ZY-3 MUX传感器数据的缨帽变换系数推导

缨帽变换是一种实用性都很强的遥感影像增强方法,已被成功地应用于各种遥感领域。然而,对于缺少中红外波段的4波段高分卫星传感器,采用常规的Gram-Schmidt正交化方法难以推导出缨帽变换的湿度分量,即便少量推导出湿度分量的算法也存在着结果失真的问题。因此,开展针对4波段传感器缨帽变换系数的推导,提出了先确定湿度分量、再确定亮度和绿度分量的逆推算法,并将其应用在ZY-3 MUX传感器数据上。实验结果表明:(1)逆推方法可以有效地推导出ZY-3 MUX缨帽变换的湿度分量,较好地解决了前人研究中出现的湿度分量失真问题;(2)新方法求出的3个分量的散点在其三维特征空间中呈现典型的"缨帽"特征,较于传统的GramSchmidt正交化方法,新方法的散点在水体、植被和建筑用地/裸土之间的空间分布位置可以更好地相互分离,不会造成不同地类之间的混淆;(3)采用新方法所得到的缨帽变换系数的精度好于传统的Gram-Schmidt正交化方法,体现在新方法具有较高的R值和较低的RMSE误差。本研究可为ZY-3 MUX数据提供一套有效的缨帽变换系数,同时也为缺乏中红外波段的高空间分辨率遥感影像提供一种新的缨帽变换系数推导方法,解决了常规GramSchmidt正交化方法无法准确表示湿度分量的问题。     

基于缨帽变换影像融合的遥感影像植被信息提取

充分结合缨帽变换、遥感影像融合以及归一化植被指数(NDVI)的优点,提出了一种基于缨帽变换的遥感影像融合与NDVI相结合的植被信息提取方法,并与传统的NDVI方法进行了对比。实验表明,该方法很好地改善了遥感影像植被信息的提取精度,特别是提取道路两旁的行道树以及居民小区中的绿地信息,效果更好。     

TM 图像的信息量分析及特征信息提取的研究

图像信息量分析是图像处理的基础,为此,本文研究了三个不同植被覆盖类型区,即多林区(森林覆盖在40％以上)、一般林地分布的丘陵区(森林覆盖10-30％)和农田为主的丘陵与平原区的图像信息量。分析同一地区冬夏两季的图像信息特征后得知,红外波段的信息量高于可见光波段,其中信息量最大的是TM5波段,最小的是TM2波段。同时对不同情况下波段间的相关性、均值和标准差等统计特征值也进行了分析。据此就图像增强、信息特征提取方法,如主成分分析、缨帽变换(KT变换)、比值等方法以及波段组合等进行了系统研究,并就其实用条件进行了探讨和评价。     

基于缨帽变换的遥感图像融合方法研究

本文阐述了图像融合和缨帽变换的意义,针对缨帽变换在植被信息提取方面的优点,提出一种新的基于缨帽变换的遥感图像融合方法。为了验证算法的有效性,在城区遥感图像融合中,将其与其他几种图像融合算法进行比较。实验结果表明,基于缨帽变换的遥感图像融合方法得到的结果图像纹理清晰、结构完整、光谱保持能力强,在城市零星绿地提取中有着显著的效果。     

卫星遥感估测土壤水分的一种方法

对于植被影响土壤水分遥感的大小 ,提出了“光学植被盖度”新概念来衡量 ,并用遥感法进行估测。用分解象元法排除植被干扰来提取土壤水分光谱信息。采用土壤水分光谱法并借助回归分析建立土壤水分遥感的TM数据模型。以此为桥梁 ,建立了AVHRR数据模型     

胶东半岛果园TM影像信息的提取决策树方法

本文选取胶东半岛最具代表性的5个果品县(市)为研究区,以Landsat TM影像数据为分类影像,尝试提取果园信息。选用可以"无缝"融入多种辅助信息的决策树分类方法,综合NDVI、地形地貌和缨帽变换等多种辅助信息,利用年内物候变化最大的果园与背景地物的光谱差异,进行果园信息提取;利用SPOT影像以及野外考察资料作为检验样本进行精度验证。表明综合多种辅助信息,利用决策树分类法提取TM影像果园信息可行且准确性较高。     

基于TM图像的南京市气溶胶光学厚度反演

气溶胶是影响地气能量平衡和气候变化的重要因素,对人类生活环境质量有着重要的影响.利用Landsat5TM图像,通过6S大气辐射传输模型建立查找表;在此基础上进行回归分析,得到暗像元的气溶胶光学厚度(aerosol optical depth,AOD);再通过克里金插值计算得到南京市的AOD空间分布数据;最后通过大气校正对结果的合理性进行检验,并对反演结果进行了分析.结果表明:将暗像元法应用于TM图像可以较好地反演出南京市AOD的空间分布;南京市AOD总体呈现北高南低的分布特点,植被、城市建成区、地形的分布是影响南京市AOD分布的主要因素;对于南京市来说,采用高空间分辨率的图像能够获得比一般空间分辨率图像更多气溶胶分布的细节信息.     

特征变换的延安建筑物提取研究

针对高空间分辨率卫星遥感数据多光谱波段数较少和原始波段组合光谱特征利用有限等问题,该文提出了一种基于特征变换的建筑物信息提取方法。以陕西省延安市宝塔地区为研究区,基于快鸟数据采取特征变换、波段选择、数据融合等解决高空间分辨率原始光谱特征利用有限等问题,采用知识规则的面向对象分类方法进行建筑物识别研究。实验表明,缨帽变换波段能有效地突出建筑物信息,4种融合算法中主成分变换融合适用进一步面向对象分类,建筑物识别的总体精度达到89.3%。此方法能有效识别沿坡脚或滑坡体分散分布的建筑物,为快速获取居民空间分布信息和辅助灾害应急评估等提供参考。     

蚀变信息和线性体提取在某铀矿区的应用(摘要)

随着遥感技术在地质上的应用不断深入,单一波段图像或常规的合成图像已不能满足地质解译的需要,作者通过对TM数据进行多种功能复合处理,期望能直接从影像上获得蚀变信息和线性体(构造)发育程度的信息。 蚀变信息的提取根据白云母化、绢云母化和绿泥石化等蚀变岩石波谱曲线和正常岩石波谱曲线的差异,通过TM 5/TM7的比值提高蚀变岩石的灰度。由于南方植被发育,这一比值也使植被信息得到提高,而TM 4/TM3是植被的指示系数,因此利用这两个比值进行变换分类,就可消除植被对蚀变信息的干扰。 线性体提取及其密度统计线性体与断裂构造是密切相关…     

几种TM影像的水体自动提取方法比较

本文对几种常用的TM影像水体方法进行论述,并提出了两种水体自动提取方法,一种是将TM2+TM3>TM4+TM5的谱间关系法与IHS彩色空间构建模型相结合提取水体;另一种是基于卫星图像数据的LBV变换与归一化植被指数NDVI的遥感影像水体信息自动提取的方法。并通过实验比较几种方法的优点和缺点,IHS彩色空间构建模型的方法可以有效地提高细小水体提取的精度,LBV变换与NDVI相结合的方法可以准确地区分水体与低密度覆盖的水植混合区。     

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).     

基于3S技术的雅江县植被类型调查与分析

使用TM遥感影像,借助GIS软件进行了影像的处理和解译;制作了植被类型图,计算主要植被类型的面积及其变化情况;分析了3S技术在植被调查中的优势,并调查了植被动态变化,为植被恢复模式的建立提供了依据。     

Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands

Water and energy balance interactions with vegetation in mountainous terrain are influenced by topographic effects, spatial variation in vegetation type and density, and water availability. This is the case for the mountainous areas of northern Portugal, where ancestral irrigated meadows (lameiros) are a main component of a complex vegetation mosaic. The widely used surface energy balance model METRIC was applied to four Landsat images to determine the spatial and temporal distribution of the energy balance terms in the identified land cover types (LCT). A discussion on the variability of evapotranspiration (ET) through the various vegetation types was supported by a comparison between the respective crop coefficients and those available in the literature corresponding to the LCT, which has shown the appropriateness of METRIC estimates of ET. METRIC products derived from images of May and June – NDVI, surface temperature, net radiation, soil heat flux, sensible heat flux, and ET – were used to characterize the LCTs, through application of principal component analysis. Three principal components explained the variance of observed variables and their varimax rotated loadings allowed a good explanation of the behaviour of the explanatory variables in association with the LCTs. Information gained contributes to improve the characterization of the study area and may further support conservation and management of these mountain landscapes.     

基于RS的北京山区植被覆盖变化空间特征

利用1979年和2005年的Landsat TM/MSS影像,采用基于植被指数的像元二分法分别计算了北京山区的植被覆盖度,分析了植被覆盖变化及地貌对植被覆盖变化的影响。结果显示:北京山区高海拔地区植被覆盖较稳定,低海拔区变化剧烈;陡坡区生态较脆弱,缓坡区生态修复和植被退化的概率较大;同一时相阳坡植被覆盖度小于阴坡,西北坡生态较脆弱,东南坡植被生态修复机率较大。     

THE DEPARTMENT OF PHOTOGRAMMETRY AND REMOTE SENSING AT WUHAN TECHNICAL UNIVERSITY,PEOPLE'S REPUBLIC OF CHINA

Many remote sensing applications are predicated on the fact that there is a known relationship between climate and vegetation dynamics as monitored from space. However, few studies investigate vegetation index variation on individual homogeneous land cover units as they relate to specific climate and environmental influences at the local scale. This study focuses on the relationship between the Palmer Drought Severity Index (PDSI) and different vegetation types through the derivation of vegetation indices from Landsat 7 ETM+ data (NDVI, Tasseled Cap, and SAVI). A series of closely spaced through time images from 1999 to 2002 were selected, classified, and analyzed for an area in northeastern Ohio. Supervised classification of the images allowed us to monitor the response in individual land cover classes to changing climate conditions, and compare these individual changes to those over the entire larger areas. Specifically, the images were compared using linear regression techniques at various time lags to PDSI values for these areas collected by NOAA. Although NDVI is a robust indicator of vegetation greenness and vigor, it may not be the best index to use, depending on the type of vegetation studied and the scale of analysis used. A combination of NDVI and other prominent vegetation indices can be used to detect subtle drought conditions by specifically identifying various time lags between climate condition and vegetation response.     

滑坡蠕变与遥感影像上植被异常关系

以滑坡蠕变阶段坡体的蠕变会引起环境条件的改变，进而影响植被生长状况的野外考察客观现实为依据，提出一种间接监测滑坡变化的新方法。利用高分辨率光学遥感技术，对滑坡蠕变阶段遥感影像上坡体上覆植被的异常特征进行判识，建立遥感影像上植被异常与滑坡蠕变的关系，反映滑坡的演化过程，弥补GPS技术、InSAR技术及部分地面监测手段在地势高、地形陡峭、植被茂盛等条件下监测工作的不足，为后续的滑坡预测研究提供帮助。以植被覆盖度较高的新磨村山体高位滑坡为例，首先，对研究区域进行分区；其次，计算各分区的植被覆盖度；最后，利用植被覆盖度分析遥感影像上的植被异常与滑坡蠕变的关系，并根据滑后遥感影像和实地考察情况进行验证。结果显示，2014年—2016年，滑坡的主要物源区、变形体上方细长局部崩滑区和泉眼及冲沟周边的植被覆盖度出现明显的下降，即随着滑坡发生时间的临近，植被受滑坡蠕变的影响变大，植被生长状况变差；而且随着距裸地等滑坡风险较大区域的距离增大，植被受滑坡蠕变的影响变小，植被生长状况变好。这表明，植被异常与滑坡蠕变存在明显的时空相关性，体现了滑坡蠕变阶段遥感影像上植被异常与滑坡蠕变的内在联系，反映了滑坡逐步失稳的演化过程，为进一步预测滑坡的发生提供依据。     

Peatland subsidence and vegetation cover degradation as impacts of the 2015 El niño event revealed by Sentinel-1A SAR data

Indonesia has the world’s largest tropical peatland, mostly located in the southern province of Sumatra, the south of Kalimantan, and Papua. The catastrophic fires between June and October 2015 induced by the El Niño event burnt most of these peatland areas. We analyzed spatio-temporal peat subsidence during pre- and post-fires in the peat hydrological unit of Sungai Sugihan – Sungai Saleh (KHGSS), South Sumatra using Sentinel-1 images by applying DInSAR-SBAS algorithm. Based on our analysis, the linear subsidence rate after the 2015 peat fires increased by a factor 6.4 compared to that of pre-fires. Generally, the estimated subsidence is temporally well-correlated with the precipitation variation. In addition, the subsidence patterns are spatially correlated with the hotspot distribution, peat thickness, and drainage networks. Furthermore, we mapped vegetation cover over the KHGSS by using the Sentinel-1 images as well. The results show that the vegetation degradation is correlated with the hotspot distribution and the highly-degraded vegetation associated with the 2015 peat fires. It demonstrated that the 2015 El Niño event has significant impacts on increasing the amount of the subsidence and the vegetation degradation in KHGSS area.     

多频率InSAR提取沼泽湿地DEM精度对比分析

选取3种波长的干涉SAR数据对提取沼泽湿地区域的DEM，并随机从1：10 000地形图中选取111个点数据进行精度验证，最后对比分析了沼泽湿地植被对于不同SAR波长的干涉相干性差异。结果表明：L-band ALOS-1 PALSAR精细模式的HH单视复数数据与1：10 000地形图数据吻合度较好，76.58%的高程值差异在3 m以内，其相干系数比C-band Sentinel-1A IW模式的VV单视复数数据和X-band TerraSAR HH单视复数数据要高；更适合利用雷达干涉测量技术提取沼泽湿地的DEM；不同湿地植被类型的相干系数有较大差异，岛状林和灌草结合的湿地植被分布区相干系数值较大，而浅水沼泽植被区和深水沼泽植被区相对较低。     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

基于RS和GIS的松花江流域植被覆盖动态变化研究

利用遥感和地理信息系统技术对1989,1995年的Landsat TM数据和2002年Landsat ETM＋三期遥感数据进行处理,反演和计算松花江流域的归一化植被指数（NDVI）,在此基础上,获取研究区域植被覆盖度。在ArcGIS9.2软件空间分析模块的支持下,对研究区域三期植被覆盖影像进行叠加分析,以流域尺度和栅格尺度分析植被覆盖变化的时间和空间特性,获取研究区域植被覆盖度空间格局分布特征,为该区域植被覆盖度的自动化监测提供很好的技术支持。