Vegetation Components Quantitative Inversion By Hyper Spectral Remote Sensing In Coal Mining Area

A mass of toxic element came from gas drainage, spontaneous combustion of waste heap et al came from production process all seriously polluted air environment in coal mining area, even healthy stress the growth of vegetation through dust holding directory or indirectly. Hence, the monitoring and evaluation of vegetation stress of coal mining area through the acquisition of vegetation biochemical components content can make great sense on reasonable utilization of mining resource.In this paper, hyperspectral images were used to research vegetation biochemical components inversion and to supply the basis of vegetation effects caused by mining. The vegetation biochemical components to be inversed were chlorophyll and water content. The HYPERION image with more bands, hyperspectral and high spatial resolution, broad coverage area, were used to do inversion. The Yanzhou coal mining area was selected as research area.The key point of the passage is the construction of inversion model. The field spectral and vegetation biochemical components were used to research the inversion model. Because of the complicated surface feature and sparse vegetation distribution, the anti-soil capacity of inversion model parameters was analyzed using LOPEX'93 dataset by the application of theory of mixed pixel. The relevance between every band/band combination/vegetation index and vegetation biochemical components were simulated under different soil background proportion. The results show that the models composed of the spectral parameters of the reflectance and its variants to inverse vegetation chlorophyll, the reflectance and the logarithm of reciprocal reflectance of 730 nm and 400 nm combination can keep a high correlation coefficient while the area ratio of soil component changes from 10 percent to 90 percent, the correlation coefficient between the reflectance and chlorophyll was around 0.645, and the correlation coefficient between the logarithm of reciprocal reflectance and chlorophyll was 0.650. To inverse water content, the combination of 1100 nm,1170 nm,1000 nm,1040 nm, 1080 nm reflectance, and the combination of 1170 nm,960 nm,1210 nm,1090 nm,1080 nm, 950 nm,1220 nm,1210 nm logarithm of reciprocal reflectance show a strong anti-soil capacity, the correlation coefficients between the two models and water content were all larger than 0.99. In the models composed of the spectral parameters of the spectral position variables, the parameter of red edge-green peak-red valley was selected as the strongest Anti-soil capacity parameter, the correlation coefficients were distributed around 0.530. In the models composed of the vegetation index to retrieve vegetation chlorophyll,, the anti-soil capacity was poor but when the model is used to retrieve vegetation water content the correlation is stable though the soil area ratio is changable and the correlation coefficients are as high as 0.980 and 0.960 at the two typical water indices of Ratio975 and Ratio 1200, respectively. These conclusions can be used to guide the vegetation biochemical component inversion for sparsely vegetated regions.Due to the major atmospheric particulate matter and the complex atmospheric components, the sensitive characteristic of band involved in Hyperion foundation model to AOT, H2O, CO2 et al different atmospheric components were analyzed. According to the sensitivity, the atmospheric components were accurately confirmed. In the process of atmospheric correction, the aerosol optical thickness and water vapor were retrieved as the input model parameters. The 6S transfer model was used for atmospheric correction and the results show that:the precision of atmospheric correction is high enough for vegetation chlorophyll and water content inversion.Hyperion images were used to do the inversion experiment of chlorophyll and water content on the basis of constructed vegetation biochemical components inversion model and precise atmospheric correction. The field samples were used to validate. The results show that the water content had a better inversion result than chlorophyll. The relative error of inversed chlorophyll less than30% and water content less than 15%.