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1.
Landsat系列卫星对地观测40年回顾及LDCM前瞻 总被引：7，自引：0，他引：7
Landsat系列卫星数据凭借其长期连续、全球覆盖、适中的时间空间分辨率和科学的数据存档与分发策略等优势,逐渐成为地表特征和地球系统科学研究中最有效的遥感数据之一,并广泛应用于生态环境、农林地矿、能源资源、教育科研和政府管理等领域。而第8代陆地卫星--陆地卫星数据连续任务卫星(LDCM)于2013年2月发射升空,该卫星携带了运行性陆地成像仪(OLI)和热红外传感器(TIRS)两种传感器。与Landsat 7/ETM+相比,OLI/TIRS在波段设置、辐射分辨性能和扫描方式上都得到很大改进,其中OLI共包括9个波段,新增海岸带(coastal)监测和卷云(cirrus)识别波段,TIRS则设置了两个热红外波段。如果LDCM能够成功升空运行,它将继续承担起长期连续对地观测的使命。 相似文献
2.
Rei Sonobe Yuki Yamaya Hiroshi Tani Xiufeng Wang Nobuyuki Kobayashi Kan-ichiro Mochizuki 《国际地球制图》2019,34(8):839-855
Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification. 相似文献
3.
地形校正可以减小地形起伏对地物光谱的影响,提高计算机分类在山区的精度。设计了针对全球土地覆盖分类的Landsat TM/ETM+数据地形校正方法 SCOS(Smoothed COS余弦),首先对地形的坡度角进行抹平处理,很大程度上削弱了地表非朗伯性对地形校正的影响,然后利用简单有效的余弦校正去除地形效应。该方法与其他常用地形校正算法的对比分析是通过对全球不同区域、不同地表覆盖的有代表性的6景Landsat TM/ETM+数据的试验,采用统计分析与目视判读的方式,从过度校正和类内均一性两个方面进行的。结果表明,该方法在目视效果和统计结果上优于常规方法,并且更加简单有效,无需复杂的大气参数及传感器参数,满足全球地表覆盖分类对地形校正的需求。 相似文献
4.
Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge. 相似文献
5.
老挝是一个发展中国家,境内的大多数地方没有开展过土地利用/土地覆盖调查。本文选择老挝琅勃拉邦省的Phonxay区为研究区域,利用Landsat OLI数据进行土地利用/土地覆盖遥感调查与分析。研究过程中,利用ArcGIS Desktop选择训练样本和验证样本,通过Python和ArcPy编程开发了图像分类、精度评价以及面积统计的工作流程序,实现了快速得到分类结果和精度评价信息,分类结果的总精度为89.53%,Kappa系数为0.81。 相似文献
6.
Coffee is a commodity of international trade significance, and its value chain can benefit from age-specific thematic maps. This study aimed to assess the potential of Landsat 8 OLI to develop these maps. Using field-collected samples with the random forest classifier, splitting coffee into three age classes (Scheme A) was compared with running the classification with one compound coffee class (Scheme B). Higher overall classification accuracy was obtained in Scheme B (90.3% for OLI and 86.8% for ETM+) than in Scheme A (86.2% for OLI and 81.0% for ETM+). The NIR band of OLI was the most important band in intra-class discrimination of coffee. Landsat 8 OLI mapped area closely matched farm records (R2?=?0.88) compared to that of Landsat 7 ETM+ (R2?=?0.78). It was concluded that Landsat 8 OLI data can be used to produce age-specific thematic maps in coffee production areas although disaggregating coffee classes reduces overall accuracy. 相似文献
7.
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
8.
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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10.
This study assesses the usefulness of Nigeriasat-1 satellite data for urban land cover analysis by comparing it with Landsat and SPOT data. The data-sets for Abuja were classified with pixel- and object-based methods. While the pixel-based method was classified with the spectral properties of the images, the object-based approach included an extra layer of land use cadastre data. The classification accuracy results for OBIA show that Landsat 7 ETM, Nigeriasat-1 SLIM and SPOT 5 HRG had overall accuracies of 92, 89 and 96%, respectively, while the classification accuracy for pixel-based classification were 88% for Landsat 7 ETM, 63% for Nigeriasat-1 SLIM and 89% for SPOT 5 HRG. The results indicate that given the right classification tools, the analysis of Nigeriasat-1 data can be compared with Landsat and SPOT data which are widely used for urban land use and land cover analysis. 相似文献
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12.
TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda. 相似文献
13.
Habes A. Ghrefat Philip C. Goodell 《International Journal of Applied Earth Observation and Geoinformation》2011
The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method. 相似文献
14.
"中缅油气管道"是我国陆上第三大能源通道,该项目的建设将改变其沿线周边的土地利用/覆盖现状,同时对沿线地区的自然环境和社会经济发展产生重要影响。本文选取中缅油气管道沿线的国内11个重要节点和缅甸境内4个重要节点作为研究区,以2012年和2015年2期Landsat7 ETM+和Landsat8 OLI影像数据作为数据源,利用决策树分类算法提取2012年和2015年2期中缅油气管道沿线重要节点的土地利用/覆被信息,分析2012年和2015年2个时期的中缅油气管道沿线15个重要节点土地利用/覆被的时空变化。研究结果表明：①15个节点中,除德宏芒市、保山隆阳区、缅甸若开邦外,其他11个节点的土地覆被变化均在20%左右;②15个节点中,最主要的土地覆被变化为植被和裸土的相互转换,其次为其他土地覆被类型向建筑的转换;③由于中缅油气管道项目的辐射作用,带动当地经济发展,改变当地的经济作物结构,因此造成大量的植被和裸土的相互转换,并造成建筑用地需求增加,出现大量的其他地表覆被类型向建筑的转换。 相似文献
15.
M. Heinl J. Walde G. Tappeiner U. Tappeiner 《International Journal of Applied Earth Observation and Geoinformation》2009
The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification. 相似文献
16.
综合比较Landsat 8 OLI遥感影像与地面同步水质监测结果,发现Landsat 8的近红外波段与其他波段的组合和水体浊度具有较高的相关性,以此为基础运用OLI的第1、3、5波段组合建立了汉江中下游浊度的遥感反演数学模型。根据该模型生成了2013年4~11月共3幅汉江中下游浊度分布图,并进行了空间分析。精度验证表明,模型相对误差在15%左右,R2=0.71,表明运用Landsat 8 OLI可有效地监测该区域水体浊度分布情况。 相似文献
17.
Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = −10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor. 相似文献
18.
Land cover mapping forms a reference base for resource managers in their decision-making processes to guide rural/urban growth and management of natural resources. The aim of this study was to map land cover dynamics within the Upper Shire River catchment, Malawi. The article promotes innovation of automated land cover mapping based on remote sensing information to generate data products that are both appropriate to, and usable within different scientific applications in developing countries such as Malawi. To determine land cover dynamics, 1989 and 2002 Landsat images were used. Image bands were combined in transformations and indices with physical meaning; together with spatial data, to enhance classification accuracy. A maximum likelihood classification for each image was computed for identification of land cover variables. The results showed that the combination of spatial and digital data enhanced classification accuracy and the ability to categorise land cover features, which are relatively inhomogeneous. 相似文献
19.
Acquisition probability differences in cloud coverage of the available Landsat observations over mainland Southeast Asia from 1986 to 2015 总被引：1，自引：0，他引：1
Landsat data are the longest available records that consistently document global change. However, the extent and degree of cloud coverage typically determine its usability, especially in the tropics. In this study, scene-based metadata from the U.S. Geological Survey Landsat inventories, ten-day, monthly, seasonal, and annual acquisition probabilities (AP) of targeted images at various cloud coverage thresholds (10% to 100%) were statistically analyzed using available Landsat TM, ETM+, and OLI observations over mainland Southeast Asia (MSEA) from 1986 to 2015. Four significant results were found. First, the cumulative average acquisition probability of available Landsat observations over MSEA at the 30% cloud cover (CC) threshold was approximately 41.05%. Second, monthly and ten-day level probability statistics for the 30% CC threshold coincide with the temporal distribution of the dry and rainy seasons. This demonstrates that Landsat images acquired during the dry season satisfy the requirements needed for land cover monitoring. Third, differences in acquisition probabilities at the 30% CC threshold are different between the western and eastern regions of MSEA. Finally, the ability of TM, ETM+, and OLI to acquire high-quality imagery has gradually enhanced over time, especially during the dry season, along with consequently larger probabilities at lower CC thresholds. 相似文献
20.
The operational land imager (OLI) is the latest instrument in the Landsat series of satellite imagery, which officially began normal operations on 30 May 2013. The OLI includes two bands that are not on the thematic mapper series of sensors aboard Landsat-5 and 7; a cirrus band and a coastal/aerosol band. This paper compares the classification and regression tree and the kernel-based extreme learning machine (KELM) for mapping crops in Hokkaido, Japan, using OLI data, except the cirrus band and the pan band. The OLI data acquired on 8 July 2013 was used for crop classification of beans, beets, grassland, maize, potatoes and winter wheat. The KELM algorithm performed better in this study and achieved overall accuracies of 90.1%. According to the Jeffries–Matusita (J–M) distances, the short wavelength infrared band provides the greater contribution (the highest value was observed for band 6 in OLI data). 相似文献