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  Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series

Hamunyela, E., Reiche, J., Verbesselt, J., & Herold, M. (2017). Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series. Remote Sensing, 9(6): 515. doi:10.3390/rs9060515.

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http://dx.doi.org/10.3390/rs9060515 (Publisher version)
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 Creators:
Hamunyela, Eliakim1, Author
Reiche, Johannes, Author
Verbesselt, Jan, Author
Herold, Martin, Author
Affiliations:
1External Organizations, ou_persistent22              

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Free keywords: Earth Observation; Regional Validation
 Abstract: Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the trade-off between spatial and temporal accuracy. Timely detection of forest disturbance is often accompanied by many false detections, and existing approaches for reducing false detections either compromise the temporal accuracy or amplify the omission error for forest disturbances. Here, we propose to use a set of space-time features to reduce false detections. We first detect potential forest disturbances in the Landsat time series based on two consecutive negative anomalies, and subsequently use space-time features to confirm forest disturbances. A probability threshold is used to discriminate false detections from forest disturbances. We demonstrated this approach in the UNESCO Kafa Biosphere Reserve located in the southwest of Ethiopia by detecting forest disturbances between 2014 and 2016. Our results show that false detections are reduced significantly without compromising temporal accuracy. The user’s accuracy was at least 26% higher than the user’s accuracies obtained when using only temporal information (e.g., two consecutive negative anomalies) to confirm forest disturbances. We found the space-time features related to change in spatio-temporal variability, and spatio-temporal association with non-forest areas, to be the main predictors for forest disturbance. The magnitude of change and two consecutive negative anomalies, which are widely used to distinguish real changes from false detections, were not the main predictors for forest disturbance. Overall, our findings indicate that using a set of space-time features to confirm forest disturbances increases the capacity to reject many false detections, without compromising the temporal accuracy.

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 Dates: 2017-05-212017-05-232017-05-23
 Publication Status: Issued
 Pages: 17
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: BEX565
DOI: 10.3390/rs9060515
 Degree: -

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Project name : BACI
Grant ID : 640176
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Remote Sensing
Source Genre: Journal
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Publ. Info: Basel : Molecular Diversity Preservation International (MDPI)
Pages: 17 Volume / Issue: 9 (6) Sequence Number: 515 Start / End Page: - Identifier: ISSN: 2072-4292
CoNE: https://pure.mpg.de/cone/journals/resource/2072-4292