A comparative study of satellite and ground-based phenology

Long time series of ground-based plant phenology, as well as more than two decades of satellite-derived phenological metrics, are currently available to assess the impacts of climate variability and trends on terrestrial vegetation. Traditional plant phenology provides very accurate information on individual plant species, but with limited spatial coverage. Satellite phenology allows monitoring of terrestrial vegetation on a global scale and provides an integrative view at the landscape level. Linking the strengths of both methodologies has high potential value for climate impact studies. We compared a multispecies index from ground-observed spring phases with two types (maximum slope and threshold approach) of satellite-derived start-of-season (SOS) metrics. We focus on Switzerland from 1982 to 2001 and show that temporal and spatial variability of the multispecies index correspond well with the satellite-derived metrics. All phenological metrics correlate with temperature anomalies as expected. The slope approach proved to deviate strongly from the temporal development of the ground observations as well as from the threshold-defined SOS satellite measure. The slope spring indicator is considered to indicate a different stage in vegetation development and is therefore less suited as a SOS parameter for comparative studies in relation to ground-observed phenology. Satellite-derived metrics are, however, very susceptible to snow cover, and it is suggested that this snow cover should be better accounted for by the use of newer satellite sensors.     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.     

Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.     

基于通量塔净生态系统碳交换数据的植被物候遥感识别方法评价

选择北美洲72座通量塔观测的净生态系统碳交换（NEE）数据来计算植被物候,并以此作为参考数据,从可行性和准确性两方面对阈值法、移动平均法和函数拟合法三大类常用的植被物候遥感识别方法进行了综合评价.结果表明： 基于局部中值的阈值法对植被物候识别的可行性和准确性均最优;其次为Logistic函数拟合法中的一阶导数方法;移动平均法对植被物候识别的可行性和准确性与移动窗口的大小有关,对于16 d合成的归一化差值植被指数（NDVI）时间序列数据来说,移动窗口大小为15时能获得较优的结果;而全局阈值法对植被物候识别的可行性和准确性均最差;Logistic函数拟合法中的曲率变化率方法在识别植被物候时虽然与基于NEE数据得到的植被物候在数值上存在较大偏差,但二者之间具有较高的相关性,说明基于曲率变化率方法识别出的植被物候能较真实地反映植被物候在时空上的变化趋势.     

Variability and evolution of global land surface phenology over the past three decades (1982–2012)

Monitoring land surface phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP provides the potential to fill the gap between traditional phenological (field) observations and the large‐scale view of global models. In this study, we review and evaluate the variability and evolution of satellite‐derived growing season length (GSL) globally and over the past three decades. We used the longest continuous record of Normalized Difference Vegetation Index data available to date at global scale to derive LSP metrics consistently over all vegetated land areas and for the period 1982–2012. We tested GSL, start‐ and end‐of‐season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period. We evaluated trends using global environmental stratification information in place of commonly used land cover maps to avoid circular findings. Our results confirmed an average lengthening of the growing season globally during 1982–2012 – averaging 0.22–0.34 days yr^?1, but with spatially heterogeneous trends. About 13–19% of global land areas displayed significant GSL change, and over 30% of trends occurred in the boreal/alpine biome of the Northern Hemisphere, which showed diverging GSL evolution over the past three decades. Within this biome, the ‘Cold and Mesic’ environmental zone appeared as an LSP change hotspot. We also examined the relative contribution of SOS and EOS to the overall changes, finding that EOS trends were generally stronger and more prevalent than SOS trends. These findings constitute a step towards the identification of large‐scale phenological drivers of vegetated land surfaces, necessary for improving phenological representation in terrestrial biosphere models.     

A systematic review of vegetation phenology in Africa

The study of vegetation phenology is important because it is a sensitive indicator of climate changes and it regulates carbon, energy and water fluxes between the land and atmosphere. Africa, which has 17% of the global forest cover, contributes significantly to the global carbon budget and has been identified as potentially highly vulnerable to climate change impacts. In spite of this, very little is known about vegetation phenology across Africa and the factors regulating vegetation growth and dynamics. Hence, this review aimed to provide a synthesis of studies of related Africa's vegetation phenology and classify them based on the methods and techniques used in order to identify major research gaps. Significant increases in the number of phenological studies in the last decade were observed, with over 70% of studies adopting a satellite-based remote sensing approach to monitor vegetation phenology. Whereas ground based studies that provide detailed characterisation of vegetation phenological development, occurred rarely in the continent. Similarly, less than 14% of satellite-based remote sensing studies evaluated vegetation phenology at the continental scale using coarse spatial resolution datasets. Even more evident was the lack of research focusing on the impacts of climate change on vegetation phenology. Consequently, given the importance and the uniqueness of both methods of phenological assessment, there is need for more ground-based studies to enable greater understanding of phenology at the species level. Likewise, finer spatial resolution satellite sensor data for regional phenological assessment is required, with a greater focus on the relationship between climate change and vegetation phenological changes. This would contribute greatly to debates over climate change impacts and, most importantly, climate change mitigation strategies.     

The remote sensing approach in broad‐scale phenological studies

Abstract. Satellite imagery provides a unique tool for monitoring seasonal dynamics of the Earth's vegetation on a global scale. The combination of the normalized difference vegetation index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR) with a daily repeat cycle and 1 km spatial resolution makes weather satellites operated by the National Oceanic and Atmospheric Administration very well suited for deriving broad‐scale phenological metrics from satellite images. In this paper, similarities and differences between remotely sensed phenological studies and traditional symphenological studies conducted by ground‐based observations are summarized. Finally, major shortcomings in deriving phenological metrics from NDVI time series are discussed.     

祁连山不同植被类型的物候变化及其对气候的响应

基于1982—2006年GIMMS NDVI和2000—2014年MODIS NDVI遥感数据,利用double logistic拟合方法提取了1982—2014年祁连山区不同植被的生长季始期、生长季末期和生长季长度3个重要的物候参数,分析了不同植被物候期的时间变化趋势、空间分异特征及对气候因子的响应。结果表明:(1)祁连山区不同植被的生长季始期和生长季末期随年际变化表现出波动提前或推迟,其中沼泽植被的变化波动最大;草甸植被、灌丛植被、阔叶林植被和栽培植被生长季长度出现延长趋势;(2)祁连山区植被生长季始期集中在5月初,其中阔叶林植被生长季开始最早,荒漠植被生长季开始最晚,植被生长季末期集中在9月,栽培植被生长季结束较早,荒漠植被、沼泽植被生长季结束较晚,植被生长季长度集中在110—140 d,其中阔叶林植被、针叶林植被生长季长度较长,而荒漠植被、高山植被生长季长度较短;(3)祁连山植被物候期变化趋势的空间分布表明植被生长季始期、生长季末期主要表现为提前不明显和推迟不明显,生长季长度主要表现为缩短不明显和延长不明显;(4)物候要素与气候要素相关性表明前期温度的积累有利于植被的开始生长,但当年3月的降水量对植被生长季始期同样有重要作用,不同植被生长季末期与8月、9月温度相关性较大,而与10月、11月降水的相关性较大。     

Area-averaged vegetative cover fraction estimated from satellite data

The relationship was analysed between the vegetation cover factor expressed as a percentage and the area-averaged normalized difference vegetation index (NDVI). On selected days the NDVI was calculated from channel 1 and 2 reflectance data of the National Oceanic and Atmospheric Administration (NOAA—11) satellite's advanced very high-resolution radiometer (AVHRR) for five test areas under agricultural and forestry use. No ground-based reflectance measurements could be made for validation of these data. Therefore the land surface NDVI, which varied with time, and percentage vegetation cover of the test areas were deduced from time-independent but site-specific statistical land use data updated by temporal phenological observations, and from surface-specific reflectance curves published in the literature. The result indicated that the area-averaged NDVI, as obtained from the NOAA—11 radiometer, was less than the value calculated from the land surface NDVI. After correction to reduce the offset of the data, the values would be a suitable indicator of the fraction of vegetation cover.     

日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪

植被物候期(春季返青和秋季衰老)是表征生物响应和陆地碳循环的基础信息。由于常绿针叶林冠层绿度的季节变动较弱,遥感提取常绿针叶林的物候信息存在着较大的不确定性,是目前区域物候监测中的难点。利用MODIS植被指数(归一化植被指数NDVI和增强型植被指数EVI)、GOME-2日光诱导叶绿素荧光(SIF)和通量数据(总初级生产力GPP)估算2007—2011年亚热带常绿针叶林物候期,用来比较三类遥感指数估算常绿针叶林物候的差异。结果表明:基于表征光合作用物候的通量GPP数据估算得到5年内亚热带常绿针叶林生长季开始时间(SOS_(GPP))为第63天,生长季结束时间(EOS_(GPP))为第324天,生长季长度为272天;基于反映植被光合作用特征的SIF曲线获得物候信息要滞后GPP物候期,其中生长季开始时间滞后19天,生长季结束时间滞后2天;基于传统植被指数NDVI和EVI的物候期滞后GPP物候期的时间要大于SIF滞后期,其中植被指数SOS滞后SOS_(GPP)31天,植被指数EOS滞后EOS_(GPP)10—17天。虽然基于3种遥感指数估算的春季和秋季物候都滞后于通量GPP的物候期,但是卫星SIF的物候信息能够更好地捕捉常绿针叶林的生长阶段。同时,春季温度是影响森林生长季开始时间的最重要因素;秋季水分和辐射是影响生长季结束时间的关键因素。由此可见,SIF估算的亚热带常绿针叶林的春季和秋季物候的滞后时间要短于传统植被指数,能更好地追踪常绿林光合作用的季节性,为深入研究陆地生态系统碳循环及其对气候变化的响应提供重要的基础。     

发展NECT土地覆盖特征数据集的原理、方法和应用

着重探讨了建立中国东北样带 (NortheastChinatransect, NECT) 土地覆盖特征数据集的原理、方法及其在全球变化研究方面的重要应用。NECT土地覆盖特征数据集是以多时相的 1km分辨率的NOAA/AVHRR归一化植被指数NDVI (Normalizeddifferencevegetationindex) 数字影像为基础, 同时采用高程、气候、土壤、植被、土地利用、土地资源、生态区域、行政边界、经济、社会等多源数据作为数据源, 并经过标准化处理 (如数字化、空间插值、几何配准、投影转换 ) 集成而成。在土地覆盖特征数据集的主要应用方面, 如 :1) 利用多时相、1km分辨率的NOAA/AVHRR影像完成了中国东北样带土地覆盖分类图。一级分类系统包括森林、草原、荒漠和沙地、灌丛、农田、混合覆盖 类型、城镇和水体等 8类, 二级分类体系包括 12类。经过地面采样进行精度检验, 分类精度达到 81.6 1%。 2 ) 对主要植被类型的植物生长季变化进行的研究。利用多时相的遥感影像构造了能够反映植被年际、季节生长变化的遥感植被指数ND VImax、NDVI变幅xam以及NDVI的标准偏差x′s 等, 分析这 3个参数 1983~ 1999年的 17年中的变化情况。该数据集的建立是研究该样带土地覆盖特征及其变化规律的基础, 对基于样带的全球变化研究有重要的意义。     

基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析

植被物候学作为研究植被与环境条件相互作用的科学,在全球气候变化的大背景下已成为国际热点研究领域,其中森林植被在调节全球碳平衡、维护全球气候稳定的过程中有着至关重要的作用。随着遥感技术的发展,多种遥感指数被应用到森林植被物候研究中,其中以MODIS NDVI和EVI应用最为广泛,而叶绿素荧光(SIF)作为植被光合作用的"探针"也被广泛应用于森林植被物候研究中。为了探究3种指数在森林植被物候研究中的差异与特性,本文以长白山温带红松阔叶林通量观测站为研究区域,采用模型拟合结合动态阈值法提取2007—2013森林物候特征参数,并使用通量数据(总初级生产力GPP)进行验证。结果表明:NDVI与EVI、SIF相比,表现为生长季开始时间与结束时间的明显提前和滞后,与GPP数据偏差较大,且夏季生长季峰期曲线形态过宽且平坦,无法较好反映生长季变化特征;EVI相较于NDVI有所改善,整体变化趋势与SIF、GPP基本吻合,但依然存在秋季衰减时间稍迟于SIF与GPP的问题;SIF虽然存在夏季骤降现象,但依然与GPP数据一致性最好,可以较好反映出森林植被季节变化特征。SIF数据与植被光合作用的紧密关联使其在植被物候研究中具有优于植被指数的准确性,并随着遥感平台的增加和反演方法的改善,将会在多尺度、多类型的植被物候监测中发挥更加重要的作用。     

Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies.     

Climatic controls of vegetation vigor in four contrasting forest types of India—evaluation from National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer datasets (1990–2000)

Ten-day advanced very high resolution radiometer images from 1990 to 2000 were used to examine spatial patterns in the normalized difference vegetation index (NDVI) and their relationships with climatic variables for four contrasting forest types in India. The NDVI signal has been extracted from homogeneous vegetation patches and has been found to be distinct for deciduous and evergreen forest types, although the mixed-deciduous signal was close to the deciduous ones. To examine the decadal response of the satellite-measured vegetation phenology to climate variability, seven different NDVI metrics were calculated using the 11-year NDVI data. Results suggested strong spatial variability in forest NDVI metrics. Among the forest types studied, wet evergreen forests of north-east India had highest mean NDVI (0.692) followed by evergreen forests of the Western Ghats (0.529), mixed deciduous forests (0.519) and finally dry deciduous forests (0.421). The sum of NDVI (SNDVI) and the time-integrated NDVI followed a similar pattern, although the values for mixed deciduous forests were closer to those for evergreen forests of the Western Ghats. Dry deciduous forests had higher values of inter-annual range (RNDVI) and low mean NDVI, also coinciding with a high SD and thus a high coefficient of variation (CV) in NDVI (CVNDVI). SNDVI has been found to be high for wet evergreen forests of north-east India, followed by evergreen forests of the Western Ghats, mixed deciduous forests and dry deciduous forests. Further, the maximum NDVI values of wet evergreen forests of north-east India (0.624) coincided with relatively high annual total precipitation (2,238.9 mm). The time lags had a strong influence in the correlation coefficients between annual total rainfall and NDVI. The correlation coefficients were found to be comparatively high (R²=0.635) for dry deciduous forests than for evergreen forests and mixed deciduous forests, when the precipitation data with a lag of 30 days was correlated against NDVI. Using multiple regression approach models were developed for individual forest types using 16 different climatic indices. A high proportion of the temporal variance (>90%) has been accounted for by three of the precipitation parameters (maximum precipitation, precipitation of the wettest quarter and driest quarter) and two of the temperature parameters (annual mean temperature and temperature of the coldest quarter) for mixed deciduous forests. Similarly, in the case of deciduous forests, four precipitation parameters and three temperature parameters explained nearly 83.6% of the variance. These results suggest differences in the relationship between NDVI and climatic variables based upon the time of growing season, time interval and climatic indices over which they were summed. These results have implications for forest cover mapping and monitoring in tropical regions of India.     

中国北方草地与农牧交错带植被的NDVI-Ts空间的年内变化特征

利用多时相或时序植被指数(normalize difference vegetation index,NDVI)数据进行地表覆盖研究已取得了大量成果.随着陆地表面温度(1and surface temperature,Ts)遥感反演精度的不断提高,将Ts与NDVI结合起来进行地表植被动态变化的监测已成为可能.本文主要包括以下三部分内容:1)介绍了基于卫星遥感数据的NDVI、Ts和Ts/NDVI计算方法.2)讨论NDVI、Ts和Ts/NDVI数据对植被覆盖信息表达的差异,并分析了中国北方草地与农牧交错带植被在NDVI-Ts空间的年内变化特征.3)利用信息熵和平均梯度,定量分析了NDVI、Ts和 Ts/NDVI数据在信息表达丰富度方面的差异,并对在不同地表植被覆盖下,Ts/NDVI数据对信息提高程度的敏感性进行了讨论.     

新疆植被物候时空变化特征

基于MODIS-NDVI数据,提取新疆2001—2016年典型植被物候期,分析新疆不同生态分区的山地-绿洲系统植被物候期的时空演变趋势和空间分异特征,并结合同期气象数据,探讨植被物候与气候变化的响应关系。结论为:(1)新疆植被物候具有明显的纬向分布和垂直地带性分布特征,海拔在物候的地域分异中扮演着重要作用。新疆植被生长季开始时间(Start of season,SOS)集中于3月中旬至5月上旬,生长季结束时间(End of season,EOS)集中于10月中旬至12月下旬。(2)与全球大背景下典型植被物候特征变化趋势相反,新疆植被SOS呈推迟趋势,推迟幅度为1.9d/10a;EOS呈提前趋势,提前幅度为3.66d/10a;生长季长度(Length of season,LEN)呈缩短趋势,缩短幅度为5.6d/10a。除东疆地区外,全疆及不同分区均呈现出绿洲及平原SOS较早,山地区域较迟;全疆及不同分区均呈现出山地EOS结束较早,绿洲结束较迟;除东疆地区外,全疆及不同分区的LEN均为绿洲及平原区域山地,同样显示出垂直地带性分布的特征。(3)通过冗余分析(Redundancy analysis,RDA)解释了物候特征与气象因子关系的绝大部分信息,生长季开始时间受春季气温、前一年冬季降水量和日照时数的显著影响。夏季和秋季降水量是新疆植被生长季结束时间的重要影响因素,在总体上受气温和日照时数的影响较小。     

中国北方草地与农牧交错带植被的NDVI-Ts空间的年内变化特征

利用多时相或时序植被指数(normalize difference vegetation index,NDVI)数据进行地表覆盖研究已取得了大量成果。随着陆地表面温度(1and surface temperature,TS)遥感反演精度的不断提高,将Ts与NDVI结合起来进行地表植被动态变化的监测已成为可能。本文主要包括以下三部分内容：1)介绍了基于卫星遥感数据的NDVI、Ts和Ts／NDVI计算方法。2)讨论NDVI、Ts和Ts／NDVI数据对植被覆盖信息表达的差异,并分析了中国北方草地与农牧交错带植被在NDVI-TS空间的年内变化特征。3)利用信息熵和平均梯度,定量分析了NDVI、Ts和Ts／NDVI数据在信息表达丰富度方面的差异,并对在不同地表植被覆盖下,Ts／NDVI数据对信息提高程度的敏感性进行了讨论。     

Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa

Questions: What are the patterns of remotely sensed vegetation phenology, including their inter‐annual variability, across South Africa? What are the phenological attributes that contribute most to distinguishing the different biomes? How well can the distribution of the recently redefined biomes be predicted based on remotely sensed, phenology and productivity metrics? Location: South Africa. Method: Ten‐day, 1 km, NDVI AVHRR were analysed for the period 1985 to 2000. Phenological metrics such as start, end and length of the growing season and estimates of productivity, based on small and large integral (SI, LI) of NDVI curve, were extracted and long‐term means calculated. A random forest regression tree was run using the metrics as the input variables and the biomes as the dependent variable. A map of the predicted biomes was reproduced and the differentiating importance of each metric assessed. Results: The phenology metrics (e.g. start of growing season) showed a clear relationship with the seasonality of rainfall, i.e. winter and summer growing seasons. The distribution of the productivity metrics, LI and SI were significantly correlated with mean annual precipitation. The regression tree initially split the biomes based on vegetation production and then by the seasonality of growth. A regression tree was used to produce a predicted biome map with a high level of accuracy (73%). Main conclusion: Regression tree analysis based on remotely sensed metrics performed as good as, or better than, previous climate‐based predictors of biome distribution. The results confirm that the remotely sensed metrics capture sufficient functional diversity to classify and map biome level vegetation patterns and function.     

基于NDVI_Ts特征空间的中国土地覆盖分类研究

 归一化植被指数（NDVI）与地表温度（Ts）是描述地表覆盖特征的两个重要参数, 其构成的NDVI_Ts特征空间具有丰富的地学和生态学内涵。该文在NOAA/AVHRR连续时间序列数据反演Ts的基础上,通过主成分分析、非监督分类和基于DEM的分类后处理等方法,以Ts/NDVI为指标对中国土地覆盖进行分类。结果表明,Ts/NDVI对中国较大尺度上不同土地覆盖类型的差异具有较强的敏感性,其对中国土地覆盖分类结果的野外抽样检验精度比传统的单独利用NDVI时间序列进行非监督分类提高了3.3%,Kappa系数提高了0.020 2;在综合其它反映植被特征及其环境的指标（如气候、地形等）的基础上,利用Ts/NDVI将有可能较为准确 地提取中国植被或土地覆盖的信息,有利于对其进行分类和变化监测,具有深远的研究潜力 和应用价值。     

Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006

Aim We intend to characterize and understand the spatial and temporal patterns of vegetation phenology shifts in North America during the period 1982–2006. Location North America. Methods A piecewise logistic model is used to extract phenological metrics from a time‐series data set of the normalized difference vegetation index (NDVI). An extensive comparison between satellite‐derived phenological metrics and ground‐based phenology observations for 14,179 records of 73 plant species at 802 sites across North America is made to evaluate the information about phenology shifts obtained in this study. Results The spatial pattern of vegetation phenology shows a strong dependence on latitude but a substantial variation along the longitudinal gradient. A delayed dormancy onset date (0.551 days year^?1, P= 0.013) and an extended growing season length (0.683 days year^?1, P= 0.011) are found over the mid and high latitudes in North America during 1982–2006, while no significant trends in greenup onset are observed. The delayed dormancy onset date and extended growing season length are mainly found in the shrubland biome. An extensive validation indicates a strong robustness of the satellite‐derived phenology information. Main conclusions It is the delayed dormancy onset date, rather than an advanced greenup onset date, that has contributed to the prolonged length of the growing season over the mid and high latitudes in North America during recent decades. Shrublands contribute the most to the delayed dormancy onset date and the extended growing season length. This shift of vegetation phenology implies that vegetation activity in North America has been altered by climatic change, which may further affect ecosystem structure and function in the continent.