Schepaschenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, Bastin, J.-F., Mollicone, D., Tsendbazar, N.-E., Bastin, L., McCallum, I. ORCID: https://orcid.org/0000-0002-5812-9988, Laso Bayas, J.C. ORCID: https://orcid.org/0000-0003-2844-3842, Baklanov, A. ORCID: https://orcid.org/0000-0003-1599-3618, Perger, Ch., Dürauer, M., & Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549 (2019). Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. Surveys in Geophysics 40 (4) 839-862. 10.1007/s10712-019-09533-z.
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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.
Item Type: | Article |
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Uncontrolled Keywords: | Forest cover; Biomass; Forest monitoring; Remote sensing; Satellite imagery; Visual interpretation; Geo-Wiki |
Research Programs: | Ecosystems Services and Management (ESM) |
Depositing User: | Luke Kirwan |
Date Deposited: | 13 May 2019 06:01 |
Last Modified: | 04 Jan 2024 13:50 |
URI: | https://pure.iiasa.ac.at/15903 |
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