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基于TM卫星影像获取北京市水体密度指数与植被覆盖指数的方法 总被引:5,自引:0,他引:5
以北京市为研究区域,分析了该区域的TM(Thematic Mapper;专题制图仪)卫星影像特征,探讨了水域、农田、林地、草地、城市用地以及云和云影在TM的7个波段上的光谱可分性,提出了NDCI(Normalized Difference Cloud Index;归一化云指数),分析建立了基于NDCI、NDVI(Normalized Difference Vegetation Index;归一化植被指数)、NDBI(Normalized Difference Built-up Index;归一化建筑指数)、MNDWI(Modified Normalized Difference Water Index;改进型归一化水体指数)和坡度数据的简单决策树模型,对研究区的几类主要地物、云和云影的信息进行了提取,并对结果进行了精度评价.在GIS支持下计算了水域、林地、草地和农田的面积,计算了北京市2005年第3季度的水体密度指数和植被覆盖指数.结果表明:该方法的总体提取效果较好,在分类过程中阈值的选取简单、有效,分类结果能够满足计算水体密度指数和植被覆盖指数的要求,从而将遥感技术运用到生态质量气象评价中去,并取得了较为满意的结果. 相似文献
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局部标准差图像(Local Standard Deviation image,LSD)在卫星图像的空间一致性检测中有着重要的作用,然而利用传统的滑动窗技术计算局部标准差图像时,由于大量的循环过程使运算速度较慢,特别是当卫星图像较大而滑动窗较小时,这种运算更为耗时.采用了矩阵运算的思路,提出了根据滑动窗大小将卫星图像数组按一定方向和偏移量进行整体平移,然后对经过平移后的图像数组进行数学运算来获取标准差图像的快速算法.通过计算2005年1月1日的NOAA-16/AVHRR通道4亮温局部标准差图像实例表明,采用快速算法的计算效率相对于传统滑动窗算法计算效率提高明显. 相似文献
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中国气象局沈阳大气环境研究在2014年基于CUACE和CMAQ建立了东北区域空气质量数值预报业务系统,本文介绍了空气质量数值模式的研究进展以及业务现状,为东北区域空气质量和雾霾预报提供了技术支撑。然而,随着预报精细化和更长预报时效的业务发展需求,存在预报准确率不高、计算资源短缺、科技创新能力不足等问题。本文基于存在的问题提出了东北区域空气质量数值预报未来发展建议与展望,包括加强大气污染源清单研究与技术规范制定、观测资料同化技术研究与业务应用、物理过程参数化方案的改进优化、发展数值预报产品订正技术、开发高分辨率7—10 d数值预报产品、加强人才引进和科技创新等。 相似文献
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利用华北地区2007年的NOAA18 1B卫星资料和402个气象站点的观测数据,建立了利用NOAA18 1B卫星资料和气象资料估算光能利用率(Light Utility Efficiency,LUE)的新技术方法,并研究了华北地区植被LUE及其时空变化。结果表明:利用NOAA18 1B卫星资料来估算华北地区植被LUE,效果较好。华北地区植被的年LUE介于0%~1.13%之间,最大LUE为2.83%。北京、河北、天津、山西及内蒙古各省(区)平均LUE依次为0.57%,0.52%,0.47%,0.39%和0.26%。各种植被类型LUE为森林与灌丛为0.35%~0.74%,草原为0.11%~0.35%,农田为0.44%~0.51%,建筑用地为0.30%,荒漠裸地仅为0.02%。华北各省(区)夏季平均LUE为0.52%~1.14%;秋季和春季平均LUE分别为0.16%~0.49%和0.08%~0.31%;冬季的大部分时间LUE极低,接近零。 相似文献
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A modified Becker’s split-window approach for retrieving land surface temperature from AVHRR and VIRR 下载免费PDF全文
In order to provide a long time-series,high spatial resolution,and high accuracy dataset of land surface temperature(LST) for climatic change research,a modified Becker and Li’s split-window approach is proposed in this paper to retrieve LST from the measurements of Advanced Very High Resolution Radiometer(AVHRR) onboard National Oceanic and Atmospheric Administration(NOAA)-7 to-18 and the Visible and InfraRed Radiometer(VIRR) onboard FY-3A.For this purpose,the Moderate Resolution Transmittance Model(MODTRAN) 4.1 was first employed to compute the spectral radiance at the top of atmosphere(TOA) under a variety of surface and atmosphere conditions.Then,a temperature dataset consists of boundary temperature T s(which is one of the input parameters to MODTRAN),and channels 4 and 5 brightness temperatures(T 4 and T 5) were constructed.Note that channels 4 and 5 brightness temperatures were simulated from the MODTRAN output spectral radiance by convolving them with the spectral response functions(SRFs) of channels 4 and 5 of AVHRRs and VIRR.The coefficients of modified Becker and Li’s split-window approach for various AVHRRs and VIRR were subsequently regressed based on this temperature dataset using the least square method.As an example of validation,one AVHRR satellite image over Beijing acquired at 0312 UTC 27 April 2008 by AVHRR onboard NOAA-17 was selected to retrieve the LST image using the modified Becker and Li’s approach.The comparison between this LST image and that from the MODIS level-2 LST product provided by the University of Tokyo in Japan indicates that the correlation coefficient is 0.88,the bias is 0.6 K,and the root mean square deviation(RMSD) is 2.1 K.Furthermore,about 70% and 37% pixels in the LST difference image,which is the result of retrieved LST image from AVHRR minus the corresponding MODIS LST image,have the values within ± 2 and ± 1 K,respectively. 相似文献