基于反射光谱预测土壤重金属元素含量的研究

本文利用实验室实测的土壤反射光谱以及铅、镉、汞等重金属元素数据,进行土壤重金属元素含量快速预测的可行性研究。本文利用偏最小二乘回归方法,研究了反射率(R)、一阶微分(FDR)、反射率倒数的对数(lg(1/R))和波段深度(BD)等对预测精度的影响,对这几种光谱指标预测土壤重金属含量的能力进行了分析和评价,同时分析了多光谱数据估算土壤重金属元素含量的可行性。结果表明,反射率倒数的对数lg(1/R)是估算土壤重金属元素含量最好的光谱指标,尤其是Cd和Pb,检验精度R超过0.82。有机质、铁锰氧化物和黏土矿物对土壤重金属元素的吸附是可见光—近红外—短波红外光谱估算其含量的机理。多光谱数据同样具有估算土壤重金属元素含量的能力,但实际数据则要考虑多种因素的影响。     

矿区土壤重金属Pb、Cd污染状况高光谱分类建模

针对矿区土壤重金属含量高度变异性及样本不均衡导致重金属污染状况分类误差较大的问题,本文在光谱预处理及光谱变换基础上,采用主成分分析（PCA）对光谱进行降维处理,并通过SMOTE算法生成虚拟样本均衡各污染等级样本,最后应用随机森林（RF）对Cd、Pb进行回归与分类。研究结果表明：定量反演重金属Pb、Cd含量精度很低;在定性分析试验中对降维前光谱样本应用SMOTE算法,土壤重金属Pb、Cd污染等级分类精度较原始样本分类精度均有较大提升,且少数类别误判率也降低明显。其研究为大面积监测矿区土壤重金属污染状况提供了一种有效、精确的方法。     

特征变量选择结合SVM的耕地土壤Hg含量高光谱反演

为探讨应用高光谱数据反演耕地土壤重金属汞(Hg)含量,对原始光谱进行10 nm重采样和SG平滑处理,用不同光谱变换数据与土壤重金属Hg含量进行相关性分析,采用IRIV、Random Frog和PCC提取光谱特征波段,分别建立SVM与GWO-SVM土壤Hg含量高光谱反演模型,获取Hg含量最优反演路径.研究表明,一阶微分变...     

GIS的矿区土壤重金属污染评价及空间分布

针对土壤中的重金属含量超标会对人体健康造成极大危害的问题,为检测矿区土壤重金属含量超标状况及空间分布特征,以江西省信丰县4个矿区为例,在28个点位的20~60cm处测定Hg、Cd、As、Cu、Pb、Ni的含量,采用地统计学和地理信息系统相结合的方法进行分析。结果表明,单因子污染指数显示Pb的污染程度最大,污染程度为中度污染;插值分析图显示Cu以西南方向的虎山矿区含量较高,Ni以西南方向的虎山矿区和北部的赤岗矿区含量较高,Pb的污染区域贯穿于整个分布区;重金属含量随深度的增加无明显变化。结合在污染修复方面的经验,建议通过植物修复技术、物理与化学方法进行污染治理和修复。     

基于GIS的土壤重金属数据库构建及应用

本文首先探讨了利用GIS技术构建上海市基本农田土壤重金属数据库的方法,该数据库集空间数据、采样点监测数据和元数据于一体,对于土壤重金属信息的管理、应用服务等具有重要的基础作用,是农产品安全生产环境认证和土壤环境长效管理的重要依据;接着,基于数据库进行了重金属元素的描述性统计、动态累积和Kriging插值分析;结果表明,除As外,其余重金属平均含量均高于该区域的背景值含量,尤其是Cd、Hg、Zn、Cu累积指数较高,是上海市农田土壤重金属污染的主要元素。     

光谱解混下耕地土壤铬含量GMDH模型反演

针对遥感影像反射率与重金属元素间的光谱响应弱,土壤重金属经典反演模型精度较低等问题,本文以Sentinel-2号遥感影像为数据源,利用像元二分模型进行影像光谱解混,筛选出相关性较高的特征光谱作为光谱参量,构建基于像元线性解混和不同光谱变换下土壤反射率与重金属Cr含量的PLS模型和GMDH模型。研究结果表明,解混后的光谱与重金属Cr含量间的显著相关波段数增多,相关性增强。基于解混后的土壤光谱与重金属Cr含量构建的GMDH模型,其模型稳定性较好,预测能力更强,精度更好。该方法拓展了传统的利用遥感影像进行反演的思路,可为大范围监测土壤重金属的污染状况提供有益参考。     

Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data—A case study along Le’an River,China

It is necessary to estimate heavy metal concentrations within soils for understanding heavy metal contaminations and for keeping the sustainable developments of ecosystems. This study, with the floodplain along Le’an River and its two branches in Jiangxi Province of China as a case study, aimed to explore the feasibility of estimating concentrations of heavy metal lead (Pb), copper (Cu) and zinc (Zn) within soils using laboratory-based hyperspectral data. Thirty soil samples were collected, and their hyperspectral data, soil organic matters and Pb, Cu and Zn concentrations were measured in the laboratory. The potential relations among hyperspectral data, soil organic matter and Pb, Cu and Zn concentrations were explored and further used to estimate Pb, Cu and Zn concentrations from hyperspectral data with soil organic matter as a bridge. The results showed that the ratio of the first-order derivatives of spectral absorbance at wavelengths 624 and 564 nm could explain 52% of the variation of soil organic matter; the soil organic matter could explain 59%, 51% and 50% of the variation of Pb, Cu and Zn concentrations with estimated standard errors of 1.41, 48.27 and 45.15 mg·kg?; and the absolute estimation errors were 8%–56%, 12%–118% and 2%–22%, and 50%, 67% and 100% of them were less than 25% for Pb, Cu and Zn concentration estimations. We concluded that the laboratory-based hyperspectral data hold potentials in estimating concentrations of heavy metal Pb, Cu and Zn in soils. More sampling points or other potential linear and non-linear regression methods should be used for improving the stabilities and accuracies of the estimation models.     

模拟多光谱的土壤重金属含量反演研究初探

根据多光谱传感器的光谱响应函数,采用实测ISI921VF反射光谱数据模拟Landsat卫星ETM+传感器多光谱数据,在模拟光谱的基础上,通过光谱特征提取、构建土壤指数对土壤重金属Cu,Pb,As进行预测分析。研究显示,Cu,Pb与模拟ETM+光谱的B2,B3波段显著相关,As与DSI,RSI,NDSI相关系数在0.6以上,基于模拟多光谱建立的Cu,As模型精度较高,平均相对误差分别为7.9%,2.7%,表明模拟的Landsat卫星ETM+传感器多光谱具有预测耕地土壤重金属的潜力,为实现大范围监测土壤重金属污染提供新思路。     

土壤重金属污染灰色综合评价模型

针对稀疏采样难以准确估测区域土壤重金属综合污染情况和迁移变化规律的问题,提出基于GIS的多属性决策组合赋权灰色综合评价模型。首先采用GIS技术揭示土壤重金属空间变异和污染分布格局;然后利用最大化熵理论集成主客观因素,架构优化组合赋权的土壤重金属污染灰色综合评价体系;最后以试验区土壤中8种(铜、锌、铅、镉、砷、铬、汞、镍)重金属的综合污染情况为例,检验该方法应用效果。结果表明:最优组合权重的灰色综合分析方法兼顾主观偏好和客观属性,其评价结果具有更高的可信度和风险辨识度,提高了综合评价的合理性与有效性,可为土壤重金属污染监测提供方案参考。     

Assessing Metal-Induced Changes in the Visible and Near-Infrared Spectral Reflectance of Leaves: A Pot Study with Sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

The aim of this study was to monitor changes in leaf spectral reflectance due to phytoaccumulation of trace elements (Cd, Pb, and As) in sunflower mutant (M5 mutant line 38/R4-R6/15-35-190-04-M5) grown in spiked and in situ metal-contaminated potted soils. Reflectance spectra (350–2500 nm) of leaves were collected using portable ASD spectroradiometer, and respective leaves sample were analyzed for total metal contents. The spectral changes were quite noticeable and showed increased visible and decreased NIR reflectance for sunflower grown in soil spiked with 900 mg As kg^?1, and in in situ metal-contaminated soils. These changes also involved a blue-shift feature of red-edge position in the first derivatives spectra, studied vegetation indices and continuum removed absorption features at 495, 680, 970, 1165, 1435, 1780, and 1925 nm wavelength. Correlograms of leaf-metal concentration and reflectance values show highest degrees of overall correlation for visible, near-infrared, and water-sensitive wavelengths. Partial least square and multiple linear regression statistical models (cross-validated), respectively, based on Savitzky–Golay filter first-order derivative spectra and combination of spectral feature such as vegetation indices and band depths yielded good prediction of leaf-metal concentrations.     

徐州市九里矿区土壤重金属插值分析及污染评价

通过对徐州市九里矿区的表层土壤进行采样测定,进而对该矿区土壤重金属富集与污染状况进行了分析与评价。结果表明,该矿区表层土壤中5种重金属(Cd、Cu、Zn、Pb、Cr)含量均略高于中国土壤元素背景值,土壤中Cd富集程度较高,污染较严重;Cu、Zn、Pb、Cr富集程度较低,污染程度较轻。在GIS环境下利用空间数据插值方法对重金属的空间分布特征研究发现,该城区土壤重金属含量与该区的工矿活动和交通活动等密切相关。     

基于GIS的县域土壤重金属生态风险评价

为研究经济快速发展区农田土壤中重金属的含量及污染状况,本文以浙江省慈溪市为研究对象,研究土壤中铜、汞、镉、铅、砷、铬、锌七种重金属含量特征,并采用潜在生态危害指数法对其进行评价,并绘制生态风险危害指数分级图。结果表明:土壤中除汞元素含量较高外,其他各元素含量仅稍高于当地土壤背景值。七种元素的单因子污染指数Cfi值均属于中等的污染参数,综合污染指数Cd上限值已处于高污染指数的范围,但平均值为属于中等污染水平。从潜在生态风险评价结果来看,七种元素的单项潜在生态风险参数Eri的值也只有汞达到了强生态危害,七种元素综合潜在生态危害指数RI刚刚达到中等生态危害水平,说明该区农田土壤尚处于较低的生态风险状态。生态危害指数插值结果表明,慈溪市重金属元素的高风险区分布在中南部人类活动较为活跃、城乡工业较发达的区域,在今后的土地利用中,应高度重视人类活动对土壤重金属污染的影响。     

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area.     

结合野外与实验室光谱的土壤Pb含量反演

重金属污染日益加剧,重金属在土壤中的聚集不仅破坏了生态平衡,也对人类的健康生活造成了影响,因此快捷、准确地获取土壤中的重金属含量成为土壤污染监制与治理的重要环节。高光谱遥感技术的发展使得快速低成本反演土壤重金属含量成为可能。针对野外光谱受环境因素（土壤粒径、含水量等）的影响,且现有研究中普遍存在样本量不足的问题,提出结合野外光谱与实验室光谱构建土壤铅(Pb)反演机理模型的方法,首先,采用直接矫正（direct standardization,DS）算法对野外光谱进行环境因素校正;其次,通过引入实验室光谱联合建模的方式,提高样本的差异性;最后,提取铁氧化物特征谱段用于建模以增加反演的机理性。利用中国河北雄安一般农作区的70个土壤样本野外光谱数据研究表明,未经DS校正的野外光谱全谱段单独建模,反演精度R2仅为0.220 0,而所提方法的反演精度R2可达0.914 6, 模型具有出色的估算能力,表明在去除环境因素对野外光谱影响基础上,综合利用野外光谱与实验室光谱的铁氧化物特征谱段建模能够显著提高Pb含量的反演精度。     

Estimating soil zinc concentrations using reflectance spectroscopy

Soil contamination by heavy metals has been an increasingly severe threat to nature environment and human health. Efficiently investigation of contamination status is essential to soil protection and remediation. Visible and near-infrared reflectance spectroscopy (VNIRS) has been regarded as an alternative for monitoring soil contamination by heavy metals. Generally, the entire VNIR spectral bands are employed to estimate heavy metal concentration, which lacks interpretability and requires much calculation. In this study, 74 soil samples were collected from Hunan Province, China and their reflectance spectra were used to estimate zinc (Zn) concentration in soil. Organic matter and clay minerals have strong adsorption for Zn in soil. Spectral bands associated with organic matter and clay minerals were used for estimation with genetic algorithm based partial least square regression (GA-PLSR). The entire VNIR spectral bands, the bands associated with organic matter and the bands associated with clay minerals were incorporated as comparisons. Root mean square error of prediction, residual prediction deviation, and coefficient of determination (R²) for the model developed using combined bands of organic matter and clay minerals were 329.65 mg kg⁻¹, 1.96 and 0.73, which is better than 341.88 mg kg⁻¹, 1.89 and 0.71 for the entire VNIR spectral bands, 492.65 mg kg⁻¹, 1.31 and 0.40 for the organic matter, and 430.26 mg kg⁻¹, 1.50 and 0.54 for the clay minerals. Additionally, in consideration of atmospheric water vapor absorption in field spectra measurement, combined bands of organic matter and absorption around 2200 nm were used for estimation and achieved high prediction accuracy with R² reached 0.640. The results indicate huge potential of soil reflectance spectroscopy in estimating Zn concentrations in soil.     

土壤中镉、铜伤害对水稻光谱特性的影响

本文讨论了水稻受重金属镉和铜污染伤害后的光谱反射特性的变化,为遥感监测污染提供基本依据。使用高分辨率光谱辐射计在自然状态下实地测量了受污染的水稻光谱特性,比采集叶片在室内测量更接近于实际情况,便于结合遥感图像进行定性和定量分析研究。 结果表明,镉和铜拌土生长的水稻在分蘖期受到的影响最明显,无论是在生理上还是在反射光谱方面变化都比较显著。因此,对水稻受重金属污染的遥感监测最佳时间为分蘖期,有效波段为0.54—0.58,0.64—0.69,0.74—0.80微米。综合对水稻光谱的各种分析方法,如波形分析,微分光谱,绿度指数和主成分变换等技术,水稻在分蘖期,对高浓度的监测效果较好,而对低浓度效果不甚明显。     

土壤Cu含量高光谱反演的BP神经网络模型

以高光谱数据为基础,针对传统土壤重金属反演模型拟合度低、预测效果差的缺点,提取光谱预处理后的特征波段数据进行相关性分析,选取860nm一阶微分光谱反射率建立基于Matlab的重金属Cu含量BP神经网络预测模型,模型的拟合优度为0.721,预测精度达82.3%,高于传统单元线性回归模型0.414的拟合优度与76.1%的预测精度。研究表明,BP神经网络模型具有良好的拟合优度与预测能力,能更有效预测土壤中重金属Cu的含量。     

基于集合经验模态分解的土壤重金属Cd定量反演

针对高光谱定量反演土壤重金属含量的模型精度不足的问题,本文从时频空间的角度引入时频分析法——集合经验模态分解（EEMD）。采用EEMD法分解土壤高光谱,获得不同频率的本征模态（IMF）分量,通过分析IMF分量与重金属含量的相关性,提取特征光谱,构建EEMD-SVM定量反演模型。研究结果表明,通过EEMD法分解土壤光谱,可有效地提取土壤光谱中的微弱信息;构建EEMD-SVM模型可较好地反演土壤重金属Cd含量,模型的决定系数R²为0.920 3,明显高于基于一阶微分处理光谱数据后构建的SVM模型的R²（0.786 6）。即说明在土壤重金属定量反演领域,EEMD可作为一种新的光谱处理方法。     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Predicting soil organic carbon stocks under commercial forest plantations in KwaZulu-Natal province,South Africa using remotely sensed data

ABSTRACT

Commercial forest plantations are increasing globally, absorbing a large amount of carbon valuable for climate change mitigation. Whereas most carbon assimilation studies have mainly focused on natural forests, understanding the spatial distribution of carbon in commercial forests is central to determining their role in the global carbon cycle. Forest soils are the largest carbon reservoir; hence soils under commercial forests could store a significant amount of carbon. However, the variability of soil organic carbon (SOC) within forest landscapes is still poorly understood. Due to limitations encountered in traditional systems of SOC determination, especially at large spatial extents, remote sensing approaches have recently emerged as a suitable option in mapping soil characteristics. Therefore, this study aimed at predicting soil organic carbon (SOC) stocks in commercial forests using Landsat 8 data. Eighty-one soil samples were processed for SOC concentration and fifteen Landsat 8 derived variables, including vegetation indices and bands were used as predictors to SOC variability. The random forest (RF) was adopted for variable selection and regression method for SOC prediction. Variable selection was done using RF backward elimination to derive three best subset predictors and improve prediction accuracy. These variables were then used to build the RF final model for SOC prediction. The RF model yielded good accuracies with root mean square error of prediction (RMSE) of 0.704 t/ha (16.50% of measured mean SOC) and 10-fold cross-validation of 0.729 t/ha (17.09% of measured mean SOC). The results demonstrate the effectiveness of Landsat 8 bands and derived vegetation indices and RF algorithm in predicting SOC stocks in commercial forests. This study provides an effective framework for local, national or global carbon accounting as well as helps forest managers constantly evaluate the status of SOC in commercial forest compartments.