MODIS数据在乌鲁木齐地区植被景观动态监测中的应用

根据EOS／MODIS卫星时次高覆盖面广的特征，选取从4．10月的MODIS晴空资料，对整个乌鲁木齐地区植被景观进行动态监测，利用植被指数最大合成法制作了每月植被指数专题图。为了能够结合物候更好地分析乌鲁木齐地区植被随季节变化趋势以及不同时期植被生长状况，我们还制作了植被景观专题图及其变化评价图。评价图的优势在于能直观地反映植被变化的趋势，并能将变化的程度数字化，还可以根据不同的评价图叠加DEM做相应的分析。为乌鲁木齐地区植被变化研究提供有效的依据。     

Discrimination between climate and human-induced dryland degradation

In this study we present a technique to discriminate between climate or human-induced dryland degradation, based on evaluations of AVHRR NDVI data and rainfall data. Since dryland areas typically have high inter-annual rainfall variations and rainfall has a dominant role in determining vegetation growth, minor biomass trends imposed by human influences are difficult to verify. By performing many linear regression calculations between different periods of accumulated precipitation and the annual NDVI_max, we identify the rainfall period that is best related to the NDVI_max and by this the proportion of biomass triggered by rainfall. Positive or negative deviations in biomass from this relationship, expressed in the residuals, are interpreted as human-induced. We discuss several approaches that use either a temporally fixed NDVI peaking time or an absolute one, a best mean rainfall period for the entire drylands or the best rainfall period for each individual pixel. Advantages and disadvantages of either approach or one of its combinations for discriminating between climate and human-induced degradation are discussed. Depending on the particular land-use either method has advantages. To locate areas with a high likelihood of human-induced degradation we therefore recommend combining results from each approach.     

水土流失区马尾松林植被提取的土壤调节指数分析

为实现水土流失区植被遥感信息的准确提取,本文采用2007年ALOS 10 m多光谱影像,利用土壤调节植被指数SAVI和MSAVI,对福建长汀水土流失区马尾松林不同植被覆盖密度的3个实验区进行植被提取,并选用不同的土壤调节因子（L=0.25,0.5,0.75,1）做实验,将结果和以NDVI植被指数提取的结果进行对比,分析了提取效果及受土壤噪音的影响程度。实验表明,SAVI指数能提高水土流失区的植被提取精度。在中、低植被覆盖区,其提取的总精度比NDVI高出2%~7%,Kappa系数高出7%~18%;而土壤调节因子L的取值对植被信息的提取也呈现出一定的规律性,即：随着L从0向1递增,SAVI提取稀疏植被的能力上升而探测阴坡植被的能力下降。总体来看,对于低植被覆盖和中等植被覆盖地区,可分别用SAVI（L取0.75）和SAVI（L取0.5）来提取植被信息,对于高植被覆盖区,仍可直接用NDVI进行植被信息提取;研究发现MSAVI在植被信息提取中并不具有特别的优势。     

Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle

Recent studies in Amazonian tropical evergreen forests using the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) have highlighted the importance of considering the view-illumination geometry in satellite data analysis. However, contrary to the observed for evergreen forests, bidirectional effects have not been evaluated in Brazilian subtropical deciduous forests. In this study, we used MISR data to characterize the reflectance and vegetation index anisotropies in subtropical deciduous forest from south Brazil under large seasonal solar zenith angle (SZA) variation and decreasing leaf area index (LAI) from the summer to winter. MODIS data were used to observe seasonal changes in the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Topographic effects on their determination were inspected by dividing data from the summer to winter and projecting results over a digital elevation model (DEM). By using the PROSAIL, we investigated the relative contribution of LAI and SZA to vegetation indices (VI) of deciduous forest. We also simulated and compared the MISR NDVI and EVI response of subtropical deciduous and tropical evergreen forests as a function of the large seasonal SZA amplitude of 33°. Results showed that the MODIS-MISR NDVI and EVI presented higher values in the summer and lower ones in the winter with decreasing LAI and increasing SZA or greater amounts of canopy shadows viewed by the sensors. In the winter, NDVI reduced local topographic effects due to the red-near infrared (NIR) band normalization. However, the contrary was observed for the three-band EVI that enhanced local variations in shaded and sunlit surfaces due to its strong dependence on the NIR band response. The reflectance anisotropy of the MISR bands increased from the summer to winter and was stronger in the backscattering direction at large view zenith angles (VZA). EVI was much more anisotropic than NDVI and the anisotropy increased from the summer to winter. It also increased from the forward scatter to the backscattering direction with the predominance of sunlit canopy components viewed by MISR, especially at large VZA. Modeling PROSAIL results confirmed the stronger anisotropy of EVI than NDVI for the subtropical deciduous and tropical evergreen forests. PROSAIL showed that LAI and SZA are coupled factors to decrease seasonally the VIs of deciduous forest with the first one having greater importance than the latter. However, PROSAIL seasonal variations in VIs were much smaller than those observed with MODIS data probably because the effects of shadows in heterogeneous canopy structures or/and cast by emergent trees and from local topography were not modeled.     

MODIS NDVI和AVHRR NDVI 对草原植被变化监测差异

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。     

Vegetation cover variation in the Qilian Mountains and its response to climate change in 2000–2011

An understanding 0f variati0ns in vegetati0n c0ver in resp0nse t0 climate change is critical f0r predicting and managing future terrestrial ec0system dynamics. Because scientists anticipate that m0untain ec0systems will be m0re sensitive t0 future climate change c0mpared t0 0thers, 0ur 0bjectives were t0 investigate the impacts 0f climate change 0n variati0n in vegetati0n c0ver in the Qilian M0untains （QLM）, China, between 2000 and 2011. T0 acc0mplish this, we used linear regressi0n techniques 0n 250-m MODIS N0rmalized Difference Vegetati0n Index （NDVI） datasets and mete0r0l0gical rec0rds t0 determine spati0temp0ral variability in vegetati0n c0ver and climatic fact0rs （i.e. temperature and precipitati0n）. Our results sh0wed that temperatures and precipitati0n have increased in this regi0n during 0ur study peri0d. In additi0n, we f0und that gr0wing seas0n mean NDVI was mainly distributed in the vertical z0ne fr0m 2,700 m t0 3,600 m in elevati0n. In the study regi0n, we 0bserved significant p0sitive and negative trends in vegetati0n c0ver in 26.71% and 2.27% 0f the vegetated areas. C0rrelati0n analyses indicated that rising precipitati0n fr0m May t0 August was resp0nsible f0r increased vegetati0n c0ver in areas with p0sitive trends in gr0wing seas0n mean NDVI. H0wever, there was n0 similar significant c0rrelati0n between gr0wing seas0n mean NDVI and precipitati0n in regi0ns where vegetati0n c0ver declined thr0ugh0ut 0ur study peri0d. Using spatial statistics, we f0und that veeetati0n c0ver freauentlvdeclined in areas within the 2,500-3,100 m vertical z0ne, where it has steep sl0pe, and is 0n the sunny side 0f m0untains. Here, the p0sitive influences 0f increasing precipitati0n c0uld n0t 0ffset the drier c0nditi0ns that 0ccurred thr0ugh warming trends. In c0ntrast, in higher elevati0n z0nes （3,900-4,500 m） 0n the shaded side 0f the m0untains, rising temperatures and increasing precipitati0n impr0ved c0nditi0ns f0r vegetati0n gr0wth. Increased precipitati0n als0 facilitated vegetati0n gr0wth in areas experiencing warming trends at l0wer elevati0ns （2,000-2,400 m） and 0n l0wer sl0pes where water was m0re easily c0nserved. We suggest that spatial differences in variati0n in vegetati0n as the result 0f climate change depend 0n l0cal m0isture and thermal c0nditi0ns, which are mainly c0ntr0lled by t0p0graphy （e.g. elevati0n, aspect, and sl0pe）, and 0ther fact0rs, such as l0cal hydr0l0gy.     

中国比辐射率空间分布特征分析

使用2003-2013年MOD/MYD11C3地表比辐射率光谱数据、MOD/MYD13C2植被指数光谱数据,合成全国各月地表比辐射率、NDVI(Normalized Difference Vegetation Index)。基于DEM数据分析比辐射率与NDVI随海拔、坡向的变化规律。结果表明:(1)比辐射率低值段(0.960~0.970)主要分布在我国西北荒漠地区,面积比例全年变化不显著,代表了干燥裸土下低比辐射率的特征;中值段(0.970~0.975)分布于我国大部分植被覆盖地区,面积比例夏高冬低,代表植被覆盖下混合像元的中比辐射率特征;高值段(0.975~0.980)位于我国部分高海拔和高纬度地区,面积比例冬高夏低,代表冰雪与植被混合像元的高比辐射率特征。(2)比辐射率与NDVI随坡向变化呈明显的"双峰双谷"分布。东南坡、西坡为峰值,最大值位于东南坡;南坡、北坡为谷值,最小值位于北坡。两者变化一致性很高。受不同坡向太阳方位角下的地形敏感性与植被覆盖综合影响,比辐射率表现出随坡向的峰谷变化规律。(3)随海拔升高,比辐射率呈垂直地带性变化。存在3个下降区:250 m~1250 m、2500 m~3000 m和4750 m~6000 m;3个上升区:1250 m~2500 m、3000 m~4750 m和6000 m~6500 m。这与NDVI随海拔变化特征类似,反映垂直下垫面植被变化对比辐射率空间分布的影响。     

小花间流域旱情监测模型分析

利用2002年5月份的小花间流域的MOD IS数据提取归一化差值植被指数(NDVI)和陆地表面温度(LST),选取合适的NDVI提取窗口尺度,构建LST-NDVI特征空间,采取适宜的干边和湿边的斜率确定方法,计算温度植被干旱指数(TVDI),对小花间流域进行旱情监测,平均相对湿度进行定性验证。结果表明:采用多尺度的像元提取窗口,依据像元直方图确定干边和湿边,建立温度植被干旱指数(TVDI)进行小花间流域旱情监测能够较好反映当地旱情。基于旱情监测模型的生产需要,探讨旱情与地表温度以及归一化植被指数之间的关系,认为地表温度能够反映当地2002年5月份旱情,而归一化植被指数的作用较小。     

利用NOAA NDVI数据集监测冬小麦生育期的研究

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。     

热带山区高时空分辨率NDVI融合精度及其影响因素分析

高时空分辨率NDVI时序数据作为遥感应用中的重要数据源,对土地覆被动态变化监测具有重要意义,特别是在地表高程变化显著、气候条件复杂、景观异质性强烈的热带山区。虽然当前学者们提出了诸多时空数据融合模型,但针对这些模型在热带山区的NDVI数据融合精度及其影响因素分析尚不多见。对此,本文选取3类时空数据融合方法(权重函数法、概率统计法和多种混合法)中具有代表性的4个模型:STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)、RASTFM(Spatial and Temporal Adaptive Reflectance Fusion Model)、ESTARFM(Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model)、BSFM(Bayesian Spatiotemporal Fusion Model)(STARFM、ESTARFM为权重函数法;BSFM为概率统计法;RASTFM为多种混合法),选择位于我国热带山区的纳板河流域作为研究区。对融合模型的...