基于生态地理分区的大兴安岭植被物候时空变化

植被与气候的关系十分密切,植被物候可作为全球气候变化的指示器.大兴安岭位于我国最北部,对气候变化较为敏感,研究该区植被物候的时空变化对评估全球变化对陆地生态系统的影响具有重要意义.依据中国生态地理区划图,将大兴安岭划分为4个生态研究区域,本文利用GIMMS NDVI 3g遥感数据集分析1982—2012年大兴安岭整体及各生态地理分区植被物候变化.结果表明: 研究期间,所有分区植被生长季开始日期均表现为提前趋势,生长季结束日期均表现为推迟趋势.植被物候对气候因子变化敏感,尤其是对气温的敏感程度高于降水,其中,北段山地落叶针叶林区植被生长季开始日期与春季温度呈显著负相关;除南段草原区外,其他3个分区植被生长季结束日期均与秋季降水呈显著负相关.从整体来看,植被物候随海拔、纬度的变化趋势明显.     

植物物候变化研究进展

全球变化背景下,植物物候对气候变暖的响应已经成为研究热点。本文就植物物候对温度、水分、光照等气象因子的响应做了总结,并对国内外物候研究方法进行概述,特别是综合了近年来国内物候变化研究的文献,对我国植物春季物候变化情况作了统计:全球变化对我国大部分地区植被的影响主要是生长季提前,但新疆干旱区植被生长季开始日期在区域尺度上没有显著提前或者延迟趋势。由于物候研究方法的差异以及研究尺度的不同,在一定程度上弱化了研究结果的可比性,建议我国应基于目前的中国通量观测网以及各级生态监测网络,建立统一的物候监测平台,同时完善通量数据提取植物物候信息的方法,特别是阈值判定标准,为分析植物物候响应气候变化提供参考。     

植物物候与气候研究进展

植物物候及其变化是多个环境因子综合影响的结果,其中气候是最重要、最活跃的环境因子。主要从气候环境角度分析了植物物候与气候以及气候变化间的相互关系,概述了国内外有关植物物候及物候模拟等方面的研究进展。表明,温度是影响物候变化最重要的因子;同时,水分成为胁迫因子时对物候的影响也十分重要。近50a左右,世界范围内的植物物候呈现出了春季物候提前,秋季物候推迟或略有推迟的特征,从而导致了多数植物生长季节的延长,并成为全球物候变化的趋势。全球气候变暖改变了植物开始和结束生长的日期,其中冬季、春季气温的升高使植物的春季物候提前是植物生长季延长的主要原因。目前对物候学的研究方向主要集中在探讨物候与气候变化之间的关系,而模型模拟是定量研究气候变化与植物物候之间关系的重要方式,国内外已经开发出多种物候模型来分析气候驱动与物候响应之间的因果关系。另外遥感资料的应用也为物候模型研究提供了新的方向。物候机理研究、物候与气候关系以及物候模型研究将是研究的重点。     

基于遥感技术的植被识别方法研究进展

植被具有明显的年际变化特征, 其演替对气候调节以及水土保持具有重要作用。遥感技术是研究区域乃至全球尺度地表植被分布状况以及覆盖度变化的有效手段, 可对地表进行长期、连续的观测, 具有宏观性、广阔性、多样性以及适应性强等优势。综述和归纳了国内外基于遥感监测技术的植被物候以及植被覆盖度时空动态变化的研究现状、不同卫星遥感数据在植被分类中的应用、遥感植被识别的分类方法, 剖析了遥感植被识别分类中决策树等方法及其优缺点, 提出今后遥感植被识别分类仍需进一步研究的若干问题。     

基于不同光谱指数的植被物候期遥感监测差异

植被物候是陆地生态系统响应气候和环境变化的一项综合性指标.遥感光谱已经被广泛用于提取植被物候期,但遥感提取的物候期与站点观测差别很大,其物理意义尚不明确.本文选取中国东北部的一景MODIS数据(2000—2014年),分析了基于红波段和近红外波段的归一化差值植被指数(NDVI)和简单比植被指数(SR)提取的植被生长季起始期(SOS)和结束期(EOS)的差异.结果表明:两者的物候期存在显著差别,基于NDVI提取的SOS比SR提取的SOS平均早18.9 d,基于NDVI提取的EOS比SR提取的EOS平均晚19.0 d,NDVI得到的生长季长度更长.基于NDVI和SR提取的物候期的年际变化也存在显著差别,超过20%的像元SOS和EOS甚至表现出相反的年际变化趋势.上述差异与两种植被指数自身的季节曲线特征和抗噪性差异有关.NDVI与SR观测数据来源完全一致,仅数学表达形式不同,提取的物候期结果却存在显著差异.说明遥感监测的植被物候期高度依赖于植被指数的数学表达形式,如何建立可靠的植被物候期遥感提取方法仍需进一步研究.     

近地遥感在森林冠层物候动态监测中的应用

近地遥感技术是原位观测森林冠层物候的重要手段,具有高时间分辨率的优点,而且空间尺度适中,是实现物候尺度推绎的有力工具.本研究首先评述了利用3种光学传感器(辐射表、光谱仪和数码相机)监测森林物候的近地遥感方法;结合帽儿山通量观测站的实测数据分析识别物候期的不确定性来源,发现最重要的误差来自物候提取方法;剖析近地遥感与其他物候观测方法的衔接以及该技术自身存在的问题.最后提出该领域的重点研究方向: 加强冠层光学(或冠层结构)物候与功能(生理、生态过程)物候的联系;整合各区域冠层物候观测网络,实现冠层尺度的全球物候联网观测与数据共享;充分发挥近地遥感的优势,整合多源多尺度物候数据;发展近地遥感物候模型,改进动态全球植被模型中物候模拟.     

北京地区气候变化和植被的关系——基于遥感数据和物候资料的分析

 气候变化对陆地生态系统的影响及其反馈是全球变化研究的焦点之一。本文利用1951～2000年的气温、降水等气候资料、1982～2000年的NOAA/AVHRR遥感数据和1951～2000年北京春季物候的代表性指标——山桃（Prunus davidiana）始花的物候数据,分析了在年际和年内时间尺度上北京地区各气候参量与植被变化之间的关系。结果显示：植物生长与温度之间的关系远比其与降水之间的关系密切;各气候参量和植被生长状况之间的关系因时间尺度而不同。1)月际水平上,具有显著生态学意义的气候指标对植被生长状况的影响更明显。2)温度与NDVI指标的相互作用最大为零时滞：年际水平上,影响时效约为1年;月际水平上,约为1个月。3)植物物候期与温度之间的关系远比其与降水之间的关系密切。年际尺度上,气候参量和植物物候期的相互作用是同时的,其中气温的影响时效为2年;月际尺度上,实际温度和植物物候期的相互作用时效约为1个月。     

民勤荒漠植被对气候变化的响应

运用民勤荒漠区1974-2009年物候观测资料和2002-2010年植被样方观测资料以及同期气象资料,分析了荒漠植被对气候变化的响应.结果表明:1961-2010年,民勤荒漠区气温升高,空气湿度增大,年均气温升高速率大于全球水平和中国近百年平均水平;植物对气温变化的响应主要表现在春季物候提前、秋季物候推迟、生长季延长;植被对降水量变化的响应主要表现为植被盖度和纯盖度随降水量减少而降低,植株密度、植物多度随降水量变化而波动;植被盖度和纯盖度与年降水量的相关性较高,然后依次为6-7月和4-5月的降水量;植株密度和植物多度与9月降水量呈正相关;植物春季物候提前的次序是芽初膨大期＞芽开放期＞开花始期＞展叶始期和展叶盛期＞花蕾序出现＞开花盛期＞开花末期＞果实成熟期;秋季物候推迟的次序是叶全变色期＞落叶始期＞叶初变色期＞落叶末期.春季气温升高对民勤荒漠区植物物候的影响大于秋季气温升高对物候的影响.     

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。     

东北地区植被物候时序变化

植被与气候的关系非常密切,植被物候可作为气候变化的指示器。东北地区位于我国最北部,是气候变化的敏感区域,研究该区植被物候对气候变化的响应对阐明陆地生态体统碳循环具有重要意义。利用GIMMS AVHRR遥感数据集得到了东北地区阔叶林、针叶林、草原和草甸4种植被25a(1982—2006年)的物候时序变化,得出4种植被春季物候都表现出先提前后推迟的现象,秋季物候的变化则比较复杂,阔叶林和针叶林整体上呈现出秋季物候推迟的趋势,草原和草甸则表现为提前-推迟-提前的趋势。应用偏最小二乘(Partial Least Squares)回归分析了该区域植被物候与气候因子之间的关系,结果表明:春季温度与阔叶林、针叶林和草甸春季物候负相关,前一年冬季温度与草原春季物候正相关,降水与植被春季物候的关系有点复杂;4种植被秋季物候与夏季温度均呈正相关,除草原外,其余3种植被秋季物候均与夏季降水负相关。植被春季物候可能主要受温度影响,而秋季物候很可能主要受降水控制。     

Plant phenology and global climate change: Current progresses and challenges

Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground‐ and remote sensing‐ based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process‐based phenology modeling, and on the scaling of phenology from species to landscape‐level.     

Macrophenology: insights into the broad-scale patterns,drivers, and consequences of phenology

Plant phenology research has surged in recent decades, in part due to interest in phenological sensitivity to climate change and the vital role phenology plays in ecology. Many local-scale studies have generated important findings regarding the physiology, responses, and risks associated with shifts in plant phenology. By comparison, our understanding of regional- and global-scale phenology has been largely limited to remote sensing of green-up without the ability to differentiate among plant species. However, a new generation of analytical tools and data sources—including enhanced remote sensing products, digitized herbarium specimen data, and public participation in science—now permits investigating patterns and drivers of phenology across extensive taxonomic, temporal, and spatial scales, in an emerging field that we call macrophenology. Recent studies have highlighted how phenology affects dynamics at broad scales, including species interactions and ranges, carbon fluxes, and climate. At the cusp of this developing field of study, we review the theoretical and practical advances in four primary areas of plant macrophenology: (1) global patterns and shifts in plant phenology, (2) within-species changes in phenology as they mediate species' range limits and invasions at the regional scale, (3) broad-scale variation in phenology among species leading to ecological mismatches, and (4) interactions between phenology and global ecosystem processes. To stimulate future research, we describe opportunities for macrophenology to address grand challenges in each of these research areas, as well as recently available data sources that enhance and enable macrophenology research.     

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.     

气候变化背景下橡胶树物候研究进展

橡胶树(Heveabrasiliensis)是广布于热带地区的经济林木，是战略物资天然橡胶的主要来源，其物候学的研究对胶园生产管理和评估热带地区植被对全球气候变化的响应方面具有重要意义。早期的物候研究主要服务于苗木繁育、割胶规划和抗逆栽培等生产应用；利用遥感监测植被物候日趋成熟，已广泛应用于橡胶树并成为主流的物候监测方法；橡胶树物候具有明显的时空异质性，对气候变化的响应较为复杂，其中温度和降水是关键影响因子，同时内因(品系、基因和树龄等)和外因(种植密度、地理位置和农业措施等)也共同影响了其物候。为更好服务天然橡胶产业的可持续发展和热区气候变化科学研究，未来的橡胶树物候研究应重点关注多源遥感数据的协同重建、物候指标提取算法的普适化和遥感预测模型的精准化。该文系统梳理了橡胶树物候的监测方法、服务价值、时空格局，提出了存在问题及未来研究方向。     

全球变化下植物物候研究的关键问题

总结了全球变化下植物物候研究的主要进展,针对该领域国内外的几个热点问题进行了讨论。植物物候研究的重心从以前的野外观测和初步统计分析逐步过渡到以揭示物候周期的调控机制和环境效应为主,研究手段从植物物候对环境变化做出反应的表象描述转移到多尺度、多要素耦合关系的综合分析。随着学科交叉研究的不断深入,植物物候研究从植物个体及居群适应性研究转向植物物候变化对生态系统、气候演变、农业生产乃至人类健康等方面影响的系统评估。并且在该转变过程中出现了几个关键性问题,如不同温度带大气温度与光周期对植物物候期贡献力问题、植物物候变化对气候变暖的非线性响应特征、群落水平上植物物候研究的复杂性、以及农业生态系统中作物物候研究的重要性等。对我国植物物候研究现状和管理体系中亟待解决的问题提出了建议。     

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

Understanding large‐scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County‐level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short‐term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county‐level corn yields. By conflating heterogeneous phenology‐based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology‐based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end‐of‐the‐season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root‐mean‐square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high‐dimensional (spectral, spatial, and temporal) input features to achieve a robust county‐level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.     

近十年的国际植被物候研究文献发展态势分析

基于Web of Science数据库的检索结果,利用Histcite、Bibexcel和Netdraw对国际植被物候研究文献进行计量分析。结果表明:1 060篇相关文献刊载于288种期刊,平均载文3.68篇; 共分33个研究方向; 3 380位作者(第一作者904位)、69个国家或地区、1 172个组织参与; 国际合作发文310篇,占比29.25%; 其中中美合作居第一(19次)。分析还表明:2002～2007年是该领域重要发展期; 国际植被物候研究热点主要集中在基于气候(climate)-物候的田间局地观测和基于遥感(remote sensing)的大尺度物候研究。     

Temperature and snowfall trigger alpine vegetation green‐up on the world's roof

Rapid temperature increase and its impacts on alpine ecosystems in the Qinghai–Tibetan Plateau, the world's highest and largest plateau, are a matter of global concern. Satellite observations have revealed distinctly different trend changes and contradicting temperature responses of vegetation green‐up dates, leading to broad debate about the Plateau's spring phenology and its climatic attribution. Large uncertainties in remote‐sensing estimates of phenology significantly limit efforts to predict the impacts of climate change on vegetation growth and carbon balance in the Qinghai–Tibetan Plateau, which are further exacerbated by a lack of detailed ground observation calibration. Here, we revealed the spatiotemporal variations and climate drivers of ground‐based herbaceous plant green‐up dates using 72 green‐up datasets for 22 herbaceous plant species at 23 phenological stations, and corresponding daily mean air temperature and daily precipitation data from 19 climate stations across eastern and southern parts of the Qinghai–Tibetan Plateau from 1981 to 2011. Results show that neither the continuously advancing trend from 1982 to 2011, nor a turning point in the mid to late 1990s as reported by remote‐sensing studies can be verified by most of the green‐up time series, and no robust evidence for a warmer winter‐induced later green‐up dates can be detected. Thus, chilling requirements may not be an important driver influencing green‐up responses to spring warming. Moreover, temperature‐only control of green‐up dates appears mainly at stations with relatively scarce preseason snowfall and lower elevation, while coupled temperature and precipitation controls of green‐up dates occur mostly at stations with relatively abundant preseason snowfall and higher elevation. The diversified interactions between snowfall and temperature during late winter to early spring likely determine the spatiotemporal variations of green‐up dates. Therefore, prediction of vegetation growth and carbon balance responses to global climate change on the world's roof should integrate both temperature and snowfall variations.     

植物物候学研究进展

植物物候变化在研究陆地生态系统对气候变化的响应时被誉为"矿井中的金丝雀"，全球气候变化愈演愈烈，重新引起了人们对植物物候研究的广泛关注。随着观测技术的发展，在各种空间和生态尺度上收集到的物候观测数据迅速累积，尽管已经在多个尺度上（物种、群落和景观尺度）观察到物候变化，但物候变化的机理仍然没有得到很好的理解。回顾了国内外植物物候研究的发展历程；总结了物候数据收集技术进展和全球物候变化的主要趋势；归纳了植物物候变化的机理与驱动因素；探讨了物候模型研究及物候对气候变化响应研究的主要方向。随着物候观测技术在不同尺度上应用的增加，物候研究进入了一个新的阶段。未来物候研究需要制定跨区域标准化观测指南，融合所有相关学科，改进物候模型，拓展研究区域；同时融合有效的历史物候资料，采用新技术和长期收集的物候数据为大数据时代植物物候学研究提供基础。