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

Research output: Contribution to conferenceConference paperAcademic

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 languageEnglish
Pages1-4
Publication statusPublished - 2013
Event7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada -
Duration: 25 Jun 201327 Jun 2013

Workshop

Workshop7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada
Period25/06/1327/06/13

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  • 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. 1-4. Paper presented at 7th International Workshop on Analysis of MultiTemporal Remote Sensing Data, Banff, Alberta, Canada, . https://edepot.wur.nl/305585