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。     

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。     

叶面积指数(LAI)的遥感定量方法综述

总结了当前遥感定量研究叶面积指数(LAI)的两种主要方法:统计模型法和光学模型法,阐述了各自的机理和研究进展,在此基础上,讨论了两种方法的优缺点及未来的发展趋势。     

基于新型机载多角度传感器AMTIS的LAI反演

以实地测量数据为先验知识,利用AMTIS数据对顺义地区的一块小麦地进行了叶面积指数(LAI)反演实验研究,并用实地LAI数据进行了验证。通过利用实测数据作为模型参数,以及对干湿土壤分类和匹配表的调整,使反演结果和反演速度得到了提高。     

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of Tm3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.     

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.     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.     

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。