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植被覆盖区卫星高光谱遥感岩性分类 总被引：1，自引：0，他引：1
植被高覆盖区岩石和土壤在遥感图像上表现为弱信息、小目标,如何利用卫星高光谱遥感提取岩性弱信息是目前遥感地质应用中的最大挑战之一。以黑龙江呼玛地区为例,选择美国EO-1卫星Hyperion高光谱数据。由于植被与下伏岩石-土壤的光谱混合,分别计算研究区含土壤因子和不含土壤因子的植被指数,并对两类不同的植被指数进行主成分分析,以此分离植被和岩石-土壤组分。在含土壤因子植被指数主成分分析的二维组分散点图上,明显区分出背景植被与异常岩石-土壤组分,证实了植被与岩石-土壤组分经主成分分析分离的效果。同时在不添加土壤因子植被指数的分析中,明显区分出植被覆盖信息。通过对实验区典型岩石进行野外光谱测试,然后对光谱进行连续统去除处理,将其作为参考光谱,与分离后的岩石-土壤光谱进行光谱特征拟合（SFF）,从而成功地识别出研究区内不同岩石类型,特别是玄武岩、流纹岩、砂砾岩、安山质凝灰岩、大理岩和石英片岩识别效果较好。根据研究区内不同岩石地层单元内岩石组合特征,通过分离后的组分合成图像,成功地实现了岩性分类。与已知地质图叠加,证实通过卫星高光谱数据提取的不同岩石类型颜色边界与地质图岩性界线吻合较好。结果表明：通过植被与岩石-土壤光谱组分分离,结合高光谱遥感的光谱特征拟合,能够识别不同的岩石类型,实现植被覆盖区岩性分类。 相似文献
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植被的发育限制了遥感在地质学方面的应用, 在植被覆盖区进行岩石填图, 首先要考虑去除植被干扰影响.以内蒙古东乌旗地区为例, 选择先进星载热发射和反射辐射仪(advanced spaceborne thermal emission and reflection radiometer, ASTER)数据, 分别计算研究区内含土壤因子植被指数和不含土壤因子的植被指数, 并对两类不同的植被指数进行主成分分析, 挑选出植被信息被抑制和岩石-土壤信息突出的主成分进行岩性分类, 和利用最大似然法的分类结果进行对比分析, 评价两种方法的岩性分类性能, 植被抑制法的总体分类正确率为82.946 8%, 最大似然法的总体分类正确率为76.364 3%.结果说明在植被覆盖区, 利用植被指数来抑制植被信息是可行的, 和常规分类方法中的最大似然法相比, 大大提高解译的准确性. 相似文献
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基于植被指数和土地表面温度的干旱监测模型 总被引：79，自引：4，他引：79
干旱是一种周期性发生的自然现象,其发生过程中有关参数如地表覆盖度、温度和土壤表层含水量等可以通过遥感的途径进行定量反演,而这些参数客观地反映了地表的综合特征。综述了运用遥感反演产品---土地表面温度和归一化植被指数在干旱监测中的应用前景和进展,分析了距平植被指数、条件植被指数、条件温度指数和归一化温度指数等干旱监测方法的优缺点,在前人研究的基础上,提出了条件植被温度指数的干旱监测模型,探讨了其应用前景。 相似文献
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利用陆地卫星多光谱遥感数据,在研究草被、针叶林、阔叶林、耕地等不同植被类型波谱特征的基础上,引入景观分区信息,进行空间特征与波谱特征相结合的遥感植被类型信息提取,其精度和效果优于单纯依赖植被指数的分类方法,能够适用于研究区植被类型的详细划分。 相似文献
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银川平原土壤盐渍化与植被发育和地下水埋深关系 总被引：9，自引：0，他引：9
土壤盐渍化是制约银川平原内部植被生长最主要的生态环境地质问题,也是影响区域农业生产的第一障碍性问题。基于遥感数据,结合表层土壤含盐量及地下水位观测资料,对银川平原土壤盐渍化与植被和地下水的关系进行了定量研究。结果表明：随着表层土壤含盐量的增大,NDVI(归一化植被指数)逐渐减小,植被发育较好的区域土壤含盐量均小于3 g/kg;植被主要生长在没有盐渍化或轻微盐渍化的地区,在中度和重度盐渍化地区几乎没有植被发育;在枯水季节,研究区土壤盐渍化最严重的地下水位埋深为1.5 m,而土壤盐渍化较为严重的地下水位埋深范围为1~3 m。 相似文献
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卫星遥感信息系统在林火及干旱监测中的应用 总被引：1，自引：0，他引：1
根据几种投影方式的特点，再结合贵州地理位置，采用麦卡托投影方式来进行火灾检测，火灾自动监测系统能迅速找出火点位置并计算火面积，监测范围广，时效较常规地面监测手段提前1－3天。旱情的遥感监测基于土壤水分和植被状况，对于裸地卫星遥感重点是土壤的含水量，对于有植被覆盖的区域，遥感的重点是植被指数的变化以及植被冠层蒸腾状况的变化。从贵州的实际情况和地表条件，分别选用植被指数法、第四通道遥感下垫面温度法、植被供水指数法及热惯量方法来进行干旱监测。通过实例认为，气象卫星确实能大范围地对干旱进行宏观监测，其监测面积精度高，不仅可以迅速准确确定受灾面积，而且还可精确到县，在实际运用中可操作性很强，能为政府部门抓好防旱抗旱提供详细的数据参考，做好灾情预报和指导抗灾救灾工作服务。 相似文献
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黄河三角洲地表特征参数的遥感研究 总被引：5，自引：0，他引：5
地表特征参数与下垫面特征密切相关,它是研究地表物质平衡和能量平衡的基础,因而应用遥感方法反演区域地表特征参数日益受到重视。本文主要应用现有的地表特征参数遥感反演模型,利用AVHRR和TM数据反演了黄河三角洲的地表特征参数:地表反照率、地面温度和植被指数等,并根据反演结果研究了地表特征参数及其合理组合所反映的地表特征。农田植被和天然植被的植被指数变化规律不同;在植被全覆盖区域,植被指数与反照率成幂函数关系,在极干或极湿情况下,地表温度与反照率成线性关系;地表特征参数的合理组合反映出黄河三角洲下垫面覆盖度低,裸地较多,地表较湿润,蒸发量较大。 相似文献
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宁夏地区水资源对植被生长的影响研究 总被引：6，自引：6，他引：0
植被状况的变化是反映区域性生态环境状况的重要指标之一,多年植被指数的变化则反映了植被生态环境随时间的变化规律。采用定量遥感技术及斜率法,以全球植被指数MODIS NDVI数据作为数据源,对宁夏地区2000-2004年期间的植被指数变化幅度及变化趋势进行了研究。结果表明,从2000年开始,宁夏部分地区的植被指数呈现下降趋势,植被出现退化现象。在此基础上,对贺兰山、六盘山地区植被变化与降雨量的关系做了定量研究,并对红寺堡及银川平原等地区的水资源开发利用对植被的影响进行了较为详细的分析。贺兰山、六盘山的植被主要受降雨量的控制,而红寺堡地区的植被退化是由于水资源的不合理应用导致土壤盐渍化的结果,银川平原的植被则与引用黄河水量的减少和地下水的过量开采有关。 相似文献
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土壤水分与干旱遥感研究的进展与趋势 总被引：47，自引：2，他引：47
对土壤水分和干旱遥感监测的国内外发展情况进行了回顾,依据土壤水分和干旱遥感监测的原理,围绕着可见光与近红外、热红外、微波光谱波段的使用,分别以土壤和植被为观测对象,对国内外土壤水分与干旱遥感现状与发展趋势进行讨论。重点对微波遥感土壤湿度的算法发展和研究趋势进行了较详细的论述。在总结国内外土壤水分遥感监测研究实验中所用过的方法与原理的基础上,对目前遥感监测土壤水分研究领域的重点、难点和未来的发展方向进行了评述。土壤水分遥感监测是一个复杂的间接过程,波段及变量选择、传感器的性能等因素对土壤水分监测是至关重要的,土壤的各种理化性状、地形的分异作用以及气候变化和人为的土壤管理措施对土壤水分均有不同的影响,地表特征与土壤水分也存在着一定的相关性。改进的热惯量法和作物缺水指数法是当前土壤水分遥感监测中较为成熟的方法。GIS技术的广泛应用、土壤水分遥感监测模型的优化。微波遥感的应用将是该领域的重点研究方向。 相似文献
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联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究 总被引：1，自引：0，他引：1
土壤水分是气候、水文学研究中的重要变量,微波遥感是获取区域地表土壤水分的重要手段,而L波段更是微波土壤水分反演的最优波段。依托HiWATER黑河中游绿洲试验区的地面观测及机载PLMR微波辐射计亮温数据,利用微波辐射传输模型L-MEB,并将MODIS地表温度产品(MOD11A1)和叶面积指数产品(MYD15A2)作为模型及反演中的先验辅助信息,借助LM优化算法,通过PLMR双极化多角度的亮温观测,针对土壤水分、植被含水量(VWC)和地表粗糙度这3个主要参数,分别进行土壤水分单参数反演、土壤水分与VWC或粗糙度的双参数反演以及这3个参数的同时反演。通过对不同反演方法的比较可以得出结论,多源辅助数据及PLMR双极化、多角度信息的应用可以显著降低反演的不确定性,提高土壤水分反演精度。证明在合理的模型参数和反演策略下,SMOS的L-MEB模型和产品算法可以达到0.04 cm3/cm3的反演精度,另外无线传感器网络可以在遥感产品真实性检验中起到重要作用。 相似文献
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高光谱遥感是一种利用成像光谱仪同时获取地物目标辐射、光谱和空间等多重信息遥感的技术,水文地质是高光谱遥感重要的应用领域之一,通过高光谱遥感图像分析,能够提取大区域包气带及含水系统的水文地质信息,可为水文地质环境的识别及实时监测、地下水资源高效管理、地下水数值模型构建等提供科学数据。通过调研凝练了高光谱遥感基本原理方法及其在水文地质方面的应用研究,重点总结了地下水环境污染和水文信息反演方面的高光谱遥感应用研究进展,分析了高光谱遥感水文地质应用所面临的挑战,并展望了高光谱遥感水文地质应用研究的发展趋势,主要包括:通过土壤和植被的光谱信息实现对研究区包气带土壤重金属污染状况的大面积、短周期连续快速监测;在高光谱图像中提取构造、地层岩性等地质和水文地质特征,以环境指示因子（如植被）为有效补充,获取饱和带地下水环境信息;通过高光谱图像识别植被类型、计算植被覆盖度、分析叶片反射光谱特征并建立模型反演土壤含水量和地下水位,为研究和保护缺水地区生态环境提供数据支撑。 相似文献
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Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy. 相似文献
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As wheat represents the main staple food and strategic crop in Egypt and worldwide and since remote sensing satellite imagery is the tool to obtain synoptic, multi-temporal, dynamic, and time-efficient information about any target on the Earth, the main objective of the current study is to use remote sensing satellite imagery to generate remotely sensed empirical preharvest wheat yield prediction models. The main input parameters of these models are spectral data either in the form of spectral reflectance data released from Satellite Pour l’Observation de la Terre (SPOT) 4 satellite imagery or in the form of spectral vegetation indices. The other input factor is leaf area index (LAI) that was measured by LAI Plant Canopy Analyzer. The four spectral bands of SPOT4 imagery are green, red, near-infrared, and middle infrared; the five vegetation indices that are forms of ratios between red and near-infrared bands are normalized difference vegetation index, ratio vegetation index, soil-adjusted vegetation index, difference vegetation index, and infrared percentage vegetation index. Another vegetation index is green vegetation index that is calculated through a ratio between green band and near-infrared band. Each of the above-mentioned factors was used as an input factor against wheat yield to generate wheat yield prediction models. All generated models are site-specific limited to the area and the environment and could be applicable under similar conditions in Egypt. The study was carried out in Sakha experimental station by using the dataset from two wheat season 2007/2008 and 2009/2010. The total wheat area was 1.3 ha cultivated by Sakha 93 cultivar. Modeling and validation process were carried out for each season independently. Modeled yield was tested against reported yield through two common statistical tests; the standard error of estimate between modeled yield and reported yield, and the correlation coefficient for a direct regression analysis between modeled and reported yield with each generated model. Generally, as shown from the correlation coefficient of the generated models, green and middle infrared bands did not show good accuracy to predict wheat yield, while the other spectral bands (red and near-infrared) bands showed high accuracy and sufficiency to predict yield. This was proven through the correlation coefficient of the generated models and through the generated models with the wheat crops for the two seasons. Accordingly, the green vegetation index that is generally calculated from green and near-infrared bands showed relatively lower accuracy than the rest of the vegetation index models that are calculated from red and near-infrared bands. LAI showed high accuracy to predict yield as shown from the statistical analysis. The models are applicable after 90 days from sowing stage and applicable in similar regions with the same conditions. 相似文献
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鉴于吐哈盆地地表植被覆盖稀少,故利用遥感技术提取亮度、绿度、湿度指数并加以合成,以反映盆地及其周边的反射蒸腾强度、植被覆盖程度和土壤湿度的信息,显示出盆地排泄构造的空间展布特征,进而分析成矿期间研究区的水动力环境,并划分出主要补给区(觉罗塔格山、天山山脉)和排泄区(鲁克沁附近及位于两者之间的径流区)。利用吐哈盆地现代三维地形图,结合盆地常规地质研究成果,反演成矿期间研究区及其周围地形高差特点,揭示出了墩隆起和西缘艾丁湖斜坡带的长期存在,以及它们影响了盆地的水动力系统,提供了南高北低长径流范围的成矿地形优势。 相似文献
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遥感技术在陆面过程研究中的应用进展 总被引：10，自引：1，他引：10
探讨了当前陆面过程 (LSP)研究的特点 ,指出遥感在陆面过程研究中的应用以及陆面过程国际合作实验是突出的特点 ,进而对遥感技术的陆面参数获取、地表能量通量的计算以及与 LSP模式的结合研究及进展进行了综述。根据不同特征的地表参数选择光学遥感或微波遥感已成共识 ,而综合利用不同遥感数据获取同一种地表参数也已成为研究热点 ,当前及今后发射的携载多种遥感仪器的众多遥感卫星为此项研究提供了条件 ;遥感与 LSP模式的结合研究是遥感在陆面过程研究中深入应用的一个方面 ,国际陆面过程合作实验是这项研究的重要保证。 相似文献
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Extracting desertification from Landsat TM imagery based on spectral mixture analysis and Albedo-Vegetation feature space 总被引：1，自引：0，他引：1
Land desertification has been a worldwide environmental problem. Desertification monitoring and evaluation are very important content in desertification context. Scientific and accurate evaluation of desertification can provide scientific basis for decision making in mitigating desertification. Because of the advantage of large amount of information, short cycle and broad scope of data, less restrictions on the human and material resources and so on, remote sensing has become an important technology to monitor land desertification in the past 30 years. Desertification is the most typical and serious form of desertification in China, especially in the oasis zone distributed along inland rivers or in the lower reaches of inland rivers in northwestern China. Quantitative evaluation of the current desertification remote sensing methods used is mostly obtained through the vegetation index and vegetation cover, to gain information on the extent of desertification. As the arid and semiarid sparse vegetation cover, soil and soil moisture on the most common vegetation index have a greater effect. First, based on the spectral mixture analysis model, three kinds of endmember consisting of vegetation, water and bare soil were selected. The image dimensionality was reduced by the minimum noise fraction (MNF). The pixel purity index transformation was used to narrow the range of the endmember. On the scatter plot of MNF, three kinds of endmember were selected, and relative abundance distribution of each component was obtained by using linear spectral mixture model. Second, a spectral feature space composed of vegetation component and land surface albedo retrieved from Landsat TM Imagery was constructed to evaluate desertification present condition and degree quantificationally. Last, an empirical study was carried out taking the middle reaches of Heihe River as an example. Results indicated that this method makes full use of multi-dimensional remote sensing information, reflecting the desertification land cover, water, thermal environment and its changes, with a clear biophysical significance, and the index is simple, easy to obtain, high precision, and is conducive to quantitative analysis, monitoring and desertification assessment of desertification. It was rather ideal to assess desertification on the basis of Albedo-Vegetation feature space: correct prediction proportion of testing samples reached 90.3 %. This method was beneficial to the desertification quantitative analysis and monitoring with the characteristics of simple index, easy accessibility and high accuracy. 相似文献
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Satellite remote sensing applications for surface soil moisture monitoring: A review 总被引：6，自引：0，他引：6
Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and
energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture
is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that
soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This
paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches
for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles
and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms
and key issues that have to be addressed in the near future are also discussed. 相似文献