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及时监测干旱与半干旱区光合/非光合植被覆盖度时空变化,可以为指导荒漠化防治工程及植被衰退机制研究提供重要信息。本文以甘肃民勤典型植被白刺灌丛为研究对象,通过地面控制性光谱实验获取混合光谱、端元光谱与丰度信息,开展线性与非线性光谱混合模型(包括核函数非线性和双线性混合模型)估算光合和非光合植被覆盖度的对比研究,采用全限制最小二乘法进行模型解混,分别获取各样本数据中各类端元丰度及其精度信息,通过模型分解的均方根误差(RMSE)与地面验证精度确定用于光合和非光合植被覆盖度估算的最佳光谱混合模型,其中参考端元丰度采用神经网络(NNC)分类算法对数字影像进行分类获取。结果表明:(1)引入阴影端元的四端元模型相对于传统的三端元模型(光合/非光合植被与裸土)能有效提高光谱解混的精度,并提高光合和非光合植被覆盖度估算精度;(2)对白刺灌丛来说,光合植被、非光合植被、裸土及阴影间多重散射混合效应存在,但混合效应不够显著;考虑非线性参数的核函数非线性光谱混合模型表现略低于线性光谱混合模型,因此非线性光谱混合模型在估算白刺灌丛光合和非光合植被覆盖度时相对于线性光谱混合模型没有明显优势;(3)基于光合/非光合植被、裸土与阴影四端元的线性光谱混合模型可以实现白刺灌丛光合和非光合植被覆盖度的准确估算,光合植被覆盖度估算RMSE为0.11 77,非光合植被覆盖度估算RMSE为0.0835。 相似文献
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针对单一的地表物质组成并不能充分反映城市地表热环境特点这一问题,该文基于热混合影像,利用线性光谱分解方法获取地表组成信息,然后利用光谱分解热混合、线性回归、决策树方法估算地表温度。结果表明:只研究单一地表组成对地表温度的影响,有可能扩大其环境效应;决策树模型在不同规则下能更好地模拟地表温度的空间异质性;光谱分解热混合模型只需要两组数据即可估算出不同地表覆盖下的地表温度,且估算精度较其他模型高;光谱分解热混合模型和多元回归模型结合4种地表组成监测其对地表温度的影响,决策树方法通过不透水面、水体、植被预测地表温度,前两者估算精度比后者高,因此综合考虑城市典型地表组成能更好反映其对地表温度的作用。 相似文献
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不透水面作为城市生态环境的一个重要指标,被广泛应用于城市扩张监测、热岛效应分析及人类活动影响等方面的研究中。线性光谱混合模型构造简单、物理含义明确,是估算不透水面的主要方法。但是全约束的线性光谱混合模型容易在不透水面覆盖较低的地区(0~20%)出现高估,而在不透水面覆盖较高的地区(80%~100%)出现低估。因此,以黑龙江省富锦市为实验区,利用Landsat5 TM图像,讨论了线性光谱混合模型在不同端元数目和约束条件下对不透水面的估算精度,发现三端元(高反射率地物、植被及土壤)半约束条件的线性光谱混合模型估算结果最优,其均方根误差为16.71%,并结合地表温度和植被覆盖度辅助分析,去除了水田对不透水面估算的影响,提高了不透水面的估算精度。 相似文献
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《北京测绘》2019,(8)
城市化是目前全球关注的热点问题之一。城市化导致城市范围的显著扩展,并引起局地气候的变化,从而进一步导致城市区域植被物候的变化。因此,利用多年的时间序列遥感影像提取城市扩展与植被物候的变化,可为分析城市化及其影响提供依据。Landsat系列卫星数据的免费开放,为分析城市的时空变化及其环境影响提供了丰富数据。本文利用由Landsat时间序列数据及标准化光谱混合模型得到的光谱端元组分时间序列,探索了城市及周边植被物候变化提取以及城市扩展提取的新方法,并以北京市为例,提取了1984～2015年32年间的城市扩展以及3个年段的植被物候变化,分析了城市扩展的时空演化以及植被物候的时空变化,并定性分析了二者的关联。论文的主要创新点包括三个方面:(1)提出了基于Landsat时间序列数据和标准化光谱混合模型的城市及周边区域植被物候及变化的提取方法。首先,该方法针对城市区域光谱混合问题及现有植被指数的饱和等问题,采用基于标准化光谱混合模型的全球端元组分的植被丰度时间序列进行植被物候及变化的提取,避免了光谱混合分析中端元选择的问题,同时植被丰度结果在不同地区不同时间上具有可比性;其次,该方法考虑城市及周边区域植被类型多样及变化复杂的特点,适用于多种物候模型的植被物候提取,能同时提取出不同植被类型的物候结果,同时,植被物候变化结果中剔除了不同地物类型及不同植被类型的变化区域,能更好进行物候变化分析。(2)提出了基于标准化光谱混合模型及全球端元组分的城市指数(SVDUI)。该指数基于物理统计的光谱混合分析进行构建,与现有基于像元构建的指数相比更适于光谱混合严重的城市区域的研究。该指数能更好地表达城市与其它地物类别的差异,突出城市特征,并更好地提取出城市范围,因此,SVDUI指数可为城市范围提取提供一种新的途径。此外,该指数能保持时间上的一致性与可比性,具有广泛适用性。(3)提出了一种基于SVDUI城市指数的时间序列变化检测方法,该方法可以快速提取城市扩展的时间、空间及强度信息。该方法能快速、有效地提取出长时间序列的城市扩展结果,同时能充分利用年内及年际变化的时间信息,有效去除单时相结果上的噪声影响,不依赖于单时相影像的结果好坏。 相似文献
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以新疆准噶尔盆地古尔班通古特沙漠为研究区,以中等分辨率成像光谱仪(MODIS 1B)数据为例,辅以MODIS光谱响应函数(SRF)和全波段光谱仪(ASD)准同步采集的雪面反射光谱,运用线性光谱混合模型(LSMM)实现了稀疏植被区积雪遥感信息提取.结果表明:①利用SRF对雪面反射光谱进行端元光谱到像元光谱的转换,生成对应于MODIS1-7波段的离散光谱,将其与用最小噪声分离(MNF)变换和像元纯度指数(PPI)法获得的MODIS影像端元光谱进行对比,发现MODIS1波段光谱值远大于转换光谱值,MODIS2-7波段光谱值与转换光谱值接近;②MODIS2-7波段影像端元光谱值适用于LSMM估算稀疏植被区积雪分量,积雪分量估算值与归一化差分积雪指数(NDSI)拟合结果显示,剔除MODIS1波段后估算的积雪分量与NDSI的相关性显著提高,表明所提取的积雪分量可以作为估算积雪的典型指数. 相似文献
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利用Landsat8数据,以成都市为研究对象,采用光谱混合分解模型研究估算成都市的不透水面分布信息。通过MNF变换和PPI指数运算,确定高、低反照率,植被及土壤四类光谱端元,并利用Landsat8新增的卷云波段去除云、土壤、沙地等噪声和MNDWI指数去除水体。修正后的高、低反照率组分含量之和即为不透水面含量的估计值。通过将不透水面信息与利用TIRS11波段反演的地表温度进行回归分析,发现两者呈正相关。 相似文献
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基于高光谱Hyperion数据的线性光谱混合模型与神经网络模型的比较 总被引：1，自引：1，他引：0
混合像元问题是定量遥感中的热点问题之一,为了改进从遥感数据中提取定量信息,人们建立了各种混合光谱分解技术,其中线性光谱混合模型和神经网络模型就是两种比较成熟的方法。以陕西省横山地区的高光谱Hyperion数据为研究基础,通过最小噪声变换(MNF)、像元纯度指数(PPI)转换和RMS误差分析的迭代方法相结合提取影像中的纯净像元作为终端端元。分别运用神经网络模型和线性光谱混合模型对影像进行光谱分解,得到各个组分的分解图像。以标准植被指数(NDVI)影像为衡量标准,选取训练样本点,分别对两种模型进行回归分析,结果显示NDVI影像与线性光谱混合模型植被分解图像的判定系数(R2=0.91)要大于其与神经网络模型的判定系数(R2=0.81)。进一步分析表明在一般情况下,线性光谱混合模型具有比神经网络模型略高的分离精度,但是神经网络模型对细部信息的提取的效果要好于线性光谱混合模型,最后提出了端元均方根误差(EAR)指数,一种新的混合像元分解的思路。 相似文献
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准确地估测植被覆盖度对于生态环境、自然资源评估有着重要的意义.本文通过无人机获取多光谱影像结合DEM,对拍摄区域植被面积进行估测;利用无人机遥感平台搭载的Sequoia多光谱相机获取影像数据,研究了常见的4种植被指数(归一化差值植被指数(NDVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)、绿度归一化植被指数(GNDVI))在植被面积估测中的适用性.实验结果表明,无人机多光谱影像结合DEM,在植被面积估测中具有可行性.其中,归一化差值植被指数(NDVI)可使植被从土壤、水体、阴影等复杂背景因素中分离出来,能较为准确地统计植被覆盖面积.通过无人机多光谱影像估测绿植覆盖面积,可为精细化作物管理、农业估产提供决策依据. 相似文献
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以2001年7月11日LandsatETM7影像和2009年7月16日TM影像为数据源,基于V-I-S理论模型,采用归一化光谱分解模型提取了乌鲁木齐市区范围内2个时段的植被、土壤、不透水层3个连续地表参数分量。通过对不透水层不同阈值的划分,提取了2时段的乌鲁木齐市城市发展的空间信息,结果较为满意;通过空间叠加计算方式获取了8年来乌鲁木齐市城市化发展的空间信息和主要拓展方向。结果表明,乌鲁木齐城市化发展速度较快,特别是北扩趋势显著。 相似文献
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Quantifying impervious surfaces in urban and suburban areas is a key step toward a sustainable urban planning and management strategy. With the availability of fine-scale remote sensing imagery, automated mapping of impervious surfaces has attracted growing attention. However, the vast majority of existing studies have selected pixel-based and object-based methods for impervious surface mapping, with few adopting sub-pixel analysis of high spatial resolution imagery. This research makes use of a vegetation-bright impervious-dark impervious linear spectral mixture model to characterize urban and suburban surface components. A WorldView-3 image acquired on May 9th, 2015 is analyzed for its potential in automated unmixing of meaningful surface materials for two urban subsets and one suburban subset in Toronto, ON, Canada. Given the wide distribution of shadows in urban areas, the linear spectral unmixing is implemented in non-shadowed and shadowed areas separately for the two urban subsets. The results indicate that the accuracy of impervious surface mapping in suburban areas reaches up to 86.99%, much higher than the accuracies in urban areas (80.03% and 79.67%). Despite its merits in mapping accuracy and automation, the application of our proposed vegetation-bright impervious-dark impervious model to map impervious surfaces is limited due to the absence of soil component. To further extend the operational transferability of our proposed method, especially for the areas where plenty of bare soils exist during urbanization or reclamation, it is still of great necessity to mask out bare soils by automated classification prior to the implementation of linear spectral unmixing. 相似文献
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Linear spectral mixture analysis (LSMA) is widely employed in impervious surface estimation, especially for estimating impervious surface abundance in medium spatial resolution images. However, it suffers from a difficulty in endmember selection due to within-class spectral variability and the variation in the number and the type of endmember classes contained from pixel to pixel, which may lead to over or under estimation of impervious surface. Stratification is considered as a promising process to address the problem. This paper presents a stratified spectral mixture analysis in spectral domain (Sp_SSMA) for impervious surface mapping. It categorizes the entire data into three groups based on the Combinational Build-up Index (CBI), the intensity component in the color space and the Normalized Difference Vegetation Index (NDVI) values. A suitable endmember model is developed for each group to accommodate the spectral variation from group to group. The unmixing into the associated subset (or full set) of endmembers in each group can make the unmixing adaptive to the types of endmember classes that each pixel actually contains. Results indicate that the Sp_SSMA method achieves a better performance than full-set-endmember SMA and prior-knowledge-based spectral mixture analysis (PKSMA) in terms of R, RMSE and SE. 相似文献
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Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models. 相似文献
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由于人口快速增长和农村人口向城市迁移,城市不透水面积也在持续快速增长。加速的城市扩张和无监控的城市开发会导致诸多生态环境问题。本文利用Landsat影像,采用附有限制条件的线性光谱混合分解、植被覆盖度与不透水面负相关模型、监督分类三种方法对南京城区的不透水表面分布进行空间分析。通过评估这三种方法提取的不透水面的精度和分析和种方法受其主要人为因素的影响大小以及不透水面的提取过程,表明了线性光谱混合分解方法较优。 相似文献
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Quantification of the urban composition is important in urban planning and management. Previous research has primarily focused on unmixing medium-spatial resolution multispectral imagery using spectral mixture analysis (SMA) in order to estimate the abundance of urban components. For this study an object-based multiple endmember spectral mixture analysis (MESMA) approach was applied to unmix the 30-m Earth Observing-1 (EO-1)/Hyperion hyperspectral imagery. The abundance of two physical urban components (vegetation and impervious surface) was estimated and mapped at multiple scales and two defined geographic zones. The estimation results were validated by a reference dataset generated from fine spatial resolution aerial photography. The object-based MESMA approach was compared with its corresponding pixel-based one, and EO-1/Hyperion hyperspectral data was compared with the simulated EO-1/Advanced Land Imager (ALI) multispectral data in the unmixing modeling. The pros and cons of the object-based MESMA were evaluated. The result illustrates that the object-based MESMA is promising for unmixing the medium-spatial resolution hyperspectral imagery to quantify the urban composition, and it is an attractive alternative to the traditional pixel-based mixture analysis for various applications. 相似文献
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基于混合光谱分解的城市不透水面分布估算 总被引：10，自引：0，他引：10
城市化的一个重要表现就是不透水面分布比率的上升,城市内部不透水面分布是城市生态环境的一个重要指标。对于规模较大的大城市,采用高性价比的中等分辨率影像,获取不透水面的分布,是当前国际研究的一个热点。本研究利用Landsat 7的ETM 影像,在线性光谱分解的技术上,提取了上海市的不透水面分布并对其空间特征进行了分析。研究揭示,ETM 影像对于城市尺度的信息提取,其成本是较低的;对于城市地域来说,利用植被、高反照度、低反照度和裸露的土壤四种最终光谱端元的线性组合,可以较好地模拟ETM 波谱特征,而除了水面以外的高反照度、低反照度两种最终光谱端元,可以较好地表达城市不透水表面信息。结果显示,利用中等分辨率影像对上海中心城区不透水面分布提取的精度还是令人满意的,总体上,上海市不透水面分布比率较高,不透水面分布的空间差异进一步揭示了城市土地覆被空间结构以及城市空间扩展的差异性。 相似文献
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利用雷达干涉数据进行城市不透水层百分比估算 总被引：2，自引：0，他引：2
人工不透水层是城市地区的重要特征.作为城市生态环境的关键指数,不透水层百分比(Impervious Surfaces Percentage, ISP)常用于城市水文过程模拟、水质面源污染及城市专题制图等研究中.本文利用ERS-1/2 重复轨道雷达干涉数据,采用分类与回归树(CART)算法探究了雷达遥感在城市ISP估算中的可行性和潜力,并与SPOT5 HRG光学遥感图像的估算结果进行了分析比较.香港九龙港岛实验区的初步研究结果表明,雷达干涉数据在城市不透水层研究中具有一定的应用潜力,特别是裸土和稀疏植被的ISP估算结果要好于光学遥感,这主要得益于雷达干涉数据(特别是长时间相干图像)在人工建筑物和裸土或稀疏植被之间具有很强的区分能力,另外,雷达干涉数据和光学遥感数据间的融合能够提高ISP估算精度. 相似文献
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利用Landsat ETM+分析城市热岛与下垫面的空间分布关系 总被引：3，自引：0，他引：3
采用数理统计与空间统计相结合的方法,利用Landsat ETM 数据对北京、上海、沈阳和武汉等4个大城市的夏季城市热岛相对强度与城市下垫面的空间分布关系进行对比研究。用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面;用城市地表亮温与水体亮温差值表征城市热岛相对强度。结果显示,4个城市的植被覆盖、不透水面与热岛强度的分布呈较强的空间正自相关,并且存在较为一致的自相关范围,该范围相当于城市街道与建筑组合特征尺度;自相关引起的结构性是导致3者空间分布异质性的主要因素。植被覆盖对城市热岛的缓解效果与不透水面对城市热岛的增强作用均呈分段线性特征,但区域差异较为明显;交叉相关系数曲线则显示出相关性的空间异质性与多尺度现象,同时存在一个约550 m的空间作用特征尺度。该研究结果有助于在城市规划实践中合理配置建筑与植被的间隔和比例,以缓解城市热岛效应。 相似文献
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Dengsheng Lu Emilio Moran Scott Hetrick 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(3):298-306
Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two-step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50%-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem. 相似文献