白茎绢蒿是一种广泛分布于新疆富蕴县各个矿区的一种植物。 在矿区进行矿产勘查时, 由于植物等障碍信息的存在, 传统的勘查方法已经难以发挥作用, 急需一些新方法、 新思路。 遥感植物地球化学方法可以巧妙地利用植物这一天然的信息源, 把植物从障碍信息转换为了有用信息。 帮助人们快速、 经济地获取植物屏障下的矿产有用信息。 由于其具有大面积、 快速、 无损性等优点, 受到了越来越多学者的关注, 成为当下的研究热点。 近些年虽然有学者综合考虑“吸收系数”和“衬度系数”这两个指标, 证明了白茎绢蒿是对隐伏矿床的勘查具有较好指示性作用的植物, 生在在矿床上部的植物可以较好的吸收土壤中的成矿元素, 在其体内形成地球化学异常, 相比于其他植物异常信息更加清晰可见。 但是目前没有人研究是否可以从光谱的角度来发现白茎绢蒿体内的地球化学异常, 进而为隐伏矿床的勘查提供参考。 因此, 本研究首次尝试从白茎绢蒿的光谱信息中寻找出与地球化学异常密切相关的特征波段或者特征值, 然后构建基于植物光谱的隐伏矿床预测模型。 采取的方法是首先利用ASD FieldSpec3 型光谱仪分别对生长在矿床上部和背景区的植物进行光谱测定, 然后从原始光谱、 一阶导数光谱、 二阶导数光谱、 一阶导数的分形维数、 二阶导数的分形维数五个层面对生长在这两个区域的植物光谱进行对比分析, 最终优选出了10个差异显著的特征波段, 分别为: R′824, R′834, R′1 533, R′1 573, R′1 633, R′1 643, R″1 284, R″1 703, 一阶导数的分形维数以及二阶导数的分形维数。 这些特征波段可以作为植物地区寻找隐伏矿床的植物地球化学标志。 以优选出的10个特征波段作为输入参数, 分别用随机森林 (RF)和偏最小二乘-支持向量机(PLS-SVM)构建了基于植物光谱数据的隐伏矿床预测模型。 结果表明: （1）两种模型均可以取得较好的效果, 但是相比于随机森林模型, 偏最小二乘-支持向量机模型具有更好的鲁棒性, 泛化能力也更强; （2）利用植物的光谱异常寻找隐伏矿床具有较大的潜力, 因为相比于传统方法, 更加简单、 快速。 课题组已经利用动力三角翼和HySpex成像高光谱传感器构建了“超低空探测平台”, 可以实现对地“亚米级”的观测。 但是如何有效的解决“空间尺度”和“光谱尺度”问题, 如何把地面试验场建立的模型更好的应用于超低空探测平台, 实现研究区大面积地、 快速地植物异常信息提取将是我们下一步的研究重点。

Seriphidium terrae-albae is a kind of plant widely distributed in various mining areas of Fuyun County, Xinjiang, China. The traditional exploration methods are difficult to play a role due to the existence of plant information and other obstacles, and some new methods and new ideas are urgently needed. The remote sensing plant geochemistry method is a kind of natural information source that smartly utilizes plants, transforming the plant from the barrier information to the useful information. Help people quickly and economically obtain the useful information about minerals under plant barriers. Because of its large area, being fast and non-destructive and other advantages, it has attracted more and more attention of scholars, and has become the current research hotspot. In recent years, although some scholars have synthetically considered “absorption coefficient” and “contrast coefficient” to prove that Seriphidium terrae-albae can be used as a good indicator for the exploration of concealed deposits. The plants in the upper part of the deposit can absorb the ore-forming elements in the soil better, but at the same time they form geochemical anomalies in their bodies, and the information is more visible than other plant anomalies. However, no one has studied whether the geochemical anomalies in Seriphidium terrae-albae can be found from the spectral point of view, then providing some references for the exploration of concealed deposits. Therefore, our study first tries to look for the feature bands or eigenvalues closely related to geochemical anomalies, and then construct the prediction model of hidden deposit based on plant spectrum. First, the method adopted was to measure the reflectance spectra of plants growing in the upper part of deposit and background area by ASD FieldSpec3 spectrometer respectively. Then the spectra of plants growing in these two regions were analyzed and compared from five aspects, including the original spectrum, the first derivative spectrum, the second derivative spectrometry, the first derivative fractal dimension and the second derivative fractal dimension. Finally the 10 characteristic bands that were notably different were selected including R′824, R′834, R′1 533, R′1 573, R′1 633, R′1 643, R″1 284, R″1 703, the first derivative fractal dimension and the second derivative fractal dimension. These characteristic bands can be used as botanogeochemistry marks for seeking exploration of concealed deposits. Taking these ten optimized bands as input parameters, random forest (RF) and partial least squares support vector machine (PLS-SVM) were used to construct a prediction model that seeks the position of hidden deposits based on abnormal spectrum of plant. The results showed that these two models can obtain satisfactory results, but compared with the random forest model, the partial least squares support vector machine model has a better robustness and stronger generalization ability. The results also indicated that it has great potential in looking for hidden deposit using extraordinary spectrum of plants, due to the advantages of being simple and quick. Our team has built a “very low altitude detection platform” using dynamic delta wing and HySpex imaging hyperspectral sensor, which can realize the observation of “sub-meter”. But the problems will be our next research focus as follows, how to effectively solve the problem of “spatial scale” and “spectral scale”? How to better apply the model established on the ground test ground to the very low altitude detection platform, and how to extract the plant anomaly information in a large area and quickly in the research area?