土壤光谱特征分析及盐渍化信息提取——以新疆渭干河/库车河绿洲为例

土壤盐渍化严重制约土地生产力,实时监测土壤盐渍化有利于农业正常生产。选择新疆渭干河—库车河绿洲的光谱反射率数据,研究不同程度盐渍化土壤的光谱特征;并对绿洲所在的库车县的环境与灾害监测预报小卫星的高光谱数据进行盐渍化信息提取。提取步骤为：首先对土壤光谱反射率数据进行14种形式的变换,再与土壤含盐量进行相关分析、逐步回归分析,建立估算不同盐渍化程度的土壤含盐量方程,用均方根误差验证方程的精度;其次,建立植被和土壤波谱库;最后,在波谱库的数据基础上,使用波谱角分类法（SAM）对环境与灾害监测预报小卫星的高光谱数据进行分类。用同步实测数据对分类效果进行精度评价,效果较好,这一结果为今后该区域的高光谱应用奠定了基础,对区域农民耕作方式提出了警示,为区域可持续发展实践提供了参考。     

塔里木河中游典型绿洲盐渍化土壤的反射光谱特征

研究盐渍化土壤的光谱特性是利用遥感技术实现在区域尺度上进行土壤盐渍化监测和评价的工作基础, 是建立地面数据和遥感数据关系的桥梁。本文以塔里木河中游典型绿洲--渭干河-库车河三角洲绿洲为研究对象, 采用光谱学技术以及多元统计相结合的方法, 研究干旱区典型绿洲盐渍化土壤的反射光谱特征。首先, 对光谱数据进行预处理(去噪、剔除水分吸收波段), 以便消除仪器本身噪声及外界条件的影响, 并且计算了部分盐渍地样本的光谱吸收特征参数, 说明相同程度的盐渍化土壤具有相似的吸收特征;其次, 研究盐渍化土壤的反射光谱与盐分因子(八大离子、电导率(EC)、含盐量(salt content)、pH、总溶解固体(TDS)等) 之间的关系, 并选择具有代表性的盐分因子与野外实测光谱数据建立定量回归模型, 通过多元线性回归分析得出含盐量、SO₄^2-、TDS、EC与原始光谱数据的相关性分别是0.746、0.908、0.798 和0.933, 达到了理想的效果。本研究对于干旱区典型绿洲盐渍土的光谱特征研究有着重要指示意义, 为发展和完善中国盐渍土理化特征的可见光-近红外反射光谱分析理论奠定科学积累, 并进一步为干旱区土壤盐渍化、沙漠化灾害等环境恶化问题的解决提供新的科学技术手段。     

ALOS融合影像质量评价及其土地盐渍化应用研究

将经过配准的同一地区不同空间分辨率和光谱分辨率的遥感影像进行融合是提高土地覆盖/土地利用分析精度的有效途径。采用PCA、IHS、HPF和小波变换融合法对内蒙古杭锦后旗中部地区的ALOS全色和多光谱影像进行融合,并对融合结果进行了定性和定量评价。基于地物光谱特征、解译标志和监督分类法提取试验区土地盐渍化信息,比较多光谱影像和融合影像的土地盐渍化信息提取精度。结果显示,PCA、IHS和HPF融合影像的空间细节表现能力得到提升,而PCA和小波变换融合影像的光谱保真度优于IHS和HPF融合影像;PCA融合影像的盐渍化分类精度、总分类精度和Kappa系数均为最高,是最适于试验区土地盐渍化分类研究的融合方法。     

基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究

以塔里木盆地北缘绿洲--渭干河-库车河三角洲绿洲为例,借助ENVI遥感软件,利用ETM+数据,探讨了该绿洲土壤盐渍化信息提取的方法.传统的遥感图像分类方法多数在解决问题上存在精度不高、分类效率较低、不确定性强的缺陷,所以,选择好的分类方法对于提取盐渍化信息是至关重要的.近年来,将SVM应用于遥感图像分类已成为新的发展趋势.文章提出了基于纹理特征的支持向量机(Support Vector Machine,SVM)的分类方法,得出以下结论:分别结合3×3,5×5,7×7,9×9,11×11,13×13窗口纹理特征和光谱的SVM分类精度都很高,达到93%以上.并且在验证分类精度时,发现结合光谱和9×9窗口纹理信息的SVM分类的结果更符合实际情况.所以说加入纹理特征后使得光谱信息比较接近的3类地物(重度、中度、轻度盐渍地)的区分性增大,从而使精度提高.因此,基于纹理特征的SVM分类方法更有利于遥感图像分类和盐渍化信息监测,是地物遥感信息提取的有效途径.     

干旱区土壤盐渍化遥感监测方法研究

准确提取盐渍化信息对土壤盐渍化监测和研究盐渍化动态变化具有重要意义。先对除去TM6含有6个多光谱波段的TM图像分别做主成分、缨帽变换，然后在对遥感数据和其派生数据分析和实验的基础上，得出TM数据的可见光和近红外波段之一和第三主成分及缨帽变换后的绿度特征做彩色合成能较好的揭示土壤盐渍化信息，TM数据多光谱波段的选取依研究区而定，此方法的效果受土壤含水量的影响。最后对合成图像分类得出了研究区盐渍地的数量和分布情况。     

阿拉善地区土壤盐渍化的遥感反演及分布特征

土壤盐渍化是威胁干旱区土地的重要环境问题。利用遥感技术对土壤盐渍化进行动态监测,分析土壤盐度水平与空间分布,有利于掌握土壤盐渍化现状,为土地资源可持续利用提供理论依据。现有研究多在田间尺度,随着土壤环境问题涉及的范围越来越大,区域斑块信息的提取已无法满足宏观地模拟和展示整体土壤环境的空间分布。以阿拉善地区为例,结合遥感光谱指数与实测土壤盐分数据,运用偏最小二乘回归（PLSR）方法,构建区域尺度范围的土壤盐渍化反演模型,实现大面积地区土壤盐度的精准模拟和定量监测。结果表明,构建的模型验证精度达到0.8788,达到极显著水平,预测结果与实际情况相符,可以较准确地模拟研究区土壤盐渍化状况。受地形、气候、景观类型、农业活动以及土地管理等因素的综合影响,阿拉善地区约20%的区域土壤呈现出不同程度的盐渍化,其中黑河下游河岸带、雅布赖山西侧及贺兰山西侧冲积扇土壤盐渍化程度最为严重。本研究可为大面积区域土壤盐分状况的快速监测及遥感定量反演提供可行的方法,同时为该区域不同程度盐渍化土壤的治理和土地利用管理提供依据。     

基于MODIS时间序列的松嫩平原盐渍地提取

松嫩平原是我国内陆盐渍土三大分布区之一,土壤盐渍化是该区最主要的环境问题。以多时相中分辨率成像光谱仪(MODIS)的归一化植被指数(NDVI)时间序列影像为主要数据源,通过Savizky-Golay滤波重构NDVI时序数据,依据研究区7种主要土地覆被类型的时间序列曲线差异性,应用分类回归树(Classification And Regression Tree,CART)方法确定像素归属类别,得到松嫩平原2013年盐渍土的分布数据;并基于不同盐渍化程度土壤的植被物候特征差异性建立CART决策树区分不同程度盐渍土。分类结果为:盐渍地掩膜提取精度达98.13%,Kappa系数为0.83;不同程度盐渍土识别的精度达到86.08%,Kappa系数为0.78。该研究表明多时相MODIS数据在大尺度盐渍土信息识别中具有可行性。     

塔里木河流域中游渭干河-库车河绿洲盐渍土水盐信息光谱特征研究

土壤水分、盐分时空变异强,是影响土壤光谱特征的两个重要因素。土壤水分与盐分之间的关系以及土壤水分、盐分与土壤光谱特征间的关系直接关系到利用遥感光谱信息监测土壤盐渍化的精度。该文运用多元统计学及可见光-近红外反射光谱分析方法对2010年10月渭干河-库车河三角洲绿洲盐渍土水分、盐分数据和盐渍土野外光谱数据进行分析,探讨该绿洲盐渍土表层(0～10cm)水盐信息与野外光谱特征间的关系。结果表明:1)土壤水分和土壤电导率可用Cubic曲线拟合,相关系数R=0.8503,土壤盐分和土壤电导率也可用Cubic曲线拟合,相关系数R=0.842,但土壤水分与盐分之间的显著性较弱,相关系数R=0.74。2)与原始野外光谱相比,包络线消除后光谱波段与土壤水分和土壤电导率之间的Pearson相关性都有不同程度的提高,利用包络线消除法后的波段分别建立盐渍土土壤水分、土壤电导率后向回归预测方程,为动态水盐条件下的盐渍土遥感监测提供了理论依据。     

利用遥感信息决策树方法分层提取荒漠化土地类型的研究探讨

选择甘肃省民勤县绿洲作为典型荒漠化区域,根据荒漠化土地分类体系确定决策树的结构及各类地物在树形中的位置。基于各类地物的光谱反射特性和图象数据反映的综合特征,采取相应的识别和提取方法,以最大限度地利用遥感数据源。对于非荒漠化土地分类,利用土壤调节植被指数、阈值数字信号统计可以分离成一类树枝;而重点讨论的3种荒漠化土地类型的分层分类,则相继采用光谱特征提取、几何特征提取、纹理特征提取、监督分类以及植被指数等复合识别指标进行分枝。结果表明:利用决策树分层提取法可以有效地排除和避免提取地物时所有多余信息的干扰及影响,目标明确。同时,为提高分类的精度,开展野外遥感调查和特征分析是极其重要的。     

基于GF-1遥感影像的艾比湖区域田间尺度土壤盐渍化监测方法

土壤盐渍化是制约农业生产和发展的主要障碍。目前土壤盐渍化的遥感监测主要基于中、低分辨率卫星影像,并采用传统的基于像元分类方法,对盐渍化信息的细节描述不足,监测精度不高。本文使用国产GF-1影像,结合自上而下的多尺度分割技术和面向对象的信息提取技术,针对田间尺度下的盐渍化信息进行精确地提取、分类,并与传统分类方法进行了对比。结果表明：面向对象法和最大似然法的分类总体精度分别为92.72%和84.31%,Kappa系数分别为0.90和0.78。该技术能准确提取田间尺度下的盐渍地信息,在未来的农田盐渍化高精度监测研究中具有一定应用价值和发展潜力。     

基于热红外光谱的干旱区土壤盐分监测研究

土壤盐渍化是新疆最常见的土地退化过程,已经严重威胁到了当地的农业生产、生态稳定和经济发展。通过对渭库绿洲土壤含盐量和土壤热红外光谱分析,探讨了土壤含盐量与热红外发射率之间的定量关系。结果表明：（1）盐渍化土壤的发射率随着含盐量的变化而发生变化,当土壤盐分增加时,发射率也随之增大。（2）土壤含盐量与热红外发射率光谱数据相关性在8.5～9.5 μm波段范围内表现尤为显著,相关系数超过0.8,最高为0.90,对应波段范围9.259～9.271 μm。（3）运用回归模型一阶导数变换形式下建模效果和预测精度都是最优的,R²达到了0.899,RMSE最小为1.734。热红外光谱技术可以反演土壤含盐量,为利用热红外遥感识别土壤盐分信息提供技术支撑。     

国际草地资源遥感研究新进展

系统地评论了国际上有关草地资源遥感研究的最近文献。首先,分析了草地遥感的可行性,确定了监测和评估草地资源最有效的光谱波段,阐述了各种遥感影像在草地资源调查与评估中的实用价值。之后,介绍了草地资源遥感的常用方法,即植被指数和其他指数法,对比和分析了各种指数在草地资源遥感应用中的有效性及应用范围。这些草地资源遥感的应用包括草地盖度监测与制图、生物量估算、草地退化监测及草地资源定量分析。最后,探讨了草地资源遥感的发展趋势,包括摄像遥感、高分辨率影像(如CASI,AVIRIS和IKONOS)和GPS的运用。此外,GIS的引入及其与数字影像处理的集合会使草地资源遥感由简单监测逐渐向动态预报和模拟过渡。这些研究新动向对国内相关科学家在选题时具有极大的参考意义。     

Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico

Mapping soil salinity is difficult due to its large spatial and temporal variability. Remote sensing is widely used to lower survey costs, but existing studies usually analyze bare soils and make little reference to the halophytic plants and their role as salinity indicators.This paper aims to correlate soil characteristics (electric conductivity in saturation extract (EC_e) and sodium absorption ratio (SAR) with the spectral response of plant species and bare soils, integrating an algorithm to allow multi-scale mapping using remote sensors.Ground radiance was measured on different plant species and bare soils. A Combined Spectral Response Index (COSRI) was calculated for bare soils and vegetation by adjusting the normalized difference vegetation index (NDVI). EC_e and SAR were determined in surface soil samples. Correlation coefficients between COSRI and soil salinity were obtained and a model was adjusted to predict soil salinity. Landsat-ETM and airborne digital images were used to calculate raster maps of COSRI, and EC_e and SAR were estimated using adjusted models. Correlation between COSRI and EC_e and SAR was of −0.885 and −0.857, respectively. Variance accounted for by exponential models for EC_e and SAR was of 82.6% and 75.1%, respectively. It may be concluded that the method is an easy, low-cost procedure to map salt-affected areas.     

Data processing and interpretation of sea radiance factor measurements

Problems in the interpretation of passive optical remote sensing data obtained by telespectrometric measurements on board a research vessel (or aircraft) are discussed. Two methods are considered: (1) The correlation method, where correlative relationships between the remotely sensed spectra and concentrations of optically active substances in the water are determined and corresponding regression formulae found; (2) the similarity method, where the remotely sensed spectrum is compared with the multitude of spectra obtained by means of model calculations. The application of these methods and analysis of the results are made using our remote and in situ data. It is found that the correlation method is far from being general (the regression parameters depend on the location, season and weather conditions), but it has the advantage of being applicable without the need to describe the aquatic environment by a theoretical model. The similarity method is much more general but involves difficulties in including the optical properties of the aquatic environment in the theoretical model, especially the backscattering properties of several types of suspended matter in the water bodies.
Some aspects of detecting oil-slick pollution on the sea surface by means of passive optical remote sensing methods are discussed and corresponding examples are shown.     

植被高光谱特征分析及其病害信息提取研究

高光谱(hyperspectral)遥感是20世纪末地球观测系统中最重要的技术突破之一。根据植被高光谱数据的植被冠层光谱反射特征和诊断性光谱吸收特征,利用光谱连续统去除法,探讨光谱一阶微分反射比(FDR)和从连续统去除的光谱吸收特征中获得的波段深度(BD)、连续统去除后微分反射比(CRDR)、波段深度比(BDR)和归一化波段深度指数(NBDI)等光谱特征参量。结合多时相的条锈病小麦PHI航空高光谱影像,分析条锈病对小麦光谱的影响及其光谱特征,并运用光谱特征参量和波谱角制图(SAM)技术监测和识别小麦条锈病。     

Modelling patterned ground distribution in Finnish Lapland: an integration of topographical, ground and remote sensing information

New data technologies and modelling methods have gained more attention in the field of periglacial geomorphology during the last decade. In this paper we present a new modelling approach that integrates topographical, ground and remote sensing information in predictive geomorphological mapping using generalized additive modelling (GAM) . First, we explored the roles of different environmental variable groups in determining the occurrence of non‐sorted and sorted patterned ground in a fell region of 100 km2 at the resolution of 1 ha in northern Finland. Second, we compared the predictive accuracy of ground‐topography‐ and remote‐sensing‐based models. The results indicate that non‐sorted patterned ground is more common at lower altitudes where the ground moisture and vegetation abundance is relatively high, whereas sorted patterned ground is dominant at higher altitudes with relatively high slope angle and sparse vegetation cover. All modelling results were from good to excellent in model evaluation data using the area under the curve (AUC) values, derived from receiver operating characteristic (ROC) plots. Generally, models built with remotely sensed data were better than ground‐topography‐based models and combination of all environmental variables improved the predictive ability of the models. This paper confirms the potential utility of remote sensing information for modelling patterned ground distribution in subarctic landscapes.     

南北过渡带1∶5万植被类型图遥感制图案例研究

编制南北过渡带地区1∶25万和典型山地1∶5万植被类型图是南北过渡带综合科学考察的主要任务之一。以往植被类型图的编制都是采用大量的地面调查来完成的,地面调查方法虽然精确,但费时费力,并且由于自然条件的限制,地面调查往往只能覆盖较小的范围。遥感数据因为其全覆盖的优势,可以很好地弥补样方调查的局限性,但目前大范围的植被类型遥感信息提取尤其是自动提取方面仍然存在一定的困难和瓶颈。本文以1∶5万太白山植被类型图的编制为例,利用多源多时相的高分辨率遥感数据,结合地面调查数据、以往的各种比例尺的植被类型图数据和森林资源调查数据等,探讨并研究基于山地垂直带谱的中大比例尺植被类型图的遥感提取方法和制图方法。研究结果表明：① 山地植被垂直带谱可以有效地支持1∶5万山区植被类型图的遥感制图。利用太白山植被垂直带谱和1∶1万数字表面模型数据（DSM）可以生成具有垂直带谱信息的地形约束因子;将地形约束因子与多源多时相高分辨率遥感数据、地面调查数据、以往的小比例尺植被类型图数据等相结合,可以有效提取各级植被类型,从而实现中大比例尺植被类型图的编制。② 典型山地1∶5万植被类型图的遥感制图基本流程为植被型组解译→植被群系组、群系、亚群系解译→植被型、植被亚型分类,采取自上而下和自下而上相结合的分类方法来分类。本文的研究成果可以为中大比例尺植被类型图的编制提供示范和科学依据。     

Use of remotely sensed data in mapping underwater landscapes of Srednyaya Bay (Peter the Great Gulf,Sea of Japan)

We examine the selection criteria for satellite images and methods of processing them in the process of mapping underwater landscapes using remotely sensed data, discuss the interpretation principles and algorithms as well as some issues related to the support of observations with field material. It is shown that a detailed landscape mapping of shallow marine waters by methods of visual and automated interpretation requires multispectral superhigh spatial resolution images. Results of investigations made on underwater profiles by using lightweight diving outfits were employed to describe seven types of underwater landscapes, and echo sounder measurements were used in constructing the digital elevation model for the bottom of Srednyaya Bay. It is established that the regions for which it was possible to carry out a reliable interpretation of data from the IKONOS-2 spacecraft are in the range of depths between 0 and 10 m and make up about three-fourths of the area of the bay bottom. Ten facies were identified and put on the map, for each of which we determined the area, the range of depths and the mean depth of propagation. Remotely sensed data were used to assess the contribution (in the spatial structure of the geosystem) of algal vegetation on the littoral; the eelgrass fields were ranked according to the degree of projective cover. As a result of a clustering according to the similarity of spectral attributes, we identified ten groups of pixels of the image analyzed. An analysis is made of the agreement between the distribution of facies identified by expert interpretation and results of an automated classification of pixels, and the contours of landscape units were updated. The conclusion is drawn regarding integration of the computer-aided and visual approaches to interpretation of remotely sensed data for shallow marine waters leading to a “hybrid” express method of mapping landscapes of shallow marine waters.     

Detecting biophysical properties of a semi-arid grassland and distinguishing burned from unburned areas with hyperspectral reflectance

This paper examines the utility of hyperspectral remote sensing to detect fresh and dry biomass, water content and plant area index of burned and unburned grassland in southern California. Contrary to many previously published reports, the normalized difference vegetation index (NDVI, a vegetation greenness index) was not a good indicator of any of these important biophysical properties in either the burned or unburned area, especially after regeneration. Rather, the water band index (WBI, an index of water status in vegetation) showed better promise of estimating these biophysical properties in this semi-arid ecosystem. Despite the post-regeneration similarities in visual and harvested values of these two areas, we found that the full range of hyperspectral reflectance in ‘visible to infrared’ (400–1000 nm) wavelengths when used in a cluster analysis can readily differentiate the burned and unburned areas. This demonstrates the utility of hyperspectral remote-sensing in mapping subtle features that may not be detectable from conventional remote-sensing indices (e.g. NDVI) alone.