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。     

HJ-1CCD与Landsat ETM+在土地利用中的应用比较

为研究环境一号(HJ-1)CCD影像在土地利用中的应用能力,选取HJ-1 A/B CCD和Landsat ETM+作为数据源,在遥感影像土地利用要素识别、土地利用计算机分类精度、土地利用信息提取二级分类精度和土地利用分类等方面,对HJ-1A/B CCD数据和Landsat ETM+数据在土地利用分类中的应用能力进行对比研究,结果表明土地利用分类应用中,HJ-1 A/B CCD基本可以替代Landsat ETM+,且HJ-1 A/B CCD数据光谱敏感性更强,反映地物细节的能力更强,比Landsat ETM+数据更有优势的结论。     

不同尺度反演土壤重金属铜含量研究

采用实测土壤高光谱遥感数据和多光谱遥感影像数据采用单元回归分析法对土壤重金属铜含量建立反演预测模型。利用单元回归分析法分别建立模型,得出高光谱的最佳预测波段是R_(942),模型决定系数R2=0.634,多光谱最佳预测波段为B2,模型决定系数R2=0.625。通过显著性检验,均达到显著水平。结果表明多光谱遥感影像数据在本研究区内具有预测重金属铜含量的能力。     

频率域光谱的土壤铜铅浓度差异区分及反演研究EI北大核心CSCD

土壤是人类生存环境的重要载体,土壤重金属污染问题一直备受关注。随着可见光-短波红外(VNIR-SWIR)高光谱和X-射线荧光(XRF)技术的发展,因其具有光谱信息量大、高效便利、可无损监测等的优势,在土壤重金属浓度监测中取得了越来越多的成果。总结其应用成果来看,只能借助光谱域变换反演土壤Cu、Pb浓度,鲜有对Cu、Pb污染土壤光谱从频率域角度进行局部细节信息的深入挖掘。     

基于电磁感应的干旱区土壤盐渍化定量遥感研究

以南疆典型干旱区Landsat 7 ETM+遥感图像为数据源,利用决策树分类法提取农业用地,并对农业用地进行移动式电磁感应调查(简称磁感调查)和光谱特征提取,同时分析磁感数据和图像光谱特征与土壤盐分含量的相关性,从而建立土壤盐分的定量反演模型。研究结果表明:土地利用类型决策树的分类精度达到93.75%,Kappa系数达0.915 4;经多元逐步回归分析,磁感调查获得的土壤盐分含量与差值植被指数(DVI)、ETM+图像第二波段像元值(B2)以及比值植被指数(RVI)间具有显著相关性,由此建立的遥感反演模型可用于土壤盐分含量的定量反演。经89个样点检验,基于磁感调查的土壤盐分遥感反演精度虽低于基于磁感调查的地统计空间分析的精度,但遥感定量反演值与磁感调查实测值仍具有良好的相关性,而且精度较高,因此利用本文方法进行土壤盐渍化大面积监测是快速有效的途径。     

ZY-1 02C星图像与ETM~+图像融合方法及效果评价

资源一号(ZY-1)02C卫星的发射成功填补了我国自主研发高分辨率遥感卫星的空白。根据该卫星的图像特点和数据质量,以俄罗斯西伯利亚地区的ZY-1 02C卫星P/MS传感器数据为例,通过解译该地区的地质矿产信息,对P/MS图像数据的处理和质量情况进行分析;同时结合该地区的美国Landsat卫星ETM+数据,将P/MS全色波段数据与ETM+多光谱数据融合,并采用HSV,Brovery,Gram-Schmidt和PCA等4种融合方法进行实验对比。实验结果表明,用Gram-Schmidt法融合后的图像,光谱信息丢失最少,融合效果最好,可有效提高融合图像的空间分辨率,进而提高该地区的遥感地质解译精度。该研究成果将为进一步推进我国国产高分辨率遥感卫星的应用发挥积极作用。     

全球30m分辨率多光谱影像数据自动化处理的实践与思考

全球地表覆盖遥感制图与关键技术研究项目要求对两个基准年度(2000年、2010年)全球30 m分辨率的多光谱遥感数据进行辐射处理和几何精纠正处理,为地表覆盖制图完成数据准备。数据以Landsat TM/ETM+为主,HJ-1A/B CCD数据为补充,共计2万多景影像需要进行辐射处理,有1000多景HJ-1A/B CCD影像需要几何精纠正。如此大规模的数据处理,自动化处理是必然的选择。本文介绍了HJ-1A/B CCD图像几何精纠正自动化实现中关键问题的解决方法和精度评价结果,Landsat TM/ETM+和HJ-1A/B CCD图像自动化辐射校正中关键问题的解决方法和精度评价结果,以及大规模的数据处理活动引发的一些思考。     

一种拓展的半物理时空融合算法及其初步应用

Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。     

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。     

Comparison between four methods for data fusion of ETM+ multispectral and pan images

Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation (SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat—7 ETM+ multispectral bands with ETM+ panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM+ pan image.     

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。     

基于小波变换的土壤重金属铬含量定量反演

耕地污染日益严重,耕地土壤的重金属高光谱信息属于非线性的微弱信号。小波变换作为常用的非线性微弱信号处理手段,在保留更多微弱信号的基础上可以更好的提取出土壤重金属的微弱光谱信息。文中研究在Db4小波对土壤原始光谱进行分解与重构的基础上提取特征波段,利用特征波段与重金属含量的相关性建立偏最小二乘模型反演土壤重金属铬含量。研究表明,利用Db4小波函数对原始光谱进行分解和重构可以有效提取土壤重金属铬的特征光谱信息;利用小波分解与重构所提取的特征光谱信息与重金属铬含量之间的相关性所建立的PLS模型的决定系数明显高于基于传统一阶微分处理土壤光谱所建立的PLS模型的决定系数。     

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.     

Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle

A time series of leaf area index (LAI) of a managed birch forest in Germany (near Dresden) has been developed based on 16-day normalized difference vegetation index (NDVI) data from the Landsat ETM+ sensor at 30 m resolution. The Landsat ETM+ LAI was retrieved using a modified physical radiative transfer (RTM) model which establishes a relationship between LAI, fractional vegetation cover (fC), and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. In situ measurements of photosynthetically active radiation (PAR) and vegetation structure parameters using hemispherical photography (HSP) served for calibration of model parameters, while data from litter collection at the study site provided the ground-based estimates of LAI for validation of modelling results. Influence of view-illumination conditions on optical properties of canopy was simulated by a view angle geometry model incorporating the solar zenith angle and the sensor viewing angle. Effects of intra-annual and inter-annual variability of structural properties of the canopy on the light extinction coefficient were simulated by implementing variability of the leaf inclination angle (LIA), which was confirmed in the study site. The results revealed good compatibility of the produced Landsat ETM+ LAI data set with the litter-estimated LAI. The results also showed high sensitivity of the LAI retrieval algorithm to variability of structural properties of the canopy: the implementation of LIA dynamics into the LAI retrieval algorithm significantly improved the model accuracy.     

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...     

Cross-sensor calibration between Ikonos and Landsat ETM+ for spectral mixture analysis

Spectral mixture analysis is an algorithm that is developed to overcome the weakness in traditional land-use/land-cover (LULC) classification where each picture element (pixel) from remote sensing is assigned to one and only one LULC type. In reality, a remotely sensed signal from a pixel is often a spectral mixture from several LULC types. Spectral mixture analysis can derive subpixel proportions for the endmembers from remotely sensed data. However, one frequently faces the problem in determining the spectral signatures for the endmembers. This study provides a cross-sensor calibration algorithm that enables us to obtain the endmember signatures from an Ikonos multispectral image for spectral mixture analysis using Landsat ETM+ images. The calibration algorithm first converts the raw digital numbers from both sensors into at-satellite reflectance. Then, the Ikonos at-satellite reflectance image is degraded to match the spatial resolution of the Landsat ETM+ image. The histograms at the same spatial resolution from the two images are matched, and the signatures from the pure pixels in the Ikonos image are used as the endmember signatures. Validation of the spectral mixture analysis indicates that the simple algorithm works effectively. The algorithm is not limited to Ikonos and Landsat sensors. It is, in general, applicable to spectral mixture analysis where a high spatial resolution sensor and a low spatial resolution sensor with similar spectral resolutions are available as long as images collected by the two sensors are close in time over the same place.