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

15903 17 87540 2 248765 document 87540 Schepaschenko2019_Article_RecentAdvancesInForestObservat.pdf application/pdf bc174c936b6bf44f54b5f662a4545c77 MD5 1372653 2019-05-13 05:57:26 https://pure.iiasa.ac.at/id/eprint/15903/1/Schepaschenko2019_Article_RecentAdvancesInForestObservat.pdf 15903 1 1 application/pdf text en public cc_by_4

Schepaschenko2019_Article_RecentAdvancesInForestObservat.pdf

published 87541 1 248773 document 87541 indexcodes.txt text/x-c++ c9421e18c8c8fd9651b1e3a84b295a88 MD5 18544 2019-05-13 06:07:30 https://pure.iiasa.ac.at/id/eprint/15903/2/indexcodes.txt 15903 2 2 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/87540 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/87540 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/87540 archive 353 disk0/00/01/59/03 2019-05-13 06:01:18 2024-01-04 13:50:58 2019-05-13 06:01:18 article show Schepaschenko D. 7435 0000-0002-7814-4990 See L. 8571 0000-0002-2665-7065 Lesiv M. 8477 0000-0001-9846-3342 Bastin J.-F. Mollicone D. Tsendbazar N.-E. Bastin L. McCallum I. 1731 0000-0002-5812-9988 Laso Bayas J.C. 2106 0000-0003-2844-3842 Baklanov A. 2067 0000-0003-1599-3618 Perger Ch. 1899 Dürauer M. 2013 Fritz S. 8130 0000-0003-0420-8549 Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery pub prog_esm Forest cover; Biomass; Forest monitoring; Remote sensing; Satellite imagery; Visual interpretation; Geo-Wiki 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. 2019-07 published Springer Netherlands 10.1007/s10712-019-09533-z 279 276 181 202 172 22 228 73 98 public Surveys in Geophysics 40 4 839-862 TRUE 0169-3298 Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754) A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring (LANDSENSE, H2020 689812) Geo-Wiki FALSE yes info:eu-repo/grantAgreement/EC/FP7/617754/EU//CROWDLAND; info:eu-repo/grantAgreement/EC/H2020/689812/EU//LANDSENSE info:eu-repo/semantics/article info:eu-repo/semantics/openAccess