Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data

B.R. DeVries; A.K. Pratihast; J. Verbesselt; L. Kooistra; S. de Bruin; M. Herold

doi:10.1109/Multi-Temp.2013.6866022

Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data

B.R. DeVries, A.K. Pratihast, J. Verbesselt, L. Kooistra, S. de Bruin, M. Herold

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic

5 Citations (Scopus)

Abstract

In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Break detection For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

Original language	English
Title of host publication	MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images
Subtitle of host publication	Proceedings
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	9781479924134
DOIs	https://doi.org/10.1109/Multi-Temp.2013.6866022
Publication status	Published - 2013
Event	7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada - Duration: 25 Jun 2013 → 27 Jun 2013

Workshop

Workshop	7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada
Period	25/06/13 → 27/06/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/Multi-Temp.2013.6866022

Cite this

DeVries, B. R., Pratihast, A. K., Verbesselt, J., Kooistra, L., de Bruin, S., & Herold, M. (2013). Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data. In MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images: Proceedings IEEE. https://doi.org/10.1109/Multi-Temp.2013.6866022

@inproceedings{749ef48dfeaa4e5fa691d4e150d8155a,

title = "Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data",

abstract = "In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Break detection For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.",

author = "B.R. DeVries and A.K. Pratihast and J. Verbesselt and L. Kooistra and {de Bruin}, S. and M. Herold",

year = "2013",

doi = "10.1109/Multi-Temp.2013.6866022",

language = "English",

booktitle = "MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images",

publisher = "IEEE",

address = "United States",

note = "7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada ; Conference date: 25-06-2013 Through 27-06-2013",

}

DeVries, BR, Pratihast, AK, Verbesselt, J, Kooistra, L , de Bruin, S & Herold, M 2013, Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data. in MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images: Proceedings. IEEE, 7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada, 25/06/13. https://doi.org/10.1109/Multi-Temp.2013.6866022

Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data. / DeVries, B.R.; Pratihast, A.K.; Verbesselt, J. et al.
MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images: Proceedings. IEEE, 2013.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic

TY - GEN

T1 - Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data

AU - DeVries, B.R.

AU - Pratihast, A.K.

AU - Verbesselt, J.

AU - Kooistra, L.

AU - de Bruin, S.

AU - Herold, M.

PY - 2013

Y1 - 2013

N2 - In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Break detection For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

AB - In this paper, we present an integrated near real-time forest disturbance monitoring system which utilizes temporally dense Landsat time series in combination with a continuous local expert based system in a tropical forest ecosystem in southern Ethiopia. Landsat time series were analyzed using the Break detection For Additive Season and Trend Monitor (BFAST Monitor) method and in situ local expert data was in turn facilitated by the use of mobile devices programmed to be able to classify land use changes. BFAST Monitor was found to be able to describe forest change dynamics using irregular Landsat time series data with frequent cloud and SLC-off gaps. Disturbance data collected by local experts enhanced the BFAST Monitor results by providing contextual information such as the specific area and local drivers of disturbance events.

U2 - 10.1109/Multi-Temp.2013.6866022

DO - 10.1109/Multi-Temp.2013.6866022

M3 - Conference paper

BT - MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images

PB - IEEE

T2 - 7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada

Y2 - 25 June 2013 through 27 June 2013

ER -

Near real-time tropical deforestation detection using dense Landsat time series and local expert monitoring data

Abstract

Workshop

UN SDGs

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