浙江省作物多熟种植制度遥感反演及其时空格局

结合作物物候特征,利用2001-2010年的MODIS/NDVI时间序列数据,在基于改进的Savitzky -Golay滤波算法重构高质量序列的基础上,对浙江省作物熟制进行遥感反演,并分析了浙江省作物熟制的时空格局变化特征.结果表明:空间分辨率为250 m的16 d合成MODIS/NDVI时间序列数据能够准确定量地反演出作物熟制的时空格局;NDVI峰值集中出现于每年的2-3月、5-6月和8-9月;浙江省作物种植以一年二熟制为主,从2001-2010年的种植情况来看,一年二熟作物种植面积最大,年际变化幅度较小,一年一熟和一年三熟作物种植面积相当,年际变幅相对较大;2001年以来浙江耕地复种指数整体呈缓慢增加趋势,2010年复种最大为205.5,但距其潜力理论值288仍有一定距离,表明浙江复种潜力仍较大,耕地利用程度有待提高.     

鄱阳湖农业区多熟种植时空格局特征遥感分析

农业土地利用活动是人类作用于地球系统最为直接的扰动因素,其变化会因改变生态系统过程与格局以及生态系统资源有效性而对生态系统功能在局地到全球尺度都产生重要的影响.中国南方普遍实施的多熟种植制度是高强度土地利用的重要特征之一,在中国传统的三熟农业区之一--江西省鄱阳湖区域,以农户为管理单元的农业种植制度由于受到洪水风险、经济效益及农业政策的调节,其时空格局动态也因此更加复杂.以分布在鄱阳湖平原的农田为研究区,结合多时相MODIS影像表达的作物生长规律和农业物候观测记录,检测并分析农业多熟种植的时空格局特征.研究结果表明空间分辨率500m、8d合成的MODIS/EVI时间序列数据能够定量表达出农业种植的多熟制特征,可以应用于区域农业多熟种植制度时空分析,研究区种植制度时空格局的形成是农户对气候、社会经济及粮食安全状况响应及适应的结果.在空间和时间上清晰地认识农业多熟种植的特征,将会对掌握高强度土地利用的过程和特点,模拟与评估土地利用对粮食安全及生态环境安全的影响有重要的意义.     

2003-2019年山东省冬小麦关键物候期时空特征

农作物物候的变化会直接影响农作物的生长过程,进而影响其质量和产量。基于MODIS EVI遥感数据,提取2003-2019年山东省冬小麦种植区以及主要物候期（返青期、拔节期、抽穗期、成熟期）信息,并分析各物候期的时空变化特征。结果表明：2003-2019年山东省冬小麦返青期、拔节期、抽穗期、成熟期分别集中在一年中的第45-70天、第75-100天、第103-125天、第135-155天;4个物候期的空间分布均呈鲁南早于鲁北、内陆早于沿海的格局特征。区域尺度上,返青期、拔节期、抽穗期、成熟期年际变化率分别为1.56 d/10a、0.65 d/10a、-0.54 d/10a、2.51 d/10a,变化趋势均不显著。像元尺度上,返青期与拔节期变化以推迟趋势为主,呈显著推迟趋势的区域分别占总面积的1.65%、3.05%,这些区域主要分布于鲁东沿海地区和鲁南内陆地区;抽穗期变化以提前趋势为主,呈显著提前趋势的区域占总面积的6.56%,集中分布于菏泽东北部地区;成熟期变化以推迟趋势为主,呈显著推迟趋势的区域占总面积的10.23%,多出现在滨州、德州以及济宁等地区。研究结果可为山东省农田和冬小麦管理以及农业生态保护等提供数据支持和科学参考。     

黄淮海多熟种植农业区作物历遥感检测与时空特征

多熟种植是高强度农业土地利用的重要特征,但由于缺乏在空间和时间上清晰描述农业多熟种植和作物种植历时空分布的数据,使得区域尺度农田生态系统碳动态估计、农田生产力监测与模拟等有很大的不确定性。黄淮海农业区是以冬小麦-夏玉米二熟制为主的我国粮食主产区,冬小麦和夏玉米分别为光合作用途径为C3和C4的作物,已有研究证明如果在估算生态系统生产力时不考虑一年两季作物及其光能利用率的差异则会导致生产力估算结果过低。研究结合农业气象站点地面作物物候观测数据和空间分辨率500m、8d合成的MOD IS时间序列数据,分析研究区二熟制作物的生长过程、物候特征和作物历的空间差异,发展基于EVI和LSWI时间序列曲线检测多熟区各季作物种植历的方法,获取黄淮海农业区空间表述清晰的熟制和各季作物的生长开始与结束时间数据,并应用农业气象站点数据对方法和所获取的作物历数据进行了比较验证。论述的方法和提取的各季作物的作物历时空数据将能够应用于区域尺度农田生产力估算、生物地球化学循环模拟和农业生态系统监测。     

藏北高原植被物候时空动态变化的遥感监测研究

 利用遥感数据提取的植被物候格局及时空变化特征能很好地反映区域尺度上植被对全球变化的响应。目前关于青藏高原地区植被物候的少量报道基本上是基于物候站点的观测记录展开分析的。该文基于非对称高斯拟合算法重建了藏北高原2001–2010年的MODIS EVI (增强型植被指数)时间序列影像, 然后利用动态阈值法提取整个藏北高原2001–2010年植被覆盖的重要物候信息, 包括植被返青期、枯黄期与生长季长度, 分析了植被物候10年间平均状况的空间分异特征以及年际变化情
况, 并结合站点观测记录分析了气温和降水对植被物候变化的影响, 结果表明: (1)藏北高原植被返青期在空间上表现出从东南到西北逐渐推迟的水平地带性与东南高山峡谷区的垂直地带性相结合的特征, 近60%区域的植被返青期提前, 特别是高山地区; (2)植被枯黄期的年际变化不太明显, 大部分地区都表现为自然的年际波动; (3)生长季长度的时空变化特征由植被返青期和枯黄期二者决定, 但主要受返青期提前影响, 大部分地区生长季长度延长; (4)研究区内不同气候区划植被物候的年际变化以那曲高山谷地亚寒带半湿润区和青南高原亚寒带半干旱区的植被返青期提前和生长季延长程度最为明显; (5)基于气象台站数据分析气候变化对物候的影响发现, 返青期提前及生长季延长主要受气温升高的影响, 与降水的关系尚不明确。     

山西植物功能型划分及其空间格局

随着全球气候变化的加剧,作为沟通陆地生态系统与气候变化的桥梁,植物功能型(Plant Functional Types,PFTs)越来越受到生态学家的关注。PFTs不仅是简化生态系统复杂性的有效工具,而且可将植物的生理生态过程、生物物理特征及物候变化等引入到动态植被模型中,研究气候变化下的植被反应及其反馈机制。为了在区域尺度上研究气候变化和植被反应,基于"生态-外貌"原则,依据植物特征(如生长型、叶的性状)及其对水分、温度的需求,结合区域的气候与地理条件,对山西植被进行植物功能型的划分,并在此基础上对其空间格局进行分析。结果表明:(1)山西植被可划分为19类植物功能型(其中包括4类栽培作物功能型),分别是:寒温性常绿针叶林、温性常绿针叶林、寒温性落叶针叶林、温性落叶阔叶林、高寒落叶灌丛、温性落叶灌丛、多年生禾草草原、多年生禾草草丛、多年生禾草草甸、多年生莎草草甸、多年生杂类草草原、多年生杂类草草丛、多年生杂类草草甸。1年生杂类草草甸、多年生豆科草原、果树、一年一熟栽培作物、一年二熟栽培作物和二年三熟栽培作物。植物功能型的划分和分布与山西植被区划有较好的一致性,基本反映了植物固有特征及其对水热条件的需求。(2)农作物在山西占有较大比重,占植被类型面积的53.15%,森林类型以温性常绿针叶林和温性落叶阔叶林为主,灌丛类型以温性落叶灌丛为主,草本类型中多年生禾草草丛占较大比例,占草本类型面积的50.98%。(3)由于水热条件及地理条件的差异,植物功能型(不考虑栽培作物)在各区域表现出较大差异,如多年生杂类草草原主要分布于北部地区,在南部并不存在这种植物功能型;森林类型的功能型主要分布于中、南部地区,且结构复杂、类型多样。(4)除栽培作物表现出较好的整体性和连通性,其他植物功能型均表现出不同程度的破碎化和离散化。(5)山西植物功能型整体上表现出较高的多样性,其中中部地区比其他地区的多样性和破碎化程度高,斑块类型更加趋向于离散的小斑块状,北部地区则以一年一熟栽培作物占明显优势,表现出较强的优势度,而南部地区并没有表现出很强的破碎度或优势度。     

中国主要农作物产量波动影响因素分析

为研究我国主要农作物(粮食、水果、油料、糖料和棉花作物)产量波动特征及相关影响因素,以我国1981 ～2010年主要农作物产量和种植面积资料为基础,同时利用4种方法对农作物单产进行趋势拟合与分解.结果表明:(1)近30年来我国农作物总产和单产都逐步提高,特别是水果产量的增长最快,其中柑橘单产年均增长率达7.09％;各类作物趋势产量也呈上升趋势;但气象产量波动剧烈,年际间缺乏连续性,其中水果气象产量波动幅度最大(柑橘为17.76％、苹果为15.83％).(2)作物总产的年际波动中,种植面积和单产因素的贡献率因作物类型不同而有所差异,其中粮食作物和水果总产波动的主要原因是单产波动,糖料作物和棉花产量波动主要因种植面积波动导致,油料作物总产的波动中单产和种植面积贡献相当.(3)各类作物单产年际波动均取决于气象因素,农业政策和科技进步等社会因素对单产年际波动的贡献较低.(4)作物趋势产量的4种拟合方法,其结果无显著差异.各影响因素对我国农作物产量波动的贡献率因作物类型不同而有所差异;重视作物单产提高、关注气候变化对农作物生产的影响是目前我国农业安全生产的重要任务.     

1996—2016年松嫩平原传统大豆种植结构的时空演变

在转基因大豆冲击下,我国传统大豆种植面积大幅下降,研究大豆主产区传统大豆种植结构及其动态变化对我国大豆进出口贸易和保障国家粮食安全意义重大.本研究基于1996—2016年Landsat TM/OLI遥感影像,以遥感、地理信息系统技术为支撑,利用随机森林方法提取大豆空间分布信息,并结合景观格局指数分析松嫩平原大豆种植结构时空演变特征.结果表明: 1996—2016年,松嫩平原大豆种植面积呈波动变化;在与其他地物类型面积转换中,大豆与其他作物面积间的转换最明显;大豆斑块面积占作物总面积的比例呈先增后减特征,而斑块密度指数和分离度指数均呈先减后增趋势.松嫩平原传统大豆种植结构时空演变表现出种植面积波动变化、破碎度增加、空间分布趋于离散的特征;国内外市场及政策导向成为传统大豆种植结构变化的重要影响因素.     

基于MODIS-EVI数据和Symlet11小波识别东北地区水稻主要物候期

作物物候信号能够反映温度和降水等变化对植被生长的影响,是进行农作物动态分析和田间管理的重要依据。基于2008年EOS-MODIS多时相卫星遥感数据,研究了我国东北地区水稻的主要物候期的识别方法。首先提取研究区24个农业气象观测站所在位置的MODIS-EVI(Enhanced Vegetation Index,增强型植被指数)指数的时间序列;同时利用小波滤波消除时间序列上的噪音,小波滤波选用函数包含Daubechies(7-20),Coiflet(3-5)和Symlet(7-15)共26种类型。然后根据水稻移栽期、抽穗期和成熟期在EVI时间序列上的表现特征来识别水稻主要物候期。最后与东北地区24个站点水稻物候观测资料对比并分析误差。结果表明,Symlet11小波滤波的效果最好,其移栽期识别结果的误差绝大部分在±16 d,抽穗期和成熟期识别结果的误差在±8 d。表明通过此方法可以较好地识别东北水稻主要物候期,并可进一步应用到整个东北地区水稻的物候空间分布和时间变化特征研究上。     

农作物景观生态研究:概念框架与研究方向

针对当前农业系统中农作物景观变化迅速、人地关系趋于复杂、粮食安全保障需求迫切，提出景观生态学和农业科学交叉产生的农作物景观生态研究这一新兴研究方向。通过构建农作物景观生态研究的概念框架，辨明农业生产中复杂的景观等级结构，为农业可持续发展提供理论依据。综述从农业景观到农作物景观的研究变化，指出农作物景观具有高动态性、尺度性和格局复杂特征。针对由种植者、消费者、耕地及其周边自然环境组成的农业景观，需要遵循相应的生态和农业经济发展规律，研究农作物种植格局变化、农作物的社会-生态效应和农作物种植可持续性的景观生态途径。重点开展以下四个研究方面的内容，农作物景观时空动态及其形成机理、农作物种植的多功能评价与权衡、景观农艺管理措施及情景模拟、可持续的农作物景观生态评价。从而构建"格局-过程-服务-管理-可持续性"的农作物景观生态研究范式，为农业可持续发展提供综合的空间分析与管理途径，为实现农业农村现代化与城乡人类福祉提升提供科学支撑。     

黄淮海地区耕地复种指数的时空格局演变

耕地复种指数是土地利用强度的重要表征,时空动态特征有助于理解人类活动与生态环境的耦合作用。以黄淮海地区2001—2015年MODIS(Moderate-resolution Imaging Spectroradiometer,中分辨率成像光谱仪)NDVI(Normalized Difference Vegetation Index,归一化植被指数)遥感影像为数据源,使用Savitzky-Golay滤波对时间序列曲线平滑重构后,结合研究区物候信息设置含有阈值的二次差分算法提取复种次数,最后在R环境下绘制复种指数空间分布图。结果表明:(1)河南省复种指数最高(169.3%),山东省次之,天津市最小;(2)各省市年际变化趋势大体一致,经历了升高-降低-升高的过程;从空间分布特征来看,耕地复种指数具有明显的地域性差异,二熟制主要集中于南部,东部和北部受地形和纬度影响,主要以一熟制为主。研究结果对于黄淮海农耕区的土地利用强度辨识、人类活动方式确定具有参考价值,同时也证明了该方法具有更大尺度推广的潜力。     

Functional differences between summer and winter season rain assessed with MODIS‐derived phenology in a semi‐arid region

Questions: We asked several linked questions about phenology and precipitation relationships at local, landscape, and regional spatial scales within individual seasons, between seasons, and between year temporal scales. (1) How do winter and summer phenological patterns vary in response to total seasonal rainfall? (2) How are phenological rates affected by the previous season rainfall? (3) How does phenological variability differ at landscape and regional spatial scales and at season and inter‐annual temporal scales? Location: Southern Arizona, USA. Methods: We compared satellite‐derived phenological variation between 38 distinct 625‐km² landscapes distributed in the northern Sonoran Desert region from 2000 to 2007. Regression analyses were used to identify relationships between landscape phenology dynamics in response to precipitation variability across multiple spatial and temporal scales. Results: While both summer and winter seasons show increases of peak greenness and peak growth with more precipitation, the timing of peak growth was advanced with more precipitation in winter, while the timing of peak greenness was advanced with more precipitation in summer. Surprisingly, summer maximum growth was negatively affected by winter precipitation. The spatial variations between summer and winter phenology were similar in magnitude and response. Larger‐scale spatial and temporal variation showed strong differences in precipitation patterns; however the magnitudes of phenological spatial variability in these two seasons were similar. Conclusions: Vegetation patterns were clearly coupled to precipitation variability, with distinct responses at alternative spatial and temporal scales. Disaggregating vegetation into phenological variation, spanning value, timing, and integrated components revealed substantial complexity in precipitation‐phenological relationships.     

Three‐dimensional change in temperature sensitivity of northern vegetation phenology

Understanding how the temperature sensitivity of phenology changes with three spatial dimensions (altitude, latitude, and longitude) is critical for the prediction of future phenological synchronization. Here we investigate the spatial pattern of temperature sensitivity of spring and autumn phenology with altitude, latitude, and longitude during 1982–2016 across mid‐ and high‐latitude Northern Hemisphere (north of 30°N). We find distinct spatial patterns of temperature sensitivity of spring phenology (hereafter “spring S_T”) among altitudinal, latitudinal, and longitudinal gradient. Spring S_T decreased with altitude mostly over eastern Europe, whereas the opposite occurs in eastern North America and the north China plain. Spring S_T decreased with latitude mainly in the boreal regions of North America, temperate Eurasia, and the arid/semi‐arid regions of Central Asia. This distribution may be related to the increased temperature variance, decreased precipitation, and radiation with latitude. Compared to spring S_T, the spatial pattern of temperature sensitivity of autumn phenology (hereafter “autumn S_T”) is more heterogeneous, only showing a clear spatial pattern of autumn S_T along the latitudinal gradient. Our results highlight the three‐dimensional view to understand the phenological response to climate change and provide new metrics for evaluating phenological models. Accordingly, establishing a dense, high‐quality three‐dimensional observation system of phenology data is necessary for enhancing our ability to both predict phenological changes under changing climatic conditions and to facilitate sustainable management of ecosystems.     

农作物需水量随气候变化的响应研究

利用甘肃省近80个气象站1961~2000年的常规气象观测资料以及夏秋主要粮食作物平均生育期资料,采用FAO推荐的Penm an-M on te ith公式结合作物系数,计算了各站夏秋主要粮食作物的需水量,分析作物需水量随时间和空间的变化特点以及对气候变化的响应.结果表明:农作物需水量与种植区的气候类型关系十分密切,从干旱-半干旱-半湿润-湿润地区其需水量呈现减小的趋势,越是干旱的地区作物需水量越大,越是湿润的地区作物需水量越小.作物需水量随气候变化的响应比较明显,在干旱、半干旱地区表现尤为突出.不同作物品种需水量相差较大,对气候的响应机制也有差异,一般来说夏粮的需水量小于秋粮,夏粮需水量对气候变化的响应比秋粮敏感.     

Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere

The inter-annual shift of spring vegetation phenology relative to per unit change of preseason temperature, referred to as temperature sensitivity (days °C⁻¹), quantifies the response of spring phenology to temperature change. Temperature sensitivity was found to differ greatly among vegetation from different environmental conditions. Understanding the large-scale spatial pattern of temperature sensitivity and its underlying determinant will greatly improve our ability to predict spring phenology. In this study, we investigated the temperature sensitivity for natural ecosystems over the North Hemisphere (north of 30°N), based on the vegetation phenological date estimated from NDVI time-series data provided by the Advanced Very High Resolution Radiometer (AVHRR) and the corresponding climate dataset. We found a notable longitudinal change pattern with considerable increases of temperature sensitivity from inlands to most coastal areas and a less obvious latitudinal pattern with larger sensitivity in low latitude area. This general spatial variation in temperature sensitivity is most strongly associated with the within-spring warming speed (WWS; r = 0.35, p < 0.01), a variable describing the increase speed of daily mean temperature during spring within a year, compared with other factors including the mean spring temperature, spring precipitation and mean winter temperature. These findings suggest that the same magnitude of warming will less affect spring vegetation phenology in regions with higher WWS, which might partially reflect plants’ adaption to local climate that prevents plants from frost risk caused by the advance of spring phenology. WWS accounts for the spatial variation in temperature sensitivity and should be taken into account in forecasting spring phenology and in assessing carbon cycle under the projected climate warming.     

黄土丘陵区旱地作物水分生态适应性系统评价

根据调查资料和田间试验结果,系统地分析了旱地不同作物生长发育与降水分布的时序关系、旱地作物水分潜在利用率和旱地主要作物水分供需平衡与错位特征,并利用水分生态适应性数学模型,对宁南黄土丘陵区主要作物的水分生态适应性进行了定量评价.结果表明,秋熟作物生长发育与降水分布耦合性较好,夏熟作物生长发育与降水分布耦合性较差.不同作物的降水潜在利用率存在差异,其基本规律是:多年生牧草>薯类作物>谷类作物;秋熟作物>夏熟作物.作物的水分满足率和生态适应性,秋熟作物优于夏熟作物,丰水年份优于干旱年份.旱地6种主要作物的水分生态适应性指数排序依次为:谷子>马铃薯>糜子>胡麻>豌豆>春小麦.     

Landscape controls on the timing of spring,autumn, and growing season length in mid‐Atlantic forests

The timing of spring leaf development, trajectories of summer leaf area, and the timing of autumn senescence have profound impacts to the water, carbon, and energy balance of ecosystems, and are likely influenced by global climate change. Limited field‐based and remote‐sensing observations have suggested complex spatial patterns related to geographic features that influence climate. However, much of this variability occurs at spatial scales that inhibit a detailed understanding of even the dominant drivers. Recognizing these limitations, we used nonlinear inverse modeling of medium‐resolution remote sensing data, organized by day of year, to explore the influence of climate‐related landscape factors on the timing of spring and autumn leaf‐area trajectories in mid‐Atlantic, USA forests. We also examined the extent to which declining summer greenness (greendown) degrades the precision and accuracy of observations of autumn offset of greenness. Of the dominant drivers of landscape phenology, elevation was the strongest, explaining up to 70% of the spatial variation in the onset of greenness. Urban land cover was second in importance, influencing spring onset and autumn offset to a distance of 32 km from large cities. Distance to tidal water also influenced phenological timing, but only within ~5 km of shorelines. Additionally, we observed that (i) growing season length unexpectedly increases with increasing elevation at elevations below 275 m; (ii) along gradients in urban land cover, timing of autumn offset has a stronger effect on growing season length than does timing of spring onset; and (iii) summer greendown introduces bias and uncertainty into observations of the autumn offset of greenness. These results demonstrate the power of medium grain analyses of landscape‐scale phenology for understanding environmental controls on growing season length, and predicting how these might be affected by climate change.     

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.     

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.