基于线性混合光谱方法的时间序列植被覆盖度提取

为了准确地从遥感数据中获取城市植被覆盖度(VFC)信息,以线性混合光谱模型(LSMM)为原理基础,参考各城市端元模型,利用MODIS时间序列提取武汉研究区2007至2014年间植被覆盖度,获得植被覆盖度年际变化情况。在对遥感影像进行端元提取及确定时,结合四种城市端元模型分别对研究区植被覆盖度进行估算,并对像元分解精度作对比分析。结果表明,MODIS时间序列使用混合光谱分析法能够科学客观地实现对研究区植被覆盖度的提取,可以有效地进行周期性监测和分析研究区植被覆盖变化。     

基于MODIS植被指数的广西区植被覆盖度动态分析

许多研究表明植被覆盖度与土壤侵蚀之间有着密切的关系,同时,植被覆盖度是通用土壤流失方程（USLE）的一个重要参数。本研究利用MODIS遥感数据,基于归一化植被指数的像元二分模型原理,对广西植被覆盖进行遥感动态监测,分析了近十年来植被覆盖空间分布及变化规律。本研究成果可为区域生态建设和可持续发展提供科学依据。     

基于多源遥感影像的多尺度城市植被覆盖度估算

以Landsat 7 ETM+、SPOT 5和IKONOS遥感影像数据为数据源,利用格网法从1∶500地形图提取的不同空间分辨率的植被覆盖度为参考依据,通过对不同辐射校正水平的遥感影像获得的植被覆盖度进行精度比较分析,对多源多尺度和多源同尺度城市植被覆盖度估算的相关问题进行研究.研究表明,在城市区域进行植被覆盖度估算时,ICM模型为较佳辐射校正模型;对于高分辨遥感影像,NDVI为植被覆盖度估算的较佳植被指数;对于中分辨率影像,植被覆盖度估算的较佳植被指数则为RVI和MSAVI;就研究区而言GI模型比CR模型估算的植被覆盖度更准确.     

基于时间序列遥感数据的植被覆盖度反演研究

基于案例分析,阐述时间序列遥感数据的植被覆盖度反演,结合GIS和RS技术,采用像元二分模型提取估算植被覆盖度,通过数理统计方法得到植被覆盖率的变化规律,从而为研究城市生态环境变化状况提供参考,并对城郊地表环境的改善及建设提供参考依据。     

基于遥感影像的漓江流域植被覆盖度动态变化研究

本文以漓江流域作为研究区域,用1991、2006年的TM影像和2012年的HJ1A影像作为数据源,利用归一化植被指数(NDVI)像元二分模型估算出的漓江流域不同时期的植被覆盖度信息结果进行了变化监测研究,分析了变化原因.结果显示,1991~2012年间,漓江流域的植被覆盖度变化以稳定为主,稳定部分的面积占总面积的41.478%;植被覆盖度增加部分的面积占总面积的40.340%,减少部分的面积占总面积的18.182%,增加部分的面积大于减少部分的面积;说明1991年至2012年漓江流域的植被覆盖度整体上呈现上升的趋势,其原因是漓江流域内林区的保护以及植树造林的措施比较到位,而部分区域植被覆盖度的下降主要是受人为因素的影响,需要在今后的发展过程中,继续加大漓江流域的生态环境保护,尽量减少人为原因的破坏.     

基于遗传算法综合Terra/Aqua MODIS热红外数据反演地表组分温度

混合像元组分温度相对来说更有应用价值,而多角度热红外遥感的发展推动了混合像元组分温度反演基础和方法的发展.根据前期数值模拟得到Terra和Aqua卫星上的MODIS测量可以认为是同一卫星在两个不同观测时间和观测角度上的测量,综合利用Terra和Aqua卫星上的MODIS数据反演混合像元内土壤和植被组分温度.根据混合像元热红外辐射模型,利用遗传算法,分别模拟Terra卫星MODIS的32和33通道,以及Terra和Aqua卫星上MODIS的32通道辐射反演了河北怀来试验区范围内植被覆盖率、土壤组分温度和比辐射率、植被组分温度和比辐射率等表面参数.通过与实测数据进行比较,综合利用上午Terra和下午Aqua卫星32通道数据反演的上午植被组分温度与地面同步测量温度偏差在1℃内,而利用上午Terra卫星32和33通道数据反演的上午植被组分温度与地面同步测量值偏差在1.4℃内.尽管利用双星数据反演的组分温度精度相对较高,但针对同一个像元,两个方案反演的结果有一定偏差.     

基于植被供水指数的旱区土壤湿度反演方法研究

植被供水指数(VSWI)是进行干旱研究的有效指标,是进行区域土壤湿度反演的重要方法。利用MODIS数据,提取归一化植被指数(NDVI)、修正的土壤调整植被指数(MSAVI)、增强型植被指数(EVI)和地表温度(Ts)等参数,建立植被供水指数、基于MSAVI的植被供水指数(VSWI-M)、基于EVI的植被供水指数(VSWI-E),并对比三种指数反演土壤湿度的效果;在此基础上,建立分区域、基于NDVI阈值的混合植被供水指数(MVSWI)模型,利用20 cm土壤墒情实测数据对模型进行检验,RE,RMSE误差结果显示,MVSWI模型具有较好的精度,可以用来估算土壤湿度。     

基于像元二分模型的植被覆盖动态监测——以北京市密云、延庆、怀柔为例

以北京市密云、延庆、怀柔三地区为研究区域,利用该区域2001年及2010年的遥感影像对两年的植被覆盖度进行反演及对比。综合运用图像预处理、NDVI计算及像元二分法确定土壤与植被NDVI阈值等方法对植被覆盖度信息提取,最终比较不同年份不同植被覆盖度的面积百分比。     

利用遥感影像软件ENVI提取植被指数

在遥感影像处理中,植被指数已被广泛应用于定性和定量评价植被覆盖及其生长活力．本文主要介绍利用ENVI遥感图像处理软件对遥感影像进行植被指数提取4的方法。对植被指数提取的关键部分进行了分析,并给出了植被指数提取的技术关键．     

基于MODIS温度植被角度指数的农作物估产模型研究

利用MODIS数据,以河北石家庄和邢台地区冬小麦产量估算为例,探讨了综合植被指数与陆表温度的温度植被角度指数在农作物估产中的应用研究.首先,根据冬小麦物候历,计算了冬小麦抽穗期四种参量指数:归一化植被指数(NDVI)、增强型植被指数(EVI)、温度植被角度指数(TVA)和增强型温度植被角度指数(ETVA);其次,将实测的冬小麦产量数据与NDVI、EVI、VTA和EVTA数据进行回归分析,建立模型.结果表明,实测产量数据与这四种指数均具有很好的线性回归关系,相关系数R2均在0.60以上(分别为0.61、0.65、0.68、0.74),其中基于TVA和ETVA的估产模型要好于NDVI和EVI模型.由此可见,综合了MODIS光学反射和辐射信息的TVA/ETVA,能有效应用于实践估产中,并提高预测的准确性.     

Atmospherically resistant vegetation index (ARVI) for EOS-MODIS

An atmospherically resistant vegetation index (ARVI) is proposed and developed for remote sensing of vegetation from the Earth Observing System (EOS) MODIS sensor. The same index can be used for remote sensing from Landsat TM and the EOS-HIRIS sensor. The index takes advantage of the presence of the blue channel (0.47.±0.01 μm) in the MODIS sensor, in addition to the red (0.66±0.025 μm) and the near-IR (0.865±0.02 μm) channels that compose the present normalized difference vegetation index (NDVI). The resistance of the ARVI to atmospheric effects (in comparison to the NDVI) is accomplished by a self-correction process for the atmospheric effect on the red channel, using the difference in the radiance between the blue and the red channels to correct the radiance in the red channel. Simulations using radiative transfer computations on arithmetic and natural surface spectra, for various atmospheric conditions, show that ARVI has a similar dynamic range to the NDVI, but is, on average, four times less sensitive to atmospheric effects than the NDVI     

基于花期果树冠层光谱反射率的果树树种辨识研究

利用冠层光谱反射率数据(Rλ),对处于开花期的7种果树的树种进行了辨识研究.通过光谱数据重采样、植被指数求算等相关数据处理,比较了6种卫星传感器与4种植被指数对果树树种的辨识效能,并在优选数据形式、优化模型参数的基础上,建立了辨识果树树种的BP神经网络模型.主要结论为:(1)6种卫星传感器辨识果树树种的效能由强到弱的排列顺序为:MODIS、ETM+、QUICKBIRD、IKONOS、HRG、ASTER;(2)在4种植被指数中,RVI对果树树种的辨识效能最强,其次是NDVI,SAVI与DVI的辨识效能相对较弱;(3)用MODIS或ETM+传感器的近红外通道与蓝光通道上的反射率数据,求算的RVI与NDVI对果树树种的辨识效能相对较强;(4)在Rλ及其22种变换数据中,波长间隔设为9nm的d1[log(1/Rλ)],是建立BP神经网络模型的首选数据形式.     

Detection of interannual vegetation responses to climaticvariability using AVIRIS data in a coastal savanna in California

Ecosystem responses to interannual weather variability are large and superimposed over any long-term directional climatic responses making it difficult to assign causal relationships to vegetation change. Better understanding of ecosystem responses to interannual climatic variability is crucial to predicting long-term functioning and stability. Hyperspectral data have the potential to detect ecosystem responses that are undetected by broadband sensors and can be used to scale to coarser resolution global mapping sensors, e.g., advanced very high resolution radiometer (AVHRR) and MODIS. This research focused on detecting vegetation responses to interannual climate using the airborne visible-infrared imaging spectrometer (AVIRIS) data over a natural savanna in the Central Coast Range in California. Results of linear spectral mixture analysis and assessment of the model errors were compared for two AVIRIS images acquired in spring of a dry and a wet year. The results show that mean unmixed fractions for these vegetation types were not significantly different between years due to the high spatial variability within the landscape. However, significant community differences were found between years on a pixel basis, underlying the importance of site-specific analysis. Multitemporal hyperspectral coverage is necessary to understand vegetation dynamics     

The moderate resolution imaging spectrometer (MODIS) science anddata system requirements

The Moderate Resolution Imaging Spectrometer (MODIS) has been designated as a facility instrument on the first NASA polar orbiting platform as part of the Earth Observing System (Eos) and is scheduled for launch in the late 1990s. The near-global daily coverage of MODIS, combined with its continuous operation, broad spectral coverage, and relatively high spatial resolution, makes it central to the objectives of Eos. The development, implementation, production, and validation of the core MODIS data products define a set of functional, performance, and operational requirements on the data system that operate between the sensor measurements and the data products supplied to the user community. The science requirements guiding the processing of MODIS data are reviewed, and the aspects of an operations concept for the production of data products from MODIS for use by the scientific community are discussed     

中国东部典型城市群AOD时空演变及预测

为精准预测我国东部典型城市群的气溶胶光学厚度(AOD),基于2010-2019年MODIS数据,分析了京津冀、长三角、珠三角区域之间以及区域内部的AOD时空差异特征,构建了小波变换与BP神经网络相结合的AOD预测模型,并对典型城市群AOD进行了预测.研究结果表明:1)各城市群气溶胶浓度峰值均出现在夏季,京津冀地区AOD...     

Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI

Retrieval of land-surface temperature (LST) using data from the METEOSAT Second Generation-1 (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) requires adequate estimates of land-surface emissivity (LSE). In this context, LSE maps for SEVIRI channels IR3.9, IR8.7, IR10.8, and IR12.0 were developed based on the vegetation cover method. A broadband LSE map (3-14 /spl mu/m) was also developed for estimating longwave surface fluxes that may prove to be useful in both energy balance and climate modeling studies. LSE is estimated from conventional static land-cover classifications, LSE spectral data for each land cover, and fractional vegetation cover (FVC) information. Both International Geosphere-Biosphere Program (IGBP) Data and Information System (DIS) and Moderate Resolution Imaging Spectrometer (MODIS) MOD12Q1 land-cover products were used to build the LSE maps. Data on LSE were obtained from the Johns Hopkins University and Jet Propulsion Laboratory spectral libraries included in the Advanced Spaceborne Thermal Emission and Reflection Radiometer spectral library, as well as from the MODIS University of California-Santa Barbara spectral library. FVC data for each pixel were derived based on the normalized differential vegetation index. Depending on land cover, the LSE errors for channels IR3.9 and IR8.7 spatially vary from /spl plusmn/0.6% to /spl plusmn/24% and /spl plusmn/0.1% to /spl plusmn/33%, respectively, whereas the broadband spectrum errors lie between /spl plusmn/0.3% and /spl plusmn/7%. In the case of channels IR10.8 and IR12.0, 73% of the land surfaces within the MSG disk present relative errors less than /spl plusmn/1.5%, and almost all (26%) of the remaining areas have relative errors of /spl plusmn/2.0%. Developed LSE maps provide a first estimate of the ranges of LSE in SEVIRI channels for each surface type, and obtained results may be used to assess the sensitivity of algorithms where an a priori knowledge of LSE is required.     

The Moderate Resolution Imaging Spectroradiometer (MODIS): landremote sensing for global change research

The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation     

Hidden Markov models applied to vegetation dynamics analysis usingsatellite remote sensing

Details hidden Markov models (HMM) with respect to their ability to represent time series of remotely sensed data as well as to analyze vegetation dynamics at large scales. The present approach is shown to be a powerful way to classify and extract various dynamics parameters as well as to detect phenological anomalies. The methodology is applied and validated using the Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) time series. The model is then used to determine vegetation active cycle and the length of the growing season in the West African savanna     

Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy

The normalized difference vegetation index (NDVI) has been widely applied in optical remote sensing. However, it has been demonstrated that NDVI is still partially affected by atmospheric path scattering and bidirectional (illumination and viewing geometry) effects. In this paper we present the benefit of using a bidirectional NDVI, and we discuss the problems in using the maximum NDVI composite method. Based on the assumption that a clear day has a larger NDVI value and a cloudy day has a smaller NDVI value (smaller reflectance in the near-infrared band and larger reflectance in red band due to atmospheric path scattering), the ratio of squared observed NDVI values and calculated NDVI values is used as a weight in our inversion method. The calculated NDVI values are derived from previously inverted bidirectional reflectance distribution functions (BRDFs). The inversion process will loop until all weights converge. Our research on the early Terra/MODIS data using a semiempirical kernel-driven BRDF model (the RossThick-LiTransit model) shows that this new method can improve inversion results whenever some cloudy pixels are not filtered out. As cloud detection and subpixel cloudiness are always a problem, this technique should still be very useful in improving the quality of BRDF inversion.