Identification of Mining Pollution Using Hyperion Data at Dexing Copper Mine in Jiangxi Province, China
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摘要: 利用高光谱图谱结合特征开展矿山污染直接识别研究.首先详细分析了德兴铜矿矿山污染(废矿、废水以及植被) 地物的光谱特征, 总结出可利用于直接识别和提取这些污染物的特征光谱, 从而利用矿区航天Hyperion高光谱数据并以矿物识别谱系技术为主有效地识别出矿区的污染类型及其分布.对于以黄铁矿等含铁矿物为主的围岩或贫矿矿石的氧化污染利用70 0nm、10 0 0nm以及2 2 0 0nm附近的特征吸收分别识别出含Fe3 + 矿物及其Fe2 + 和Fe3 + 混合矿物, 并进一步根据光谱特征识别出赤铁矿和针铁矿; 根据矿区水体在6 0 0nm附近吸收特征的差异相对区分出酸性水、碱性水和中性水; 根据植被在6 85nm附近的最大吸收深度相对地划分植被污染程度.最后建议建立矿山污染地物光谱数据库.该研究为利用高光谱的技术优势快速且有效地直接识别与提取出污染源的种类、类型并分析其潜在的污染趋势提供了新的思路, 为矿山污染监测、治理规划和复垦提供了新技术和知识支撑.Abstract: The process of contamination identification at Dexing copper mine based on the spectral feature of pollutions such as mine offal, waste water and vegetation and so on are investigated using spectral identification tree technique for Hyperion data. The spectra of various surface materials at mine are analyzed at first. And then the different contaminations, the Fe-bearing minerals including Fe3+ and mixture of Fe2+ and Fe3+ based on the spectral absorption feature of 700 nm, 1 000 nm and 2 200 nm, the pollution water and their relative pH based on the spectral feature of 600 nm, the vegetation contamination caused by mine offal and pollution water based on the maximum absorption of spectral depth between 580 nm-750 nm, are identified and extracted using Hyperion data. The spectral database of mining pollution is proposed. A good idea of identifying mining pollution quickly and directly is put forward using hyperspectral imaging technique. The project can be very practical in terms of technical support for inspecting and surveying, managing and planning, remedial action of mine environment.
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图 2 废弃物光谱特征
a.新鲜岩石, 表面颜色灰色; b.弱风化, 表面颜色黄色; c.强氧化, 表面颜色棕红色
Fig. 2. Spectra of mining offal
图 3 水体光谱特征
图中数字表示测试处离岸距离; a—c.酸性水体光谱特征; d.碱性水体光谱特征; e.大坞河河水; f.乐安河河水
Fig. 3. Spectra of water from mine
图 5 矿区部分高光谱Hyperion影像图(左) 及其辐射校正前后光谱对比(右)
影像波谱中, 蓝色表示校正前; 红色表示校正后
Fig. 5. Hyperion data cube (left) and spectral comparisons between row and recorrected data (right)
图 6 矿山污染信息(a-c: 灰白色) 提取
a.三价铁化合物; b.含Fe2+与Fe3+混合矿物; c.水域; d.植被污染
Fig. 6. Contamination information (gray in image of a-c) identification and extraction of mine
图 7 所识别铁矿物的图像波谱
a.三价铁化合物; b.Fe2+与Fe3+的混合矿物; X, Y为影像像元坐标
Fig. 7. Spectra of identified Fe-bearing mineral from image cube
图 8 MNF B1与MNF B6散点图(a)、水体酸碱度信息(b) 与水体影像光谱(c) (c图不同颜色所表示的影像光谱与b图地物对应)
Fig. 8. Relative pH information segmenting of water: scatterplot between MNF B1 and MNF B6 (a), relative pH for various water (b. red shows relative low pH; blue shows relative middle pH and green shows relative high pH) and spectra corresponding to different pH water (c)
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