摘要
土地复垦工作减轻了煤炭开采对耕地粮食生产的影响,尽早预测复垦作物产量有助于田间精准管理和保障粮食安全。无人机平台和传感器技术的发展支撑了高空间分辨率和光谱分辨率数据的获取,使快速估产成为了可能。以黑土区露天煤矿排土场复垦后种植的大豆为例,评估了将植被指数(VI)、纹理指数(TI)、植被指数和纹理指数(VTI)分别作为特征变量,采用多元线性回归、随机森林回归、反向传播神经网络和支持向量回归4种方法,建立大豆产量的估测模型。结果表明:与单独将植被指数或纹理指数作为特征变量相比,将植被指数和纹理指数作为特征变量构建的模型精度均有一定程度提高;支持向量回归方法在产量估测建模中表现出最好的稳定性和精度,R^(2)、RMSE和RPD分别为0.84、0.0039、2.53。研究成果为黑土区露天煤矿排土场大豆产量估测提供了技术支持,并可为田间精准管理和复垦效果评价提供数据支撑。
Land reclamation mitigates the impact of coal mining on grain production in cultivated land.Early prediction of reclaimed crop yields facilitates precision field management and ensures food security.Advances in unmanned aerial vehicle(UAV)platforms and sensor technology enable the acquisition of high spatial and spectral resolution data,making rapid yield estimation feasible.The analysis focuses on soybeans cultivated in reclaimed open-pit coal mine waste dumps within the black soil region.This study evaluates soybean yield estimation models using four methods—multiple linear regression(MLR),random forest regression(RFR),backpropagation neural network(BPNN),and support vector regression(SVR)—with vegetation index(VI),texture index(TI),and combined vegetation-texture index(VTI)as feature variables.The results show that:(1)Compared to using only VI or TI as feature variables,models incorporating both VI and TI demonstrated improved accuracy;(2)Support Vector Regression(SVR)exhibited the best stability and accuracy in yield estimation modeling,achieving R^(2)=0.84,RMSE=0.0039,and RPD=2.53.This research findings provide technical support for estimating soybean yields in the dump of open-pit coal mine in the black soil region and offer data-driven insights for precision field management and reclamation effectiveness evaluation.
作者
王培俊
李志鑫
胡振琪
李长江
史九
WANG Peijun;LI Zhixin;HU Zhenqi;LI Changjiang;SHI Jiu(School of Public Policy and Management(School of Emergency Management),China University of Mining and Technology,Xuzhou 221116,China;School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China)
出处
《矿业安全与环保》
北大核心
2025年第6期38-46,共9页
Mining Safety & Environmental Protection
基金
国家自然科学基金青年科学基金项目(41901229)。
关键词
黑土区
矿区土地复垦
作物
产量估测
机器学习
无人机
black soil region
mine land reclamation
crop
yield estimation
machine learning
unmanned aerial vehicle
