遥感图像在土地覆盖和温度分布之间对应关系研究中的应用

利用遥感手段探讨土地覆盖类型和温度之间的对应关系有新意。以日本东京地区1998年10月14日接收的TM遥感图像为主要数据,在地面实际调查的基础上,利用图像处理方法,进行了土地覆盖类型和温度分布之间关系的探讨。以该区几种代表性的土地覆盖类型作为训练区,利用TM图像的第1,2,3,4,5五个波段数据进行分类处理,获得了很好的分类结果,TM图像第6波段(10.40～12.5μm)是探测地面温度分布最佳波段,利用第6波段数据通过一系列的转换,获得了该区温度分布。温度分布和土地覆盖类型之间具有很好的对应性,如:白天的城区温度与郊区相比普遍偏高,但城区中植被覆盖率高的居民区和公园,温度偏低;城区的商业区(新宿、银座等)、机场和大型工厂等温度偏高。     

沿海丘陵地区土地覆盖及其动态变化的多源遥感研究

】针对沿海丘陵地区土地覆盖的特殊性,以ＴＭ 与ＳＰＯＴ 融合影像在厦门市的应用为例,确定该类可操作的通用土地覆盖分类体系和各覆盖类型,及其动态变化的多源遥感识别方法。重点分析建立“土地覆盖类型波谱特征曲线库”的方法,使土地覆盖类型的遥感识别方法具有通用性,同时也为实际应用中的遥感影像分类识别提供一种比较有系统性的思路     

黄土高原防护林区TM数据的土地利用／覆盖分类方法研究

利用陆地卫星５号专题制图仪（ＴＭ）ＣＣＴ数据，对位于黄土高原防护林区的山西省隰县进行了大面积（面积为１４００ｋｍ＾２）的土地利用／覆盖的计算机自动分类研究。提出了在地形破碎的黄土高原防护林做遥感图像自动分类的方法和步骤，即把无监分类和有监分类有机结合的混合分类方法，提高了分类精度，使遥感数据能够在实际应用中真正发挥其覆盖面广、周期短的特点。     

Landsat TM热红外遥感数据定量反演地下水富集带的温度信息--以甘肃河西地区石羊河流域为例

利用ＬａｎｄｓａｔＴＭ６热红外遥感数据定量反演了干旱地区的地表温度,研究结果表明,区典型地表覆盖类型的地表亮温比地表起初温度低０．４－１Ｋ,遥感反演的地面真实温度与当地３月下旬的实测温度误差在０．８Ｋ以下,这说明用ＬａｎｄｓａｔＴＭ６定量反演干旱区的地表温度是可行的。研究结果表明,地下水富集带地表温度具有异常现象,其地表温度比地表水体高５Ｋ左右,而比其它地表类型低７Ｋ以上,据此,可以利用热红外遥感     

Landsat TM热红外遥感数据定量反演地下水富集带的温度信息——以甘肃河西地区石羊河流域为例

利用ＬａｎｄｓａｔＴＭ６热红外遥感数据定量反演了干旱地区的地表温度,研究结果表明,研究区典型地表覆盖类型的地表亮温比地表真实温度低０．４～１Ｋ,遥感反演的地面真实温度与当地３月下旬的实测温度误差在０．８Ｋ以下,这说明用ＬａｎｄｓａｔＴＭ６定量反演干旱区的地表温度是可行的。研究结果还表明,地下水富集带地表温度具有异常现象,其地表温度比地表水体高５Ｋ左右,而比其它地表类型低７Ｋ以上,据此,可以利用热红外遥感技术有效地探测干旱区地下水富集带的信息。     

应用遥感信息复合方法的森林分类和动态监测研究

在森林资源清查和动态监测中，一般要求能识别树种、在树种复杂时也要求识别到树种组。成图时要求可直接使用几何精度高、类型边界清晰的中比例尺如１：２．５万的影像图。为了满足生产的需要，我们进行了遥感信息复合、遥感与非遥感信息复合及复合方法等实用技术应用的研究。实验中应用了ＭＳＳ与ＴＭ卫星图像、ＴＭ不同时相的图像、ＴＭ与Ｓｐｏｔ的复合，及复合前后信息特征变化的定量分析等研究。从而为最优波段组合、特征信息提     

Matlab 自组织竞争神经网络遥感图像分类——以福州市琅歧岛土地覆盖ö土地利用类型为例

以福州市琅歧岛土地覆盖/土地利用类型为例, 以遥感图像解译知识为基础, 使用TM、Aster的融合图像, 将DEM 因子作为待分类图像的波段加入其中, 构成新的待分类图像, 利用Matlab 平台构建自组织竞争神经网络, 在不依赖网络训练样本选取的前提下, 仿真的结果能真实的反映原始图像的特征, 分类总精度为91. 14% , Kappa 系数为0. 89, 实例证明自组织竞争神经网络分类方法是一种行之有效的分类方法。     

覆盖类型间光谱重叠的测量

为了从陆地卫星数据中选择能改善土地覆盖分类精度的通道,本文提出一精确、高效的方法。结果表明,亮度值重叠指数(BVOI)是覆盖类型之间亮度值重叠度的最佳测度。利用所有覆盖类型在每一通道内的重叠信息选择通道,不需要复杂的数学计算。在使用遥感数据进行土地利用/土地覆盖分类中,应用BVOI计算简单、概念清楚。     

基于光谱角分类器遥感影像的自动分类和精度分析研究

遥感影像是地球表面一定区域景观和覆盖的客观记录和形象显示。选择黑河弱水流域作为自动分类的典型研究区,利用该区域的Landsat 7ETM+遥感影像结合地面实况调查数据,寻找土地利用/覆盖类型自动分类的训练样本,运用光谱角分类方法对ETM+图像进行自动分类。通过分类图像与地面真实样本数据对比分析,获得适用于荒漠地表遥感影像自动分类的可行性方法,并且进一步讨论了混淆矩阵计算的分类误差,研究了以Kappa分析为基础的精度评价。     

黄河下游1993年汛期河势遥感调查

利用ＭＥＲＩＤＩＡＮ遥感图像处理系统，通过ＴＭ图像最佳波段组合、线性拉伸放大、密度分割等处理手段，首次在黄河下游河段进行卫星遥感河势调查，其优势在于可以弥补目估河势的不足，对河势流路、主流线位置、洪水浸滩范围及水边线、生产堤、主要滩区洪水进出口门位置、进水和水围村庄等准确定位并详细计算各类滩区面积与分布密度。     

使用单窗算法研究北京城区热岛效应

随着全球变暖和城市化进程的加快,大城市城区的热岛效应日益严重。城市下垫面对地表能量交换的影响巨大,引起地表温度分布的不均一性。遥感技术的发展为地表温度的反演提供了可能。近年来人们使用劈窗算法对均一的海面温度的反演很成功,但是受空间分辨率的限制以及陆面的不均一性,陆面温度的反演一直是一个没有解决好的问题。覃志豪提出了一种TM热红外波段单窗算法,可以利用辅助气象资料快速计算出地表温度。本文以北京市城区为研究区,采用LandsetETM第6波段的单窗算法,反演了亮度温度和地表实际温度,分析了城市下垫面情况下NDVI与地表温度的相关关系,并解释了北京城区热岛在空间上的分布及其可能的原因。结果表明:北京市城区热岛效应显著;地表温度与NDVI相关性显著;城区绿地和水体在区域的温度分布中起到重要作用。     

基于热红外遥感的城市热场形成机制及特征分析--以成都市为例

利用LANDSAT5的TM6热红外数据和实时的地表温度，优选了一种方法进行成都市地面温度场反演，根据反演结果对成都市地表温度场分布特征进行了分析，同时定量分析了植被覆盖和水域廊道对地表温度场分布的影响，定量分析的结果对城市区域布局和规划有积极意义。     

城市建筑材质—地表温度关系的多源遥感研究

利用PROBA CHRIS遥感影像对北京城市建筑材质和自然地表进行基于光谱先验知识的分层分类提取,并与Landsat5 TM热红外数据反演得到的北京城市地表温度叠加,采用统计学方法定量分析了主要建筑材质、自然地类与地表温度的关系,并重点就不同建筑材质对城市热岛的影响及其表面特性所起作用进行了分析。结果表明：北京城区中的砖瓦房表面温度最高,比其他材质高0.3K~4.0K,比自然地类高5.1K~7.8K;金属结构表面温度略低;混凝土、水泥和沥青的平均温度相当,他们是城市热环境异常的主要来源之一;另外,城市中的玻璃幕墙能够有效地降低其表面温度,比其它材质低3.3K~4.0K。反照率、热惯量和热传导性是建筑材质影响城市地表温度的3个重要表面特性,对于不同材质,它们存在较大差异。     

A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6

A C++ language-based software tool for retrieving land surface temperature (LST) from the data of Landsat TM/ETM+ band6 is developed. It has two main functional modules: (1) Three methods to compute the ground emissivity based on land use/cover classification image, NDVI image and the ratio values of vegetation and bare ground and (2) Converting digital numbers (DNs) from TM/ETM+ band6 to LST. In the software tool, Qin et al.'s mono-window algorithm and Jiménez-Muňoz and Sobrino's single channel algorithm are programmed to retrieve LST. It will be a useful software tool to study the thermal environment of ground surface or the energy balance between the ground and the bottom atmosphere by using the thermal band of Landsat TM/ETM+.     

Mapping the invasive species,Chinese tallow,with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress‐tupelo forest, senescing Chinese tallow with red leaves (‘red tallow’), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress‐tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non‐active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs.     

Application of spectral mixture analysis for terrain evaluation studies

In this article, we describe an approach to calculate the spectral mixture within pixels and classify multispectral images. The results are compared with the classified images by traditional supervised rules such as Maximum Likelihood and appreciable results were accomplished. The method considers the number of endmembers that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. The only requirement for this method is a radiometrically corrected image because the endmembers are directly selected from the image. To make the method presented here more efficient, we propose to apply it only to the classes having low accuracy after a traditional supervised classification. Because the land cover classes in this study are related to a geomorphological terrain unit, we propose to mask the terrain units having problematic classes and decompose these units into their endmembers. A geomorphological analysis of the study area (Tonle Sap basin in Cambodia) was made to establish the relationship between land cover, landforms and soils through terrain mapping units. Then we performed a supervised classification of a Landsat Thematic Mapper (TM) image and of the same image merged with a SPOT-panchromatic (PAN) image, based on the land covers corresponding to the terrain mapping units. Then we masked a terrain unit having problematic spectral classes and applied the spectral mixture analysis which allowed an efficient separation of the land cover classes agglomerated in the preliminary classification. The result of this re-classification was re-inserted into the first classification and was compared statistically with the results obtained in the preliminary classification. We consider this procedure an efficient method to improve the results obtained from a supervised classification. The method can separate different land covers that were agglomerated in the preliminary segmentation. In our case, the classification accuracy for the terrain unit used (the fluvial terrace) increases from 62% (using only the TM bands) and 69% (using TM+ SPOT) to 83%.     

Coastal wetland vegetation classification with a Landsat Thematic Mapper image

Coastal wetland vegetation classification with remotely sensed data has attracted increased attention but remains a challenge. This paper explored a hybrid approach on a Landsat Thematic Mapper (TM) image for classifying coastal wetland vegetation classes. Linear spectral mixture analysis was used to unmix the TM image into four fraction images, which were used for classifying major land covers with a thresholding technique. The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method. Expert rules were finally used to modify the classified image. This research indicates that the hybrid approach employing sub-pixel information, an analyst's knowledge and characteristics of coastal wetland vegetation distribution shows promise in successfully distinguishing coastal vegetation classes, which are difficult to separate with a maximum likelihood classifier (MLC). The hybrid method provides significantly better classification results than MLC.     

基于Landsat TM图像的北京城市地表温度遥感反演研究

利用北京地区Landsat TM热红外波段数据，采用单通道算法反演得到北京地区地面温度分布图。从反演结果可以看出，北京城区地面温度比郊区地表温度高，郊区地表温度较低，密云水库、官厅水库等水体的温度最低，总体上北京城市热岛效应显著。地表比辐射率是通过Van经验公式反演得到，通过对比分析，表明该方法对自然地表的比辐射率反演效果较好。     

The effects of endmember selection on modelling impervious surfaces using spectral mixture analysis: a case study in Sydney,Australia

In urban areas, spectral mixture analysis (SMA) is a common technique for deriving the fractions of land covers within a pixel and information on the distribution of impervious surfaces. This study examined how the selection of endmembers affected the quantification of impervious surfaces using TM and ASTER imagery. Multiple subsets of endmembers derived using (1) extreme pixels from a minimum noise fraction (MNF) transformation, and (2) a manual approach using a priori knowledge of the study area were analysed. Two data sets were used to assess accuracy: (1) simulated image data comprising unmixed and mixed pixels of 10 typical and spectrally different urban land covers, and (2) detailed data derived from high-resolution aerial photography. The dimensionality of the imagery limited the number of endmembers, and as a result, unmixed land covers were modelled using multiple endmembers and some cells had abundance values that summed to more than one or were negative. The land covers of red roofs and concrete were the largest contributors to the error in impervious surfaces. The Sequential Maximum Angle Convex Cone (SMACC) endmember model was also used to unmix the images; however, the larger number of endmembers did not resolve the use of multiple endmembers to model the unmixed land covers and the accuracy was similar to that using SMA. The relationship between the pervious fraction estimated using the vegetation endmember and the ground reference data was stronger than that for the impervious fraction, although the fraction was underestimated. The problems in modelling highly variable impervious surfaces with a limited number of endmembers suggest that in urban environments with substantial vegetation, modelling the vegetation component as the inverse of the impervious fraction may lead to improved results.