Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery.
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| Title: | Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. |
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| Authors: | Schepaschenko, Dmitry1 (AUTHOR) schepd@iiasa.ac.at, See, Linda1 (AUTHOR), Lesiv, Myroslava1 (AUTHOR), Bastin, Jean-François2,3 (AUTHOR), Mollicone, Danilo2 (AUTHOR), Tsendbazar, Nandin-Erdene4 (AUTHOR), Bastin, Lucy5,6 (AUTHOR), McCallum, Ian1 (AUTHOR), Laso Bayas, Juan Carlos1 (AUTHOR), Baklanov, Artem1,7 (AUTHOR), Perger, Christoph1 (AUTHOR), Dürauer, Martina1 (AUTHOR), Fritz, Steffen1 (AUTHOR) |
| Source: | Surveys in Geophysics. Jul2019, Vol. 40 Issue 4, p839-862. 24p. |
| Subject Terms: | *Forest management, *Forest monitoring, *Estimation theory, *Automatic classification, *Deforestation, *Time series analysis |
| Company/Entity: | Food & Agriculture Organization of the United Nations |
| 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. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01693298 |
| DOI: | 10.1007/s10712-019-09533-z |
