Literature DB >> 29017128

Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review.

K J Lees1, T Quaife2, R R E Artz3, M Khomik3, J M Clark4.   

Abstract

Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration.
Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  GPP; NEE; Respiration; Restored peatlands; Satellites; Vegetation indices

Year:  2017        PMID: 29017128     DOI: 10.1016/j.scitotenv.2017.09.103

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  2 in total

1.  Peatland vegetation composition and phenology drive the seasonal trajectory of maximum gross primary production.

Authors:  Matthias Peichl; Michal Gažovič; Ilse Vermeij; Eefje de Goede; Oliver Sonnentag; Juul Limpens; Mats B Nilsson
Journal:  Sci Rep       Date:  2018-05-22       Impact factor: 4.379

2.  A data science challenge for converting airborne remote sensing data into ecological information.

Authors:  Sergio Marconi; Sarah J Graves; Dihong Gong; Morteza Shahriari Nia; Marion Le Bras; Bonnie J Dorr; Peter Fontana; Justin Gearhart; Craig Greenberg; Dave J Harris; Sugumar Arvind Kumar; Agarwal Nishant; Joshi Prarabdh; Sundeep U Rege; Stephanie Ann Bohlman; Ethan P White; Daisy Zhe Wang
Journal:  PeerJ       Date:  2019-02-28       Impact factor: 2.984

  2 in total

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