Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 424129
Title Near real-time disturbance detection using satellite image time series
Author(s) Verbesselt, J.P.; Zeileis, A.; Herold, M.
Source Remote Sensing of Environment 123 (2012). - ISSN 0034-4257 - p. 98 - 108.
DOI http://dx.doi.org/10.1016/j.rse.2012.02.022
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) land-surface phenology - monitoring structural-change - terrestrial ecosystems - vegetation indexes - ndvi - dynamics - patterns - exchange - models
Abstract Near real-time monitoring of ecosystem disturbances is critical for rapidly assessing and addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a multi-purpose time-series-based disturbance detection approach that identifies and models stable historical variation to enable change detection within newly acquired data. Satellite image time series of vegetation greenness provide a global record of terrestrial vegetation productivity over the past decades. Here, we assess and demonstrate the method by applying it to (1) simulated time series of vegetation greenness data from satellite data, (2) real-world satellite greenness image time series between February 2000 and July 2011 covering Somalia to detect drought-related vegetation disturbances. First, simulation results illustrate that disturbances are successfully detected in near real-time while being robust to seasonality and noise. Second, major drought-related disturbance corresponding with most drought-stressed regions in Somalia are detected from mid-2010 onwards. The method can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling. While the data and methods used are appropriate for proof-of-concept development of global scale disturbance monitoring, specific applications (e.g., drought or deforestation monitoring) mandate integration within an operational monitoring framework
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