山西霍山植物群落主要层LAI垂直分布规律

在山西霍山七里峪林场，采用间接的光学测量方法对沿着海拔分布的植物群落主要层叶面积指数（LAI）进行了测量，从不同的角度对LAI的垂直分布规律进行分析。结果表明：1．海拔不同，LAI表现出很大的差异，海拔1750m左右的LAI值大于较高与较低海拔的；对LAI和海拔之间的关系进行回归，得到LAI分布的拟合方程：y=b0＋b1x＋b2x^2＋b3x^3（R=0．712，P〈0．01）。2．同一海拔样带，阴坡LAI均大于阳坡，但对同种种群来说，阴坡树种平均胸径并不一定均大于阳坡。3．植被类型LAI比较表现为：低山针叶林〈低山灌丛〈落叶阔叶林〈落叶针叶林〈常绿针叶林；而主要群系LAI比较表现为：油松林（0．38）〈落叶松林（0．78）〈山杨林及其混交林（1．09）〈白桦林（2．00）〈辽东栎林及其混交林（3．73）。4．典型群落LAI值变化分析和人工林与天然林以，比较表明，LAI值在一定海拔达到最大值，这对于山西霍山人工林栽培和丰产林培育等具有重要意义。     

树冠叶面积体密度和叶面积指数的间接估值方法研究

本文论述了根据计算机断层成像的原理，对树冠的叶面积体密度和叶面积指数等构造参数进行间接估值的方法。说明了对树冠多角度底视数据的获取，对树冠外形的反投影重构，和获得冠层内叶面积体密度的空间分布的原理和方法。总结了测量工作经验，并用测量数据对估值方法进行了检验。     

基于森林模型参数先验知识估算高分辨率叶面积指数

目前,估算高分辨率叶面积指数LAI（Leaf Area Index）的常用方法是采用大量地面测量数据和遥感数据建立统计模型,再用统计模型估算LAI。然而,与农田地面测量实验相比,森林地面测量实验获取的观测数据更加有限,这使得基于统计模型的森林高分辨率LAI的估算精度低,难以满足应用需求。为此,本文提出一种基于森林模型参数先验知识、使用森林研究区少量的LAI地面测量数据和归一化植被指数NDVI数据估算森林高分辨率LAI的方法。首先,获取全球20个森林实验区的LAI地面测量数据和NDVI数据,建立LAI-NDVI统计模型并提取森林模型参数的先验知识。然后,以一个新的森林站点Concepción作为研究区,将该研究区的数据分为建模数据和验证数据两个部分。使用研究区有限的建模数据对森林模型参数先验知识进行本地化校正得到优化模型,优化模型用于估算森林高分辨率LAI,使用验证数据评价LAI的估算精度。同时,选取了Camerons站点、Gnangara站点、Hirsikangas站点评价本文方法的LAI估算精度。使用地面测量LAI验证基于森林模型参数先验知识估算高分辨率LAI的结果精度,经验证4个森林站点的均方根误差分别为0.6680,0.4449,0.2863,0.5755。研究结果表明：在仅有少量观测数据时,采用本方法能有效地提高森林高分辨率LAI的估算精度。因此,本方法可为森林高分辨率LAI的遥感估算提供参考。     

On the relationship between historical land‐use change and water availability: the case of the lower Tarim River region in northwestern China

Natural ecosystems in the region of the lower Tarim River in northwestern China strongly deteriorated since the 1950s due to an expanding desertification. As a result, the downstream Tarim River reaches became permanently dry land. This historical evolution in land‐use change is typically the result of the anthropogenic impact on natural ecosystems. On the basis of a spatially distributed hydrological catchment model bidirectionally linked with a fully hydrodynamic MIKE11 river model, land‐use changes characterized by historical changes in leaf area index (LAI) of vegetation, as well as the evolution of irrigated surface areas, can be causally related to changes in water resources (groundwater storage and surface water resources). An increased surface area of irrigated (agricultural) land, together with a majority of inefficient irrigation methods, did lead to a strong increase of water resources consumption of the farmlands located in the upper Tarim River area. Evidently, this evolution influenced available water resources downstream in the Tarim basin. As a result, farmland has been gradually relocated to the upstream regions. This has led to reduced flows from the upper Tarim stream, which subsequently accelerated the dropping of the groundwater level downstream in the basin. This study moreover demonstrates that land surface biomass changes (cumulative LAI) along the lower Tarim River are strongly related to the changes in groundwater storage. Copyright © 2012 John Wiley & Sons, Ltd.     

基于植被信息遥感反演的东亚飞蝗监测研究

植被是东亚飞蝗发生和成灾的重要指示因子。运用遥感技术对植被生长进行监测,对东亚飞蝗的预测和防治具有重要意义。以河北省黄骅市为研究区,利用实地获取的植被冠层孔隙度数据反算的LAI数据以及Landsat-5 TM影像提取的各种VI数据,进行了LAI(LAI-2000改进型算法的反算结果)与TM影像上反演的VI之间的相关分析。结果表明,RDVI最适合反映研究区植被生长状况。分析RDVI与飞蝗发生面积的关系,发现两者呈负线性相关,即随着RDVI减小,飞蝗的发生面积呈线性增大。     

祁连山亚高山灌丛林叶面积指数与冠层氮、磷的关系

氮和磷作为植物体内重要的生命元素,在植物群落的生长发育过程中发挥着重要的作用。为了明确祁连山亚高山灌丛林叶面积指数与冠层氮、磷之间的关系,本文通过对祁连山亚高山灌丛林不同植被类型(箭叶锦鸡儿、高山吉拉柳、金露梅)及不同放牧处理(羊群、牦牛,未放牧)条件下灌丛群落的叶面积指数(LAI)与叶片氮积累量(TFN)、叶片磷积累量(TFP)比较发现,在整个亚高山灌丛群落中,LAI与TFN和TFP之间都有较强的相关性,并且TFN和TFP比值的变化表明不同植被类型叶片的生长都受到N、P的共同限制,只是随着LAI的增加,高山吉拉柳主要受到氮素的限制,箭叶锦鸡儿主要受到磷素的限制,而金露梅则受到N、P的共同限制;在不同放牧条件下,单位面积LAI对应的TFN的值较高而TFP的值较低,说明动物通过对植被的啃食可能会改变群落的模式,在一定程度上限制磷的摄入。LAI、N、P之间的耦合关系表明了亚高山灌丛群落的LAI在物种组成、放牧和冠层密度上存在差异,但仍然受到N和P的约束。研究结果有利于探索水分限制条件下祁连山灌丛林生态系统植物叶片与养分元素之间关系,对于研究干旱区高寒灌丛生态系统在全球气候变化中的作用及其对全球气候变化的响应与反馈,具有重要的理论价值和实践意义。     

基于不同景观的LAI-ET时空协同效应研究——以蒙古高原和青藏高原为例（英文）

近几十年来,蒙古高原和青藏高原的增温速度高于全球变暖的平均水平,导致生态系统的结构和功能发生了显著变化。叶面积指数(LAI)和蒸散发(ET)在塑造陆地表面过程和气候方面发挥着重要作用。在文中,我们重点关注LAI和ET的时空变化及其相互关系。基于2000-2014年的MODIS产品,我们发现蒙古高原的LAI和ET之间存在普遍的正相关关系,而青藏高原则没有协同作用。总体而言,青藏高原LAI的显著增加(减少)区域占总面积的49.38%(50.62%),蒙古高原则为94.92%(5.09%);青藏高原ET增加区域面积占总面积的21.70%(124.10×10~3 km^2),蒙古高原为88.01%(341.60×10~3 km^2)。更重要的是,随着时间的推移,这种关系在整个空间中发生了很大的变化,并且在景观的某些部分发现了不匹配。需要通过观测和/或实验研究来探讨这些关系,包括植被特征及其干扰的影响。     

基于神经网络方法的芦苇叶面积指数遥感反演

提出了一种从TM图像上获取芦苇冠层叶面积指数的方法:首先对芦苇的生长背景进行分类;然后,对不同的背景光谱利用冠层反射率(FCR)模型计算得到查找表;最后,利用实测数据和查找表中的数据作为参数进行BP神经网络模型训练,从而得到芦苇冠层LAI。结果表明,人工神经网络方法有很强的非线性拟合能力,能够消除背景对反演结果的影响,有效提高LAI反演的精度。     

Seasonal variations and mechanism for environmental control of NEE of CO_2 concerning the Potentilla fruticosa in alpine shrub meadow of Qinghai-Tibet Plateau

The study by the eddy covariance technique in the alpine shrub meadow of the Qing-hai-Tibet Plateau in 2003 and 2004 showed that the net ecosystem carbon dioxide exchange (NEE) exhibited noticeable diurnal and annual variations, with more distinct daily changes during the warmer seasons. The CO2 emission of the shrub ecosystem culminated in April and September while the CO2 absorption capacity reached a maximum in July and August. The absorbed carbon dioxide during the two consecutive years was 231.4 and 274.8 g CO2·m-2 respectively, yielding an average of 253.1 gCO2·m-2 per year: that accounts for a large proportion of absorbed CO2 in the region. Obviously, the diurnal carbon flux was negatively related to temperature, radiation and other atmospheric factors. Still, minute discrepancies in kurtosis and duration of carbon emission/absorption were detected between 2003 and 2004. It was found that the CO2 flux in the daytime was similarly affected by photosynthetic photon flux density in both years. Temperature appears to be the most important determinant of CO2 flux: specifically, the high temperature during the plant growing season inhibits the carbon absorption capacity. One potential explanation is that soil respiration is enhanced under such condition. Analysis of biomass revealed that the annual net carbon fixed capacity of aboveground and belowground biomass was 544.0 in 2003 and 559.4 g Cm"2 in 2004, which coincided with the NEE absorption capacity (63.1 g C·m-2 in 2003 and 74.9 g C·m-2 in 2004) in the corresponding plant growing season.     

Net ecosystem CO_2 exchange and controlling factors in a steppe——Kobresia meadow on the Tibetan Plateau

Knowledge of seasonal variation of net ecosystem CO2 exchange (NEE) and its biotic and abiotic controllers will further our understanding of carbon cycling process, mechanism and large-scale modelling. Eddy covariance technique was used to measure NEE, biotic and abiotic factors for nearly 3 years in the hinterland alpine steppe--Korbresia meadow grassland on the Tibetan Plateau, the present highest fluxnet station in the world. The main objectives are to investigate dynamics of NEE and its components and to determine the major controlling factors. Maximum carbon assimilation took place in August and maximum carbon loss occurred in November. In June, rainfall amount due to monsoon climate played a great role in grass greening and consequently influenced interannual variation of ecosystem carbon gain. From July through September, monthly NEE presented net carbon assimilation. In other months, ecosystem exhibited carbon loss. In growing season, daytime NEE was mainly controlled by photosynthetically active radiation (PAR). In addition, leaf area index (LAI) interacted with PAR and together modulated NEE rates. Ecosystem respiration was controlled mainly by soil temperature and simultaneously by soil moisture. Q10 was negatively correlated with soil temperature but positively correlated with soil moisture. Large daily range of air temperature is not necessary to enhance carbon gain. Standard respiration rate at referenced 10℃(R10) was positively correlated with soil moisture, soil temperature, LAI and aboveground biomass. Rainfall patterns in growing season markedly influenced soil moisture and therefore soil moisture controlled seasonal change of ecosystem respiration. Pulse rainfall in the beginning and at the end of growing season induced great ecosystem respiration and consequently a great amount of carbon was lost. Short growing season and relative low temperature restrained alpine grass vegetation development. The results suggested that LAI be usually in a low level and carbon uptake be relatively low. Rainfall patterns in the growing season and pulse rainfall in the beginning and at end of growing season control ecosystem respiration and consequently influence carbon balance of ecosystem.