Use Of Hyper/Multiple-Spectral Data On Monitoring Heavy Metal Pollution In Soil-Rice System Nearby Baoshan Mines

Mining supports national economic development with large amount of energy sources and materials, however, over-mining and laggard environmental protection measures make many mines be faced with challenge of sustainable development and cause serious heavy metal pollution in the nearby soil-crop system. Security and health of agricultural environment is directly correlated to the subsistence security of people and constructing of harmonious society. As a quick, convenient and lossless method, remote sensing was gradually focused and has been used as an alternative and effective solution for monitoring environmental quality of farmlands. This dissertation was supported by the opening project funded by National Science Foundation of China (40571130) and Key Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of Agriculture.Pb, Zn, Cu, Cd and As entered into soil-paddy plant system when being mined from Baoshan Mine which is a Pb-Zn mine exploitated for many years. Pb, Zn, Cd and As have no spectral feature in the region of visible-near-infrared light, meanwhile, Cu shows spectral characteristics only if its concentration in soils exceeds 4000 mg/kg. However, heavy metal concentrations in agricultural soils are very little. It is hard to predict heavy metal concentrations in agricultural soils by soil spectral reflectance. Whereas, the close correlation between heavy metals and Fe that has significant spectral characteristics should be the basis on which heavy metal concentrations in agricultural soils can be predicted by soil spectral reflectance. Heavy metals absorbed by paddy plants would inhibit growth of root system, synthesis of chlorophyll and photosynthesis of leaves, which should be betrayed by canopy reflectance spectra of paddy plants. Therefore, heavy metal concentrations in rice canopy leaves should be predicted by characteristic spectra extracted from rice canopy reflectance spectra. Heavy metals accumulated in rice corns make passivation for rice protein and would threaten people's health through food chain. Close correlation between heavy metals and protein that has significant spectral feature should also be the basis on which heavy metal concentrations in rice corns can be predicted by corn reflectance spectra.Based on above theories, heavy metal concentrations and spectra of soil-paddy plant system in the farmlands nearby Baoshan Mine were studied to research the feasibility of reflectance spectra of soils, rice canopy and rice com in predicting and assessing heavy metal pollution in soil-paddy plant system. This study can contribute to the usage of spaceborne or airborne multi/hyper spectral remote sensing in environmental pollution monitoring. The main results of this dissertation are listed below:1, Pb concentration in agricultural soils was 1767.42 mg/kg and exceeded much higher than the level required by Soil Environmental Quality Standard made by Ministry of Environmental Protection of P.R.C for rice producing area. Zn, Cu, Cd and As were similarily higher than the corresponding level required by Soil Environmental Quality Standard. At the same time, edible quality of rice is poor because Pb concentration in rice is much higher than the safety level, which should be seriously treated by relational government departments.2, On the basis of intercorrelation between heavy metals and Fe, spectral reflectance of soils was used to construct assessing models for the indirect estimation of heavy metal concentrations in polluted soils. Appropriate spectral pretreatments can promote cability of models. This reserach indicated that remote sensing can be an alternative solution of convenience and speed to investigate and monitor heavy metal pollution in agricultural soils.3, It is these spectral parameters such as vegetation indices, red edge position, slope of green peak polarization, and area of triangle absorption extracted from rice canopy spectra at the tillering stage that makes the best of canopy reflectance spectra of paddy plants polluted by heavy metals. By means of these spectral features, regression models were constructed for heavy metal concentrations in rice canopy leaves. Inversed Gaussian was used to fit reflectance spectra in the region from red light to near-infrared light for red edge position, which would be profitable for the sufficient usage of multispectral data. On the basis of extraction of red edge position, conception of spectral critical value was defined to discriminate physiological critical value for paddy plants polluted by heavy metal.4, Prediction for protein in rice by coarse rice reflectance spectra pretreated by multiplicative scatter correction was the best because the pretreatment promote predicting ability of regression models. It indicates that heavy metals in rice can also be indirectly predicted by coarse rice spectra on the basis of close correlation between heavy metals and rice protein.Prediction of protein content and heavy metal concentrations in coarse rice by rice canopy spectra at the tillering stage was not as good as that by coarse rice reflectance spectra. However, this method is one compensatory solution to predicting rice quality and assessing rice edibility.