IDEAS home Printed from https://ideas.repec.org/p/inn/wpaper/2011-18.html
   My bibliography  Save this paper

Near Real-Time Disturbance Detection in Terrestrial Ecosystems Using Satellite Image Time Series: Drought Detection in Somalia

Author

Listed:
  • Jan Verbesselt
  • Achim Zeileis
  • Martin Herold

Abstract

Near real-time monitoring of ecosystem disturbances is critical for 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 generic time series based disturbance detection approach by modelling stable historical behaviour to enable detection of abnormal changes within newly acquired data. Time series of vegetation greenness provide a measure for terrestrial vegetation productivity over the last decades covering the whole world and contain essential information related land cover dynamics and disturbances. Here, we assess and demonstrate the method by (1) simulating time series of vegetation greenness data from satellite data with different amount of noise, seasonality and disturbances representing a wide range of terrestrial ecosystems, (2) applying it to real 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 for seasonality and noise. Second, major drought related disturbance corresponding with most drought stressed regions in Somalia are detected from mid 2010 onwards and confirm proof-of-concept of the method. The method can be integrated within current operational early warning systems and has the potential to detect a wide variety of disturbances (e.g. deforestation, flood damage, etc.). It 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 or definitions and does not require time series gap filling.

Suggested Citation

  • Jan Verbesselt & Achim Zeileis & Martin Herold, 2011. "Near Real-Time Disturbance Detection in Terrestrial Ecosystems Using Satellite Image Time Series: Drought Detection in Somalia," Working Papers 2011-18, Faculty of Economics and Statistics, Universität Innsbruck.
  • Handle: RePEc:inn:wpaper:2011-18
    as

    Download full text from publisher

    File URL: https://www2.uibk.ac.at/downloads/c4041030/wpaper/2011-18.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Achim Zeileis, 2005. "A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 445-466.
    2. Pesaran, M. Hashem & Timmermann, Allan, 2002. "Market timing and return prediction under model instability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 495-510, December.
    3. Anton Vrieling & Kirsten Beurs & Molly Brown, 2011. "Variability of African farming systems from phenological analysis of NDVI time series," Climatic Change, Springer, vol. 109(3), pages 455-477, December.
    4. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    5. Chu, Chia-Shang James & Stinchcombe, Maxwell & White, Halbert, 1996. "Monitoring Structural Change," Econometrica, Econometric Society, vol. 64(5), pages 1045-1065, September.
    6. R. B. Myneni & C. D. Keeling & C. J. Tucker & G. Asrar & R. R. Nemani, 1997. "Increased plant growth in the northern high latitudes from 1981 to 1991," Nature, Nature, vol. 386(6626), pages 698-702, April.
    7. Zeileis, Achim & Shah, Ajay & Patnaik, Ila, 2010. "Testing, monitoring, and dating structural changes in exchange rate regimes," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1696-1706, June.
    8. Achim Zeileis & Friedrich Leisch & Christian Kleiber & Kurt Hornik, 2005. "Monitoring structural change in dynamic econometric models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 99-121, January.
    9. Leisch, Friedrich & Hornik, Kurt & Kuan, Chung-Ming, 2000. "Monitoring Structural Changes With The Generalized Fluctuation Test," Econometric Theory, Cambridge University Press, vol. 16(6), pages 835-854, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jan J. J. Groen & George Kapetanios & Simon Price, 2013. "Multivariate Methods For Monitoring Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 250-274, March.
    2. Anatolyev, Stanislav, 2009. "Nonparametric Retrospection and Monitoring of Predictability of Financial Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 149-160.
    3. Zeileis, Achim & Shah, Ajay & Patnaik, Ila, 2010. "Testing, monitoring, and dating structural changes in exchange rate regimes," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1696-1706, June.
    4. Carsten J. Crede, 2019. "A Structural Break Cartel Screen for Dating and Detecting Collusion," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(3), pages 543-574, May.
    5. Czinkota, Thomas, 2012. "Das Halteproblem bei Strukturbrüchen in Finanzmarktzeitreihen [The Halting Problem applied to Structural Breaks in Financial Time Series]," MPRA Paper 37072, University Library of Munich, Germany.
    6. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    7. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO.
    8. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    9. Eklund, Jana & Kapetanios, George & Price, Simon, 2010. "Forecasting in the presence of recent structural change," Bank of England working papers 406, Bank of England.
    10. Yudong Chen & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional, multiscale online changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 234-266, February.
    11. Hsu, Chih-Chiang, 2007. "The MOSUM of squares test for monitoring variance changes," Finance Research Letters, Elsevier, vol. 4(4), pages 254-260, December.
    12. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    13. Abhijit Sharma & Kelvin G Balcombe & Iain M Fraser, 2009. "Non-renewable resource prices: Structural breaks and long term trends," Economics Bulletin, AccessEcon, vol. 29(2), pages 805-819.
    14. Bock, David, 2007. "Consequences of using the probability of a false alarm as the false alarm measure," Research Reports 2007:3, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    15. Chen, Yudong & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional, multiscale online changepoint detection," LSE Research Online Documents on Economics 113665, London School of Economics and Political Science, LSE Library.
    16. Edgar Merkle & Achim Zeileis, 2013. "Tests of Measurement Invariance Without Subgroups: A Generalization of Classical Methods," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 59-82, January.
    17. Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
    18. Anatolyev Stanislav & Kosenok Grigory, 2018. "Sequential Testing with Uniformly Distributed Size," Journal of Time Series Econometrics, De Gruyter, vol. 10(2), pages 1-22, July.
    19. Piotr Kotlarz & Michael Hanke & Sebastian Stöckl, 2023. "Regime-dependent drivers of the EUR/CHF exchange rate," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-18, December.
    20. Alberto Cazzola & Lucia Pasquini & Aurora Angeli, 2016. "The relationship between unemployment and fertility in Italy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(1), pages 1-38.

    More about this item

    Keywords

    early warning; real-time monitoring; global change; disturbance; time series; remote sensing; vegetation and climate dynamics;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inn:wpaper:2011-18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Janette Walde (email available below). General contact details of provider: https://edirc.repec.org/data/fuibkat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.