为探究土壤重金属有效波段提取方法，明确敏感波段耦合机器学习模型对土壤重金属浓度的估测潜力，以内蒙古多个废弃尾矿区周边典型污染场地为研究对象，通过高光谱遥感数据预测土壤Cu、Zn、Pb和Cr重金属的浓度。基于16种敏感波段提取方法（按过滤法、包裹法、嵌入法分类）并结合决策树（DT）、随机森林（RF）和梯度决策树（GBDT）模型，进行重金属浓度反演。结果表明，相比过滤法和嵌入法，包裹法提取的敏感波段对重金属浓度的解释性最高，敏感波段主要集中在450－750 nm和1829－2493 nm。在6种包裹法中，竞争自适应重加权抽样法（CARS）和可变迭代空间收缩法（VISSA）分别为Cu和Cr提供了关键光谱信息，而连续投影算法（SPA）则对Zn和Pb具有较高敏感度。相比DT和RF模型，GBDT在聚焦敏感波段时表现出更强大的拟合性能，耦合CARS、VISSA和SPA方法能更准确地估测土壤重金属浓度。利用独立矿区验证时，CARS、VISSA和SPA组合GBDT模型对土壤重金属浓度仍具有稳定的估测性能，Cu、Zn、Pb和Cr的决定系数（R²）分别为0.91、0.89、0.87和0.84。该研究构建的土壤重金属监测模型能有效增强土壤光谱信息可解释性，为矿区土壤重金属的快速监测提供了具有实际应用潜力的新方法。

探索自动化的激光点云分类方法对于三维建模、城市土地分类、DEM制图等应用具有重要作用。考虑到现有的点云分类算法在提取依赖邻域结构的特征参数时面临邻域尺度的选择难、数据维度高、计算复杂，并且缺乏对分类特征参数的重要性评估和选择等问题，本文提出了基于随机森林的机载LiDAR点云数据降维与分类方法。在分析点云数据的高程、回波、强度等属性特征的基础上，提取归一化高度、高度统计量、表面特征、空间分布特征、回波特征及强度特征6大类特征参数，并构建多尺度特征参数，运用随机森林的特征选择算法对分类特征集进行优化，然后进行点云分类。试验结果表明，基于随机森林的特征选择方法可以有效地降低特征维度，并且使得总体分类精度达到94.3%（Kappa系数为0.922），相比于使用全部特征分类和SVM分类方法而言，该方法的总体分类精度均有一定程度的提高；特征的重要性度量结果表明，归一化高度特征在点云分类中所起的作用最大。

Exploring automatic point cloud classification method is of great importance to 3D modeling,city land classification,DEM mapping and etc.To overcome the problem that extracting geometric feature for point cloud classification involved neighbor structure meets the challenge that the optimal neighbor scale parameter,high data dimension and complex computation,lacking efficient feature importance analysis and feature selection strategy,this paper proposed a point cloud classification and dimension reduction method based on random forest.After analyzing the characteristic of elevation,intensity and echo of laser points,this paper extracted a total of 6 feature types like normalized height feature,height statistic feature,surface metric feature,spatial distribution feature,echo feature,intensity feature,then built a multi-scale feature parameter from them.Finally,a supervised classification was conducted using a random forest algorithm to optimal the feature set and choose the best feature set to classify the point cloud.Results indicate that,the overall accuracy of the proposed method is 94.3% (Kappa coefficient is 0.922).The proposed method got an improvement in the overall accuracy when compared with no feature selection strategy and SVM classification strategy; The feature importance analysis indicates that the normalized height is the most important feature for the classification.