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。     

北京植被时空变化与气候因子相关性

基于北京市2000—2015年SPOT遥感影像归一化植被指数(NDVI),结合气温、降水量、日照时长等气候因子,采用一元线性回归分析法、趋势分析法、变异系数法及二阶偏相关系数法,剖析北京NDVI时空变化与气候因子的相关性:2000—2015年间,北京植被覆盖变化总体呈缓慢波动上升趋势;中心城区多为低植被覆盖区,郊区多为...     

中国区域蒸发潜力不均性发展趋势及其气候成因分析

气象台站20 cm蒸发皿观测资料自然正交分解显示,1980~2000年中国区域气温显著增加期间,长江中游至河套、东北等区域地表年蒸发潜力呈增加趋势;相反在长江以南、东部和西南等地区年蒸发潜力呈下降趋势.辐射观测资料分析结果表明,自20世纪70年代中国区域太阳入射能整体呈下降趋势,因此对于蒸发潜力增加的地区,太阳辐射产生的热力作用并不是决定蒸发潜力发展趋势的唯一原因.通过对大气风动力和干燥力等因子的分析证实,大气动力作用是造成中国区域地表蒸发潜力空间不均性分布的主要原因.同时1980~2000年NOAA-AVHRR遥感数据分析结果也表明,地表覆盖类型的变换以及植被覆盖率的下降,引发的地表热力作用和地表物理性质变化,是造成蒸发潜力空间分布不均性加大的另一项重要原因.     

基于决策树分类的大屯矿区地物信息提取及矿区污染分析

本文在对国内外遥感图像分类方法充分研究分析的基础上,选择决策树分类法对大屯矿区的Landsat 8遥感图像进行分类研究。选取样本提取并分析研究区典型地类光谱特征曲线,依据光谱曲线特征和归一化植被指数建立了土地利用分类决策树模型,通过反复试验和修正,筛选出适宜大屯矿区地物分类的决策树最优阈值,对研究区进行分类和精度评价,最后通过分类结果对研究区的水体污染状况进行简要分析。     

基于NDVI时间序列数据的江西省水稻种植制度变化研究

利用Savitzky-Golay滤波对覆盖江西省范围的SPOT VGT NDVI时间序列数据进行平滑处理的基础上,结合坡度数据,通过非监督分类的方法提取了江西省2000、2005和2010年水稻种植范围,并根据NDVI的年内动态变化,从水稻种植范围、水稻生长季起始时间、水稻复种指数和NDVI最大振幅等分析了江西省水稻种植和生长情况,探讨2000~2010年江西省水稻生产的变化。     

基于时序NDVI的昭觉植被覆盖度变化研究

归一化植被指数(NDVI)能精确地反映植被绿度、光合作用强度,在一定程度上反映着植被的演化信息,是评价生态环境状况的重要指标之一。本文利用Landsat TM数据分别对昭觉地区2009年和2014年的NDVI进行计算,并分别利用均值法、像元二分模型及NDVI差值植被指数对研究区域植被覆盖变化进行定量分析,结果表明:昭觉县NDVI均值上升了11.6%,植被覆盖度中极度改善的面积比例约占38%,昭觉县整体NDVI植被覆盖度显著提高,并对其变化原因进行简要分析,为生态环境建设提供决策依据。     

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.