The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters

NDVI (Normalized Difference Vegetation Index) has been widely used to monitor vegetation changes since the early eighties. On the other hand, little use has been made of land surface temperatures (LST), due to their sensitivity to the orbital drift which affects the NOAA (National Oceanic and Atmospheric Administration) platforms flying AVHRR sensor. This study presents a new method for monitoring vegetation by using NDVI and LST data, based on an orbital drift corrected dataset derived from data provided by the GIMMS (Global Inventory Modeling and Mapping Studies) group. This method, named Yearly Land Cover Dynamics (YLCD), characterizes NDVI and LST behavior on a yearly basis, through the retrieval of 3 parameters obtained by linear regression between NDVI and normalized LST data. These 3 parameters are the angle between regression line and abscissa axis, the extent of the data projected on the regression line, and the regression coefficient. Such parameters characterize respectively the vegetation type, the annual vegetation cycle length and the difference between real vegetation and ideal cases. Worldwide repartition of these three parameters is shown, and a map integrating these 3 parameters is presented. This map differentiates vegetation in function of climatic constraints, and shows that the presented method has good potential for vegetation monitoring, under the condition of a good filtering of the outliers in the data.     

Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan

Kazakhstan is the second largest country to emerge from the collapse of the Soviet Union. Consequent to the abrupt institutional changes surrounding the disintegration of the Soviet Union in the early 1990s, Kazakhstan has reportedly undergone extensive land cover/land use change. Were the institutional changes sufficiently great to affect land surface phenology at spatial resolutions and extents relevant to mesoscale meteorological models? To explore this question, we used the NDVI time series (1985-1988 and 1995-1999) from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset, which consists of 10 days maximum NDVI composites at a spatial resolution of 8 km. Daily minimum and maximum temperatures were extracted from the NCEP Reanalysis Project and 10 days composites of accumulated growing degree-days (AGDD) were produced. We selected for intensive study seven agricultural areas ranging from regions with rain-fed spring wheat cultivation in the north to regions of irrigated cotton and rice in the south. We applied three distinct but complementary statistical analyses: (1) nonparametric testing of sample distributions; (2) simple time series analysis to evaluate trends and seasonality; and (3) simple regression models describing NDVI as a quadratic function of AGDD.The irrigated areas displayed different temporal developments of NDVI between 1985-1988 and 1995-1999. As the temperature regime between the two periods was not significantly different, we conclude that observed differences in the temporal development of NDVI resulted from changes in agricultural practices.In the north, the temperature regime was also comparable for both periods. Based on extant socioeconomic studies and our model analyses, we conclude that the changes in the observed land surface phenology in the northern regions are caused by large increases in fallow land dominated by weedy species and by grasslands under reduced grazing pressure. Using multiple lines of evidence allowed us to build a case of whether differences in land surface phenology were mostly the result of anthropogenic influences or interannual climatic fluctuations.     

Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?

In the urban environment both quality of life and surface biophysical processes are closely related to the presence of vegetation. Spectral mixture analysis (SMA) has been frequently used to derive subpixel vegetation information from remotely sensed imagery in urban areas, where the underlying landscapes are assumed to be composed of a few fundamental components, called endmembers. A critical step in SMA is to identify the endmembers and their corresponding spectral signatures. A common practice in SMA assumes a constant spectral signature for each endmember. In fact, the spectral signatures of endmembers may vary from pixel to pixel due to changes in biophysical (e.g. leaves, stems and bark) and biochemical (e.g. chlorophyll content) composition. This study developed a Bayesian Spectral Mixture Analysis (BSMA) model to understand the impact of endmember variability on the derivation of subpixel vegetation fractions in an urban environment. BSMA incorporates endmember spectral variability in the unmixing process based on Bayes Theorem. In traditional SMA, each endmember is represented by a constant signature, while BSMA uses the endmember signature probability distribution in the analysis. BSMA has the advantage of maximally capturing the spectral variability of an image with the least number of endmembers. In this study, the BSMA model is first applied to simulated images, and then to Ikonos and Landsat ETM+ images. BSMA leads to an improved estimate of subpixel vegetation fractions, and provides uncertainty information for the estimates. The study also found that the traditional SMA using the statistical means of the signature distributions as endmember signatures produces subpixel endmember fractions with almost the same and sometimes even better accuracy than those from BSMA except without uncertainty information for the estimates. However, using the modes of signature distributions as endmembers may result in serious bias in subpixel endmember fractions derived from traditional SMA.     

基于植被指数的藏北牧区土壤湿度反演

土壤湿度的遥感动态监测在农牧业生产中具有重要意义。近年来,多种基于遥感指数的土壤湿度监测方法被提出并得到广泛关注,但当前对不同深度土壤湿度的反演及植被指数反映土壤湿度滞后性的研究较少。该文针对遥感指数反演土壤湿度的精度问题,对MODIS(moderate resolutionimaging spectroradiometer)的2种植被指数产品归一化差异植被指数(normalized difference vegetation index,NDVI)和增强型植被指数(enhanced vegetation index,EVI)与土壤湿度实测值进行相关分析,并利用在其中一个样点得到相关系数最高的回归模型对距离较远的其它点进行土壤湿度值估算,最后用土壤湿度实测值对模型的精度进行验证。结果表明,2种植被指数均与土壤湿度值呈现出较强的相关性,且利用植被指数估算土壤湿度的延迟天数为5～10 d。在相同气候模式、土壤类型和植被类型的条件下,高程为影响回归模型精度的主要因素。该研究可为牧区多层深度土壤湿度反演方法的选择和监测提供参考依据。     

An Anti noise Identification Methodfor Winter Wheat based on Satellite Time series Data

China As one of the major crops in the world,the spatial distribution information of winter wheat plays an important role in monitoring winter wheat growth,assisting economic decision making and addressing regional food security under climate change.This paper proposed a new anti noise identification method for winter wheat identification based on the 250 m MODIS NDVI time series dataset during the period from September 30,2014 to June 26,2015.With the method,the spatial distribution of winter wheat in Henan province was extracted based on the analysis of winter wheat phenology.Results indicated that the total identification accuracy of winter wheat was 93.0%,94.0% and 86.0% for the whole study area,fragmentary land area and regular land area,respectively.Compared with the traditional identification method for winter wheat based on satellite time series data,the identification accuracies with the proposed method in different filtering scenarios were not only high but also similar to each other.It strongly proved that the new method had a good performance in noise immunity and stability and can be applied to the rapid extraction of winter wheat in a large scale based on satellite time series dataset.This new method provided a new technical support for the operational extraction of winter wheat.     

基于亮温—植被指数—气溶胶光学厚度的MODIS火点监测算法研究

MODIS火灾产品的火点检测算法主要以4和11μm通道亮温数据来识别火点,在应用于不同地区和不同季节时有一定局限性。在分析MODIS现有火点检测算法的基础上,对算法相关阈值及参数进行适当调整,同时考虑火灾前后NDVI的变化,以及林火燃烧过程中伴生烟羽使火点下风方气溶胶光学厚度明显增加的特点,构建了基于亮温—植被指数—气溶胶光学厚度的火点识别算法,并应用多次火灾个例对本算法进行验证。结果表明:算法提高了对高温热点和低温焖烧火点的识别能力,有效降低了高温热点的误报率和低温火点的漏报率,使火点检测算法在不同环境的适应性有所增强。     

基于遥感和气象数据的蒙古高原1982~2013年植被动态变化分析

蒙古高原生态系统及其变化对中国北方乃至整个东北亚的生态安全有着重要影响,了解蒙古高原干旱半干旱区植被生长的动态如何在不同时间和空间尺度上响应气候变化十分必要。利用NDVI数据构建长时间序列,分析植被生长动态变化的过程和时空特征,并与气象数据进行相关性分析。主要结论如下:①NDVI的分布具有地带性;②大约39.5%的区域NDVI呈显著的增加(P=0.1),7.3%的区域NDVI显著减少(P=0.1),说明植被条件在蒙古高原有所好转;③蒙古高原NDVI的变异系数均值16.99%,这表明过去32年里植被覆盖变化情况有较强的波动性;④蒙古高原植被的生长状况与降水量有极显著的正相关关系,与气温有极显著的负相关关系。     

基于MODIS数据的科尔沁区植被覆盖时空变化分析

我国植被覆盖辽阔,自然环境相对优越,但是随着时代发展,植被的生态系统遭到严重破坏,因此对植被空间格局的变化进行及时的研究至关重要。以通辽市科尔沁区为例,利用该区域2004—2013年的MODIS遥感数据产品,结合时间序列、均值法、插值法等理论,分析科尔沁区归一化植被指数(Normalized Difference Vegetation Index,NDVI)的时间和空间变化特征,得出了科尔沁区近10 a的植被覆盖演变情况:近10 a间科尔沁区植被整体上呈增加趋势,增加速率为0.033/a,其中植被覆盖度较好的年份是2005,2010,2011,2012和2013年,对整个科尔沁区而言,西辽河流域植被覆盖度最高;从NDVI值看出,对植被覆盖度贡献最大的为夏季,增加速率为0.002 8/a。研究成果将为科尔沁地区日后地表生态环境的改善和治理提供决策依据和理论基础。     

考虑气候时滞效应的河北山区 NDVI 演变归因

从植被类型、植被物候、气候因子时滞效应等方面研究 2001 年以来河北山区归一化植被指数（normalizeddifference?vegetation?index,NDVI）演变规律,并将气候因子时滞效应引入多元线性回归方法中,识别气候因子和人类活动对河北山区 NDVI 演变的贡献。结果表明：2001—2022 年河北山区 NDVI 呈持续增长趋势,平均增速为0.003?7/a,其中乔木林、灌木林和草地 3 种植被类型增速分别为 0.003?5/a、0.004?0/a 和 0.003?8/a;受气候变化影响,河北山区植被生长季变长,生长季开始时间平均提前 9?d,而生长季结束时间仅提前 1?d;降水对植被 NDVI 的影响主要发生在当月,而气温和潜在蒸散发对植被 NDVI 的影响存在 1 个月的滞后性,考虑气候因子的时滞效应后,气候变化和人类活动对河北山区 NDVI 演变的贡献分别为 39% 和 61%,与不考虑气候因子时滞效应对比,多元线性回归的决定系数由 0.80 提高到 0.87。人工水保措施是河北山区植被 NDVI 增长的主导因素,对快速改善山区生态环境至关重要,同时 2001 年以来降水、气温等气候因素也有利于植被恢复,对植被条件较好的区域,应以自然恢复为主,实现自然条件下的生态平衡。研究结果对优化山区水土保持工作方案,提高植被修复措施效果具有重要意义。     

Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations

The main objective of the current paper is to evaluate and explain differences between computed green-up dates of vegetated land surface derived from satellite observations and budburst dates from ground observational networks. Landscapes dominated by deciduous broad-leaved trees in Germany are analysed. While ground observations generally record the onset of bud break, remote sensing refers to a detectable change of surface reflectance, which accounts for the unfolding of the majority of the leaves. The satellite detects, even in a homogeneous stand, two signals: the green-up of the understorey and, shortly after, the green-up of the canopy (overstorey). Results of comparisons indicate an earlier, although not consistently, satellite-derived green-up than bud break derived from ground observations.We hypothesise that this is due to heterogeneous ground cover and a detection of the greening of non-tree vegetation by the satellite. This hypothesis is tested by analysing the difference between satellite-derived green-up dates (d_GU) and budburst observed on the ground (d_BB) in function of the proportion of non-deciduous-forest (ndf) land use types in satellite scenes. The satellite data (a daily 1-km resolution AVHRR product) are analysed with progressively more restricted selection criteria regarding the land surface elements. The two sets of observations are compared using Gaussian Mixture Models to evaluate the statistical properties of the probability density functions (pdf) as produced by the two sets rather than comparisons of geographically coincident data. It is shown that a heterogeneous vegetation cover is likely to be the main factor determining the difference between the computed green-up date and date of budburst of the dominating tree species.