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。     

关中平原城市群植被覆盖的时空特征与影响因素

植被是全球及区域生态系统环境变化的重要指标,也是对人类社会活动有重要贡献的资源之一。为了研究关中平原城市群不同区域植被覆盖变化对自然和人文因子的响应,以划分为三个区域的植被作为研究对象,选取2000—2017年MODIS-NDVI遥感数据,运用趋势分析、探索性空间数据分析与地理探测器等方法,从时序演进与空间分布方面研究了18 a内植被覆盖的演化及分布特征,定量分析影响植被覆盖的主导因子。遥感数据要通过投影转换、拼接、最大值合成等方法进行处理,再运用Python程序进行影像批量裁剪,将遥感数据和气象数据进行分区统计,最后对该处理数据进行讨论研究。结论表明：（1） 研究期内关中平原城市群植被覆盖呈显著上升趋势,NDVI平均值增速为0.077·（10 a）^–1,阶段性变化特征明显,其中2005—2007阶段和2011—2013阶段极显著增加,最大上升速率达到了0.05·a^–1。（2） 空间上总体呈现“南高北低”的分布特征,研究区总体得到改善;高值区主要分布在南部秦岭北坡,受气候因子的影响更大,植被覆盖增加速度缓慢,达到轻度改善水平;低值区聚集在黄土高原边缘地区,植被增加趋势明显;中部关中平原极少部分地区植被覆盖出现了轻度退化或严重退化,以西安市及临近城市最为典型。（3） 热点区主要分布在秦岭山区及关中平原中部地区,冷点区则集中于黄土高原边缘地区,植被覆盖总体以增长为主。热点区格网数量持续增多,2013年达到最大为45.07%;冷点区域数量不断减少,2017年减少到9.82%;次热点区与次冷点区主要分布在中部平原地带及北部地区,由连片分布转化为零散分布,且总量不断减少。（4） 自然因素对植被覆盖的影响最为突出,其中气温和降水为影响植被覆盖的主导因子,决定力q值分别为0.955和0.931,且气温的影响大于降水的影响;人文因子影响力较为显著,GDP因子决定力q值达到0.387。研究可为当地改善植被覆盖环境提供理论依据。     

基于 MODIS 数据的吐鲁番盆地 2001—2017 年 植被变化及水热组合影响分析

中国西北干旱地区的气候变化及其对植被的影响一直备受关注。以地形特殊的吐鲁番盆 地为研究对象,利用实测气象站点数据、再分析气象格点数据以及 MODIS 卫星遥感植被指数,采用 趋势检验、线性回归、偏相关分析等方法,探究了该地区 2001—2017 年间的植被变化及其与水热组 合特征之间的关系。结果表明：（1）吐鲁番盆地降水量整体没有显著变化,但北部山区降水增长较 多,气温总体呈显著上升趋势,尤其是盆地底部中心区域增温较大。（2）全区域植被指数（NDVI）总 体呈极显著上升趋势,山区及中心区域 NDVI 增长率较大。（3）受水汽来源和日照时长的影响,吐鲁 番盆地周边山区高程 3 000 m 左右 NDVI 值最高,山区植被最好的坡向是西北坡。（4）吐鲁番盆地水 热组合复杂,水分条件是大部分地区植被生长的主要限制因素,降水与 NDVI 有较好的正相关,在 山区和荒漠区热量增加不利于植被生长,但中心区域受地下水和人类活动影响,水分的供给相对 稳定,热量增加利于植被生长。     

Swelter in the city: Urban greenery and its effects on temperature in Guwahati,India

Changing urban landscape with multistoried high rises, roads and pavements is continuously reducing urban green space. These structures result in high surface temperature variation within cities. To explore the relationship between surface temperature and normalized difference vegetation index (NDVI), this study estimates two models—geographically weighted regression (GWR) and a fixed effect panel data model in relation to the Guwahati Metropolitan Area (GMA), a secondary city in north east India. The results indicate the superiority of GWR regression in presence of spatial dependence. Panel data analysis shows that the densely populated urban areas in the GMA with less than 10 per cent greenery are 1°C warmer than the sub-urban areas with 50 per cent greenery.