遥感技术在黄土高原土地利用现状调查中的应用研究

本研究选取黄土高原典型丘陵沟壑区——陕西省米脂县为试验区,用TM磁带、TM假彩色合成影像、SPOT多光谱影像和彩色红外航空像片同时对试区进行土地利用现状分类与制图的比较研究。通过对土地利用现状分类系统、TM图像计算机增强处理与自动分类、TM最佳波段组合、三种影像的目视判读与制图、以及遥感信息源的综合评价等多方面的试验研究,为遥感技术在黄土高原1:5万土地利用现状调查制图中的应用提供了一套技术数据及可行的方法。     

利用SPOT卫片在黄土高原丘陵沟壑区进行土地利用现状调查的精度分析

本文探讨了利用SPOT卫片进行黄土高原丘陵沟壑区县级(1:50000)土地利用现状调查制图的可行性。从分类精度、制图精度等方面进行分析,提出在本区进行土地利用现状调查制图(1:50000),可用SPOT卫片配合黑白航片作为调查信息源。另外,还讨论了利用SPOT卫片在本区进行某些土壤类型判别的可行性。     

利用TM、SPOT图像制作土地利用图的潜力调查及精度评定

一、前言TM和SPOT图像是目前航天遥感的主要数据源。由于数据获取方便,具有较高的分辨率,因而得到广泛的应用。随着遥感图像处理系统的不断完善,数字图像处理技术的不断发展,TM和SPOT图像在资源调查、环境监测等方面取得了成功的应用。在测绘部门也有重要的应用价值,例如土地利用图的制作。利用计算机自动分类技术进行土地利用分类具有速度快、成本低等优点。从而引起人们的广泛兴趣。     

陕北黄土高原土地类型区域分异规律遥感分析

陕北黄土高原地形破碎、交通不便、土地利用不合理、水土流失严重、生态环境脆弱。为了扭转这种局面,必须查清区内土地资源的类型及其分布状况,以便合理地评价和利用土地资源,改善当地的经济状况和生态环境。 本文以陕北11个县(市)为例,对利用TM图像进行1:10万土地类型遥感调查与制图的工作方法和程序进行了讨论,对区内土地类型区域分异因素和分异规律进行了分析,并在此基础上,提出了土地类型分区及分区治理措施的建议。     

用色度空间变换处理的组合SPOT图像进行城市土地利用现状调查

组合SPOT图像是由多波段和全色波段按三种不同方式组合的。其中图像D是经色度空间坐标系统变换和边缘增强处理的,在唐山城区土地利用调查中效果最好。在1:29600的图像D上能识别5个二级类型和17个三级类型。土地利用图的判读精度约90％。文中还详述了分类体系和土地利用调查的判读标志。     

遥感制图的常规方法概述

遥感制图是指利用遥感图象制图。遥感图象有航空遥感图象和卫星遥感图象,制图方式有计算机制图与常规制图。目前应用最多及着重研究的,是利用Landsat的MSS图象制图;不久将利用北京地面接收站接收的Landsat-5的TM图象和即将发射的法国SPOT卫星的SPOT图象制图。由于多波段的卫星图象具有信息量丰富、现势性强,利用它编图周期短等优点,在制图方面得到了广泛的应用。     

论TM图像与GIS数据综合的土地利用动态监测

利用TM,SPOT卫星遥感图像以及相应卫星影像图,综合土地利用专题图等GIS非遥感数据,针对宁波土地利用动态监测的实际需要,进行有关遥感信息专题提取和应用的实证研究.     

基于植被分区的多特征遥感智能分类

为了有效地提取大范围地形复杂区域的土地利用/土地覆盖遥感信息,以位居青藏高原与黄土高原过渡地带的青海东部地区为研究区,研究基于蚁群智能优化算法(ant colony intelligent optimization algorithm,ACIOA)的土地利用/土地覆盖遥感智能分类。首先选用TM图像、DEM、坡度和坡向数据作为分类的特征波段;然后利用归一化植被指数NDVI对实验区数据进行植被分区;最后利用ACIOA算法进行分类规则挖掘,并依据分类规则进行土地利用/覆盖信息的提取。研究表明,基于植被分区的多特征蚁群智能分类的总体精度为88.85%,Kappa=0.86,优于传统的遥感图像分类方法,为大范围地形复杂区域的土地利用/土地覆盖遥感信息提取提供了有效的方法。     

海南岛土地利用的遥感调查与机助制图

海南岛土地利用调查制图在微机空间信息系统支持下,利用经几何校正的TM卫星遥感磁带数据直接转换成数字化地图。本文介绍了这次调查制图的方法,论述了海南岛土地利用现状分类以及各类土地利用的TM影像特征和有关解译标志,进行了面积统计和精度检验,并研究了各土地利用类型的特征。     

土地利用类型变化对环境影响的遥感信息分析

利用多时相、多分辨率的Ouickbird和SPOT图像进行土地利用变化的监测，对遥感图像上的信息进行分析，利用遥感图像进行土地利用变化信息的自动发现，结合野外调查，分析了土地利用变化对周围环境的积极效应和负面影响。实践表明，对遥感图像的信息分析结合野外调查对于发现土地利用变化对环境的影响是一种有效的方法。     

Evaluation of image fusion methods using PALSAR,RADARSAT-1 and SPOT images for land use/ land cover classification

This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.     

多光谱遥感影像与高分辨率全色影像融合研究

选取浙江省绍兴市作为研究区 ,探讨了IHS变换、PCA变换及Brovey变换等融合方法 ,发现Brovey变换更适合于多光谱数据与高分辨率全色数据之间的融合。     

Urban land use mapping and zoning of Bombay Metropolitan Region using remote sensing data

Bombay Metropolitan Region covering an area of about 4,360 sq. km. was selected for urban land use studies and for urban land use zoning. Urban land use mapping was carried out using SPOT multispectral linear array imagery on 1∶25,000 scale employing visual analysis tehcniques. Fifteen maps were prepared depicting the spatial distribution of various urban classes in the Greater Bombay and New Bombay regions. Sixteen urban land use maps were also prepared using Landsat TM data showing the distribution of land use pattern on 1∶50,000 scale for the entire metropolitan region. Urban land use zoning was carried out based upon suitability index on 1∶250,000 scale. This map provides information on the areas to be used for construction and areas to be kept under green belt in the metropolitan region. This study is a joint venture of Space Applications Centre with Bombay Metropolitan Development authority.     

城市居住单元绿地景观遥感环境研究

城市是人类改造最彻底的景观,城市中的居住单元是与市民最息息相关的景观单元。绿地是居住单元的重要自然要素,在调节气候、改善环境质量、吸尘消声等方面起着重要作用,在一定程度上反映了居住单元景观质量的优劣。遥感、地理信息系统与景观生态学理论共同形成了对生态系统空间格局进行研究的独具特色的研究模式,而利用景观指数对空间格局进行定量化研究是这种研究模式的基本内容。传统的景观指数研究多基于TM、SPOT影像在较大尺度上开展,基于高分辨率遥感影像在居住单元这样的小尺度上研究景观格局还较少。本文具体介绍了利用高分辨率遥感影像提取居住单元绿地景观斑块的整个过程,并选取绿地覆盖率、斑块密度、分维数、均匀度指数和最大斑块指数等指数,分析了厦门居住单元的绿地景观结构,最后采用模糊评判的方法对选取的居住单元作综合评价。     

Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban–rural landscape in the Brazilian Amazon

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method.     

The Utility of Very High Spatial Resolution Images to Identify Urban Objects

Abstract

Landsat MSS, TM and SPOT XS imageries were used in conjunction with unsupervised, supervised and hybrid classilication techniques to classify land cover types in semi‐arid savannas of Mathison Pastoral Station in the Katherine region of northern Australia. Accuracy assessment was based on field data from 246 ground survey sites over a 745‐km² study area. Of 14 land cover classes identified by traditional mapping means, all combinations of imageries and classification techniques differentiated at least seven land cover types. The overall accuracy for these classifications ranged between 43% and 67%. SPOT XS image delivered the best accuracy followed by TM and MSS; unsupervised classification performed better than supervised and hybrid methods. User's and producer's accuracy of individual land units ranged from 0% to 100%. Riparian woodlands, woodland on limestone slopes, shrubland on clay plains, woodland on limestone plains and shadows were the best‐mapped classes. The land units that were associated with undulating hills were not mapped accurately. However, incorporation of a digital elevation model (DEM) in a GIS improved the overall accuracy. The user's and producer's accuracy of dominant land cover types were also enhanced. The classification results and the efficacy of the techniques at Mathison were similar to those found for a nearby semi‐arid area (Kidman Springs) about 200 km from Mathison. However, the overall accuracy was lower at Mathison than at Kidman Springs. Spectral classification masks were developed from the SPOT XS and TM imageries at Kidman Springs, and were applied to classify SPOT XS and TM imageries at Mathison. Initial results showed that the classification mask could be successfully extrapolated to map dominant land cover types but only with moderate accuracy (50%).     

SPOT5遥感影像在土地调查中的应用

本文结合SPOT5卫星影像在某地区土地利用调查中的应用,介绍了利用SPOT5卫星遥感影像进行室内判读、结合外业调查建立土地利用数据库,进行土地利用调查的技术方法和工作流程。     

Research on auto-classification method of remote sensing images in mountainous areas—An application in Southwest of China

In mountainous areas, it is the undulant terrain, various types of geomorphic and land use that make the remote sensing images great metamorphism. Moreover, due to the elevation, there are many areas covered with shadow, clouds and snow that make the images more inaccurate. As a result, it would be very difficult to carry out auto-classification of RS images in these areas. The study took Southwest China as the case study area and the TM images, SPOT images as the basic information sources assisted by the auxiliary data of DEM, NDVI, topographical maps and soil maps to preprocess the images. After preprocessing by topographic correction and wiping off clouds, snow and shadows, all the image data were stacked together to form the images to be classified. Then, the research used segmentation technology and hierarchical method to extract the main types of land use in the area automatically. The results indicated that the qualitative accuracies of all types of land use extracted in Southwest China were above 90 percent, and the quantitative accuracies was above 86 percent. The goal of reducing workloads had been realized. Supported by the National Public Welfare Project on Environmental Protection (2007KYYW21), the Program of National Science and Technology research( 2006BAC01A01-05).     

利用波段合成研究SPOT多光谱影像水体提取方法

SPOT多光谱影像仅有4个波段,光谱信息相对较少,通常的水体提取方法受到波段数的限制,通过波段合成增加SPOT多光谱影像可用的波段数,不仅增加了水体提取方法的种类,而且,通过对矿区塌陷地积水现象进行水体提取试验,结果表明：波段合成对SPOT影像的水体提取情况有一定程度的改善,水体提取更完整,误提现象较少。