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Review of optical-based remote sensing for plant trait mapping

机译:基于光学的遥感技术用于植物性状作图的综述

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摘要

Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity. In this review, we first introduce RS concepts of light-vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/ concentration, and 45% for leaf mass per area estimates. The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists. We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood.
机译:植物性状数据已用于与生态系统功能,社区生态和生态系统服务评估有关的各种研究中。有证据表明,植物学家同意一系列关键植物性状,它们相对容易测量,并且对生态系统功能具有稳定而强大的预测响应。但是,植物性状数据的实地测量仍然仅限于小区域,特定时刻和特定物种数量。因此,遥感(RS)提供了补充甚至替代某些植物性状的田间测量的潜力。它提供瞬时的空间连续信息,覆盖更大的区域,并且在进行卫星观测的情况下,可从其重新访问能力中获利。在这篇综述中,我们首先介绍光与植被相互作用的遥感概念,用于植被研究的遥感仪器,遥感方法以及野外观测和遥感观测之间的缩放比例。进一步,我们详细讨论了用于关键植物性状作图的光学RS的当前成就和挑战。我们将讨论集中在三个类别的植物性状(植物生长和生命形式,易燃性以及光合途径和活性)和五个连续的植物性状(植物高度,叶片物候,每单位叶片质量,氮和磷的浓度或含量)上。我们回顾现有文献,以确定连续植物性状的检索准确性。使用RS的相对估计误差介于测量平均值的10%到45%之间,即高檐棚的植物高度大约为10%,短檐棚的植物高度为20%,植物氮为15%,植物磷含量为25% /浓度,每面积估计叶质量的45%。当性状与叶片的生化,光合作用和冠层结构有关时,RS绘制植物性状的潜力特别高。还存在其他植物性状,即叶绿素含量,水分含量和叶面积指数,可以从光学RS井中很好地检索到这些特征,这对植物科学家来说很重要。我们强调需要使用RS进行的未来生态系统功能评估应要求对各种植物性状以及叶片和冠层光谱特性进行全面和综合的测量。通过这样做,可以更好地理解植物结构,生理,生化,物候和光谱特性之间的相互作用。

著录项

  • 来源
    《Ecological Complexity》 |2013年第9期|1-16|共16页
  • 作者单位

    Remote Sensing Laboratories, Department of Geography, University of Zuerich, Winterthurerstrasse 190, CH-8057 Zuerich, Switzerland,Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. BOX 47, 6700 AA Wageningen, The Netherlands;

    School of Geography and Environmental Studies, University of Tasmania, Private Bag 76, 7001 Hobart, Australia;

    Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. BOX 47, 6700 AA Wageningen, The Netherlands;

    Remote Sensing Laboratories, Department of Geography, University of Zuerich, Winterthurerstrasse 190, CH-8057 Zuerich, Switzerland;

    Remote Sensing Laboratories, Department of Geography, University of Zuerich, Winterthurerstrasse 190, CH-8057 Zuerich, Switzerland;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Plant traits; Remote sensing; Plant ecology; Accuracy; Scaling;

    机译:植物性状;遥感;植物生态学准确性;缩放比例;

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