共查询到18条相似文献，搜索用时 156 毫秒
1.
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
2.
基于傅立叶变换的混合分类模型用于NDVI时序影像分析 总被引：4，自引：0，他引：4
应用2004年MODIS的时序NDVI数据，在分析湖北省不同地物类型的NDVI曲线季节性变化特征的基础上，设置对应的阈值，先后将水体、居民地与其他地物类型分离开。将去除了水体和居民地影响的剩余的NDVI序列影像傅立叶变换的1／12频率分量引入到地表覆盖分类的特征空间中，与其最大值影像和平均值影像组合，经过归一量化处理后合成一个类似具有三波段的卫星影像。在合成后的影像上利用最大似然法对其他地类进行分类。研究表明，引入傅立叶变换的特殊频率分量是分析多时相MODIS数据及提取地表植被覆盖信息的有效工具。 相似文献
3.
基于Landsat TM数据的北京城市热岛研究 总被引：21，自引：1，他引：21
利用Landsat TM热红外波段数据，根据辐射传输方程反演得到北京地区地表温度，讨论了北京城市热岛现象及其与土地覆盖类型和植被指数的关系。结果显示，北京城区地表温度明显比郊区地表温度高。通过地表温度对比分析发现，城区平均地表温度比近郊区和远郊区地表温度分别高出4．5℃和9℃，城市热岛效应明显。城区不同地表覆盖类型的地表温度也有显著差异，城市地表温度与NDVI具有明显的负相关关系，城市地表植被覆盖度低是城市热岛出现的主要原因。 相似文献
4.
NDVI-Ts空间全国土地覆盖分类方法研究 总被引：27，自引：1，他引：27
利用NDVI-Ts空间进行全国土地覆盖分类的方法。该方法利用1995年NOAA 10天合成的ch4、ch5通道亮温，先计算出陆地表面温度(Ts)，然后用最大值合成法计算每月的最大Ts和NDVI，以每月最大Ts和NDVI建立NDVI-Ts空间。根据像素点(NDVI，Ts)在空间中的位置矢量，求出矢量在空间中的方向角度，并作归一化处理，得到温度植被角度(NTVA)。对12个月NTVA做主成分变换提取前三个主分量，辅以全年总NDVI和大于O℃ Ts积温，用模糊K-均值法进行全国土地覆盖分类。研究结果表明，基于NDVI-Ts空间的NTVA与NDVI、Ts一起作为分类特征在土地覆盖分类中具有较高的分类精度，能够取得较好的分类效果。 相似文献
5.
为研究归一化植被指数(NDVI)和比值植被指数(RVI)的特点,选取德阳市旌阳区作为实验区,以2007年、2014年和2018年的Landsat影像为数据源,反演得到实验区NDVI和RVI的分布状况.结果表明,二者均能较客观地反映地表植被覆盖的整体情况;通过剖面分析发现,当NDVI值在0.5左右时,RVI反映的地表植被覆盖状况变化非常显著,当NDVI值在0.2左右时,部分RVI反映的地表植被覆盖状况与实际存在一定的差异,个别低植被覆盖区域RVI值在1附近. 相似文献
6.
MPDI在微波辐射计植被覆盖区土壤水分反演中的应用 总被引：5，自引：0，他引：5
大尺度上的土壤水分变化监测对于建立全球的水循环模型意义重大，是实现气候变化预测和洪涝监测的基础。星载辐射计为实现大尺度上土壤水分的监测提供了监测途径。但是在星载辐射计观测时，地表植被层的吸收和散射作用会对土壤向上的微波辐射产生衰减影响，这种影响在反演土壤水分的过程中必须予以计算和消除。原有的反演算法中，在计算这部分影响的时候，需要大量的关于地表植被状况的辅助数据，而这些即时的辅助数据往往不易获得。以AMSR—E数据为例，研究证明了微波极化差异指数（MPDI）能够反映地表植被覆盖状况。以中国华北、华东地区为实验区，选择2004年4月8日的AMSR—E亮温数据和MODIS数据为样本数据，建立起MPDI与NDVI之间的负指数关系方程。基于对NDVI的认识，得到植被覆盖度高、中、低三种状况所对应的MPDI域值，以此域值为依据对中等植被覆盖度地区作出自动判断，并用MPDI计算植被层不透明度。 相似文献
7.
综合主动和被动微波数据监测土壤水分变化 总被引：12，自引：1，他引：12
微波遥感测量土壤水分的方法主要分主动和被动两种，它们都是基于干燥土壤和水体之间介电常数的巨大差异。估算植被覆盖土壤表面土壤水分必须要考虑地表粗糙度和植被覆盖影响的问题。植被覆盖土壤表面的后向散射包括来自植被的体散射，来自地表的面散射和植被与地表间的交互作用散射项。本研究建立了一个半经验公式模型，用来计算体散射项，综合时间序列的主动和被动微波数据，消除植被覆盖的影响，估算地表土壤水分的变化状况。并应用1997年美国SGP‘97综合实验中的机载800m分辨辐射计ESTAR数据计算表面反射系数，综合Radarsat的SCAN-SAR数据得到体散射项，然后，由NOAA/AVHRR和TM计算得到的NDVI值加权分配50m分辨率的体散射项，最后计算50m分辨率的表面反射系数的变化值，从而得到土壤水分的变化情况，验证数据表明该计算结果与实测值一致。 相似文献
8.
融合多源遥感数据的高分辨率城市植被覆盖度估算 总被引：2，自引：0，他引：2
准确获取城市植被覆盖定量信息对城市生态环境评价,城市规划及可持续城市发展具有重要意义。遥感技术的发展为获取区域及全球植被覆盖信息提供了有效手段,目前基于单传感器、单时相遥感数据的城市植被覆盖度估算方法得到较为广泛的应用。然而,由于城市地表覆盖的复杂性、植被类型的多样性,在一定程度上影响了城市植被覆盖信息提取的精度。为此,本文提出一种基于多源遥感数据与时间混合分析的城市植被覆盖度估算方法。首先,通过时空融合、植被物候特征分析获得最佳时序的GF-1 NDVI数据;其次,基于时间序列的GF-1 NDVI及Landsat 8 SWIR1、SWIR2数据,采用时间混合分析方法以长沙市为例估算城市植被覆盖度。实验研究表明,基于多源遥感数据与时间混合分析方法获得了较高精度的城市植被覆盖度估算（RMSE为0.2485,SE为0.1377,MAE为0.1889）,相对于单时相光谱混合分析、传统的像元二分法,本文提出的方法更为稳定,在低、中、高不同植被覆盖区均能获得较高的估算精度,为城市植被覆盖度定量估算提供了有效方法。 相似文献
9.
为了明确辽东湾植被长势的时空变化特征,本文针对传统的遥感影像信息提取速度缓慢、效率较低等问题,基于奥维互动地图(Ovid interactive map, OMAP)平台,利用Landsat遥感影像提取1990—2019年辽东湾生长季NDVI数据,采用一元线性回归对NDVI时空变化特征进行讨论。结果表明：1)以2010年为转折点,近30年来,辽东湾地表植被NDVI呈现先增后减再增的变化趋势,植被覆盖总面积变化较小,但中高植被覆盖区(0.6相似文献
10.
为了解VIIRS NDVI与EVI两种植被指数的关系,对研究区两种植被指数的空间特征、地表特征可分性以及相关性进行了初步研究。结果表明,VIIRS NDVI与EVI的空间特征和地表特征可分性既有较强一致性,又有差异性,植被覆盖度越高,两种指数空间特征差异越大。针对不同地物类别,VIIRS NDVI与EVI对地物可分性的差异不同。与一次、二次多项式以及对数模型相比,三次多项式模型更能反映VIIRS NDVI与EVI的相关关系,复相关系数平均可达0.807 4,且植被覆盖度越低,关系特征越强。 相似文献
11.
In this paper, we apply lagged correlation analysis to study the effects of vegetation cover on the summer climate in different zones of China, using NOAA/AVHRR normalized difference vegetation index (NDVI) data during the time period from 1982 to 2001 and climate data of 365 meteorological stations across China (precipitation from 1982 to 2001 and temperature from 1982 to 1998). The results show that there are positive correlations between spring NDVI and summer climate (temperature and precipitation) in most zones of China; these suggest that, when the vegetation cover increases, the summer precipitation will increase, and the lagged correlations show a significant difference between zones. The stronger correlations between NDVI in previous season and summer climate occur in three zones (Mid-temperate zone, Warm-temperate zone and Plateau climate zone), and this implies that vegetation changes have more sensitive feedback effects on climate in the three zones in China. 相似文献
12.
Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue. 相似文献
13.
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
14.
In this paper, we apply lagged correlation analysis to study the effects of vegetation cover on the summer climate in different zones of China, using NOAA/AVHRR normalized difference vegetation index (NDVI) data during the time period from 1982 to 2001 and climate data of 365 meteorological stations across China (precipitation from 1982 to 2001 and temperature from 1982 to 1998). The results show that there are positive correlations between spring NDVI and summer climate (temperature and precipitation) in m... 相似文献
15.
Multitemporal NOAA/AVHRR NDVI images and monthly temperature and precipitation data were obtained across Yangtze River basin
covering the period 1981–2001. The spatial and temporal patterns of NDVI are the same, while spatial analysis shows that the
NDVI is influenced by the vegetation types growing in the study regions, and NDVI presents an increasing trend during the
study period in the whole basin. The climate indicators play an important role in the changes of vegetation cover in the river
basin. In the two Indicators, temperature has a significant effect on the NDVI values than precipitation in the whole basin.
However, in the 11 subbasins, the different rules are shown in different subbasins. 相似文献
16.
《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
17.
Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover. 相似文献
18.
利用近18年贵州茂兰自然保护区的Landsat TM/ETM+/OLI数据,针对云覆盖对影像质量的影响,提出并使用了一种基于NDVI时间变换一致性的方法,构建出较为完整的研究区植被指数时间序列,实现了小区域尺度下长时间序列的植被覆盖变化研究,并采用一元线性回归模型和相关分析法探讨研究区植被覆盖变化趋势及其对气象因子的响应关系。得出结论：NDVI时间变换一致性处理方法可以有效地消除云覆盖的影响;研究区近18年植被覆盖状况良好且正呈缓慢上升趋势,气候因子与植被覆盖变化呈显著正相关关系,其中平均温度的影响在当月最强,而降水量和平均相对湿度的影响则存在滞后性。 相似文献