基于 DTW 距离的时序 NDVI 数据植被信息提取--以秦巴山区为例

秦巴山区是我国重要的生态屏障,对该区的植被信息提取开展研究,可为区内生态服务功能及自然资源开发利用提供基础数据。通过加窗处理改进DTW距离相似性算法,结合临近度模糊分类方法对2005—2014年的MODIS NDVI时序数据进行植被信息提取。首先利用S-G滤波对MODIS NDVI时序数据进行重建;再利用2013年的采样数据构建各类植被的标准NDVI时序曲线,逐像元计算与标准NDVI时序曲线的加窗DTW距离,利用临近度模糊分类实现植被信息提取;最后验证提取精度。结果表明,算法具有较高的运行效率,可避免错误匹配,以较高的精度(总体精度83.8%,kappa系数0.77)实现长时间序列的植被信息提取。     

西南地区2001-2014年植被变化时空格局

时序植被动态变化研究一直是全球变化研究的热点之一,对地区生态治理有重要意义。基于西南地区2001至 2014年的MODIS植被指数数据集以及DEM数据和土地利用数据,进行季节合成植被指数（SINDVI）的趋势模拟、空间统计和相关分析,探讨西南地区植被变化趋势和空间分异特征,研究结果表明:（1）74.52%的区域SINDVI变化不显著,显著改善的区域占22.07%,而显著退化的区域占3.41%,改善面积远远大于退化面积。（2）从地形因子结果来看,中低海拔地区和缓坡地区植被变化趋势最明显,海拔3 500 m以下植被变化趋势比海拔3 500 m以上明显。随着坡度的增加,改善趋势和退化趋势都在变小。（3）从土地利用分析结果来看,SINDVI变化趋势在人工表面最明显,改善和退化趋势都相对较大。（4）受人类活动的影响,人工表面和裸地的增多、林地的减少是植被呈退化趋势的主要原因。      

A comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr^–1.     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands,USA

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R² values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.     

那曲地区植被NDVI时空变化及成因分析

利用GIS和遥感技术方法分析了2000—2014年那曲地区植被归一化指数(NDVI)的时空分布特征和变化趋势,探讨了NDVI与几种气象因子的关系。结果表明:空间上,研究区植被NDVI在空间上呈自西向东、自南向北逐步增大,高海拔地区小于低海拔地区的分布特点;时间上,近15a的NDVI总体上呈不显著性下降趋势,NDVI变化可以分为3个阶段,分别为2000—2005年较好,2006—2008年略差,2009—2014年好转。植被面积变化趋势表现为西北部植被处于稳定状态的面积居多,变化较明显的区域集中在中部和东南部地区的人口密集区,改善和退化区域呈现交错出现的特点。那曲地区植被变化的主要影响因素为降水量和热量因素引起的,人类活动在较短时间尺度上对植被也有较大影响。     

中国中东部区域TRMM降水产品降尺度研究及其时空特征分析

该研究以中国中东部区域(17°～50°N,98°～135°E)为研究范围,在前人研究基础上,根据水汽与降水之间的关系,基于MOD05水汽产品,采用偏最小二乘法,对中国中东部区域2001—2010年10 a平均TRMM3B43_V 7月降水产品进行降尺度,旨在得到空间分辨率为1 km×1 km的月降水空间分布。通过比较分析,发现该降尺度模型能大幅提高TRMM产品空间分辨率,估算结果平均相对误差为15.35%,与地面观测较接近,能体现中国中东部区域降水宏观分布趋势,且估算结果精度高于前人基于归一化植被指数(NDVI)的降尺度模型,能满足降水产品的精细化需求。     

黄河流域NDVI/土地利用对蒸散发时空变化的影响

基于蒸散发(ET)、归一化植被指数(NDVI)及土地利用数据利用M-K检验、Sen趋势分析等方法,研究2001—2015年黄河流域ET时空分布及不同植被覆盖/土地利用下的ET变化规律.结果 表明:(1)黄河流域年均ET呈东南高西北低的空间分布格局,与植被覆盖和土地利用的关系具有较好的一致性;(2)黄河流域ET、NDVI...     

西南地区近21年来NDVI变化特征分析

利用美国国家航天航空局（NASA）归一化植被指数（GIMMS NDVI）资料,初步分析了近21年（1982~2002）来西南地区植被变化特征。结果发现：21年来西南地区植被覆盖状况较好,总体呈增加趋势,同时也存在较明显的季节和区域差异：春季西南大部分地区植被呈较明显的增加趋势。夏季全区NDVI以显著的减小趋势为主,尤以90年代中期以后更为明显。秋季NDVI与夏季类似同样表现为减少趋势,并且范围有所增加。冬季ND-VI以增加为主,存在明显的东西反向特征,东部以减少为主,西部则以增加为主。     

基于NDVI城镇土地利用变化检测探讨

通过归一化植被指数（NDVI）结合影像差值,对经过辐射校正后的1993年和2005年武汉地区TM影像进行了土地利用变化检测,与监督法分类进行比较,得出NDVI更宜于实现变化信息探测和提取。