Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

In this study, we utilized all available Landsat images over two adjacent orbits between 1997 and 2015 for the quasi automatic detection of clearcuts and storm damages in boreal forest of Finland. Landsat time series modelling and analysis was done utilizing the Continuous Change Detection and Classification (CCDC) algorithm with a slight modification for rapid operative conditions. The change maps derived from dense time series analysis showed a good agreement compared to reference maps of clearcuts and storm damages obtained from visual interpretation of Very High Resolution image pairs, by lack of reliable reference in temporal and or spatial domain.
Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages1730-1733
Number of pages4
ISBN (Electronic)978-1-5386-7150-4, 978-1-5386-7149-8
ISBN (Print)978-1-5386-7151-1
DOIs
Publication statusPublished - 5 Nov 2018
MoE publication typeNot Eligible
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Abbreviated titleIGARSS
CountrySpain
CityValencia
Period22/07/1827/07/18

Fingerprint

storm damage
clearcutting
boreal forest
Landsat
time series analysis
image resolution
time series
modeling
automatic detection
detection
analysis

Keywords

  • Remote sensing
  • Forestry
  • Time series analysis
  • Earth
  • Artificial satellites
  • Storms
  • Satellites
  • Satellite image time series
  • clearcuts
  • storm damages
  • Landsat
  • Sentinel-2

Cite this

Molinier, M., Astola, H., Räty, T., & Woodcock, C. E. (2018). Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 1730-1733). [8518112] Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/IGARSS.2018.8518112
Molinier, Matthieu ; Astola, Heikki ; Räty, Tomi ; Woodcock, Curtis E. / Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronic Engineers IEEE, 2018. pp. 1730-1733
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Molinier, M, Astola, H, Räty, T & Woodcock, CE 2018, Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518112, Institute of Electrical and Electronic Engineers IEEE, pp. 1730-1733, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518112

Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. / Molinier, Matthieu; Astola, Heikki; Räty, Tomi; Woodcock, Curtis E.

2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronic Engineers IEEE, 2018. p. 1730-1733 8518112.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AB - In this study, we utilized all available Landsat images over two adjacent orbits between 1997 and 2015 for the quasi automatic detection of clearcuts and storm damages in boreal forest of Finland. Landsat time series modelling and analysis was done utilizing the Continuous Change Detection and Classification (CCDC) algorithm with a slight modification for rapid operative conditions. The change maps derived from dense time series analysis showed a good agreement compared to reference maps of clearcuts and storm damages obtained from visual interpretation of Very High Resolution image pairs, by lack of reliable reference in temporal and or spatial domain.

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KW - Artificial satellites

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Molinier M, Astola H, Räty T, Woodcock CE. Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronic Engineers IEEE. 2018. p. 1730-1733. 8518112 https://doi.org/10.1109/IGARSS.2018.8518112