| 煤炭矿区耕地土壤有机质无人机高光谱遥感估测 |
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| 引用本文： | 陈玮,徐占军,郭琦.煤炭矿区耕地土壤有机质无人机高光谱遥感估测[J].农业工程学报,2022,38(8):98-106. |
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| 作者姓名： | 陈玮 徐占军 郭琦 |
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| 作者单位： | 山西农业大学资源环境学院，太谷030801 |
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| 基金项目： | 国家自然科学基金项目（51304130）；山西政府重大决策咨询课题（ZB20211703）；山西省哲学社会科学规划课题（2020YJ052）；山西省基础研究计划项目（20210302123403） |
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| 摘 要： | 为监测煤炭矿区不同沉陷阶段耕地土壤质量状况，实现矿区土地复垦和耕地质量保护，以山西省长治王庄煤矿周边3种处于不同沉陷阶段的耕地为例，使用无人机搭载高光谱相机进行影像获取，并在研究区内进行土壤样品采集及室内光谱测定。通过对光谱反射率进行倒数、一阶微分、二阶微分、多元散射校正4种不同形式的变换，分析转换后的光谱反射率和实测有机质含量的相关性，筛选出相关系数较高的敏感波段。利用多元线性回归（Multiple Linear Regression，MLR）、偏最小二乘回归（Partial Least Squares Regression，PLSR）和BP神经网络（BP Neural Network，BPNN）3种模型对有机质含量建立预测模型，并对模型预测结果进行精度评价，选用较优模型代入无人机高光谱影像进行有机质含量填图，得到耕地范围内的土壤有机质分布情况，并对处于不同沉陷阶段的耕地土壤有机质空间分布差异及其驱动因子进行分析讨论。结果表明：1）采煤沉陷区耕地土壤有机质含量与经过多元散射校正变换的光谱曲线相关性最高，敏感波段为463.75～492.45 nm，870.79～932.58 nm处，最大相关系数为0.63。2）经过多元散射校正处理的光谱曲线运用偏最小二乘回归模型和BP神经网络模型预测有机质含量精度要明显高于多元线性回归模型，预测精度分别达到0.863和0.884，可以用于有机质含量的估测。3）采煤沉陷区耕地土壤有机质分布情况表现为煤炭开采未扰动区耕地土壤有机质分布较为均一，均值为26.94 g/kg，总体上处于中上等水平；煤炭开采扰动稳沉区耕地土壤有机质高低值分化明显，整体分布呈现较大空间分异性；煤炭开采扰动区介于二者之间。矿区有机质含量大小关系为煤炭开采未扰动区耕地>煤炭开采扰动区耕地>煤炭开采扰动稳沉区耕地。
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| 关 键 词： | 无人机 高光谱 有机质 预测模型 矿区复垦 |
| 收稿时间： | 2021/12/1 0:00:00 |
| 修稿时间： | 2022/3/9 0:00:00 |
Estimation of soil organic matter by UAV hyperspectral remote sensing in coal mining areas |
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| Chen Wei,Xu Zhanjun,Guo Qi.Estimation of soil organic matter by UAV hyperspectral remote sensing in coal mining areas[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(8):98-106. |
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| Authors: | Chen Wei Xu Zhanjun Guo Qi |
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| Institution: | College of Resource and Environment, Shanxi Agricultural University, Taigu 030801, China |
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| Abstract: | Abstract: This study aims to monitor the soil quality of the cultivated land in different subsidence stages of the coal mining areas, particularly for land reclamation and quality protection. Three types of cultivated land were taken as examples around the Wangzhuang Coal mine in Changzhi City, Shanxi Province, China. The images were first acquired by an Unmanned Aerial Vehicle (UAV) equipped with a hyperspectral camera. The soil samples were then collected to carry out the indoor spectrometry in the study areas. Four transformations of spectral reflectance were also performed on the images, including the reciprocal, the first-order differential, the second-order differential, and multivariate scattering correction. A correlation analysis was conducted to select the sensitive bands with the higher correlation coefficient between the converted spectral reflectance and measured organic matter content. A prediction model was established for the content of soil organic matter using the Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), and Back Propagation Neural Network (BPNN). The optimal model was achieved to map the organic matter content in the UAV aerial hyperspectral images, in order to determine the distribution of soil organic matter in the cultivated land range. The accuracy of the prediction model was evaluated to obtain the spatial differences and driving factors of soil organic matter in different subsidence stages of cultivated land. The results show that: 1) The soil organic matter content of the cultivated land in the coal mining subsidence area has the highest correlation with the spectral curve transformed by the multivariate scattering correction and the organic matter content. The sensitive band is 463.75-492.45 nm, 870.79-932.58 nm, the maximum correlation coefficient is 0.63. 2) The prediction accuracy of the organic matter content reached 0.863 and 0.884 in the PLSR and BPNN model in the spectral curve that was processed by multiple scattering correction, respectively, which were significantly higher than that of the MLR model. It infers that the prediction models were feasible to identify the organic matter content. 3) There was a relatively uniform distribution of soil organic matter in the cultivated land in the coal mining subsidence areas. Specifically, the distribution of soil organic matter presented with an average value of 26.94 g/kg in the undisturbed area of coal mining, which was in the upper-middle level in general. Nevertheless, there was great spatial heterogeneity for the overall distribution of soil organic matter in the disturbed and stable subsidence areas of coal mining. More importantly, there was an outstanding differentiation of high and low values, particularly with a proportion from Grade 1 to 6. The disturbed area of coal mining was between the above two grades. The relationship of organic matter content in the mining area was ranked as follows: the cultivated land in the undisturbed > the disturbed > the disturbed and settled, areas of coal mining. Consequently, the reason was the surface deformation, physical and chemical properties of soil, as well as the land evolution in the vegetation and human management before and after coal mining. |
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| Keywords: | UAV hyperspectrum organic matter prediction model mine reclamation |
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