共查询到19条相似文献，搜索用时 140 毫秒
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水稻种植面积估计的运行化遥感方法 总被引：21，自引：2，他引：21
该文提出了用高分子辨率遥感数据提取水稻种值面积本底数据和用可靠低廉的ＮＯＡＡＡＶＨＲＲ数据估计水稻种植面积变化趋方法。该方法具有运行化的特点，成功地应用于１９９４年度湖北省早稻种植面积估算。 相似文献
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应用神经网络模型分解AVHRR混合像元 总被引：18，自引：3，他引：15
在大面积农作物遥感估产中,应用气象卫星数据估算种植面积一直是一个难点。已有的混合像元分解法要么实际操作困难,要么不适用于ＡＶＨＲＲ数据。该文在前人研究的基础上提出了一种新的方法──应用ＡＶＨＲＲ混合像元神经网络分解模型估算种植面积。这种方法综合利用了ＴＭ数据与ＮＯＡＡ数据各自的优势,具有较好的科学性和经济可行性。 相似文献
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基于GIS的中国东北植被综合分类研究 总被引：53，自引：3，他引：50
ＮＯＡＡ／ＡＶＨＲＲ由于运行周期短、覆盖范围大、成本低、波段宽等特点,目前正越来越广泛地受到人们的普遍关注。在大尺度、中尺度植被遥感上,ＮＯＡＡ／ＡＶＨＲＲ具有陆地卫星无法比拟的优势,但在另一方面,ＮＯＡＡＡＶＨＲＲ也存在分辨率低、数据变形较大和几何畸变较严重等问题。这样,在应用ＮＯＡＡＡＶＨＲＲ数据进行大区域植被制图时,植被分类的精度仍待提高。本文从理论上探讨了将地理信息系统提供的地理数据与遥感数据复合的可行性;尝试在ＧＩＳ环境下,将气温、降水、高程３个影响区域植被覆盖的主要指标,按一定的地面网格系统和数学模式进行量化,生成数字地学影像,并使之与经过优化、压缩处理的ＮＯＡＡＡＶＨＲＲ数据进行复合,对复合后的综合影像进行监督分类。分类结果显示,与传统的应用最大似然分类方法对单一遥感图像分类相比,该综合分类方法分类精度提高了１８．３％,该研究方法改变了遥感影像的单一信息结构;丰富了图像的信息含量;完成了地理数据的数字传输、处理、存储及影像化显示。 相似文献
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文文在对ＮＯＡＡ－ＡＶＨＲＲ像元畸变特点分析和ＩＢ格式中的定位数据质量评价基础上，确定ＩＢ格式中的定位数据。尽管绝对精度很差，但相对精度较好，可以用来进行像元的相对定位，确定像元的大小和形状，在此基础上，提出了ＮＯＡＡ－ＡＶＨＲＲ像元畸变校正方法。校正后的图像经多项式纠正后，多时相复合和与辅助数据相复合的效果很好，误差为半个像元级。 相似文献
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本文主要研究对异于ＮＯＡＡＡＶＨＲＲ１Ｂ数据集的ＮＯＡＡＡＶＨＲＲ１Ａ．５数据集的定位,提出了非地标导航的定位处理方法。并在大量实验的基础上指出,从原始数据图像到等经纬度投影图的纠正过程中不宜采用间接的多项式拟合法。 相似文献
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NOAA气象卫星AVHRR1A.5数据集的定位处理研究 总被引：6，自引：0，他引：6
本主要研究对民是于ＮＯＡＡＡＶＨＲＲ１Ｂ数据集的ＮＯＡＡＡＶＨＲＲ１Ａ．５数据集的定位,提出非地标导航的定位处理方法。 相似文献
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泰国水稻种植面积月变化的遥感监测 总被引：14，自引：0，他引：14
介绍了光学和微波遥感影像相结合进行泰国水稻种植面积监测方法。泰国雨季雨量充足 ,气温适合 ,同一时间耕地上水稻的物候多样 ,每月水稻种植面积都发生变化。利用旱季的TM影像 ,获得耕地信息。同时利用TM影像覆盖的雷达区域进行非耕地去除 ,进行非监督分类 ,提取反映水稻种植不同生长期的雷达影像后向反射系数特征 ,建立各种类型的分类模型 ,采用监督分类的方法对全景雷达数据进行水稻种植情况调查 ,并分别予以识别和统计 ,反映研究区水稻月种植情况。分类结果通过类别检验和面积量算检验进行精度评价和分析。 相似文献
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将基于典型相关分析的正交变换的基础理论用于ＮＯＡＡ／ＡＶＨＲＲ数据的多元变化检测,对具体实施步骤进行了深入的探讨,并对部分中间结果进行节分析。实验表明,该方法应用于ＮＯＡＡ／ＡＶＨＲＲ数据的多元变化检测具有明显的优势,克降了传统方法存在的缺陷,具有良好的的应用前景。 相似文献
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提出了利用雷达数据进行水稻估产的技术方法,并以ASAR数据为例,探讨了雷达数据在水稻估产中的可行性.首先利用ASAR数据进行水稻制图,从各时相ASAR数据中提取水稻后向散射系数.随后,基于像元尺度,采用同化方法,以LAI为结合点,将水稻作物模型ORYZA2000与半经验水稻后向散射模型结合,建立嵌套模型模拟水稻后向散射系数.选择水稻出苗期和播种密度为参数优化对象,利用全局优化算法SCE-UA对0RYZA2000模型重新初始化,使模拟的水稻后向散射系数值与实测值误差最小,并由优化后的ORYZA2000模型计算每个像元的水稻产量,生成水稻产量分布图.结果表明,水稻产量分布图能够描绘研究区水稻实际产量的分布趋势,但由于采用潜在生长条件模拟,模拟的水稻平均产量比实测平均值高约13%,验证点的水稻产量模拟值与实测值相对误差为11.2%.由于半经验水稻后向散射模型存在对LAI变化不够敏感和对水层的简化处理,增加了水稻估产的误差.但从总体上看,利用该方法进行区域水稻估产是可行的,并为多云多雨地区的水稻遥感监测提供了重要参考. 相似文献
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Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between RiceLandsat and RiceNLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region. 相似文献
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构建时空融合模型进行水稻遥感识别 总被引：1，自引：0，他引：1
传统变化检测手段进行水稻遥感识别受"云污染"和影像间配准误差导致的变化检测误差累积及"椒盐"现象的影响,水稻遥感识别精度低。本文提出时空融合模型(Temporal-Spatial-Fusion Model,TSFM)进行水稻遥感识别,旨在综合像元在时间、空间维度上的信息定义像元的水稻时空归属度,根据时空归属度划分阈值提取水稻。实验结果表明:在不同窗口尺度下,TSFM在整体和"云污染"区域对水稻提取均达到了较高精度。当窗口尺度为3×3时,水稻提取的用户精度、制图精度和总体精度分别达到93.4%、83.5%和87.9%。在不同窗口尺度下水稻提取的用户精度、制图精度、总体精度均高于分类后比较PCC(Post-Classification Comparison)和多数投票法(Majority Voting,MV);在"云污染"区域,水稻识别总体精度均在92.0%以上,水稻制图精度比PCC、MV分别至少提高了14.0%、7.6%。有效地解决了传统变化检测作物遥感识别存在的误差累积问题,在一定程度上避免了"云污染"和"椒盐"现象对识别结果的影响。另外,初步探讨了TSFM水稻提取精度与景观特征关系,发现在景观规整区域适宜采用较小的窗口,在破碎区域适宜采用较大的窗口。该方法的成功实施,为大范围开展秋粮作物遥感识别,消除"云"影响进行了前期实验探讨。 相似文献
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R. S. Ayyangar P. P. Nagaeshwara Rao K. R. Rao 《Journal of the Indian Society of Remote Sensing》1980,8(1):23-29
Possibility of utilizing the red and infrared spectral information for assessing status of vegetation cover and consequential crop phenological information are discussed. The experiment was conducted in a potential agricultural area around Mandya town of Karnataka State and airborne spectral information was obtained through modular multispectral scanner from a height of 1000 meters above the ground level. The spectral information of red (0.66–0.70 urn) and infrared (0.77–0.86 urn) bands was extracted with the aid of an interactive computer system : the multispectral data analysis system. Based on the spectral information, the data was analysed and interpreted with the support of ground information. Crop fields without vegetation were observed to have infrared/red ratio in the range of 0.70 to 0.97 and also it was possible to distinguish wet and dry paddy field. Crop fields covered with vegetation exhibited higher infrared/red ratio depending on the nature of crop growth. For instance, rice crop exhibited spectral ratio of 0.78 at the time of planting, 3.52 at the time of maximum vegetation growth and 2.04 during the maturation phase. In case of sugarcane crop, the increase and decrease in spectral ratio were gradual because of its longer duration. From infrared and red band information it was possible to distinguish crop species based on rate of change of vegetation cover which corresponded with the change in spectral ratios. The temporal information expressed in two dimensional space for red and infrared band also enabled clearly to distinguish between rice and sugarcane. 相似文献
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ABSTRACT Researchers, policy makers, and farmers currently rely on remote sensing technology to monitor crops. Although data processing methods can be different among different remote sensing methods, little work has been done on studying these differences. In order for potential users to have confidence in remote sensing products, an analysis of mapping accuracies and their associated uncertainties with different data processing methods is required. This study used the MOD09A1 and MYD09A1 products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, from which the Enhanced Vegetation Index (EVI) and the two-band EVI (EVI2) images were obtained. The objective of this study was to analyze the accuracy of different data processing combinations for multi-year rice area mapping. Sixteen combinations of EVI and EVI2 with two cloudy pixel removal methods (QA/BLUE) and four pixel replacement methods (MO/MY/MOY/MYO) were investigated over the Jiangsu Province of southeast China from 2006 to 2016. Different accuracy results were obtained with different data processing combinations for multi-year rice field mapping. Based on a comparison of the relative performance of different MODIS products and processing method combinations, EVI2_BLUE_MYO was proposed to be the optimal processing method, and was applied to forecasting the rice-planted area of 2017. Study results from 2006 to 2017 were validated against reference data and showed accuracies of rice area extraction of greater than 95%. The mean absolute error of transplanting, heading, and maturity dates were 11.55, 8.10, and 7.78 days, respectively. In 2017, two sample regions (A and B) were selected from places where rice fractional cover was greater than 75%. Rice area extraction accuracies of 85.0% (A) and 92.3% (B) were obtained. These results demonstrated the complementarity of MOD09A1 and MYD09A1 datasets in enhancing pixel spatial coverage and improving rice area mapping when atmospheric influences are significant. The optimal data processing combination indentified in this study is promising for accurate multi-year and large-area paddy rice information extraction and forecasting. 相似文献
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垦造水田每年至少种植一次水稻,是垦造水田监管的重点内容。为了实现低成本、高效率监测,本文基于高时序的Sentinel-1A影像水稻识别技术,在广东省垦造水田监测中开展应用,并结合外业实地拍摄照片进行结果验证。选取广东省2020年前验收的垦造水田项目中新增水田为监测对象进行试验。结果显示,早稻识别结果总体精度达85.02%,晚稻识别结果总体精度达90.46%,说明该方法用于判断水稻种植情况是可行的,可有效缩小外业核查范围,提高监测效率。 相似文献
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S. Panigrahy M. Chakraborty K. R. Manjunath N. Kundu J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2000,28(1):59-65
Radarsat ScanSAR Narrow (SN2) data acquired on July 24 and August 17, 1997 were used to analyse the signature of rice crop in West Bengal, India. The analysis showed that the lowland practice of cultivation gives a distinct signature to rice due to the initial water background. The relatively stable backscatter from water bodies in temporal data enhanced the separability of rice fields from water using two date data. Around 94 per cent classification accuracy was achieved for rice crop using two date data. It was feasible to discriminate rice sub-classes based on their planting period like early and late crop. The analysis indicates the suitability of ScanSAR data for large area rice crop monitoring as it has a wide swath of 300 km. 相似文献
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冠层特征尺度是植被定量遥感的基础概念,其物理定义和数学定量表达具有重要的研究意义。首先,基于光学辐射传输角度提出的冠层特征尺度的物理定义,即水平维线性混合条件下的最小分辨率单元,建立了冠层特征尺度的数学计算模型,并引入倒置的地统计学指数模型。然后,提出了基于局部方差分析的冠层特征尺度计算方法。最后,利用森林区域高分辨率图像,对论文提出的冠层特征尺度模型进行了定量验证。结果表明,冠层特征尺度模型计算的冠层特征尺度与树林株行距存在密切联系,线性复相关系数达0.95,证明了本文方法的合理性和可行性。本文提出的冠层特征尺度模型为地表特征尺度定量计算提供了一种新方法。 相似文献
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提出了一种基于Landsat TM的地表温度二次像元分解方法,将地表温度的空间分辨率从120 m提高到30 m。首先,利用地表类型的线性统计模型（E-DisTrad）获取初次分解子像元的地表温度,计算得到初次分解子像元的辐亮度;然后,利用面向对象的图像分割方法获取二次分解子像元的权重,实现对地表温度的二次分解;最后,采用升尺度再分解的验证方法进行精度分析,并选取了北京市TM影像进行实例分析。实验结果表明,二次像元分解模型不仅能有效地提高地表温度的空间分辨率,反映出不同地表类型地表温度的空间差异性,而且保证了像元分解前后能量值的一致性,非常适合于复杂地表覆盖地区的热红外波段遥感影像数据的降尺度处理。 相似文献