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

Schepaschenko, Dmitry, See, Linda, Lesiv, Myroslava, Bastin, Jean-françois, Mollicone, Danilo, Tsendbazar, Nandin-erdene, Bastin, Lucy, Mccallum, Ian, Laso Bayas, Juan Carlos, Baklanov, Artem, Perger, Christoph, Dürauer, Martina and Fritz, Steffen (2019). Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. Surveys in Geophysics, 40 (4), pp. 839-862.

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.

Publication DOI: https://doi.org/10.1007/s10712-019-09533-z
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences
Engineering & Applied 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 (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).
Uncontrolled Keywords: Biomass,Forest cover,Forest monitoring,Remote sensing,Satellite imagery,Visual interpretation,Geophysics,Geochemistry and Petrology
Full Text Link:
Related URLs: http://link.spr ... 712-019-09533-z (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Online Date: 2019-05-11
Published Date: 2019-07-15
Authors: Schepaschenko, Dmitry
See, Linda
Lesiv, Myroslava
Bastin, Jean-françois
Mollicone, Danilo
Tsendbazar, Nandin-erdene
Bastin, Lucy ( 0000-0003-1321-0800)
Mccallum, Ian
Laso Bayas, Juan Carlos
Baklanov, Artem
Perger, Christoph
Dürauer, Martina
Fritz, Steffen

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