地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (3): 366-375.doi: 10.3724/SP.J.1047.2012.00366

• 遥感技术与应用 • 上一篇    下一篇

对Beer-Lambert定律间接测量森林LAI的误差低估分析

胡容海, 阎广建   

  1. 北京师范大学地理学与遥感科学学院,遥感科学国家重点实验室,环境遥感与 数字城市北京市重点实验室, 北京 100875
  • 收稿日期:2011-10-27 修回日期:2012-05-06 出版日期:2012-06-25 发布日期:2012-06-25
  • 通讯作者: 阎广建(1972-),男,教授,博士生导师,主要从事定量遥感方面的研究。E-mail:gjyan@bnu.edu.cn E-mail:gjyan@bnu.edu.cn
  • 作者简介:胡容海(1990-),男,本科生,主要从事叶面积指数测量方面的研究。E-mail:sea@mail.bnu.edu.cn
  • 基金资助:

    国家自然科学基金项目(40871164);国家"973"计划项目 (2007CB714402)联合资助。

Indirect Measurement of Forest LAI to Deal with the Underestimation Problem Based on Beer-Lambert Law

HU Ronghai, YAN Guangjian   

  1. State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, Beijing Normal University, Beijing 100875, China
  • Received:2011-10-27 Revised:2012-05-06 Online:2012-06-25 Published:2012-06-25

摘要:

叶面积指数(Leaf Area Index,LAI)是表征植被冠层结构的核心参数。在地面对LAI的间接测量是遥感反演算法验证和改进的重要手段,而目前基于Beer-Lambert定律的森林LAI地面间接测量方法存在着严重的低估问题。本文通过理论分析,指出Beer-Lambert定律在应用到森林叶面积指数测量时,LAI低估的根本原因来源于叶面积体密度、消光路径及叶倾角投影G函数在空间上的不均匀性,并定量评估了冠层非随机分布对LAI测量结果的影响,发现植被冠层的非随机分布会对LAI的测量带来20%~40%的误差。这一结论,对于Beer-Lambert定律的简单修正应用于森林LAI间接测量时仍存在着较大的局限性,尚未能根本上解决LAI的低估问题,故间接测量LAI的理论和方法需进一步深入研究。

关键词: Beer-Lambert定律, 遥感, 聚集效应, 叶面积指数测量, 低估

Abstract:

Leaf area index (LAI) defined as one half of the total green leaf area per unit ground surface area. It is an important parameter of canopy structure, because it relates to many biophysical and physiological processes of canopy, including photosynthesis, respiration, transpiration, carbon cycling, net primary productivity, precipitation interception, and energy exchange, etc. Accurate measurement of forest leaf area index by means of remote sensing has been an important task in remote sensing research. The direct method of LAI measurement is time-consuming, labor-intensive and may destroy plants. Compared to the direct method, indirect methods by means of optical methods are quicker and more efficient. These methods are all based on the Beer-Lambert law. As an important means to validate remote sensing LAI products, indirect LAI ground measurement is the basis and standard of remote sensing inversion. However, indirect ground measurement method based on Beer-Lambert law has serious underestimation problem in forest. The derivation of Beer's law was originally in uniform gas medium, when applied to discrete vegetation measurement on a pixel scale. Its applicability has not got enough attention and validation. In this paper, by theory analysis, we find that the underestimation of leaf area index comes from the spatial heterogeneity of foliage area volume density, extinction depth and leaf angle projection function G if Beer-Lambert law is applied to LAI measurements in forest. Quantitative assessment of impact on LAI measurement from non-random distribution of canopy was made. It was shown that non-random distribution of canopy may bring 20-40% measurement error of LAI. An important conclusion is that the simple correction of Beer-Lambert law has significant limitations on the in situ forest LAI measurement. This method is not a fundamental solution to this underestimation problem, and the theories and methods for LAI indirect measurement need to be changed.

Key words: clumping effects, LAI measurement, Beer-Lambert law, underestimation, remote sensing