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

Dmitry Schepaschenko; Linda See; Myroslava Lesiv; Jean-françois Bastin; Danilo Mollicone; Nandin-erdene Tsendbazar; Lucy Bastin; Ian Mccallum; Juan Carlos Laso Bayas; Artem Baklanov; Christoph Perger; Martina Dürauer; Steffen Fritz

doi:10.1007/s10712-019-09533-z

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

Dmitry Schepaschenko, Linda See, Myroslava Lesiv, Jean-françois Bastin, Danilo Mollicone, Nandin-erdene Tsendbazar, Lucy Bastin, Ian Mccallum, Juan Carlos Laso Bayas, Artem Baklanov, Christoph Perger, Martina Dürauer, Steffen Fritz

College of Engineering and Physical Sciences

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	839-862
Number of pages	24
Journal	Surveys in Geophysics
Volume	40
Issue number	4
Early online date	11 May 2019
DOIs	https://doi.org/10.1007/s10712-019-09533-z
Publication status	Published - 15 Jul 2019

Bibliographical 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

Biomass
Forest cover
Forest monitoring
Remote sensing
Satellite imagery
Visual interpretation

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10.1007/s10712-019-09533-zLicence: CC BY 3.0

Forest Observation
© 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.
Final published version, 1.31 MBLicence: CC BY 3.0

Cite this

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title = "Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery",

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.",

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AU - Schepaschenko, Dmitry

AU - See, Linda

AU - Lesiv, Myroslava

AU - Bastin, Jean-françois

AU - Mollicone, Danilo

AU - Tsendbazar, Nandin-erdene

AU - Bastin, Lucy

AU - Mccallum, Ian

AU - Laso Bayas, Juan Carlos

AU - Baklanov, Artem

AU - Perger, Christoph

AU - Dürauer, Martina

AU - Fritz, Steffen

N1 - © 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).

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N2 - 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.

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KW - Remote sensing

KW - Satellite imagery

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ER -

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

Abstract

Bibliographical note

Keywords

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