Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

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Authors

  • Dmitry Schepaschenko
  • Linda See
  • Myroslava Lesiv
  • Jean-françois Bastin
  • Danilo Mollicone
  • Nandin-erdene Tsendbazar
  • Lucy BastinORCiD: http://orcid.org/0000-0003-1321-0800
  • Ian Mccallum
  • Juan Carlos Laso Bayas
  • Artem Baklanov
  • Christoph Perger
  • Martina Dürauer
  • Steffen Fritz

Abstract

The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change.

Documents

  • Forest Observation

    Rights statement: © The Author(s) 2019. Open Access - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Details

Original languageEnglish
JournalSurveys in Geophysics
Early online date11 May 2019
DOIs
Publication statusE-pub ahead of print - 11 May 2019

Bibliographic note

© The Author(s) 2019. Open Access - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funding: CCI Biomass (4000123662/18/I-NB) project funded by ESA, the FP7 ERC project CrowdLand (No. 617754) and the Horizon2020 LandSense project (No. 689812).

    Keywords

  • forest cover, biomass, remote sensing, satellite imagery, visual representation

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