不同大气校正方法对中小湖泊蓝藻遥感动态监测的影响

环境小卫星可实现中小湖泊蓝藻动态监测,但不同大气校正方法对于相同影像的处理结果有很大差异。研究利用多种大气校正方法对环境小卫星影像进行辐射校正处理,利用多个感兴趣区的全局和局部特征以及多个统计量对处理结果进行分析,比较其在蓝藻动态监测中的作用。基于多光谱植被指数计算蓝藻生物量的思路,分析了影像经不同大气校正算法处理后,其归一化植被指数的差异性来源及其对蓝藻生物量计算结果的影响。结果表明:蓝藻动态监测的定量描述会因大气校正算法不同而不一致,进而对几种大气校正算法在蓝藻生物量监测和定量分析中所产生的不确定性进行了比较分析,并对各算法的有效利用提出了建议。     

基于RS与GIS的县级土地利用变化分析——-以四川省成都市双流县为例

基于遥感影像的城市土地利用变化研究是目前遥感应用研究的热点问题,是对土地利用变化进行监测的有效手段。利用1996年以来双流县TM和中巴卫星遥感影像,应用RS技术与GIS技术,对遥感影像的校正、影像增强和图像融合方法进行了系统的研究,并通过对双流县遥感影像处理,增强影像可识别性。在结合野外调查和室内判别的基础上,建立了研究区遥感影像解译标志,并以自动分类和目视判读相结合进行地理信息系统的空间分析,建立了1996年、2006年土地利用
数据库,分析、提取了双流县土地利用变化信息。分析表明,双流县1996~2006年间的土地利用变化情况反映出在我国目前经济快速发展的背景下,县级土地利用的变化随着县域经济的高速发展,建设用地需求增长较快,耕地面积迅速减少,导致耕地资源的保护压力持续加大;同时,土地利用过程中出现了土地生态环境质量有所下降,人与自然的关系局部恶化等问题。因此,加大对经济快速发展的县级土地利用变化的动态监测是非常必要的。     

滑坡稳定性与土地利用关联规则挖掘及应用 ——以三峡库区秭归到巴东段为例

三峡库区滑坡灾害广泛发育,其稳定性受土地利用变化等人类工程活动的影响。采用数据挖掘技术研究库区土地利用变化对滑坡稳定性的影响及其规律,利用三个时相的遥感影像得到实验区滑坡面上两个时段间的土地利用变化监测图,用Apriori算法挖掘出滑坡稳定性与土地利用变化类型之间的强规则,用马尔可夫链模型预测滑坡面上土地利用的变化趋势,将预测结果用于对滑坡稳定性发展的分析评估。通过实验分析,所采用的方法可用于预测滑坡稳定性变化趋势,为滑坡灾害的监测预警提供决策支持。     

一种稳健的多时相遥感图像相对辐射校正方法

变化检测是通过分析多时相遥感图像实现土地利用动态监视的一种有效方法，但在变化检测 分析前，需要经过辐射校正消除光照等因素对地物光谱辐射的影响，使同一地物在不同时相 影像中具有相同的辐射量。根据地物在不同时相遥感图像中的光谱特性满足线性关系的 特点，提出一种自动实现多时遥感图像相对辐射校正的稳健方法，首先通过最小差分回归找 出非变化地物在多时相遥感图像中的辐射关系 |然后利用变化区域证实过程消除变化区域对 辐射校正处理的影响 |最后通过循环迭代实现图像间的辐射校正。提出的方法不仅可以自动 地实现多时相遥感图像的相对辐射校正，而且能够保证图像的辐射分辨率不会因为辐射校正而降低。     

基于3S的土地利用数据库更新技术研究

以湖南省株洲市天元区土地利用数据库为研究对象,探索利用3S技术对土地利用数据库进行更新的技术和方法.利用静态GPS实测地面控制点,ERMAPPER为平台对QuickBird卫星数据进行几何精校正,校正后图像的点位误差控制在1个像元左右,以此作为标准校正SPOT-5、ETM＋和TM图像.将校正后SPOT-5图像导入MAPGIS与同期的土地利用现状图匹配,对发生变化的地块逐个进行标记,共标识图斑189个.在试验区内建立高精度GPS基站和移动站一套,利用移动站对变化图斑进行野外实测,数据经格式转换后导入MAPGIS 内,对土地利用现状的图形库和属性库分别进行更新.结果表明：采用3S技术可以快速、准确、高效地更新土地利用数据库,实现土地利用现状短周期预测预报的目的.     

基于分段线性回归的水质遥感图像校正算法

针对环境一号卫星拍摄到的水质遥感监测图,相对于实际水域的水质分布状况存在一定的差异,严重影响到水质监测报告的精确度,由此提出一种基于分段线性回归的水质遥感图像校正算法,利用监测水域实际测量到的实时数据和反演结果图的最小值、最大值和均值,将这3对数据进行分段线性回归,得到校正公式并完成校正。实验结果表明该算法校正精度高、速度快,极大地提高了水质监测报告的精确度。     

遥感技术在河北省土地调查中的应用与展望

河北省从2001年首次在土地资源调查监测中引入卫星遥感技术，开展了石家庄市土地利用卫星遥感动态监测试点研究。在试点经验的基础上，2005年部署开展了河北省土地利用遥感动态监测，对重点城市和县城开展了土地利用变化监测，构建全省土地利用遥感本底数据库。从2005年开始我省用三年时间，在全省组织实施了土地利用更新调查工作，其中遥感技术得到了更加广泛和深入的应用。本文着重分析遥感在土地调查中的作用、技术方法和流程，以及存在的主要问题，并结合未来遥感技术的发展和国家土地遥感工作的部署，对我省今后遥感技术在土地调查中应用前景作出展望。     

地中海生态系统中土地退化,土壤侵蚀和沙漠化遥感监测

简要介绍了国外应用遥感图像进行土地退化制图和监测的一种新方法。遥感图像上像元亮度值代表土地表面多种物质（土壤、植被、阴影、母岩）混合光谱辐射特征，通过多光谱图像辐射校正和典型区野外光谱测试，利用线性光谱混合模拟可以将ＡＶＩＲＩＳ和ＴＭ图像混合光谱中差异明显的几个组分进行分离，进而在各组分丰度分析的基础上，比较准确地进行植被盖度、土壤条件与土壤侵蚀的判别与制图。     

“北京一号”小卫星数据在天津市土地利用动态变化监测中的应用

应用“北京一号”小卫星CCD 数据进行天津市2000～ 2005 年土地利用动态变化监测, 研究该数据在土地利用研究领域所具有的实用性和效果。结果表明,“北京一号”小卫星遥感影像, 光谱信息丰富, 纹理结构清晰, 整体判读情况良好, 能够满足中等比例尺成图要求, 在土地利用研究领域具有实用性, 可以在土地利用ö土地覆盖的研究中广泛使用, 成为遥感数据更新的一种新数据源。动态监测显示, 2000～ 2005 年天津市不同土地利用类型之间相互转化明显。整体表现出了城镇用地通过占用近郊的耕地和其它类型用地而迅速扩展、城镇化过程显著以及不同土地利用类型之间转化明显等基本特征。     

遥感用于土地利用变化动态监测中的若干问题探讨

利用遥感与地理信息系统技术进行土地资源调查和动态变化监测已为人们所共识。由于土地 利用与土地覆盖的复杂性,利用遥感技术进行土地利用变化动态监测时,遇到了许多具体问题。从 应用的角度出发,重点在土地利用与土地覆盖的关系、影像的选择与处理、动态监测方法的选定与 综合及其它多方面知识的参与等几个方面进行了探讨。这些问题的解决,将会在很大程度上提高土 地利用变化监测的精度。     

Radiometric Consistency between Landsat 8 OLIand Sentinel-2 MSI Imagery in Mountainous Terrain

The combined use of multi\|sensor/multi\|temporal images provides more opportunities for long\|term land surface monitoring with high resolution and frequency requirements.However,as sensors differ in their orbital,spatial,or spectral configuration,uncertainty was introduced in the radiometric consistency of multi\|sourse images,and that becomes more outstanding in mountainous terrain with the sharp topographic relief.Therefore,a series of radiometric corrections need to be carry out before further application.The objective of this study was to indicate the radiometric consistency of Landsat\|8 OLI and Sentinel\|2 MSI images.Thus the radiometric differences between the corresponding bands of these two images acquired almost simultaneously by OLI and MSI over 2 areas at different latitude was calculated for the TOA reflectance images first.Then several radiometric corrections(atmospheric correction,BRDF correction and bandpass adjustment) were carried out successively and after each of them the radiometric differences were researched again to assess the performance of each correction method.The results first indicate that there is high radiometric consistency between OLI\|L1T and MSI\|L1C images with the R²greater than 0.9 for each band involved.Then higher consistency was found after the 6S atmospheric correction and C\|factor BRDF correction,while no remarkable improve was found after the fixed\|parameter bandpass adjustment.Furthermore,in area with great topographic relief,the radiometric consistency were higher for hillside facing the sun than hillside in shadow (the MAD of SWIR2 band was 0.010 and RMSD was 0.007 in sun\|light area,while the MAD was 0.005 and RMSD was 0.004 in shadowed area).The results point out that proper atmospheric correction,BRDF correction and bandpass adjustment could be used to improve the radiometric consistency,and topographic correction might also be carried out to balance the radiometric consistency differences between different hillsides.     

Generation of geometrically and radiometrically terrain corrected SAR image products

Terrain undulations affect the geometric and radiometric quality of synthetic aperture radar images. The correction of these effects becomes indispensable when quantitative image analysis is performed with respect to the derivation of geo- and biophysical parameters. The paper presents a rigorous approach for geometric and radiometric correction of SAR images. Using a digital elevation model, the imaging geometry is reconstructed and is used to perform geometric and radiometric correction of terrain induced distortions. The importance of a stringent radiometric correction based on the integration of the image brightness is emphasized. The approach guarantees that the energy contained in the image data is preserved throughout the geocoding process. The resulting backscattering images are fully terrain corrected and can be used for further quantitative investigations and may also improve qualitative studies as e.g. land cover classifications. The technique is applicable for different sensor types and image products, including already geocoded SAR images. The effect of different resolutions of digital elevation models used for the correction of the backscattering coefficient is investigated.     

Using multiple radiometric correction images to estimate leaf area index

Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI models established, the R ² of multi-variable models were generally higher than those of the single-variable ones. The independent variables of the best VI–LAI models contained all VIs from all radiometric correction levels, showing the potentials of multi-radiometric correction images in LAI estimating. The results indicated that the use of VIs from multiple radiometric correction images can better exploit the capabilities of remote-sensing information, thus improving the accuracy of LAI estimating.     

Image correction for radiometric effects in remote sensing

A general classification of radiometric correction methodologies yields new insights into related procedures as they are implemented typically on image analysis systems. Incorrect results can be obtained from the inappropriate application of different types of corrections related to physical phenomena which give rise to radiometric distortions in a scene. The major types of radiometric correction are reviewed as they apply to digital images acquired by satellite sensors in the visible and infrared portions of the spectrum.     

基于行频变化的航空高光谱成像仪相对辐射校正方法研究

对于核心部件为线阵CCD阵列的航空高光谱成像仪而言,积分时间因随飞行过程中成像行频动态变化而变化,导致影像中各行数据记录的地物灰度值不具备可比性,更使得相邻航带可能出现明显色差。为此,发展了一种基于行频变化的相对辐射校正方法,在传统的相对辐射校正模型中引入了行频的影响,实现了反映行频动态变化的航空高光谱数据辐射校正三维曲面模型。通过无人机载高光谱成像仪的航空飞行试验,利用所获取的验证场的图像进行相对辐射校正处理,通过相邻航带间图像处理前后色差比较、验证场布设均匀靶标的定量化。     

Different processing levels of SIR-C/X-SAR radar data for the correction of relief induced distortions in mountainous areas

The third Shuttle Imaging Radar (SIR-C) was the first multifrequency and multipolarization SAR system monitoring the Earth from space. The availability of several frequencies and polarizations offers the opportunity of low level radiometric enhancement of SAR data via principal component or ratio analysis to correct the relief induced distortions of SAR images in mountainous areas. The potential of this method is presented based on the example of the Timna Valley in the south of Israel. Especially in areas where no DEM for the correction is available the method to be presented has a high application potential. For comparison the SIR-C/X-SAR data were also corrected conventionally on the basis of the local incidence angle derived via a DEM. This correction technique has the great advantage of getting real backscatter values for each frequency and polarization.     

渭河定量遥感水质反演中的大气校正作用研究

以渭河陕西段为研究对象,在获取了渭河陕西段实地监测数据和相应时间段的SPOT-5遥感影像数据基础上采用黑暗像元法(DOS)、大气辐射传输模型法等7种大气校正方法对SPOT-5遥感影像进行大气校正。结合校正后的遥感数据,使用多元线性回归、支持向量机(SVM)、及BP神经网络3种方法对渭河进行定量遥感水质反演。实验结果表明,通过遥感影像对渭河进行定量水质反演是可行的,大气校正在一定程度上提高了定量遥感水质的精度。对于SPOT-5遥感影像的大气校正,采取对遥感数据辐射定标后消除各波段最暗像元的方法可以达到较好的效果。     

Temporal stability of some global NDVI products derived from NOAA/AVHRR GVI

The Normalized DilTerence Vegetation Index (NDVI) derived from NOAA's Advanced Very High Resolution Radiometer (AVHRR) has been widely used in monitoring continental and global vegetation distribution and dynamics, drought severity and location, and environmental deterioration. Since 1982, NOAA has produced the Weekly Global Vegetation Index (GVI) product from AVHRR. The analyses of the GVI product have revealed many problems due to the simplified radiometric correction involved in the processing. Those limitations have inspired several elTorts to reprocess the NOAA GVI data sets to produce an improved representation of global NDVI patterns. In this paper, the quality of three Global NDVI products resulting from very simple to rather sophisticated reprocessing was examined by using a global approach. In general, the quality of data improves with increasing sophistication of radiometric correction. However, this study reveals some significant errors common in all three products assessed. The problems include a systematic annual increase in values computed from a single satellite and jumps between consecutive satellites. These errors are large enough to alTect results of the long term time-series analyses. This pattern suggests an additional radiometric distortion in NOAA/ AVHRR data. It is found that the values computed from data of the first year after satellite launch are roughly the same statistically for NOAA satellites. Thus, the discontinuity ofNDVls between satellites appears to be mainly caused by the systematic drift. Therefore, data collected in the first year of satellite launch might be considered as a baseline for correcting the systematic errors. By comparing NDVI from the first year of satellites in space, it is also found that NDVI increases at higher latitude and decreases or keeps constant at lower latitude. This change of NDVI with time might signal the change of global climate.     

Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images

The homogeneity of time series of satellite images is crucial when studying abrupt or gradual changes in vegetation cover via remote sensing data. Various sources of noise affect the information received by satellites, making it difficult to differentiate the surface signal from noise and complicates attempts to obtain homogeneous time series. We compare different procedures developed to create homogeneous time series of Landsat images, including sensor calibration, atmospheric and topographic correction, and radiometric normalization. Two seasonal time series of Landsat images were created for the middle Ebro Valley (NE Spain) covering the period 1984–2007. Different processing steps were tested and the best option selected according to quantitative statistics obtained from invariant areas, simultaneous medium-resolution images, and field measurements. The optimum procedure includes cross-calibration between Landsat sensors, atmospheric correction using complex radiative transfer models, a non-lambertian topographic correction, and a relative radiometric normalization using an automatic procedure. Finally, three case studies are presented to illustrate the role of the different radiometric correction procedures when analyzing and explaining gradual and abrupt temporal changes in vegetation cover, as well as temporal variability. We have shown that to analyze different vegetation processes with Landsat data, it is necessary to accurately ensure the homogeneity of the multitemporal datasets by means of complex radiometric correction procedures. Failure to follow such a procedure may mean that the analyzed processes are non-recognizable and that the obtained results are invalid.