中国西北地区植被NDVI的时空变化及其影响因子分析

利用GIMMS/NDVI数据分析了中国西北地区1982-2006年植被NDVI时空变化特征及其影响因子。近25年来,中国西北地区年均植被NDVI增速为0.5%/10a,并存在明显的空间差异。天山、阿尔泰山、祁连山、青海的中东部等地区植被NDVI显著增加;青海南部地区、陕西和宁夏交界地区、甘肃部分地区,以及新疆部分地区的植被NDVI下降。从不同植被类型看:林地、草地和耕地的年均NDVI都在提高。研究表明:中国西北地区植被NDVI变化是各种自然和人为因素综合作用的结果。植被NDVI与气温、降水的年际变化整体上都呈弱的正相关。但与其年内变化则都呈显著的线性关系,当月均温量超过20℃时,植被NDVI呈下降趋势;当月降水量在0¹00mm期间,植被NDVI随降水线性增长,当月降水量超过100mm之后,不再有明显的增长趋势。农业生产水平提高和植被生态建设等人类活动对西北地区植被NDVI增加有重要影响。     

黄土高原地区NDVI与气候因子空间尺度依存性及非平稳性研究

基于MODIS传感器的植被指数产品(MOD13Q1)及50年气候数据,通过地理加权回归与普通最小二乘回归模型对比,对中国黄土高原地区NDVI与气候因子间的空间尺度依存性及非平稳性进行研究,以期准确建立二者间关系.结果表明:① 研究区域内,NDVI与气候因子间存在很强的空间尺度依存关系,相同空间尺度下,年均降水较年均温对NDVI影响的波动性更大;② 与普通最小二乘回归模型相比,地理加权回归模型能够更准确地展现二者间关系;③气候因子对该地区NDVI的影响差异明显,降水存在直接正向影响,而温度的影响则较复杂;④ NDVI与气候因子间沿东北--西南的分布格局体现出区域内不同植被--气候区差异特征.二者间的异质情况还反映出除气候外,人类活动,地形等其他因素对NDVI的影响.     

青藏高原植被覆盖变化及其与气候变化的关系

近几十年来,全球气候变化对青藏高原植被覆盖产生了重要影响。基于青藏高原1981—2005年遥感影像及同期气象数据,结合生态学模型,分析了青藏高原植被覆盖度变化趋势及其与气候变化的关系。结果显示,25 a间,青藏高原温度升高、降水量增加,植被覆盖度呈"整体升高、局部退化"趋势;地表植被改善区主要位于植被低覆盖区,退化区主要位于高覆盖区;从不同植被类型看,除针叶林、阔叶林受采伐影响覆盖度下降外,其他植被覆盖度均不同程度的上升;植被覆盖度变化与同期降水量变化、温度变化均呈正相关,且具有明显的区域差异。     

3个时期骆马湖大型水生植物的分布及变化

大型水生植物对湖泊的生物地球化学循环具有重要影响.一方面,大型水生植物在生长过程吸收营养;另一方面,其通过向水体释放氧气而影响磷元素以及其他相关因子,进而影响磷元素的生物地球化学循环.为了从宏观上了解骆马湖生态系统变化,以1990年9月20日、2000年5月2日和2008年10月15日Landsat TM/ETM+影像为主要数据源,以大型水生植物的归一化植被指数(NDVI)为测试变量,运用分类回归树(classifica-tion and regression tree,CART)方法确定分割阈值,通过构建知识决策树的方法识别骆马湖大型水生植物动态变化特征.3个时期的遥感分类的总体精度与kappa系数分别为92.28％和0.87、91.73％和0.86、93.38％和0.88,因此,该方法的分类精度完全满足骆马湖水生植物分布及变化的研究.研究结果表明,骆马湖大型水生植物分布面积由1990年的55.461 6 km2,减少到2000年的41.801 4 km2,2008年又增加到79.065 km2;大型水生植物主要分布在骆马湖北部河湖交汇区;人类活动干扰是造成骆马湖大型水生植物分布面积发生变化的主要原因.     

The aboveground biomass of desert steppe and its spatiotemporal variation in western Inner Mongolia

A precise understanding of the aboveground biomass of desert steppe and its spatio-temporal variation is important to understand how arid ecosystems respond to climate change and to ensure that scarce grassland resources are used rationally. On the basis of 756 ground survey quadrats sampled in western Inner Mongolia steppe in 2005–2011 and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS)—the normalized difference vegetation index (NDVI) dataset for the period of 2001–2011—we developed a statistical model to estimate the aboveground biomass of the desert steppe and further explored the relationships between aboveground biomass and climate factors. The conclusions are as follows: (1) the aboveground biomass of the steppe in the research area was 5.27 Tg (1 Tg=10¹² g) on average over 11 years; between 2001 and 2011, the aboveground biomass of the western Inner Mongolia steppe exhibited fluctuations, with no significant trend over time; (2) the aboveground biomass of the steppe in the research area exhibits distinct spatial variation and generally decreases gradually from southeast to northwest; and (3) the important factor causing interannual variations in aboveground biomass is precipitation during the period from January to July, but we did not find a significant relationship between the aboveground biomass and the corresponding temperature changes. The precipitation in this period is also an important factor influencing the spatial distribution of aboveground biomass (R²=0.39, P<0.001), while the temperature might be a minor factor (R²=0.12, P<0.01). The uncertainties in our estimate are primarily due to uncertainty in converting the fresh grass yield estimates to dry weight, underestimates of the biomass of shrubs, and error in remote sensing dataset.     

Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi‐scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust‐producing sources. The representation of intra‐annual and inter‐annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter‐annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas. Copyright © 2012 John Wiley & Sons, Ltd.     

基于MODIS影像多特征的CART决策树分类

以山东省为研究区域,利用2009年9月MODIS的8 d合成波段反射率产品MOD09,选择特征变量植被指数(NDVI、EVI)、NDWI、NDMI、NDSI及辅助信息DEM,通过选取其中的影像特征组合来确定分类方案,构建各波段组合的CART决策树,对MODIS影像进行分类,得到CART决策树的最优波段组合。结果表明,特征变量DEM、NDVI、EVI对分类结果贡献较大;将CART决策树的分类结果与其相对应的最大似然分类结果进行比较可知,基于影像多特征的CART决策树分类方法能明显提高分类精度。     

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.     

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

Abstract

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R ² of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.     

Autumn NDVI contributes more and more to vegetation improvement in the growing season across the Tibetan Plateau

ABSTRACT

Detecting changes in vegetation, distinguishing the persistence of changes, and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics. Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (NDVI) version NDVI_3g dataset in the Tibetan Plateau, the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982. Dynamic trends in vegetation experienced an obvious increase at regional scale, but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended. At pixel scale, areas with significant browning significantly expanded over 14 periods for all seasons, but for significant greening significantly increased only in autumn. The changes of vegetation activity in spring were the most drastic among three seasons. Increased increments of NDVI in summer, spring, and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods. Vegetation activity was mainly regulated by a thermal factor, and the dominant climatic drivers of vegetation growth varied across different seasons and regions. We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.