Remote Sensing Of Monitoring Typical Environmental Elements Change In Mining Area

Mining industry provides the basic resource for national economy and development。. However, the nature of mining processes creates a potential negative impact on the environment, such as destroying the earth’s surface, damaging water resource and waste-induced pollution, both during the mining operations and mining closure. Environment monitoring from mining area would provide confidence to miners, regulators and the public.Traditional monitoring way is time-consuming and uneffective, which can’t meet the mining environment change. Remote sensing technology has the advantage of large-scale, dynamic, effective and high accuracy on environment moniting. The spatial resolution, spectral resolution and temporal resolution should be concerned as the dramatic change and damage of water, vegetation and waste from mining area.Thus, this study is aim to monitor dynamic vegetation change using time series remoge sensing data; extract the lake-water from mining area using an unmixing method by low-resolution remote sensing data; inverse the soil moisture based on the thermal inertia method; and identify the mining wase bansed on the field spectral characteristic.(1) Vegetation monitoring based on time-series NDVI dataMODIS NDVI time series were used in this study to monitor the vegetation change. As the different compositon methods from various climate zones, in our study area of the semi-arid and arid zone, the yearly MVC method was used on the NDVI data as yearly maximum NDVI is sensitive to overall productivity and biomass. Furthermore, regression analysis was used to monitor vegetation change in long term change monitoring. The results showed that the improvement rate of the vegetation was 84.57%, while the degradation rate was 10.13% from 2002 to 2010. The ecological and environmental measure was the major factor for the vegetation improvement, while the development of mining and its resulting destroyed groundwater caused the vegetation degradation.(2) A locally adaptive unmixing method for lake-water area extraction based on MODIS 250-m bandsA locally adaptive unmixing (LAU) method was developed to extract lake-water area using 250-m MODIS images. In this method, the target objects with mixing pixel were extracted firstly, and then two classes of endmembers of each mixed pixel were determined referring to the reflectivity of the neighboring pixels with different weights. Water abundance in each mixed pixel could be calculated using single-band image. Taking Hongjiannao Lake as example, the lake area was extracted from 2002 to 2010. The results showed that the lake area decreased sharply with annual average cut area of 0.85 km2.(3) Soil moisture inversion method based on low-resolution remotely sensed data in mining areaTaking the arid-to-semiarid Shendong mining area as an example, soil moisture was retrieved based on the thermal inertia method. According to synchronous field survey, in the densely covered area, the remotely sensed result was not significantly correlated with field data; contrarily, in the sparsely covered area, the correlation was significant and the remotely sensed result was most significantly correlated with field data on 10 cm. The linear regression model was established to monitor the soil moisture in the mining area. Compared with the background, the soil of 10-cm depth did not get dry in the mining area during the last 9 years. The making use of mine drainage and the measures on environmental protection may be the main reason.(4) Mine waste extraction method by remote sensing based on spectral characteristicsAccording to spectral measurement, the reflectivity is low from 0.35 to 2.5 μm which is not more than 30%. The reflectivity of lignite coal is substantially equal to bituminous coal in the visible-near infrared (0.35-0.9μm) but much higher than bituminous coal from 1.0 to 2.5 μm. Then the distinguishing indicators between lignite and bituminous were proposed by use of spectral reflectance curves. Based on TM data, Normalized Difference Coal Index (NDCI) was established to extract lignite and bituminous coal combined with TM5.Overall, remote sensing can be used as an effective monitoring technique in mining area. The results would be benefit on decision and technical support for mining industry and goverment.