土壤含水量增墒、退墒预测模型的研建与应用

在天然旱地植被系统中,土壤水大小与大气水、地表水和蒸散量等因素关系密切,且存在水量平衡关系。依据水量平衡关系和实测水文资料,以连续无雨日数为影响因子,创建土壤含水量与连续无雨日数相关关系,该关系式即为单站无雨退墒预测模型;以降水为影响因子,创建土壤含水量增量与降水量相关关系,该关系式即为有雨增墒预测模型,实现短期内土壤含水量动态变化预测。预测模型已应用到2016年吉林省中西部墒情分析评价工作中,应用结果表明:该模型理论依据充分,符合天然旱地植被系统中的水量平衡关系;建模简单,应用便捷,可进行短期土壤含水量增墒、退墒预测,预测结果能够科学反映土壤含水量动态变化规律。     

长江流域土壤水量平衡与植被旱度研究

以流域为研究单元,采用水量平衡与估算法相结合的参考蒸散修正模型,定量分析了长江流域九个区域的不同水平年的土壤水分动态、植被耗水量、水分亏缺量及植被旱度.结果表明:长江中下游流域植被旱度较大,特别是偏枯年.不同区域四季干旱规律、干旱程度不同,各有特点.长江流域的水量平衡中水分蒸散是主要的输出形式.因蒸散力较大,所以植被旱度较大,植被恢复要充分考虑其水分条件,选择合适条件的物种.     

人工生态群落对茶园土壤物理化学性质影响的研究

采用定点定位方法进行人工生态群落对茶园土壤物理化学特性的影响研究。结果表明:人工生态群落能改善茶园土壤的物理化学性质。人工生态群落茶园土壤物理性状上下土层含水量分别较单纯茶园提高36.57%和13.50%,毛管持水量分别较单纯茶园提高28.2%和7.2%,从而增强土壤抗干旱能力,总孔隙度较单纯茶园高,容重较纯茶园低。茶园土壤营养元素分布表现为0～20cm的人工生态群落茶园>纯茶园的,而20～40cm纯茶园>人工生态群落茶园,表明人工生态群落茶园土壤营养元素有向上富集的趋势,对表层土的肥力水平有提高作用。另外本文还探讨了人工生态群落茶园土壤水热平衡作用,土壤生物信息等特征。     

陇东黃土高原土壤干旱特征分析

通过对黄土高原典型残塬"董志塬"麦田不同时期土壤各层次水分含量的分析,揭示了陇东黄土高原塬区土壤干旱特征,逐月分析了干旱的季节分布以及不同季节水分在土壤各层次的分布特征。分析认为2m土层干旱概率明显高于0.5m土层,但0.5m土层重旱出现概率明显偏高,各层次干旱出现频率均未超过45%。干旱的季节分布特征表现为:3月～6月土壤水分持续减少,干旱持续发展,6月上中旬是陇东麦田最干旱的时期。小麦收获后,7月分土壤水分开始回升,7月～9月为土壤水分恢复平衡阶段,10月为土壤水分恢复平衡后相对稳定阶段。收墒期降水可以使2m土层土壤水分基本恢复到适宜状态,土壤储水主要分布在2m土层,即2m为土壤水库下限深度。8月开始,麦田中下层土壤水分运动方向发生逆转,由前期的向上运动转变为向下运动。麦田涝渍现象出现在秋季,主要出现在土壤中下层。早春和晚秋麦田重旱发生概率较低,秋季是陇东麦田土壤水分含量最高的时期,晚秋2m土层平均含水量超过早春,土壤水分于秋季恢复平衡,晚秋-早春,即越冬期降水量小于土壤蒸散量,土壤水分有一定损耗。     

典型黑土区作物根层土壤含水量遥感估算及影响因素研究

漫川漫岗黑土区作物根层土壤含水量受植被覆盖、地形、土壤温度、降雨等多因素的影响，是农业生产和农田管理的关键要素。以友谊农场两个不同坡度的典型耕地地块（平耕地与等高种植坡耕地）为研究对象，利用Landsat 8遥感数据结合气象数据等驱动SEBAL(Surface Energy Balance Algorithm for Land)模型估算根层土壤含水量，利用地面实测数据对估算结果展开验证。在此基础上分析根层土壤含水量空间分布的影响因素。结果表明：(1)SEBAL模型估算的根层土壤含水量6月27日决定系数R2为0.85,7月13日决定系数R2为0.68，且7月13日根层土壤含水量明显高于6月27日；平耕地6月27日决定系数R2为0.65,7月13日为0.84；等高种植坡耕地6月27日决定系数为0.64,7月13日为0.50。(2)地块不同导致作物根层土壤含水量影响因素也不同，平耕地主要影响因素为坡度、植被覆盖度和土壤温度；等高种植坡耕地为高程和土壤温度。使用SEBAL模型可以较为快速准确地估算根层土壤含水量，研究结果对于黑土区耕地的水分管理、农业灌溉及水分运移研究具有重要意义。     

基于TSEB模型的黄河三角洲蒸散量估算

蒸散发作为湿地生态系统中地-气间水热交换的主要方式,很大程度上影响着湿地的水热平衡,合理准确地估算蒸散发量,对湿地生态系统的水分循环、能量平衡以及科学管理具有重要意义。黄河三角洲湿地作为世界上暖温带最广阔、最完整和最年轻的河口湿地生态系统,既是气候变化的敏感区,也是生态环境的脆弱区。针对其地理位置特殊、水资源供需矛盾尖锐等特点,利用SEBAL(Surface Energy Balance Algorithm for Land)和TSEB(Two-Source Energy Balance)模型,对黄河三角洲湿地蒸散发量进行估算:首先利用SEBAL模型计算地表的特征参数和各地表通量,然后利用TSEB模型分离土壤和植被,分别计算黄河三角洲湿地瞬时的土壤蒸发、植被蒸腾和土壤植被总蒸散发量,利用积分关系法进行时间尺度转换,得到日蒸散量。利用气象站实测蒸发值和FAO Penman\|Monteith公式计算的作物系数,对遥感估算结果进行直接和间接精度评价。结果表明反演的蒸散发结果合理,精高较高。分析蒸散的空间分布及不同地表类型的蒸散特性,对比分析芦苇沼泽和芦苇草甸的不同蒸散特点,结果表明基于两模型耦合的方法可用于黄河三角洲湿地蒸散量估算。     

黄土丘陵区植被恢复中土壤团聚体演变及其与土壤性质的关系

在野外调查与室内分析的基础上,对黄土丘陵区典型草原带植被自然恢复过程中土壤水稳性团聚体及其主要影响因子的演变规律进行了研究,并用通径分析法对二者的相互关系进行了定量分析。结果表明:随着植被演替,土壤中大粒级水稳性团聚体含量逐步增加,>5mm粒级团聚体在土壤团粒结构中占主导地位,含量占50%~80%。其次是5~2mm含量,占到10%~15%左右。不同群落之间>5mm团聚体含量在2m深土层加权平均值比较结果为:大羽茅群落>长芒草群落>铁杆蒿群落>百里香群落>香茅草群落,其中大羽茅群落是香茅草群落>5mm团聚体含量的近5倍,长芒草群落也是香茅草群落的近4倍左右。主要影响因子对团聚体直接作用系数的大小为:物理性粘粒>有机质>全氮>全铁>阳离子交换量>全量铝>游离铁>全磷>pH值>粘粒>碳酸钙。物理性粘粒、有机质和全氮是影响水稳性团聚体含量的主要因子。     

盘锦芦苇湿地土壤微生物生物量C的季节动态

基于盘锦湿地生态系统野外观测站芦苇群落2005年4月～10月的定位观测资料,分析了盘锦芦苇湿地土壤微生物生物量C的季节变化规律及生态特性:盘锦湿地土壤微生物量生物量C在三个土壤层次都表现出随着芦苇生长季的变化呈现先增长、后下降的"M型"变化趋势.相关分析结果表明:气候因子中降水量和空气相对湿度对微生物生物量碳的影响大于气温;土壤因子中土壤热通量和土壤平均温度的影响大于土壤含水量.通径分析结果表明:在环境因子的综合作用中,起直接作用的因子主要是气温和土壤平均温度,其它因子影响较小.     

江河源区高寒嵩草草甸土壤氨基酸成分和含量特征分析

通过高效液相色谱技术分析了青海省果洛州达日县窝赛乡原生嵩草草甸、严重退化草地及人工草地三类植被土壤中各种氨基酸成分及含量。结果表明:(1)三种类型土壤中都检测出19种常见氨基酸:精氨酸、天冬氨酸、丝氨酸、谷氨酸、苏氨酸、丙氨酸、甘氨酸、氨基丁酸、脯氨酸、蛋氨酸、缬氨酸、苯丙氨酸、异亮氨酸、亮氨酸、胱氨酸、组氨酸、鸟氨酸、赖氨酸、酪氨酸;(2)测定结果表明原生嵩草草甸土壤的氨基酸总量显著高于人工恢复重建草地和严重退化土壤氨基酸,而后两者之间差异不显著。原生高寒草地的土壤(6316.28μgg-1)严重退化草地土壤(2977.10μgg-1)人工恢复重建草地土壤(2975.90μgg-1)。(3)原生高寒草地土壤氨基酸总体呈现下降趋势:5月氨基酸含量最高,随后6月7月的显著下降,8月稍微有所回升,9月氨基酸含量到达最低;严重退化草地土壤与人工恢复重建草地土壤氨基酸含量季节变化相似,氨基酸总量在6月份到达最高点,随后7月8月显著下降,9月份稍微有所回升。     

盘锦芦苇湿地土壤微生物数量研究

基于盘锦湿地生态系统野外观测站芦苇群落生长季6~9月的定位观测资料,分析了芦苇湿地土壤微生物不同时间及不同层次上的动态。结果表明:在三个层次时间动态土壤的细菌数量最大,其次是放线菌,最少的是真菌。不同层次中10～20cm土壤层中的细菌、放线菌和真菌数量最大。细菌数量从6月的最高值逐渐减少,到8月达到最低,而后逐渐增加;放线菌数量从6月最高值急剧减少,至8月达到最低值,然后逐渐增加;真菌数量则表现为从6月开始逐渐增加,至9月达到最大值。芦苇湿地土壤微生物不同层次上的百分比变化表明:在0～10cm、10～20cm和20～30cm的3个层次上及微生物总数中土壤的细菌所占比率最大,而且在3个层次上的比率从上到下是逐渐增大的;其次是放线菌,且在3个层次上的比率是逐渐减少的;最少的是真菌,在3个层次上相差不大,接近为零,在整个微生物中所占比率为最少的。     

基于ETM＋的北京西部山区乔木群落结构研究

利用遥感技术结合地面调查的方法对2008年春季北京西部山区乔木群落的多样性进行了研究。提取归一化植被指数（NDVI）,并计算NDVI随时间的变化率（△NDVI）;利用地面调查数据计算研究区的健康指数、Mar-galef丰富度指数、Shannon-Wiener多样性指数和Simpson多样性指数,并分析△NDVI、健康指数和多样性指数之间的关系。结果表明：（1）健康指数和多样性指数之间存在正相关关系,群落多样性指数越高,群落的整体健康状况越好。（2）△NDVI与健康指数之间呈正相关关系,健康指数越高的群落,单位时间内NDVI的增加值越大,植被群落的生长变化越明显。（3）△NDVI的高低可以代表区域植被的健康程度,反映区域植物群落的丰富度、多样性。△NDVI的值越高,植被群落的健康程度越好,植被群落的丰富度、多样性指数越高。本文的研究较好的将遥感技术和地面调查相结合,对△NDVI与群落植被健康程度、多样性指数进行了研究和验证,为以后研究植被群落多样性提供了一定的借鉴。     

Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona

Hyperspectral water retrievals from AVIRIS data, equivalent water thickness (EWT), were compared to in situ leaf water content and LAI measurements at a semiarid site in southeastern Arizona. Retrievals of EWT showed good correlation with field canopy water content measurements. Statistical analysis suggested that EWT was significantly different among seven community types, from savanna to agriculture. Four band-ratio indexes (NDVI, EVI, NDWI, and NDII) were derived from MODIS showing strong spatial agreement between maps of AVIRIS EWT and MODIS indexes, and good statistical agreement for the range of habitats at the site. Temporal patterns of these four indexes in all vegetation communities except creosote bush and agriculture showed distinct seasonal patterns that responded to the timing and amount of precipitation. Moreover, these time series captured different ecological responses among the different vegetation communities.     

改进的动态图社区演化关系分析方法

传统基于相邻时间片分析所获得的社区演化关系无法完备地刻画动态图社区演化的整个过程。为此提出了一种改进的社区演化关系分析方法。首先,定义社区事件,并根据发生的社区事件来描述社区的演化状态;然后,对两个不相同时间片内的社区进行事件匹配,从而获得社区演化关系;最后,通过实验将所提方法与传统方法进行比较。实验结果表明,所提方法发现的社区事件总数是传统方法的2倍以上,可为动态图社区演化过程的描述提供更丰富的信息。     

基于权重信息挖掘社会网络中的隐含社团

社团结构是一种普遍存在于各类真实网络中的结构特性.挖掘网络的社团结构对于理解网络的功能与行为有着重要作用.然而,现有的各种社团挖掘算法仅仅基于网络拓扑结构信息,而忽视了蕴涵于真实社会网络边权信息中丰富的语义信息.目前普遍使用的基于模块性最大化的社团挖掘算法倾向于将小社团合并,这使得语义上丰富的小社团容易湮灭于基于拓扑结构信息所挖掘出的大社团中.而挖掘出这些隐含于大社团中的有着丰富语义内涵的小社团对于加深社会网络语义层面的理解有着重要作用.为此,提出一个接近线性复杂度的有权网络社团挖掘算法.通过充分利用权重信息,算法可以将社会网络划分为富含语义信息的粒度较细且相对较小的隐含社团.通过对基于DBLP作者合作网络的实证分析,证实了新算法的有效性和高效性.     

Sense of virtual community: A follow up on its measurement

Understanding the dynamics of virtual communities has become an important issue for research. One concept that explains the participation in online communities is a sense of virtual community (SOVC), which is based on the offline equivalent sense of community (SOC) and describes a “spirit of belonging together”. Although these two concepts are similar, their measurement is problematic. Inspired by earlier studies, which investigated whether traditional SOC measures are appropriate for measuring SOVC, we adopted the SOC index 2 (SCI2) recently developed by Chavis et al. in a virtual setting. Our aim was to determine whether the refined SOC measurement is more suitable for virtual communities than their forerunners. We tested the SCI2 in a popular German community on 312 respondents. Our results showed that a thorough measure of SOVC still needs further refinement. We also discuss possibilities for improvement.     

LPX: Overlapping community detection based on X‐means and label propagation algorithm in attributed networks

Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics.     

The new neighbourhood in the internet era: network communities serving local communities

The past two decades have witnessed many attempts to transform online communities in new neighbourhoods of the Internet era. In particular, one of the most interesting applications of Internet Technologies in this field have been ‘network communities’, that differ from online communities because they refer to a specific territory and, for this reason, serve as a social catalyst for the corresponding territorial community. Network communities, as virtual neighbourhoods, have the purpose of allowing a better understanding of physical ones, contributing to the creation and the proliferation of services most suited to the needs of residents. For this reason, municipalities and local governments should consider the opportunity to exploit network communities as useful tools for local community management. Following this lead, this article analyses a real case study and highlights the existence of a positive correlation between a constructive utilisation of a network community by its members, their sense of community and the degree of their involvement in local problems.     

Detecting community structure in networks by representative energy

Network community has attractedmuch attention recently, but the accuracy and efficiency in finding a community structure is limited by the lower resolution of modularity. This paper presents a new method of detecting community based on representative energy. The method can divide the communities and find the representative of community simultaneously. The communities of network emerges during competing for the representative among nodes in network, thus we can sketch structure of the whole network. Without the optimizing by modularity, the community of network emerges with competing for representative among those nodes. To obtain the proximate relationships among nodes, we map the nodes into a spectral matrix. Then the top eigenvectors are weighted according to their contributions to find the representative node of a community. Experimental results show that the method is effective in detecting communities of networks.     

Detecting community structure in networks by representative energy

Network community has attractedmuch attention recently, but the accuracy and efficiency in finding a community structure is limited by the lower resolution of modularity. This paper presents a new method of detecting community based on representative energy. The method can divide the communities and find the representative of community simultaneously. The communities of network emerges during competing for the representative among nodes in network, thus we can sketch structure of the whole network. Without the optimizing by modularity, the community of network emerges with competing for representative among those nodes. To obtain the proximate relationships among nodes, we map the nodes into a spectral matrix. Then the top eigenvectors are weighted according to their contributions to find the representative node of a community. Experimental results show that the method is effective in detecting communities of networks.     

一种基于多维遗传算法的重叠社区发现方法*

社区结构的发现是社交网络分析研究的重要内容,与传统的重叠社区不同,最近的研究表明某些真实网络中在社区重叠部分要比社区内部节点间的连接更加密集,而现有的算法没有考虑此类社区结构。基于遗传算法,提出了一个新颖的方法来发现此类社区划分。为了刻画节点属于多个社区的重叠现象,首次将多维染色体和均匀块交叉算子引入到社区发现算法中。通过实验证明,提出的算法可以很好地发现社交网络中重叠和非重叠的社区结构。