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.     

基于MODIS黄河三角洲湿地NPP与NDVI相关性的时空变化特征

应用EOS/MODIS卫星遥感资料,分析2001-2006年黄河三角洲地区植被净初级生产NPP及NDVI时空变化特征,在到海岸线不同距离的区域内,讨论了年植被净初级生产力( aNPP)与年均归一化植被指数(ANDVI)的相关性,并建立一元回归分析模型.可以看出,黄河三角洲湿地生态系统分布自海向陆具有明显的空间异质性,滩涂湿地分布于沿海地区,生产力高,该地区aNPP值高且与ANDVI呈显著正相关;其他区域由于是盐碱荒地或农业生态系统,因土壤盐分含量高而系统生产力低,或农耕地受人类干扰严重,一年总的生产力下降,而导致这些区域ANDVI与aNPP呈负相关.利用NDVI进行黄河三角洲生产力监测的可行区域,在距离海岸线小于6_km的范围内,所研究的6a回归模型均通过α=0.01的显著水平的F检验,模型的R2值为0.86-0.97.同时在生产力无法估测的区域,可以使用NDVI有效管理黄河三角洲生态系统,提高其生产力水平.     

基于MODIS EVI时间序列的鄱阳湖湿地植被覆盖和生产力的动态变化

鄱阳湖是我国最大的淡水湖, 是与长江保持自由连通的两个湖泊之一, 也是最为重要的候鸟越冬地之一, 其生境质量对全球的生物多样性保护至关重要。枯水期的鄱阳湖由众多子湖构成, 不同子湖具有不同的水文控制与管理模式, 尤其是位于长江上游的三峡大坝2006年正式运行之后, 不同水文控制模式下的子湖展现出不同的退水机制, 对退水期洲滩出露的时间与湿生植被覆盖和生产力产生了不同的影响。近年来, 遥感和生态模型在研究植被变化中应用广泛。本文以MODIS增强植被指数(enhanced vegetation index, EVI)时间序列表示地表属性, 并利用EVI时间序列模型, 建立了2000-2014年植被覆盖和生产力的时空变化趋势。在研究区内建立的网格中, 随机提取了107个斑块, 采集其每16天间隔的MODIS EVI时间序列(2000年2月至2015年4月), 将自适应Savitzky-Golay平滑算法应用于EVI时间序列分析, 提取了4个关键的植被生长指标, 即生长季开始的日期、生长季长度、生长季EVI峰值和生产力。研究结果表明: (1)具有不同水文控制模式下的湿地植被生长特征表现出显著的差异, 尤其位于自由连通子湖的植被与其他模式的子湖相比: 生长季开始的时间更晚, 生长季较短, EVI峰值较低, 并且生长季节的初级生产力较低; (2)由于水文情势的改变, 自由连通子湖2006年前后的双生长周期湿地植被的生长特征差异明显, 秋季生长季提前, 导致了生物量的过度积累, 降低了越冬雁类食源的适口性; 但位于局部水文控制子湖的湿地植被不存在这种差异。(3)自由连通与局部水文控制的子湖对鄱阳湖越冬候鸟的保护均具有十分重要的意义, 需要保证这两种类型子湖的面积, 为越冬候鸟提供更广阔的食源; 当水文情势发生改变时, 局部的水文人为控制可在一定程度上减缓鄱阳湖水情变化对湿地植被生长带来的影响。     

黄河三角洲典型植被与地下水埋深和土壤盐分的关系

土壤盐分和地下水埋深是影响黄河三角洲植被发育和分布的重要因素.本文通过野外调查与统计分析,研究了黄河三角洲区域典型植被(翅碱蓬 柽柳、刺槐、芦苇和棉花)、地下水埋深、土壤盐分之间的关系.结果表明: 研究区地下水埋深显著影响土壤盐分,平均影响系数为0.327,地下水埋深在0.5～1.5 m的土壤盐渍化最严重;整个研究区内植被发育较差,其中,研究区78%面积的归一化植被指数(NDVI)<0.4,地下水埋深、土壤盐分对天然植被分布有显著影响;土壤盐分对研究区翅碱蓬-柽柳、刺槐、芦苇及棉花NDVI的影响显著,地下水埋深对翅碱蓬-柽柳NDVI的影响显著,对芦苇、棉花、刺槐NDVI的影响不显著.     

基于MODIS/NDVI时间序列的森林灾害快速评估方法——以贵州省为例

以2005—2008年中分辨率成像光谱仪(MODIS)的归一化植被指数(NDVI)数据集为主要数据源构建研究区时间序列,结合统计学分析方法设计一套快速有效的森林灾害评估体系,用以探测森林资源损失的空间分布范围与灾害等级,并利用森林资源雪凝灾害损失的实际调查数据对评估结果进行一致性检验。结果表明:通过计算2005—2007年研究区所有森林像元的平均值R珔2005-2007和平均绝对偏差δall,确定森林灾害阈值DT为0.048;利用该阈值,获取2008年受灾较重的森林空间分布,主要密集分布在贵州省东南部和东北部,包括了黔南自治州、黔东南自治州和铜仁市等地区;受灾森林像元数占总森林像元数的28.6%,高于凝冻灾害森林资源损失实际调查结果(17.7%);在县域尺度上,根据MODIS/NDVI关键期影像获取的森林资源损失区域和灾害等级,确定德江、沿河和都匀等11个重度受灾县(市、区)和湄潭、榕江和桐梓等10个中度受灾县(市、区),与小班调查结果具有较高的一致性,其Kappa系数为0.86。方法为大区域尺度的森林灾害快速评估提供了一种新思路。     

Infinite hidden Markov models for multiple multivariate time series with missing data

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.     

Automatic reconstruction of molecular and genetic networks from discrete time series data

We apply a mathematical algorithm which processes discrete time series data to generate a complete list of Petri net structures containing the minimal number of nodes required to reproduce the data set. The completeness of the list as guaranteed by a mathematical proof allows to define a minimal set of experiments required to discriminate between alternative network structures. This in principle allows to prove all possible minimal network structures by disproving all alternative candidate structures. The dynamic behaviour of the networks in terms of a switching rule for the transitions of the Petri net is part of the result. In addition to network reconstruction, the algorithm can be used to determine how many yet undetected components at least must be involved in a certain process. The algorithm also reveals all alternative structural modifications of a network that are required to generate a predefined behaviour.     

黄河三角洲滨海滩涂不同密度柽柳林的土壤盐碱与养分特征

为探讨黄河三角洲滨海滩涂不同密度柽柳林的土壤盐碱和养分特征,明确不同密度柽柳林的“盐谷”及“肥岛”效应,以黄河三角洲山东省滨州市北海新区滨海滩涂的低密度（1100 株/hm2）、中密度（4100 株/hm2）和高密度（7100 株/hm2）柽柳林为研究对象,测定分析不同密度柽柳林及柽柳植株周边不同位置的土壤pH、电导率、速效氮、速效磷、速效钾、有机碳等指标。结果表明：（1）土壤电导率随林分密度增大表现为逐渐减小,在中、高密度柽柳林,土壤电导率分别比低密度降低28.39%、55.74%;随距离柽柳植株远近不同,中、高密度柽柳林土壤电导率表现为根部<冠幅中心<冠幅边缘<株间空地,即中、高密度柽柳林出现“盐谷”效应,而低密度林分未出现。（2）土壤速效氮、速效磷和有机碳含量随林分密度增大表现出先增大后减小,在中、高密度柽柳林,柽柳植株周边不同位置的速效氮、速效磷和有机碳含量存在显著差异（P<0.05）,表现为根部>冠幅中心>冠幅边缘>株间空地,而低密度柽柳林差异不显著（P>0.05）;即中、高密度柽柳林出现“肥岛”效应,而低密度林分未出现。（3）中密度柽柳林在养分含量、养分富集率方面高于高密度,在盐分富集率方面低于高密度,具有更强的“肥岛”、“盐谷”效应。（4）不同密度柽柳林以及柽柳植株周边不同位置的土壤pH差异均不显著（P>0.05）。林分密度显著影响土壤盐分及养分含量,中密度柽柳林具有显著提高土壤肥力的作用,高密度次之,而低密度较差。不同密度柽柳林的降盐作用表现为中、高密度较好,低密度较差。基于柽柳林改良土壤盐碱与养分的作用,建议黄河三角洲滨海滩涂柽柳合理的初植密度为4100 株/hm2。     

黄土高原泾河流域长时间序列的归一化植被指数动态变化及其驱动因素分析

泾河流域土地开发历史悠久, 是黄土高原水土流失的典型区域。研究气候变化和人类活动影响下泾河流域的植被覆盖变化及其原因, 对黄土高原的植被恢复、水土保持和景观管理等都具有重要意义。该研究应用GIMMS归一化植被指数NDVI、土地覆盖分类数据和气候数据, 采用趋势分析和相关分析方法, 研究了泾河流域1982-2005年植被覆盖变化趋势及其驱动因素。研究表明: 泾河流域24年间79.64%的区域NDVI无显著变化趋势, NDVI趋势显著增加的区域占16.33%, 主要集中在流域中部和南部, NDVI趋势显著减小的区域占4.03%, 主要集中在流域北部。流域所有气象站点的降水量均无显著变化趋势, 气温均呈显著升高趋势。分析发现气候变化不能很好地解释NDVI趋势的空间分异, 人为因素更为重要。从土地利用分析结果来看, NDVI不同趋势下各土地利用类型比例无明显变化, 但NDVI显著增加区以耕地为主, 显著减小区以草地为主, 由此推断NDVI的显著增加趋势主要由耕地NDVI增加引起, 显著减小趋势可能与林地减少和草地退化有关。通过分析不同分区的土地利用数据和社会经济资料, 着重探讨了造成植被覆盖显著变化趋势的人为因素。     

Temperature sensitivity of soil organic carbon decomposition increased with mean carbon residence time: Field incubation and data assimilation

Temperature sensitivity of soil organic carbon (SOC) decomposition is one of the major uncertainties in predicting climate‐carbon (C) cycle feedback. Results from previous studies are highly contradictory with old soil C decomposition being more, similarly, or less sensitive to temperature than decomposition of young fractions. The contradictory results are partly from difficulties in distinguishing old from young SOC and their changes over time in the experiments with or without isotopic techniques. In this study, we have conducted a long‐term field incubation experiment with deep soil collars (0–70 cm in depth, 10 cm in diameter of PVC tubes) for excluding root C input to examine apparent temperature sensitivity of SOC decomposition under ambient and warming treatments from 2002 to 2008. The data from the experiment were infused into a multi‐pool soil C model to estimate intrinsic temperature sensitivity of SOC decomposition and C residence times of three SOC fractions (i.e., active, slow, and passive) using a data assimilation (DA) technique. As active SOC with the short C residence time was progressively depleted in the deep soil collars under both ambient and warming treatments, the residences times of the whole SOC became longer over time. Concomitantly, the estimated apparent and intrinsic temperature sensitivity of SOC decomposition also became gradually higher over time as more than 50% of active SOC was depleted. Thus, the temperature sensitivity of soil C decomposition in deep soil collars was positively correlated with the mean C residence times. However, the regression slope of the temperature sensitivity against the residence time was lower under the warming treatment than under ambient temperature, indicating that other processes also regulated temperature sensitivity of SOC decomposition. These results indicate that old SOC decomposition is more sensitive to temperature than young components, making the old C more vulnerable to future warmer climate.