共查询到20条相似文献，搜索用时 218 毫秒
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快速准确地从遥感影像提取冰川堰塞湖水体信息,是研究冰坝遥感监测与应急监测的核心热点问题。以GF-1卫星遥感影像为主要数据源,采用归一化差分水体指数(NDWI)、改进阴影水体指数(ENDWI)和面向对象(SVM)对克亚吉尔冰川堰塞湖水体进行定量提取。比较分析3种水体判识方法,3种方法均可以提取完整的水体边界,并且抑制了90%以上的非水体信息。NDWI法和ENDWI法可应用于GF-1地表水体提取,能够满足冰坝水体监测与应急监测需求,但面向对象法最适宜GF-1影像的水体信息准确提取。 相似文献
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利用卫星影像快速准确地提取湖泊等地表水体范围一直是一个重要的研究课题,其对洪涝灾害监测、水资源管理与利用等具有重要意义。Sentinel-2 MSI和Landsat8 OLI数据是目前主流的开放获取的中高空间分辨率遥感影像。以鄱阳湖区为研究对象,首先,分别使用归一化差异水体指数(normalized difference water index,NDWI)、改进的归一化差异水体指数(modified normalized difference water index,MNDWI)、自动水体提取指数(automated water extraction index,AWEIsh)和基于线性判别分析的水体指数(water index,WI2015)等4种常用的水体指数从2种影像中提取湖泊水体的分布信息;然后,分析了在同种水体指数之下2种影像提取结果的差异性和同一幅影像中4种水体指数提取结果的不同;最后,利用同期的高分一号影像目视解译的结果对水体提取结果进行了精度验证。结果表明,对于2种遥感影像,4种水体指数均能成功地提取出研究区的大部分水体; AWEIsh和WI2015的提取精度最高,在Sentinel-2和Landsat8影像上分别达到了98%和94%以上,MNDWI次之,NDWI的提取精度最低;相对而言,Sentinel-2影像提取的水体细部信息更为明显,整体提取效果优于Landsat8影像。 相似文献
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利用改进的归一化差异水体指数（MNDWI）提取水体信息的研究 总被引：238，自引：7，他引：238
在对M cfeeters提出的归一化差异水体指数(NDWI)分析的基础上,对构成该指数的波长组合进行了修改,提出了改进的归一化差异水体指数MNDWI(M odified NDWI),并分别将该指数在含不同水体类型的遥感影像进行了实验,大部分获得了比NDWI好的效果,特别是提取城镇范围内的水体。NDWI指数影像因往往混有城镇建筑用地信息而使得提取的水体范围和面积有所扩大。实验还发现MNDWI比NDWI更能够揭示水体微细特征,如悬浮沉积物的分布、水质的变化。另外,MNDWI可以很容易地区分阴影和水体,解决了水体提取中难于消除阴影的难题。 相似文献
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复杂环境下高分二号遥感影像的城市地表水体提取 总被引：1，自引：0，他引：1
水体指数可以抑制背景噪声和提高地表水体的可分性,已经广泛用于地表水体提取。传统FCM聚类算法考虑了地物的不确定性,但没有顾及地物的邻域空间信息,对背景异质性比较敏感。针对传统FCM聚类算法的不足,提出一种可变邻域的区域FCM聚类算法。由于复杂环境下高分二号(GF-2)遥感影像的城市地表水体具有复杂异质背景和不确定性的特点,本文利用水体指数和区域FCM聚类算法的优点,提出一种整合水体指数和区域FCM的城市地表水体自动提取算法,该算法主要步骤包括:(1)去除影像阴影后计算归一化差分水体指数NDWI(Normalized Difference Water Index);(2)区域FCM聚类算法;(3)整合水体指数和区域FCM聚类的城市地表水体自动提取算法。最后采用两景GF-2高分辨率遥感影像(广州和武汉)进行实验,验证了该算法的有效性,并与经典地表水体提取算法进行对比分析。实验结果表明:该算法具有较高的水体提取精度,城市地表水体边界既具有较好的区域完整性又保持了局部细节,同时对城市地表水体复杂背景噪声具有较好的抑制作用,有效减少传统FCM聚类算法的"胡椒盐"现象。 相似文献
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对于一个较小研究区提取地物信息,需要更高的空间分辨率和适合的光谱信息。以柬埔寨吴哥窟西北部为研究对象,开展基于SPOT-5影像的水体信息提取方法研究。通过分析研究区内的地物光谱特征信息,发现各地物在绿色波段和短波红外波段虽然都有下降趋势,但是水体的变化程度最大。利用这个信息建立决策树的一种水体提取模型:Band3/Band41.73并且Band1Band4。通过与NDWI法、决策树模型提取精度进行对比,证明该模型提取精度有较大提高,可有效地消除水田对提取精度的影响。 相似文献
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以辽宁省为研究区,本文基于GEE遥感云平台,使用Sentinel-2遥感影像,提出了一种多特征多层次的湖库水体提取算法。该算法选择自动水体指数(AWEIsh)和改进的归一化水体指数(MNDWI)提取水体,并利用归一化植被指数(NDVI)、归一化建筑指数(NDBI)、归一化差异红边指数(NDREI)、Sentinel-2的B8和B9波段及DEM数据多层次地消除暗地物和高亮地物噪声,对提取结果中被云雾遮挡而部分缺失的水体进行修复,最后将河流及细小像素剔除。利用此算法提取了辽宁省2017—2021年每年4、7、10月的湖库水体,并对比了不同水体提取算法及不同的水体数据产品。试验结果表明,本文算法在大尺度条件下提取水体具有良好的效果,总体精度达96%以上,可以较好地去除植被、阴影等暗像元表面,并且保证了水体信息的完整性,在大尺度水体提取方面具有一定的适用性和稳定性。 相似文献
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《地理信息系统科学与遥感》2013,50(2):280-298
The spectroradiometric retrieved reflectance of a local crop, namely, beans (Phaseolus vulgaris), is directly compared to the reflectance of Landsat 5TM and 7ETM+ atmospherically corrected and uncorrected satellite images. Also, vegetation indices from the same satellite images—atmospherically corrected and uncorrected—are compared with the corresponding vegetation indices produced from field measurements using a spectroradiometer. Vegetation Indices are vital in the estimation of crop evapotransiration under standard conditions (ETc) because they are used in stochastic or empirical models for describing crop canopy parameters such as the Leaf Area Index (LAI) or crop height. ETc is finally determined using the FAO Penman-Monteith method adapted to satellite data, and is used to examine the impact of atmospheric effects. Regarding the reflectance comparison, the main problem was observed in Band 4 of Landsat 5TM and 7ETM+, where the difference, for uncorrected images, was more than 20% and statistically significant. Results regarding ETc show that omission or ineffective atmospheric corrections in Landsat 5TM,/7ETM+ satellite images always results in a water deficit when estimating crop water demand. Diminished estimated crop water requirements can result in a reduction in output or, if critical, crop failure. The paper seeks to illustrate the importance of removing atmospheric effects from satellite images designated for hydrological purposes. 相似文献
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For three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index (LAI), and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMCFW), and fuel moisture content per dry weight (FMCDW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e., Landsat 8), including narrower near-infrared (NIR) wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops. 相似文献
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利用Landsat ETM+和ASTER近红外波段数据进行了水体信息提取,然后利用知识规则对2种提取结果进行进一步分类,并分析了波谱分辨率的差异对水体信息提取结果的影响。实验表明,基于Landsat ETM+数据的水体提取总体精度为82.4%,基于ASTER数据的水体信息提取结果总体精度为92.4%;在空间分辨率相同情况下,波谱分辨率的提高可以有效地提高水体信息提取的精度。 相似文献
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绿洲—荒漠交错带地下水位分布的遥感模型研究 总被引：16，自引：0，他引：16
以利用卫星遥感数据评价干旱区绿洲－荒漠交错带地下水位的分布作为主要研究目的，使用波段Landsat-7ETM 图像，用遥感－数学－模型学融合的研究方法，在实地考察地下水位，土壤水分和其他辅助资料的基础上，建立土壤水分和地下水位的实验方程，提出了评价地下水位分布的遥感模型－GLDRS模型。利用GLDRS模型对新疆策勒绿洲－荒漠交错带进行了实地验证，结果表明，研究结果符合实际，GLDRS多波段模型优越单波段模型，理论地下水位和实测地下水位之间的相关系数为0．901。 相似文献
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Spectral analysis technique has been utilized to identify the Bauxite mineral occurrences in Panchpatmali, Orissa, India. Spectral processing of Landsat ETM+ data has been carried out by converting the digital data from quantized and calibrated values to reflectance values. Minimum noise fraction transformation is used to determine the inherent dimensionality of reflected Landsat ETM+ data, to segregate noise in the data, and to reduce the computational requirements for subsequent processing and interactively to locate pure pixels within the data-set, projecting n-dimensional scatterplots. Spectral processing results are displayed in the form of images corresponding to each group of pixels (endmembers). Mixed tune matched filtering method has been applied on Landsat ETM+ images which gave three score (abundance) images for three different classes (endmembers) such as Bauxite, vegetation and soil. Further, mineralized zones are identified using image fusion of ERS-2 SAR and Landsat ETM+ data using intensity-hue-saturation technique. 相似文献
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Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data. 相似文献
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Cyanobacterial bloom is a growing environmental problem in inland waters. In this study, we propose a method for monitoring levels of cyanobacterial blooms from Landsat/ETM+ images. The visual cyanobacteria index (VCI) is a simple index for in-situ visual interpretation of cyanobacterial blooms levels, by classifying them into six categories based on aggregation (e.g., subsurface blooms, surface scum). The floating algae index (FAI) and remote sensing reflectance in the red wavelength domain, which can be obtained from Landsat/ETM+ images, were related to the VCI for estimating cyanobacteria bloom levels from the Landsat/ETM+ images. Nine field campaigns were carried out at Lakes Nishiura and Kitaura (Lake Kasumigaura group), Japan, from June to August 2012. We also collected reflectance spectra at 20 stations for different VCI levels on August 3, 2012. The reflectance spectra were recalculated in correspondence to each ETM+ band, and used to calculate the FAI. The FAI values were then used to determine thresholds for classifying cyanobacterial blooms into different VCI levels. These FAI thresholds were validated using three Landsat/ETM+ images. Results showed that FAI values differed significantly at the respective VCI levels except between levels 1 and 2 (subsurface blooms) and levels 5 and 6 (surface scum and hyperscum). This indicated that the FAI was able to detect the high level of cyanobacteria that forms surface scum. In contrast, the Landsat/ETM+ band 3 reflectance could be used as an alternative index for distinguishing surface scum and hyperscum. Application of the thresholds for VCI classifications to three Landsat/ETM+ images showed that the volume of cyanobacterial blooms can be effectively classified into the six VCI levels. 相似文献
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Husain A. Alanazi Habes A. Ghrefat 《Journal of the Indian Society of Remote Sensing》2013,41(4):833-844
The Qurayyah Sabkha is located on the western coast of the Arabian Gulf in the Eastern Province of Saudi Arabia. The current study aims to determine the number of land cover endmembers that can be detected and mapped using Landsat 7 ETM + and ASTER. Furthermore, the study also aims to determine the spatial distribution of fractional abundances of these endmembers. Clastic sediments, calcite dominate sediments, gypsum, vegetation, water, and quartz sand were identified at the surface the Qurayyah Sabkha using Minimum Noise fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. Results from Matched Filtering (MF) and Linear Spectral Unmixing (LSU) methods showed good match and revealed that the spatial distributions of gypsum, clastic sediments, and quartz sand have nearly similar pattern as determined from Landsat 7 ETM + and ASTER data. These results also show good correspondence between spectra of sample and image. The present results also revealed good matching between the results obtained from MF, LSU, spectral analyses, and X-ray diffraction (XRD). 相似文献
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《地理信息系统科学与遥感》2013,50(2):212-231
A fine-resolution leaf area index (LAI) data set over a 150 km × 150 km region in central Kazakhstan is retrieved using Landsat ETM+ imagery and ground-based LAI inferred from hemispherical photography and direct measurements. Regression analysis and geostatistics are applied for developing empirical models of LAI from Landsat ETM+ data. The best accuracy is achieved using a model employing a canonical index that combines all the contributions of individual Landsat ETM+ bands into a single index (R 2 = 0.67; RMSE = 0.21). This model is then applied for mapping LAI at a regional scale. 相似文献