21世纪初中国农作物与自然植被物候时空差异

利用离散小波方法对2001~2012年MODIS EVI时序数据进行平滑,基于动态阈值法提取我国植被物候信息,探讨农作物和自然植被物候的时空变化特征。结果表明:(1)我国第一季农作物开始、峰值和结束日期主要以华北平原为中心随海拔的上升而推迟,而自然植被物候更早20d左右,且随海拔的上升先推迟后提前;(2)物候在时序上有显著变化的第一季区域,43.98%开始日期、52.83%峰值日期呈现提前趋势,多在开始晚、结束早的西南区及东北与内蒙古交界处,其余区域开始、峰值日期及81.80%结束日期呈推迟趋势,发生在开始早、结束晚的黄土高原及双季农作区;农作物物候推迟幅度小于自然植被。     

21世纪初期东北地区农田物候动态及其对气候的响应

利用遥感技术的物候提取方法在理解农田物候对气候变化的敏感性问题中具有较大潜力。基于MODIS的归一化植被指数（Normalized Difference Vegetation Index, NDVI）遥感产品,提取东北农田地区2005～2020年植被物候参数并用地面实测数据加以验证,然后获取农田物候与气温、降水量和日照时数间的相关关系。结果表明东北地区2005～2020年农田物候空间分布格局较为一致,只在部分区域因作物品种布局原因存在差异;农田物候的时间变化显著,多数地区生长季开始日期以1 d/a的速率在提前,生长季长度以1 d/a的速率在延长。农田物候受气温与降水量的影响较为明显,受日照时数影响不大,同时也在一定程度上受到种植结构变化的影响。而且,农田物候在很大程度上受人为因素干扰,因此有些区域气候因素对其影响表现出相反趋势。     

基于AVHRR和MODIS数据的全球植被物候比较分析

AVHRR和MODIS卫星数据在全球和区域尺度植物物候对气候变化响应研究中起着重要的作用,然而两种传感器在全球尺度物候监测的一致性有待验证。首先利用时间序列谐波分析法(HANTS)对2005年全球GIMMS AVHRR NDVI和MODIS 13A2 数据进行滤波处理;然后基于改进的动态阈值方法,提取全球植被的返青期(SOS)、枯黄期(EOS)和生长季长度(DOS);最后分区域比较和评估两种传感器提取物候参数的潜力。研究结果表明：2005年全球大部分地区植被在第100～140 d开始生长,到第260～300 d逐渐停止生长,生长季长度集中在130～180 d,并且和区域研究结果具有一致性;两种传感器提取的植被关键物候期的空间变化趋势是一致的,随着纬度升高,返青期呈现推迟趋势,枯黄期呈现提早趋势,生长季长度呈现缩短趋势;AVHRR和MODIS提取落叶林和草地的SOS、EOS和DOS在欧亚大陆和北美洲区域的相关系数大部分达到0.9以上。     

新疆棉花物候时空变化遥感监测及气温影响分析

基于2001～2016连续16a的MOD09Q1数据计算获取NDVI时序数据,利用Savitzky-Golay(S-G)滤波重构NDVI时序曲线,以动态阈值法提取新疆大地块棉花的生长季开始期、生长季结束期和生长季长度信息,并分析新疆棉花物候时空变化特征及其对气温变化的响应。结果表明:(1)南北疆棉花物候空间差异显著:生长季开始期由南向北逐渐推迟,南疆集中于第151d之前,北疆于第151～163d之间;生长季结束期逐渐提前,北疆大部分于第292d前结束生长,而南疆集中发生在第298d之后;生长季长度逐渐缩短,南疆普遍长于150d,北疆通常短于150d。(2)南北疆物候变化趋势不一致:阿克苏生长季开始期和结束期表现为"推迟—提前—推迟—提前"变化趋势,生长季长度不明显延长;沙湾县生长季开始期表现为微弱提前趋势,生长季结束期则为先推迟再提前趋势,生长季长度表现为缩短、延长、缩短的波动变化。(3)原因分析表明棉花物候主要受气温影响:生长季开始期与10℃初日、15℃初日、生长初期平均气温表现显著的相关性;生长季结束期与10℃终止日相关性较好。     

遥感叶面积指数产品提取自然植被物候期对比

物候是指示气候变化的关键因子,遥感技术的快速发展为物候监测提供了新的途径。遥感叶面积指数(LAI)产品包含了主要的物候信息,并广泛应用于植被物候的监测。了解不同数据产品在提取植被物候信息上的差异是评价遥感产品对物候期监测适用性的重要方面。以东北三省为研究区域,使用非对称性高斯函数拟合法进行数据平滑,利用动态阈值法提取MODIS、CYCLOPES和GLASS叶面积指数(LAI)产品的生长季开始时间(SGS)、生长季结束时间(EGS)和生长季长度(LGS)。研究表明:MODIS和GLASS产品提取的SGS、EGS和LGS比较接近,整体上一致性较好;CYCLOPES产品提取的SGS多数情况下晚于MODIS和GLASS产品而EGS早于MODIS和GLASS产品。通过可利用的实地物候观测数据验证表明:MODIS和GLASS产品提取林地的SGS与物候观测值比较接近,EGS略晚于物候观测值,CYCLOPES产品提取的林地的SGS和EGS更加可靠。     

京津唐地区城市化对植被物候的影响研究

城市化过程使得土地利用、地表植被覆盖发生着显著的变化,如何定量化描述城市化对植被物候的影响越来越受到各方的关注。基于京津唐地区2001~2006年NDVI时间序列影像,得出了京津唐地区植被物候空间分布格局,计算出北京、天津、唐山3个核心城市城区的距离变量与平均植被物候,并分析了城市化对植被物候指标的影响趋势。结果表明:①2001~2006年,北京城市化使得城区及离城区较近的地方植被生长开始时间提前、结束时间推后、生长季周期变长、NDVI振幅减小;②天津和唐山的城市化使得城区及离城区较近的地方植被生长开始时间延后、结束时间提前、生长季周期变短\,NDVI振幅减小;③城市化对植被物候的影响与该地区城市扩张类型存在相关性关系。     

Morlet 小波在地表物候遥感识别中尺度的选取与应用

地表物候是人类了解地球生态系统的必要参数,也是动植物保护、农耕等活动的重要依据。使用遥感数据进行地表物候提取为大尺度的地表物候变化监测提供了一种有效途径。研究发现目前应用最广泛的非对称性高斯函数拟合法和双 Logistic 函数拟合法存在一定的缺陷,尤其是在提取 NDVI 峰值谷值和物候周期不明显区域 (如干旱地区、沙地) 的物候参数时存在严重的误差,而 morlet 小波在分析地表年际变化的周期特征方面表现良好。本文使用 morlet 小波对青海湖流域 2003-2014 年的 MODIS 数据进行分析,得到地表物候在该时间段内的年度变化与趋势,进行不同区域、不同时间的差异性分析,发现青海湖流域的地表物候期整体都略有提前,但生长季的长度呈延长趋势,认为青海湖流域的地表物候整体上响应全球变暖的趋势。中部地区的生长季长度大于高海拔、高纬度区域和沙地区域,认为青海湖流域的中部地区是最适合动植物生长、活动的范围。     

基于遥感的黑河流域植被物候空间格局提取分析

植被物候的检测对于认识自然季节现象的变化规律,服务农作物生产、全球变化、生态学应用方面具有重要价值。植被指数是描述植被数量、质量、植被长势和生物量指标的重要参数。利用SPOT VEGETATION NDVI时间序列数据,采用Savitzky-Golay滤波方法重建了NDVI的年内变化序列,并利用此数据提取了黑河流域植被物候空间分布格局。结果表明,采用此方法得到的植被物候信息和该区域的农事历信息符合较好。黑河流域植被物候具有明显的空间格局。上游的高寒草地区生长季长度较短。中游地区受人类活动影响严重,较为符合该区农作物生长信息。     

2001~2010年松木希错流域植被动态变化遥感研究

遥感在区域植被变化研究中具有十分重要的作用,能为大面积监测植被状况的演化过程提供技术支持。NDVI在高植被覆盖地区存在过饱和现象,对稀疏地区的植被变化尤其敏感。以古里雅冰帽南部的松木希错流域植被相对稀疏区域为研究区,基于MODIS NDVI数据和逐月气象观测数据,以及RS和GIS平台,对该区域2001~2010年主要植被变化趋势进行了初步研究,并对植被变化与气候驱动因子的关系进行了分析和探讨。结果表明:① 2001~2010年间该区域的植被活动有加强趋势;② NDVI表明研究区植被生长季较短(5~9月),NDVI浮动区间为0.11～0.13,低于全国水平(0.3～0.35),也低于全球稀疏灌丛的平均水平(0.2～0.4);③NDVI与年均气温整体上呈正相关,而与年降水量相关性不强。表明近年来持续升温是影响该区域植被活动加强的最主要原因。     

Logistic函数方法拟合多时序NDVI数据的改进研究

单Logistic函数曲线拟合法是NDVI时间序列重建及物候遥感中关键物候期划分的重要方法之一。虽然该方法不需要设定阈值或经验系数、较适应于不同环境区域的物候遥感监测,但是在山区NDVI噪音较大的情形下,其拟合精度仍会受到较大影响。选取秦岭中部山区为研究区,在分析了多年NDVI时间序列数据特征基础上,利用山区NDVI数据序列最大值相对于最小值更为稳定的特性,对传统单Logistic模型求解方法进行改进,采用更为稳定的参数构建模型以提高NDVI时间序列重建的精度。基于秦岭样区MODIS NDVI多年遥感数据,分别在保持植被生长季特征能力和保留高质量原始真值程度两方面对原方法与改进方法的计算结果进行比较。研究表明改进的方法在上述两个方面都具有更好的效果。在植被指数噪音较大的山区,改进的方法对NDVI时间序列重建表现出更好的适用性,可为复杂的山区物候遥感相关研究提供参考。     

The use of 'overlapping' NOAA-AVHRR NDVI maximum value composites for Scotland and initial comparisons with the land cover census on a Scottish Regional and District basis

AVHRR data from April 1995 to September 1995 have been processed to produce 1 km resolution NDVI Maximum Value Composites of Scotland. Temporal profiles of mean NDVI were obtained for Scottish administrative regions. Temporal NDVI profiles for individual vegetation classes, from the Land Cover of Scotland 1988 (LCS88) dataset, were obtained and both temporal and spatial variations within these classes are also discussed. A method for enhancing the existing LCS88 dataset is proposed, based on AVHRR NDVI data, by distinguishing vegetation regional variation within a single class.     

30-second degree grid land cover classification of Asia

With the development of the global economy, environmental research has become more important than ever, especially in the Asian region. The objective of this study is to produce a land cover classification dataset for the whole of Asia using the NOAA AVHRR 1-km dataset. Ground data were mainly collected from existing thematic maps which were obtained from members of the Land Cover Working Group (LCWG) of the Asian Association of Remote Sensing (AARS). Classification was mainly based on cluster analysis of the monthly ratio of surface temperature and Normalized Difference Vegetation Index (NDVI) for seven months from April to October 1992. Additional variables, such as DEM, the maximum monthly composite NDVI in a year, and the minimum monthly composite NDVI in a year were also used in the classification processing. In order to add and improve ground data in the future, collected ground data will be published with the developed land cover dataset.     

基于多时相环境星NDVI时间序列的农作物分类研究

时相和归一化植被指数(NDVI)时间序列特征在农作物分类提取方面具有重要的应用价值。以黑龙江红星农场为研究区,利用多时相环境星HJ-1A/B CCD数据及其多期平滑重构后的NDVI时间序列曲线特征,在对象尺度上采用决策树算法开展了农作物分类研究,通过与单独利用多时相遥感数据分类结果的对比分析,研究了增加NDVI时序曲线特征对分类精度的影响。结果表明:面向对象分类方法得到的地块较为规则,平滑了地块内部同种作物间的噪声,避免了"椒盐现象",适合于我国东北地区农作物分类识别;利用NDVI时序曲线特征参与分类,增强了不同作物之间的光谱差异,提高了作物分类精度,比仅使用3个多时相HJ-1A/B CCD数据分类精度提高了5.45%,Kappa系数提高了0.09。通过该研究探讨了NDVI时序曲线特征在作物分类中的应用,拓展了遥感数据在农业领域的应用范围,具有推广价值。     

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem-atmosphere CO₂ exchange in the Amazon region.     

An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA‐9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA‐16 for 2000–2003 and NOAA‐17 for 2003–2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10‐day Africa‐only dataset. Post‐processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge‐of‐orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/gimms/).     

A ground‐validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula

An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16‐day maximum value composite data from 2000 to 2005. To create the dataset, (1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel‐specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and (2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor‐quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km×5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates (root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula.     

利用遥感监测长江三角洲森林植被物候对气候变化的响应

植物的物候与气候等环境因素息息相关,是指示气候与自然环境变化对生态影响的重要指标。目前,气候变暖日益为人所关注,使用遥感技术研究植物物候与气候变化之间的关系具有重要的意义。监测人口密度高和城市经济发达地区的植物物候对气候变暖的响应,可以揭示区域热环境变化及其产生的生态效应。本研究选取长江三角洲地区为研究区域,使用SPOT卫星VGT传感器的长时间NDVI数据序列,对经济发达区域森林植被的NDVI序列进行非对称性高斯函数拟合法平滑处理,并提取与研究其物候特征,发现①NDVI与气温具有较强相关性,随气候变暖,森林植被NDVI年均值有增加趋势;②森林植被生长活跃期起始日期提前,终止日期延后,时长有明显的延长趋势,生长活跃期内NDVI有所增加;③森林植被NDVI极大值与极小值出现日期均明显提前,NDVI极大值有增大趋势,而极小值呈下降趋势,年内极差增加,NDVI增长期缩短,衰落期延长;④森林植被在春、夏两季NDVI均值有所增长,秋季无明显变化,冬季略有降低。     

时序NDVI数据集重建综合方法研究

时序NDVI数据集已经成功地应用于全球与区域环境变化、植被动态变化、土地覆盖变化和植物生物物理量参数反演等多方面的研究。时序NDVI数据集受到云和气溶胶等大气条件和传感器自身等因素的影响包含很多噪声,影响了其进一步的应用。基于对近几年来普遍使用的5种重建方法的对比分析结果,发展了基于标准差权重和噪声点性质的两种综合方法。以黑河流域2009年16 d最大值合成的MODIS NDVI数据为例,对比了两种综合方法与5种重建方法的效果;并用2009年5月下旬至8月上旬的地面实测NDVI数据验证了两种综合方法的重建效果。结果表明这两种综合方法的效果都优于对比的5种重建方法,它们既保留了原始数据中大部分的点,又最大限度地修正了噪声点,所生产的时序NDVI数据集,可以更好地用来开展全球与区域土地覆盖和植被动态变化监测等研究。     

Vegetation greening in Spain detected from long term data (1981–2015)

ABSTRACT

This study describes a newly developed high-resolution (1.1 km) Normalized Difference Vegetation Index dataset for the peninsular Spain and the Balearic Islands (Sp_1km_NDVI). This dataset is developed based on National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA–AVHRR) afternoon images, spanning the past three decades (1981–2015). After a careful pre-processing procedure, including calibration with post-launch calibration coefficients, geometric and topographic corrections, cloud removal, temporal filtering, and bi-weekly composites by maximum NDVI-value, we assessed changes in vegetation greening over the study domain using Mann-Kendall and Theil-Sen statistics. Our trend results were compared with those derived from some widely recognized global NDVI datasets [e.g. the Global Inventory Modelling and Mapping Studies 3rd generation (GIMMS3g), Smoothed NDVI (SMN) and Moderate-Resolution Imaging Spectroradiometer (MODIS)]. Results demonstrate that there is a good agreement between the annual trends based on Sp_1km_NDVI product and other datasets. Nonetheless, we found some differences in the spatial patterns of the NDVI trends at the seasonal scale. Overall, in comparison to the available global NDVI datasets, Sp_1km_NDVI allows for characterizing changes in vegetation greening at a more-detailed spatial and temporal scale. In specific, our dataset provides relatively long-term corrected satellite time series (>30 years), which are crucial to understand the response of vegetation to climate change and human-induced activities. Also, given the complex spatial structure of NDVI changes over the study domain, particularly due to the rapid land intensification processes, the spatial resolution (1.1 km) of our dataset can provide detailed spatial information on the inter-annual variability of vegetation greening in this Mediterranean region and assess its links to climate change and variability.     

Extraction of damaged areas of windfall trees by typhoons using Landsat TM data

Two typhoons attacked, at an interval of about 2 weeks, the north part of Kyushu Island, Japan, in September 1991. Many trees, especially in artificial forests (main tree species are cedar and cypress) were felled by the typhoons. Landsat TM data taken before and after damage were collected and registered in order to extract the damaged areas. Typical damaged points were selected on the registered images, referring to aerial photographs taken immediately after damage, and the change characteristics of TM bands 1-5, 7 and Normalized Difference Vegetation Index (NDVI) due to the damage were examined. It showed that bands 5 and 7 of the middle-infrared increased more than other bands and that NDVI decreased. Bands 5, 7 and NDVI of each temporal TM data were merged into a single six-band dataset, and the damaged areas were extracted by a maximum likelihood classification using the merged dataset. The damaged areas extracted were evaluated using the aerial photographs. The damaged areas of windfall trees could be extracted with an accuracy of 90% using temporal Landsat TM data acquired before and after damage.