摘要
农田土壤重金属污染已经成为现代社会不容小视的环境问题,传统化学检测方法相对落后,进行动态、迅速、大范围的土壤重金属监测迫在眉睫。分别利用便携式地物光谱仪(ASD)和机载高光谱成像系统(Gaia Sky-mini)获取的光谱数据进行土壤重金属污染监测。结果表明,在As、Cd、Cr、Pb 4种重金属元素的偏最小二乘法(PLSR)预测模型中,基于Gaia Sky-mini光谱可以区别As的高低值,同时,还具备粗略估算样本Cd含量的能力,相对分析误差(RPD)值分别为1.13和1.50;ASD光谱数据可以粗略估算As的含量,定量估算Cd的含量,As和Cd元素RPD值最高分别为1.45和1.95。对比两种数据源回归结果,低空无人机获取的高光谱数据可以监测土壤重金属,光谱波段更宽、光谱分辨率更高的ASD数据对土壤重金属监测精度更高。低空无人机高光谱数据的应用对进一步研究快速、大尺度监测土壤重金属含量提供了更多的手段,具有重要意义。
Heavy metal pollution in farmland soils has become an environmental issue that cannot be overlooked in modern society. Traditional chemical detection methods are relatively outdated,and it is urgently needed to carry out dynamic,rapid and large-scale monitoring of soil heavy metals. In this study,heavy metal pollutions in farmland soils were monitored by using spectral data obtained from portable matter spectrometer（ ASD） and on-board hyperspectral imaging system（ Gaia Sky-mini） respectively. The results showed that Gaia Sky-mini spectroscopy can discriminate the maximum and minimum value of As from partial least squares regression（ PLSR） prediction model of As,Cd,Cr,and Pb,and roughly estimate the Cd content of samples,with residual predictive deviation（ RPD） values being 1. 13 and 1. 50,respectively.The ASD spectral data can roughly estimate As content and quantitatively estimate Cd content,with RPD values of 1. 45 and 1. 95 for As and Cd,respectively. By comparing the regression results of two data sources,it is suggested that spectral data acquired from low-altitude unmanned aerial vehicle（ UAV） can monitor heavy metals in soil with wider spectral bands and higher spectral resolution,whereas ASD spectral data has higher precision. It is important to apply lowaltitude UAV hyperspectral data in further research to carry out rapid and large-scale monitoring of soil heavy metal content.
作者
易兴松
兰安军
文锡梅
张吟
李洋
YI Xing- song;LAN An-jun;WEN Xi-mei;ZHANG Yin;LI Yang(School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China;Guizhou Mountainous Resources Institute, Guiyang 550001, China)
出处
《生态学杂志》
CAS
CSCD
北大核心
2018年第6期1781-1788,共8页
Chinese Journal of Ecology
基金
贵州省科技支撑计划项目(黔科合[2016]2595-2号)
贵州省科技计划项目(黔科合SY字[2012]3058)和黔科合平台人才([2017]5758)资助
