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      MLAOS: A Multi-Point Linear Array of Optical Sensors for Coniferous Foliage Clumping Index Measurement

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          Abstract

          The canopy foliage clumping effect is primarily caused by the non-random distribution of canopy foliage. Currently, measurements of clumping index (CI) by handheld instruments is typically time- and labor-intensive. We propose a low-cost and low-power automatic measurement system called Multi-point Linear Array of Optical Sensors (MLAOS), which consists of three above-canopy and nine below-canopy optical sensors that capture plant transmittance at different times of the day. Data communication between the MLAOS node is facilitated by using a ZigBee network, and the data are transmitted from the field MLAOS to a remote data server using the Internet. The choice of the electronic element and design of the MLAOS software is aimed at reducing costs and power consumption. A power consumption test showed that, when a 4000 mAH Li-ion battery is used, a maximum of 8–10 months of work can be achieved. A field experiment on a coniferous forest revealed that the CI of MLAOS may reveal a clumping effect that occurs within the canopy. In further work, measurement of the multi-scale clumping effect can be achieved by utilizing a greater number of MLAOS devices to capture the heterogeneity of the plant canopy.

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          A theoretical analysis of the frequency of gaps in plant stands

          T Nilson (1971)
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            Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands

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              Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

              The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                May 2014
                23 May 2014
                : 14
                : 5
                : 9271-9289
                Affiliations
                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; E-Mails: fulizhe0312@ 123456163.com (L.F.); hwc9028@ 123456163.com (W.H.); zhuyeqing0307@ 123456foxmail.com (Y.Z.); wangjd@ 123456bnu.edu.cn (J.W.)
                Author notes

                Author Contributions: Lizhe Fu, Wenchao Han and Yeqing Zhu are responsible for carrying out field experiment and preprocessing experiment data. Jindi Wang helps to analysis the results.

                [* ] Author to whom correspondence should be addressed; E-Mail: qyh@ 123456bnu.edu.cn ; Tel.: +86-105-880-2041; Fax: +86-105-880-5274.
                Article
                sensors-14-09271
                10.3390/s140509271
                4063017
                24859029
                b63cb391-b062-4032-b5ae-9a972368ae11
                © 2014 by the authors; licensee MDPI, Basel, Switzerland

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 06 January 2014
                : 23 April 2014
                : 16 May 2014
                Categories
                Article

                Biomedical engineering
                optical sensor,linear array,clumping index,coniferous
                Biomedical engineering
                optical sensor, linear array, clumping index, coniferous

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