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

Matthieu Molinier; Heikki Astola; Tomi Räty; Curtis E. Woodcock

doi:10.1109/IGARSS.2018.8518112

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

Matthieu Molinier, Heikki Astola, Tomi Räty, Curtis E. Woodcock

Boston University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

3 Citations (Scopus)

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 language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	1730-1733
Number of pages	4
ISBN (Electronic)	978-1-5386-7150-4, 978-1-5386-7149-8
ISBN (Print)	978-1-5386-7151-1
DOIs	https://doi.org/10.1109/IGARSS.2018.8518112
Publication status	Published - 5 Nov 2018
MoE publication type	Not Eligible
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Abbreviated title	IGARSS
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

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

Access to Document

10.1109/IGARSS.2018.8518112Licence: Unspecified

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). Article 8518112 IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/IGARSS.2018.8518112

@inproceedings{afc5b26432fc4b1cb9b59e2465ed1310,

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

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.",

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

author = "Matthieu Molinier and Heikki Astola and Tomi R{\"a}ty and Woodcock, {Curtis E.}",

year = "2018",

month = nov,

day = "5",

doi = "10.1109/IGARSS.2018.8518112",

language = "English",

isbn = "978-1-5386-7151-1 ",

pages = "1730--1733",

booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, IGARSS ; Conference date: 22-07-2018 Through 27-07-2018",

}

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, IEEE Institute of Electrical and Electronic Engineers, pp. 1730-1733, 38th Annual 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 et al.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE Institute of Electrical and Electronic Engineers, 2018. p. 1730-1733 8518112.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Timely And Semi-Automatic Detection of Forest Logging Events in Boreal Forest Using All Available Landsat Data

AU - Molinier, Matthieu

AU - Astola, Heikki

AU - Räty, Tomi

AU - Woodcock, Curtis E.

PY - 2018/11/5

Y1 - 2018/11/5

N2 - 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.

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.

KW - Remote sensing

KW - Forestry

KW - Time series analysis

KW - Earth

KW - Artificial satellites

KW - Storms

KW - Satellites

KW - Satellite image time series

KW - clearcuts

KW - storm damages

KW - Landsat

KW - Sentinel-2

UR - http://www.scopus.com/inward/record.url?scp=85063152362&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2018.8518112

DO - 10.1109/IGARSS.2018.8518112

M3 - Conference article in proceedings

SN - 978-1-5386-7151-1

SP - 1730

EP - 1733

BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

ER -

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this