A Method for Retrieving Water-leaving Radiance from Landsat TM Image in Taihu Lake, East China

The visible and infrared bands of Landsat Thematic Mapper (TM) can be used for inland water studies. A method of retrieving water-leaving radiance from TM image over Taihu Lake in Jiangsu Province of China was investigated in this article. To estimate water-leaving radiance, atmospheric correction was performed in three visible bands of 485nm, 560nm and 660nm. Rayleigh scattering was computed precisely, and the aerosol contribution was estimated by adopting the clear-water-pixels approach. The clear waters were identified by using the Landsat TM middle-infrared band (2.1μm), and the water-leaving radiance of clear water pixels in the green band was estimated by using field data. Aerosol scattering at green band was derived for six points, and interpolated to match the TM image. Assuming the atmospheric correction coefficient was 1.0, the aerosol scattering image at blue and red bands were derived. Based on a simplified atmospheric radiation transfer model, the water-leaving radiance for three visible bands was retrieved. The water-leaving radiance was normalized to make it comparable with that estimated from other remotely sensed data acquired at different times, and under different atmospheric conditions. Additionally, remotely sensed reflectance of water was computed. To evaluate the atmospheric correction method presented in this article, the correlation was analyzed between the corrected remotely sensed data and the measured water parameters based on the retrieval model. The results show that the atmospheric correction method based on the image itself is more effective for the retrieval of water parameters from Landsat TM data than 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) code based on standard atmospheric and aerosol models.     

珠江三角洲土地覆盖变化对地表温度的影响

由于经济的快速增长,1978年以来的25年中,珠江三角洲地区的土地覆盖变化,特别是城市扩展速度非常之快。通过把遥感和地理信息系统相结合,研究了土地覆盖变化及城市增长状况,并且评估了城市增长对地表温度的影响。利用陆地卫星多时段数据监测了土地利用和土地覆盖变化;通过陆地卫星热红外单波段反演了地表温度,研究了土地覆盖变化特别是城市增长对地表温度的影响。结果发现,珠江三角洲地区土地覆盖变化明显,城市增长显著且不平衡。城市的发展使城市化地区的地表温度提高了8.9K。研究表明,遥感和地理信息系统的结合,在监测和分析土地覆盖变化,特别是城市增长状况以及评估城市化对地表温度的影响方面,是十分有效的。     

中小城市地表温度空间分异及其与下垫面关系的遥感影像研究

以Landsat TM 影像为主要数据源,采用单窗算法反演河南省南阳市市区的地表温度。在GIS支持下,研究全区地表温度的空间分异特征,并探讨了地表温度与下垫面特征的关系。结果显示：研究区内高温斑块主要集中在仓储区和商业与公共服务区,该类区域过高的LST对城市环境有明显的负面影响;拦蓄河流所营造的水体是面积较大的低温斑块,对改善城市热环境和中小型城市拦蓄河流措施有一定意义;研究区内LST的空间自相关性在不同方向上有明显差别,东-西方向和东北-西南方向上空间自相关的范围较大,这种格局与区内水体走向有关;地表温度与道路密度指数呈明显正相关。提出中小城市可在仓储用地和交通发达地段周边规划绿地和水体,以抑制热岛呈大面积连片分布。     

Impact of GCP distribution on the rectification accuracy of Landsat TM imagery in a coastal zone

1Introduction Satelliteimagerycanbecomeavaluablecompo nentinageographicinformationsystem(GIS)data baseonlyafterithasbeengeo referencedtoaground coordinatesystemwiththedesiredprojection(Mei,etal.,2001;Wang,etal.,2002).Allimagesre ceivedatagroundreceivingst…     

Landsat与MODIS卫星数据的双向融合实验

针对如何在时间序列尺度上利用多源时空融合方法高精度地重构高分辨率遥感影像的问题,该文提出了一种基于增强字典学习样本空间的单数据对稀疏学习融合算法,并利用现有稀疏学习算法、STARFM算法以及半物理模型对Landsat与MODIS卫星数据进行双向融合实验。结果表明:随着样本尺寸及空间的拓展,改进后的稀疏学习算法能够获得比原始算法、STARFM、半物理模型等算法更优的融合结果,其中ERGAS可达15.0以内、SSIM可达84%以上,并且融合质量对高、低分辨率图像间的空间尺度差异性不敏感。通过采用更高效的在线字典学习算法,该融合方法的处理效率与应用价值有望得到极大提升。     

Integrating TM and ancillary geographical data with classification trees for land cover classification of marsh area

The main objective of this research is to determine the capacity of land cover classification combining spectral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM image texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS information (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to implement and should be applicable in other settings and over larger extents.     

森林火险遥感解译分析

本文选取张家口某地2022年4月份的陆地卫星9号(Landsat 9)影像，反演相应归一化差分植被指数、积雪指数、湿度指数和表观温度，通过主成分分析获取关键信息，结合当地地理条件讨论对林火的影响。研究表明：研究区内西南侧温度高，植被含水量低，林火风险较高，成片分布，容易发生草原森林火灾蔓延；东侧和北侧温度低，植被含水量高，积雪呈网状分布，对于林火蔓延起到很好的限制作用。研究为林火蔓延模拟等研究提供数据支持和理论依据。     

基于多源遥感数据的北京市1973-2005年间城市建成区的动态监测与驱动力分析

城市地域空间扩展情况是衡量城市化水平的重要测度指标。利用陆地卫星MSS影像、TM（或ETM＋）影像、灾害监测星座数据和“北京一号”小卫星数据,对1973—2005年北京市建成区的扩展过程、面积变化和土地利用影响进行了监测分析。结果表明：32年间,北京市建成区面积净增加超过1000km2,2005年达到1209.97km2,相当于1973年的6.58倍,年均扩展32.07km2。北京市建成区的扩展基本上呈现出以旧城区为中心向四周扩展的方式,并未在某一方向表现出特别明显的变化,但在不同时期内的扩展速度差异非常明显。北京市建成区的扩展面积中,54.34%来自于农村居民点和独立工矿用地及其他建设用地,43.57%来自于水田、旱地等耕地。人口增长对北京市建成区的扩展有显著的刺激作用;经济增长是城市变化的主要动力之一;交通的发展促进了城市用地的扩展,是城市扩展的牵引力,对城市空间扩展具有指向性作用;而政策与制度因素则在很大程度上决定了北京市发展和扩张的宏观格局。