基于SPOT NDVI的祁连山草地植被覆盖时变化趋势分析

基于RS、GIS技术和SPOT VGT-NDVI数据,运用累积平均法、均值法、趋势线分析法和影像差异法,分析了祁连山草地植被覆盖在时问和空间上的变化特征,并用Hurst指数动态预测祁连山草地植被覆盖未来变化趋势.结果表明:①1999-2007年祁连山草地植被NDVI呈缓慢增加趋势.典型草原和平原草地植被年均NDVIv和生长期NDVIg增加速率高于高寒草甸草地和沙漠草地.祁连山草地植被NDVI增加和减少的面积分别为69776 km2和15928 km2,植被NDVI增加的区域分布在冷龙岭、拉脊山、大通山、达坂山、青海南山、走廊南山、托来山、托来南山等地区,减少的区域分布在乌鞘岭、大通河、石羊河、黑河、北大河、疏勒河等河流河谷以及青海湖周边地区.②祁连山草地植被NDVI年内月和旬变化曲线均呈单峰型.③冷龙岭、宗务隆山、青海南山、镜铁山、拉脊山、乌鞘岭、大通河、黑河、北大河、疏勒河等河流河谷以及青海湖周边等地区未来草地植被覆盖有改善的趋势:走廊南山、托来山、托来南山、大通山以及湟水、石羊河等河流河谷地区未来草地植被覆盖有退化的趋势.沙漠草地、高寒草甸草地未来有改善的趋势;而典型草原和平原草地未来则有退化的趋势.     

北部湾海岸带植被覆盖时空动态特征及未来趋势

分析北部湾海岸带植被覆盖动态变化趋势,能为开展海岸带植被生态环境监测提供决策。以2000―2011年SPOT-VEGETATION逐旬NDVI数据为基础,采用MVC（最大值合成法）、标准差、线性趋势分析（SLOPE）和Hurst指数等数理统计方法对研究区植被覆盖时空变化特征及未来趋势进行定量分析。结果表明：1）研究区植被覆盖整体上处于变好的状态,在年尺度上呈现出“波动―明显改善”的趋势,且海岸带东岸与西岸的植被变化趋势快于丘陵地区;在季节尺度上NDVI的增长速率为：秋季＞夏季＞春季＞冬季;在月尺度上NDVI在6―11月植被生长迅速,而在1―4月则生长缓慢;2）NDVI均值的空间分布规律自东北―西南中心线呈现出“两头高、中心地带低”的趋势,且NDVI均值自西向东的变化规律为-0.026/1N°,具有经向地带性特点;3）NDVI的Hurst指数值为0.306 5~0.995 3,平均值为0.777 4,反持续性序列（15.78%）＜持续性序列（84.22%）,未来总体植被覆盖呈现出持续性改善趋势。未来需要重点进行保护的植被区域主要集中在十万大山的西南部、钦江流域的上游以及合浦县的西南部。     

基于改进的地表温度-植被覆盖特征空间估算地表蒸散

地表温度-植被指数特征空间被广泛应用与地表蒸散估算和土壤湿度监测,而利用散点图直接拟合干边会造成很大的不确定性。本研究采用改进的地表温度-植被覆盖特征空间进行地表通量的计算。该方法是基于能量平衡公式进行干边定位,从而减少干边确定过程中的不确定性。我们选取的17天的MODIS数据以及相应气象数据进行地表蒸散计算,并采用郑州的LAS观测站验证显热通量计算值,估算结果均方根误差（RMSE）、偏差（Bias）和相关系数平方（R^2）分别为44.06Wm^-2、36.99Wm^-2和0.71。总体来讲,通过能量平衡公式确定的理论干边相比通过散点图拟合的实测干边更合理。     

2000—2019年中国西北地区植被覆盖变化及其影响因子

中国西北地区土地荒漠化问题严重,生态环境脆弱。厘清该地区植被覆盖时空变化特征及影响因子,对生态环境保护具有重要意义。基于MOD13A3数据,通过最大值合成法处理获得2000—2019年归一化差值植被指数（Normalized Difference Vegetation Index,NDVI）时序数据,采用趋势分析、Hurst指数法及地理探测器对研究区植被覆盖的时空变化特征及影响因子进行分析。结果表明：（1）2000—2019年,研究区植被覆盖整体呈增长趋势,NDVI年增长速率为0.0027（P<0.05）,均值为0.252。空间分区年增长速率有差异,黄河流域片区（0.0062）>半干旱草原片区（0.0026）>内陆干旱片区（0.0018）。（2）研究区植被覆盖呈增长趋势的面积占55.77%,退化区域占3.76%,增长的土地利用类型以耕、林、草地为主。植被覆盖变化趋势具有持续性的区域面积占总面积的31.87%,其中持续性改善面积（17.04%）大于持续性退化面积（1.27%）,黄河流域片区增长情况及持续性增长情况最优。（3）影响植被覆盖空间分布的主要因子按影响力依次为降水、气温、日照、相对湿度,但对各分区的影响程度略有差异。黄河流域片区、内陆干旱片区空间分布受降水影响最大,半干旱草原区受日照影响最大。（4）研究区植被覆盖变化以自然因子与人类活动共同驱动为主,自然因子对植被生长的促进作用大于人类活动,且自然因子对植被覆盖变化的贡献率更高。本研究结果可为评估气候变化背景下西北地区生态环境变化提供参考。     

城市地域地表温度-植被覆盖定量关系分析--以深圳市为例

地表温度-植被覆盖间的关系一直是城市热岛研究的热点之一,两者均为描述生态系统特征的重要参数。本文利用深圳市2004年的ETM+影像,基于遥感技术提取相关的下垫面类型、地表温度和植被覆盖等信息,探讨不同下垫面类型对地表温度-植被覆盖关系的影响,并结合分形维度计算方法,比较不同分辨率下地表温度、植被覆盖及其相关关系的变化。研究结果表明,植被覆盖程度与地表温度之间存在明显的负相关,并且在不同的植被覆盖程度下,地表温度-植被覆盖关系呈现分段线性关系。下垫面类型及其组合主要通过植被覆盖的分布对地表温度产生影响。而在不同空间分辨率下(30m至960m),地表温度和植被覆盖的空间变异程度均表现为先升后降,在120m的分辨率下,两者的相关程度达到最高。结果证实区域植被覆盖状况可直接影响辐射、热动力以及土壤水分等多种地表特征,从而导致地表温度分异等。     

遥感影像中分区分类法及在新疆北部植被分类中的应用

对遥感影像提出了一种分区分类的思想，根据影像所包含的局部特征将整体影像化为几个局部的影像，然后根据局部影像的特点对图像进行分类,使得每一区域的种类数目相对于整体减少，种类的特点得以突出，分类更具有针对性,再加以高程、坡度等地貌信息，提高分类精度。该分类法在新疆北部的植被分类中得到了应用，从NOAA影像中提出的NDVI为数据源，根据该植被区域特点，将研究区的植被区域划分为四个区，即：新疆阿勒泰草原区、昭苏区、西准噶尔区和东天山区。利用GIS软件将整个研究区的NDVI指数图像化分为四块子图，根据各个区域植被的特点，采用不同的分类标准,对四块子图分类。在此之后，再利用GIS软件将所有分类子图合并为整个区域的分类图。结果表明，该类方法可以大大提高NDVI指数的植被分类精度。     

珠穆朗玛峰国家自然保护区南北坡植被覆盖变化

利用2000-2009年MODIS NDVI数据,基于每个像元的生长季NDVI峰值进行了像元水平的线性趋势分析,研究珠穆朗玛峰自然保护区南坡和北坡的植被覆盖的空间分布和变化特征.结果表明:①保护区内植被覆盖显著改善区域和轻微改善区域NDVI-Max的年平均增加率分别为3.06%和1.25%;显著退化区域和轻微退化区域NDVI-Max的年平均减少率分别为2.82%和1.09%a 22000-2009年,保护区南坡的植被覆盖整体上呈现上升趋势,22.59%的区域显著改善,19.05%的区域轻微改善,24.75%的区域保持稳定;北坡的植被覆盖整体上呈现下降趋势,19.5%的区域严重退化,24.43%的区域轻微退化,38.12%的区域保持稳定.③南坡有植被覆盖的8种土地利用类型中,山区旱地植被覆盖呈现退化趋势,其余土地利用类型都呈现上升趋势;北坡有植被覆盖的10种土地利用类型中,植被覆盖都呈现退化趋势.     

2000—2016年黄土高原不同土地覆盖类型植被NDVI时空变化

了解植被覆盖的时空变化对区域环境保护及生态环境建设具有重要意义。基于MOD13A1数据,辅以Sen+Mann-Kendall、变异系数、Hurst指数,通过分析2000—2016年间黄土高原NDVI年最大值(NDVI_ymax)和生长季均值(NDVI_gsmean)时空变化特征及趋势,以了解黄土高原实施退耕还林(草)等生态工程后的植被覆盖恢复情况。结果表明：① 2000—2016年植被NDVI_ymax和NDVI_gsmean呈现波动式增长趋势,增长率分别为0.0070/a(P<0.01)和0.0063/a(P<0.01),生态环境整体不断改善。② NDVI_ymax和NDVI_gsmean显示黄土高原植被覆盖呈增加趋势的面积远高于呈减少趋势的面积(93.42%和96.22%、6.58%和3.78%),植被覆盖状态正在不断改善。2种数据变化趋势下,不同土地覆盖类型表现略有差异,森林极显著增加趋势面积最大(73.02%和82.60%),其次为耕地(47.87%和67.43%),再次为裸地(47.03%和61.68%)。③ NDVI_gsmean的变异系数小于NDVI_ymax的变异系数,相对稳定区域面积比分别为63.31%与56.64%,2种数据分析下森林变异系数最小,植被稳定性最好。④ 从植被NDVI变化趋势与Hurst组合结果得出,NDVI_ymax未来呈现改善趋势面积占41.35%,退化趋势面积占58.65%;NDVI_gsmean呈现改善趋势面积占49.19%,退化趋势面积占50.81%。2种数据下,灌木地未来发展趋势最好,森林和耕地退化趋势面积超过了50%。研究人员应持续关注退化趋势地区的植被状态。     

基于NDVI的绿洲植被生态景观格局变化研究

使用经严格配准的同一季相 1990年和 1998年Landsat -TM和SPOT -HRV图像数据为基础底料 ,编制规一化植被指数 (NDVI)图 ,进而简化生成植被盖度图像。根据策勒绿洲特有的生态景观特征 ,利用两个图像所提供的各植被覆盖级的数量和空间密度分配状况 ,来评价图像所包括时段植被生态环境质量的变化 ,提出绿洲植被生态景观若干保护措施     

武汉市地表亮温与植被覆盖关系定量分析

利用2002年7月9日的ETM影像,提取武汉市的下垫面类型、地表亮温和植被覆盖度,探讨不同下垫面类型对地表亮温的影响;并采用分形维度计算方法, 研究代表样带的地表亮温和植被覆盖的分维值之间的相关性。结果表明:植被覆盖度越高,地表亮温越低。除水域外的下垫面类型中植被覆盖度与地表亮温存在显著的负相关,这种相关性高于地表亮温与NDVI之间的相关关系。对于样带的地表亮温和植被的分维值研究表明,亮温和植被覆盖度的分维值之间的相关系数高于亮温和NDVI的分维值之间的相关系数。     

Start of vegetation growing season on the Tibetan Plateau inferred from multiple methods based on GIMMS and SPOT NDVI data

In this study, we have used four methods to investigate the start of the growing season (SGS) on the Tibetan Plateau (TP) from 1982 to 2012, using Normalized Difference Vegetation Index (NDVI) data obtained from Global Inventory Modeling and Mapping Studies (GIMSS, 1982-2006) and SPOT VEGETATION (SPOT-VGT, 1999-2012). SGS values estimated using the four methods show similar spatial patterns along latitudinal or altitudinal gradients, but with significant variations in the SGS dates. The largest discrepancies are mainly found in the regions with the highest or the lowest vegetation coverage. Between 1982 and 1998, the SGS values derived from the four methods all display an advancing trend, however, according to the more recent SPOT VGT data (1999-2012), there is no continuously advancing trend of SGS on the TP. Analysis of the correlation between the SGS values derived from GIMMS and SPOT between 1999 and 2006 demonstrates consistency in the tendency with regard both to the data sources and to the four analysis methods used. Compared with other methods, the greatest consistency between the in situ data and the SGS values retrieved is obtained with Method 3 (Threshold of NDVI ratio). To avoid error, in a vast region with diverse vegetation types and physical environments, it is critical to know the seasonal change characteristics of the different vegetation types, particularly in areas with sparse grassland or evergreen forest.     

自然因子对四川植被NDVI变化的地理探测

许多研究已表明基于遥感的植被指数在地表过程和全球变化研究中具有重要作用,对认识植被变化的驱动因素具有重要意义,但自然因子对植被变化影响仍然难以量化。应用地理探测器模型,研究四川地区自然因子变化对植被分布的空间模式和植被变化的交互影响,并确定了促进植被生长的各主要自然因子最适宜特征。结果表明：① 2000-2015年,四川植被覆盖度状况良好,中高、高植被覆盖面积之和均超过94%;归一化植被指数（NDVI）转化表现为NDVI > 0.4以上区域转化明显,中高和高植被覆盖区面积分别呈显著下降和上升趋势;植被覆盖时空变化差异显著,植被覆盖较高区域位于四川盆地东北部、川西北高原地区,植被覆盖较低区域分布于四川盆地中部城市密集区域。② 土壤类型、高程和年均温度变化等因子较好地解释了植被状况的可变性。③ 自然因子对植被NDVI影响存在交互作用,自然因子协同效应呈现相互增强和非线性增强关系,两种因子交互作用增强了单因子的影响。④ 研究揭示的促进植被生长的各主要因子最适宜特征,有助于更好地理解自然因素对植被NDVI变化的影响及其驱动机制。     

基于遥感方法的长白山地区植被物候期变化趋势研究

目前,越来越多的遥感数据被用来监测大面积植物物候的动态变化。利用长时间序列的SPOT/NDVI旬合成数据,通过double logistic模型获取了1999~2008年长白山地区植被的3个关键物候参数:生长季始期、生长季末期和生长季长度的多年平均值,并绘制了它们的变化趋势空间格局图。结果表明,林地的生长季开始日期为第100~120天,草地和耕地相对较晚,分别为第130~140天和第140~150天;林地和草地生长季的结束日期为第275~285天,耕地的相对较早,为第265~275天;林地、草地和耕地的生长季长度范围分别为160~180 d、140~160 d和110~130 d。植被物候期的变化趋势表现为一定的空间差异性,生长季长度延长区域主要分布在长白山地区的中东部,平均每年延长约0.7 d;缩短的区域在西北地区,平均每年缩短1.1 d。最后通过部分物候观测数据及前人在相同研究区的结果验证了利用double logistic模型提取预测长白山植被物候期的可行性。     

Explaining NDVI trends in northern Burkina Faso

Many studies have shown a ‘greening of the Sahel’ on the basis of analysis of time series of satellite images and this has shown to be, at least partly, explained by changes in rainfall. In northern Burkina Faso, an area stands out as anomalous in such analysis, since it is characterized by a distinct spatial pattern and strongly dominated by negative trends in Normalized Difference Vegetation Index (NDVI). The aim of the paper is to explain this distinct pattern. When studied over the period 2000–2012, using NDVI data from the MODIS sensor the spatial pattern of NDVI trends indicates that non-climatic factors are involved. By relating NDVI trends to landscape elements and land use change we demonstrate that NDVI trends in the north-western parts of the study area are mostly related to landscape elements, while this is not the case in the south-eastern parts, where rapidly changing land use, including. expansion of irrigation, plays a major role. It is inferred that a process of increased redistribution of fine soil material, water and vegetation from plateaus and slopes to valleys, possibly related to higher grazing pressure, may provide an explanation of the observed pattern of NDVI trends. Further work will focus on testing this hypothesis.     

Temporal and spatial response of vegetation NDVI to temperature and precipitation in eastern China

Temporal and spatial response characteristics of vegetation NDVI to the variation of temperature and precipitation in the whole year, spring, summer and autumn was analyzed from April 1998 to March 2008 based on the SPOT VGT–NDVI data and daily temperature and precipitation data from 205 meteorological stations in eastern China. The results indicate that as a whole, the response of vegetation NDVI to the variation of temperature is more pronounced than that of precipitation in eastern China. Vegetation NDVI maximally responds to the variation of temperature with a lag of about 10 days, and it maximally responds to the variation of precipitation with a lag of about 30 days. The response of vegetation NDVI to temperature and precipitation is most pronounced in autumn, and has the longest lag in summer. Spatially, the maximum response of vegetation NDVI to the variation of temperature is more pronounced in the northern and middle parts than in the southern part of eastern China. The maximum response of vegetation NDVI to the variation of precipitation is more pronounced in the northern part than in the middle and southern parts of eastern China. The response of vegetation NDVI to the variation of temperature has longer lag in the northern and southern parts than in the middle part of eastern China. The response of vegetation NDVI to the variation of precipitation has the longest lag in the southern part, and the shortest lag in the northern part of eastern China. The response of vegetation NDVI to the variation of temperature and precipitation in eastern China is mainly consistent with other results, but the lag time of vegetation NDVI to the variation of temperature and precipitation has some differences with those results of the monsoon region of eastern China.     

Assessing vegetation cover and biomass in restored erosion areas in Iceland using SPOT satellite data

Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs.Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R² and RMSE values for most VIs do not vary very much. For percent VC, R² values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R² values for low-biomass areas (AGB < 800 g/m²) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m² and 136.38 g/m². The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m²), achieved R² values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m² to 259.20 g/m². The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced.These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m² can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.     

生态建设背景下陕西省植被覆盖时空变化及其影响因素研究

为了逆转近年来生态退化趋势,中国实施了一系列国家级生态建设项目,植被恢复是其核心目标。基于MODIS NDVI数据产品、气象站点观测数据及统计年鉴等资料,分析了2002—2016年陕西省植被覆盖时空变化,并采用面板数据固定效应模型进行了归因研究。结果表明:(1)实施退耕还林及天然林保护工程以后,陕西省植被覆盖状况总体呈改善趋势,但存在明显的时间波动与空间差异。(2)生态建设项目对陕西省植被恢复产生了显著正向影响,且存在时滞效应;造林率增加1个百分点,3 a后各县NDVI值将平均增加0.012 3(p<0.01),等同于年降水量增加56 mm的作用。(3)降水、气温的提高对陕西省植被恢复产生了显著正向影响,而人口与经济增长则产生了显著负向影响。因此,陕西省应继续加大生态建设项目投资力度,并加强监督与监测评价;积极推行生态移民政策,缓解人类活动带来的生态压力;调整经济结构、转变经济增长方式,摒弃消耗资源牺牲环境的粗放型发展模式。     

Remote sensing of the impact of reindeer grazing on vegetation in northern Fennoscandia using SPOT XS data

Digital image analysis and SPOT XS satellite data recorded on 1 August 1991 were used to monitor vegetation in the border region between Enontekio municipality, Finland, and Kautokeino municipality, Norway. A supervised classification on the red wave length channel was undertaken using training areas for maximum likelihood classifier to produce seven spectral land cover classes. The classification revealed great differences in vegetation between Finland and Norway. The largest difference was the lack of Cladinalichens on the Finnish side of the border. The area on the Finnish side is used for grazing all year round, while the area on the Norwegian side is a winter range only. According to official data for the local reindeer population, the average annual grazing pressure (head/area/time) is higher on the Norwegian side of the reindeer fence. The Finnish grazing practice is believed to have a detrimental impact on the vegetation, and has lead to pasture land degradation.     

Monitoring ecosystem dynamics in northwestern Ethiopia using NDVI and climate variables to assess long term trends in dryland vegetation variability

Dryland ecosystems are highly vulnerable to environmental changes. Monitoring is vital in order to evaluate their response to fluctuating rainfall and temperature patterns for long-term ecosystem safeguarding. Monitoring of long term changes of normalized difference vegetation index (NDVI) and climate variables are fundamental for better understanding of change trajectories in dryland ecosystem, and to ascertain their potential interaction with anthropogenic drivers. In this study, we identify determinant factors of dryland changes by using MODIS NDVI, precipitation and temperature data for Breaks for Additive Seasonal and Trend (BFAST) and Mann Kendall test statistic. BFAST predicts iteratively time and number of changes within a time series data to depict the size and direction of changes. Analysis of NDVI, precipitation and temperature time series data showed substantial changes during the study period of 2000–2014. There is a reduction trend in vegetation showed by the decline in NDVI, with significant breakpoints till 2009 and recovery afterwards, without a significant change in annual trends of precipitation (α < 0.05) for the same study period. Furthermore 2 positive climate trends were founded: a) a significant positive trend on long term annual rainfall during the main rainy seasons and; 2) a significant (α < 0.05) annual increment of the long term mean minimum and mean maximum temperature of 0.03 °C/year and 0.04 °C/year, respectively. This assessment showed that climate variables cannot be considered as the main factors in explaining the observed patterns of vegetation dynamics. Seasonal and interannual precipitation changes have a lower weight as driving factors for the reduction in vegetation trends. Hence, the decline in vegetation productivity of the region can be attributed to the increasing pressure of human activities.