properties of Dawu river and Yuean river were analyzed. Finally, contamination expansion trend and location were figured out based on water pollution indicator. Result: Yuean river was contaminated by Dawu river at the conjunction point of these two river. Before join with Dawu river Lean river was clean. During year 1986 to 1992 Yuean river was slightly contaminated. But from year 1992 to 1996 water quality progressively deteriorated. Due to environmental recover water quality turn to be better than year 1996 in year 2000.(2) A vegetation deterioration statistic method based on spectrums fusion technology was carried out. This method was used to figure out the deterioration size from year 2004 to year 2005. Study on NDVI indicator of vegetation pollution demonstrated that the environment change can be divided into three stages. From year 1986 to 1992 environment deteriorated slowly. But between year 1992 to 1998 environment deteriorated sharply because of neglect of environmental protection. In 1998 the situation can not be worse. Fortunately, after year 1998 the Dexing copper mine company has been paying more attention to environment protection. Then the environment started to recover day by day. In year 2000 the NDVI indicator shows that situation is much better than year 1998.(3) Land-use change information was extracted from multi-temporal and multi-resolution satellite image. In addition, the ever expanding mine tailing volume was figured out based on DEM data. This method solved the problem of distinguishability of the waste rock and building. Result: waste rock pile size expanded from 2.4KM~2 in 1992 to 5.6 KM~2 in year 2000. Volume expanded 1 time than in year 1992.(4) In order to deal with geological hazard survey, a method based on GIS and remote sensing was put forward. Analysis the correction of the land cover and landslide. Determining of highly endangered regions for landslides based on topographic factors extracted from SRTM, the vegetation indicators from NDVI.Further mapping the landslides bodies quantitatively on spots.(5) Based on terrain analysis the capability of 4# tailing reservoir and catchment basin size were figured out. Analyzing the early warning mechanism and model of Dexing 4# tailing reservoir collapse are established.(6) 3D mine virtual reality software was developed for Dexing environment demonstration. It utilized 3 D visualization technology on large amount data.