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。     

How changes of groundwater level affect the desert riparian forest ecosystem in the Ejina Oasis,Northwest China

Groundwater is a key factor controlling the growth of vegetation in desert riparian systems. It is important to recognise how groundwater changes affect the riparian forest ecosystem. This information will not only help us to understand the ecological and hydrological process of the riparian forest but also provide support for ecological recovery of riparian forests and water-resources management of arid inland river basins. This study aims to estimate the suitability of the Water Vegetation Energy and Solute Modelling(WAVES) model to simulate the Ejina Desert riparian forest ecosystem changes,China, to assess effects of groundwater-depth change on the canopy leaf area index(LAI) and water budgets, and to ascertain the suitable groundwater depth for preserving the stability and structure of desert riparian forest. Results demonstrated that the WAVES model can simulate changes to ecological and hydrological processes. The annual mean water consumption of a Tamarix chinensis riparian forest was less than that of a Populus euphratica riparian forest, and the canopy LAI of the desert riparian forest should increase as groundwater depth decreases. Groundwater changes could significantly influence water budgets for T. chinensis and P. euphratica riparian forests and show the positive and negative effects on vegetation growth and water budgets of riparian forests. Maintaining the annual mean groundwater depth at around 1.7-2.7 m is critical for healthy riparian forest growth. This study highlights the importance of considering groundwater-change impacts on desert riparian vegetation and water-balance applications in ecological restoration and efficient water-resource management in the Heihe River Basin.     

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。     

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.     

基于植被-土壤二向反射模型的土壤含水量遥感

以北京地区为例，利用像元信息分解法，定量提取出区域植被盖度，由植被盖度得到区域叶面积指数，在只考虑一次散射的情况下，利用植被-土壤二向反射模型，提取出下层湿润土壤反射率。通过引入粗糙度因子建立起粗糙地表下土壤反射率与叶面积指数的函数关系，进而得出土壤含水量。     

Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status by estimating leaf chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content. Trials were conducted with two potato cultivars under different N fertility rates (0–300 kg N ha⁻¹). Canopy reflectance, leaf area index (LAI) and leaf chlorophyll and N contents were measured. The PROSAIL model was able to predict leaf chlorophyll content with reasonable accuracy later in the growing season. The low estimation accuracy earlier in the growing season could be due to model sensitivity to non-homogenous canopy architecture and soil background interference before full canopy closure. Canopy chlorophyll content (leaf chlorophyll content × LAI) was predicted less accurately than leaf chlrophyll content due to the low estimation accuracy of LAI for values higher than 4.5.     

基于PROSPECT＋SAIL模型的遥感叶面积指数反演

以PROSPECT+SAIL模型为基础，从物理机理角度反演植被叶面积指数(LAI)。首先，通过FLAASH模型进行大气校正，使得图像像元值表达植被冠层反射率； 然后，根据LOPEX 93数据库和JHU光谱数据库选择植物生化参数和光谱数据，以PROSPECT模型模拟出的植物叶片反射率和透射率作为SAIL模型的输入参数，得到植被冠层反射率，将结果与遥感影像的植被冠层反射率对应，回归出植被LAI； 最后，以地面实测数据对遥感反演数据进行验证，并分析了误差的可能来源。     

利用不同方法反演冬小麦叶面积指数研究

基于高光谱数据的叶面积指数监测是快速获取冬小麦叶面积指数的重要方法。为了探究回归方法和高光谱数据变换对冬小麦叶面积指数反演精度的影响,采用逐步回归和偏最小二乘回归方法,分别建立基于冬小麦拔节期冠层高光谱数据、一阶导数光谱数据、二阶导数光谱数据和对数光谱数据的叶面积指数多元线性回归模型。结果显示,导数和对数变换能够提高冬小麦LAI反演精度,以蓝紫光、绿光、红光和近红外波段建立的一阶导数光谱数据逐步回归模型最优,建立回归模型的决定系数R2为0.974,交叉验证的RMSE为0.131,可为冬小麦LAI估算的方法选择和数据处理提供依据和参考。     

Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R² of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise.