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


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © The Author(s) 2019. Open Access - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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).
Uncontrolled Keywords: Biomass,Forest cover,Forest monitoring,Remote sensing,Satellite imagery,Visual interpretation,Geophysics,Geochemistry and Petrology
Publication ISSN: 1573-0956
Last Modified: 22 Jul 2024 07:21
Date Deposited: 16 May 2019 12:45
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Related URLs: http://link.spr ... 712-019-09533-z (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2019-07-15
Published Online Date: 2019-05-11
Accepted Date: 2019-04-14
Authors: Schepaschenko, Dmitry
See, Linda
Lesiv, Myroslava
Bastin, Jean-françois
Mollicone, Danilo
Tsendbazar, Nandin-erdene
Bastin, Lucy (ORCID Profile 0000-0003-1321-0800)
Mccallum, Ian
Laso Bayas, Juan Carlos
Baklanov, Artem
Perger, Christoph
Dürauer, Martina
Fritz, Steffen



Version: Published Version

License: Creative Commons Attribution

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