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1.
基于波段选择的MODIS全国土地覆盖分类 总被引：1，自引：0，他引：1
以MODIS多光谱和多时相数据为输入参数进行了全国土地覆盖分类研究。从试验区2007年MODIS 8 d数据的合成影像(MOD 09)中提取EVI、NDWI和NDSI 3个指数,并将其作为特征波段与原有的7波段(B1～B7)形成10波段影像。以统计分类J-M距离平均值和SVM分类总精度为标准评价不同波段对土地覆盖分类的贡献。在全国范围内,选择贡献最大的EVI、B7和B4这3个波段的月合成值,并分别对其作PCA变换,选取各PCA变换后的前3个波段进行分类运算。研究结果表明,在没有其他辅助信息的境况下,基于MODIS贡献最大的前3个波段结合多时相信息能够在中分辨率区域土地覆盖分类中取得较好的分类结果,其精度为78.04%。 相似文献
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本文利用2002年陕西省ETM卫星影像及相关植被地理信息,在其影像上选取覆盖全省包含不同地物类型的85个地面样地(Ground Truth),通过2003年陕西省MODIS卫星影像获取所选样地全年的NDVI时间序列曲线。采用决策树分类方法,结合NDVI时间序列曲线,实现基于时相和波谱信息的植被分类。最后通过混淆矩阵与Kappa系数等方法对分类结果进行正确率评价,结果表明,文中所给方法优于传统分类方法,所得结果与其他调查结果相一致。 相似文献
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在喀斯特分布区,基岩、植被、裸地等多种地表覆盖交错分布,地物覆盖高度异质,并且呈现出短周期规律性变化和长期动态趋势变化,单一时相的影像进行土地覆盖分类精度非常有限。针对这一问题,本文提出一种顾及物候特征的多时相遥感影像分类策略,利用具有高时间分辨率的MODIS NDVI时间序列产品作为数据源,选择BFAST(Breaks For Additive Seasonal and Trend)方法进行NDVI时间序列的物候分解,采用动态阈值法对时序分解的物候轨迹进行标记,最后将物候标记特征与原始光谱时序综合特征进行组合,选择支持向量机(SVM)分类器进行土地利用覆盖分类,并且对比了不同特征空间下的分类结果。以云南省壮族苗族自治州丘北县和砚山县为研究区进行分类实验,结果表明,BFAST模型可以有效地分解出NDVI时序中的关键物候特征,相比基于单纯光谱特征的分类,物候驱动的喀斯特断陷盆地区土地覆盖分类精度有明显的提升,在NDVI、光谱和物候组合特征空间下,土地覆盖分类精度最高,总体精度和Kappa系数分别为88.94%和0.8693,尤其在灌木林、有林地、石旮旯地与稀疏植被的区分中,SOS、POS和GSG等物候特征具有较强的可分性,表明物候特征在地物识别中的有效性。 相似文献
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《北京测绘》2019,(11)
为了分析辽阳地区的地表覆盖变化情况,本文选取辽阳矿区为研究区域,以2003年、2009年、2015年Landsat TM/OLI遥感影像及ASTER GDEMV2高程数据为基本数据源,提取NDVI、NDBI、MNDWI、高程值及单波段亮度遥感指数构建决策树分类模型,完成辽阳矿区地表覆盖的分类。对分类结果进行地表覆盖类型数量变化利用土地转移矩阵分析,并对驱动力因素进行研究。结果表明;决策树模型适用于辽阳矿区地表覆盖分类,分类精度较高。研究区域土地覆盖类型发生显著变化,主要表现为建设用地大幅增长,采矿用地大面积减少,植被、水体、裸地面积变化较小。人口增长、经济发展、矿区生态治理成为该矿区地表覆盖特征变化的主要驱动因素。 相似文献
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遥感数据反演高时空分辨率NDVI对监测植被动态变化过程具有重要意义,然而受天气影响,单颗卫星难以提供时间连续的高空间分辨率NDVI数据。以华北平原中东部为实验区,联合HJ-1 CCD数据和MODIS数据,对STARFM算法进行了改进,(1)考虑了不同地物对光谱响应的差异,为减少分类错误利用统计学上()对分类数据进行筛选,按照不同地物类型分别利用线性拟合方法修改光谱距离权重;(2)定义了预测半径,对HJ-1 CCD数据因外界影响而缺失的影像进行了预测。结果表明,与真实影像相比,预测结果呈现了较好的空间一致性,相关系数均达到了极显著相关,改进算法的预测精度要高于原算法。利用该方法将HJ-1 CCD NDVI的空间变化信息与MODIS NDVI时间变化信息有机结合重构了高时空分辨率NDVI序列,有效补充了HJ-1CCD NDVI的缺失数据集。 相似文献
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基于6S辐射传输模型,对连续6个时相的MODIS影像进行逐像元大气校正获取地表反射率,以此实现MODIS NDVI的时相归一化。归一化后,地物NDVI的时相趋势与校正前相比差异显著,能更加准确地描述地物随时间变化的规律,在浓密植被区域表现更为明显。开展对NDVI时相归一化的研究,对全球变化、作物物候监测等遥感时序分析相关应用具有着重要的意义和实用价值。 相似文献
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高光谱-LiDAR多级融合城区地表覆盖分类 总被引：3，自引：3，他引：0
城市地区地表覆盖分类在城市研究中是一个十分重要的方向。遥感作为获取地物物理属性的一种重要技术手段,已初步应用于分类研究中。然而,随着城镇化的不断推进,城市内部地物类型越来越复杂,单一的遥感影像已无法满足城区地表覆盖分类中高精度的要求。高光谱影像和LiDAR数据能够分别表征地物的光谱信息及高程而被广泛应用。因此,根据两者之间互补的优势,本文提出了基于高光谱影像和LiDAR数据多级融合的城区地表覆盖分类方法。首先对两幅影像分别进行特征提取,将提取到的光谱、空间及高程信息进行层叠实现特征级融合。对得到的特征影像的所有像素点进行分类,然后利用LiDAR点云数据提取的建筑物掩膜,对非建筑物部分进行分类,再次实现特征级融合,以此改善建筑物区域与非建筑物区域的混淆。然后将未使用掩膜得到的分类结果与利用掩膜得到的分类结果进行投票实现决策级融合。最后利用条件随机场模型对分类结果进行后处理操作,达到平滑图像去除噪声点的目的。 相似文献
8.
城郊地区地物类型较为复杂,利用单一传感器图像进行分类精度有限,而多传感器数据结合起来则可以"优势互补",有利于遥感影像的解译和分析。本文以武汉城郊某地块为实验对象,结合ALOS卫星三种传感器数据(包括全色、多光谱以及SAR影像)的信息,采用向对象的方法进行分类,以获取更高的分类精度。实验结果表明适当运用sar影像与光学影像结合起来进行分类,可以较好地区分出水体与阴影、水体与农作物等,对于道路、居民地等地物类型的分类也有所帮助。 相似文献
9.
土地利用/覆盖分类通常是利用地物的波谱反射特征进行监督或非监督分类,分类结果由于"同物异谱、异物同谱"现象的存在,往往分类精度不高。而植被指数和地表温度作为表征地表覆盖状况的生物物理参数,已成功用于宏观尺度的土地利用/覆盖分类,使得分类结果有所提高,而对于区域尺度的土地利用/覆盖分类却少见报道。本文充分利用TM数据的多光谱特征,从中提取了植被指数NDVI、地表温度Ts、温度植被角度TVA和温度植被距离TVD这四种分类特征进行监督分类,通过对7种组合方案(反射率波段组合、NDVI与反射率波段组合、Ts与反射率波段组合、NDVI与Ts和反射率波段组合、TVA与反射率波段组合、TVD与反射率波段组合、TVA与TVD和反射率波段组合)的分类结果进行比较,得出以下结论:①NDVI、Ts、NDVI和Ts、TVD作为分类特征参与到多波段地表反射率影像分类中,能够提高分类精度,而TVA、TVA和TVD的加入却没有改善分类结果;②总体分类精度受到训练样本与检验样本比例的影响。 相似文献
10.
基于Freeman散射熵和各向异性度的极化SAR影像分类算法研究 总被引：1，自引：0，他引：1
极化SAR影像中阴影、水体和裸露的耕地3种地物类型有非常相似的极化散射特性,常规基于非相干分解的分类方法难以将其有效地区分。对此,本文引入基于Freeman分解的散射熵Hf和各向异性度Af两个特征参数,并将其用于极化SAR影像分类。首先利用Hf和Af参数将阴影和水体提取出来,然后将其他地物按散射机制分为3大类,并对每一类再次利用Hf和Af参数进行细分,最后通过基于Wishart分布的聚类和迭代分类,得到最终的分类结果。通过利用Radarsat-2在河南登封获取的全极化SAR数据进行试验,表明该算法执行效率高,能够有效地区分阴影、水体和裸露的耕地,并且对其他地物类型也有很好的分类效果。 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
13.
《地理信息系统科学与遥感》2013,50(3):371-393
This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact. 相似文献
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Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved. 相似文献
16.
The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results. 相似文献
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An extensive land cover change was triggered by a series of typhoons, especially Typhoon Morakot in 2009 in Taiwan. The normalized difference vegetation index (NDVI) series from multiple satellite images were applied to monitor the change processes of land cover. This study applied spatiotemporal analysis tools, including empirical orthogonal functions (EOF), and multiple variograms in analyzing space–time NDVI data, and detected the effects of large chronological disturbances in the characteristics of land cover changes. Spatiotemporal analysis delineated the temporal patterns and spatial variability of NDVI caused by these large typhoons. Results showed that mean of NDVI decreased but spatial variablity of NDVI increased after typhoons in the study area. The EOF can clarify the major component of NDVI variations and identify the core area of the NDVI changes. Various approaches showed consistent results that Typhoon Morakot significantly lowered the NDVI in land cover change process. Furthermore, the spatiotemporal analysis is an effective monitoring tool, which advocates the use of the index for the quantification of land cover change and resilience. 相似文献
18.
MODIS NDVI和AVHRR NDVI 对草原植被变化监测差异 总被引：5，自引：0，他引：5
以草地作为研究载体,对比分析草原植被AVHRR NDVI和MODIS NDVI两种NDVI序列的年内、年际变化特征,讨论两种NDVI序列对降水量、平均气温和水汽压3种气候因子的响应差异,为合理选择NDVI序列对植被进行监测研究提供参考。结果表明：(1)两种NDVI序列所反映的草原植被年内变化趋势相似,但MODIS NDVI对各类草原的区分度优于AVHRR NDVI;(2)两种NDVI序列所反映的2000年—2003年草原植被年际变化差异明显。较之于MODIS NDVI,AVHRR NDVI变化趋势分类图表现出更强的植被改善趋势,植被改善面积在AVHRR NDVI变化趋势分类图中占94.25%,在MODIS NDVI中为83.33%;两种NDVI变化趋势分类图反映的植被变化趋势吻合度为52.88%。(3)两种NDVI序列与水汽压、降水量相关性差异显著。MODIS NDVI与各站点平均气温的相关系数均大于GIMMS NDVI;而MODIS NDVI与水汽压的相关系数83%(10个站点)小于GIMMS NDVI,与降水量的相关系数67%(8个站点)小于GIMMS NDVI。 相似文献
19.
基于获取的塔河流域2000~2014年历年4~10月间逐月MODIS植被指数产品,采用时间序列谐波分析法(HANTS)对最大值合成的逐月NDVI时间序列数据进行了重建,用趋势线分析法对塔河流域近15年生长季(4~10月)MODIS NDVI的时间变化进行计算,用一元线性回归趋势法计算得到了塔河流域近15年生长季(4~10月)NDVI变化趋势的空间分布。结合植被类型分布图对计算得到的实验结果进行了研究分析,总结了塔河流域多年植被覆盖的时空分布及其变化规律,成果可为塔河流域综合治理及生态环境评价提供依据。 相似文献
20.
Xia Zhang Rui Sun Bing Zhang Qingxi Tong 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):476-484
Moderate Resolution Imaging Spectroradiometer (MODIS) data have played an important role in global environmental and resource research. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results. In this research, the high temporal resolution of MODIS was employed to improve the accuracy of land cover classification of the North China Plain using MODIS_EVI time series from 2003. Harmonic Analysis of Time Series (HANTS) was performed on the MODIS_EVI image time series to reduce cloud and other noise effects. The improved MODIS_EVI time series was then classified into 100 clusters by the Iterative Self-Organizing Data Analysis Technique (ISODATA). To distinguish ambiguous land cover classes, a decision tree was built on five phenological features derived from EVI profiles, Land Surface Temperature (LST) and topographic slope. The overall accuracy of the final land cover map was 75.5%, indicating the promise of using MODIS EVI time series and decision trees for broad area land cover classification. 相似文献