Forest Cover Dynamics During Massive Ownership Changes – Annual Disturbance Mapping Using Annual Landsat Time-Series

  • Patrick GriffithsEmail author
  • Patrick Hostert
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 22)


Remote sensing is a core tool for forest monitoring. Landsat data has been widely used in forest change detection studies but many approaches lack capabilities such as assessing changes for long temporal sequences. Moreover, most methods are not capable of detecting gradual long term processes, such as post disturbance recovery. Following the open Landsat data policy implemented in 2008, but also due to the improved level 1 processing standards, Landsat remote sensing experienced considerable innovation, with many novel algorithms for automated preprocessing and also for change detection. Among these, trajectory based change detection methods provide new means for assessing forest cover changes using Landsat data. For example, disturbances can be assessed on a yearly basis and residual noise in the time series is effectively reduced, enabling the previously impossible detection of gradual changes (e.g. recovery, degradation). We here demonstrate the analytic power of an annual time series approach (using the Landsat based detection of trends in disturbance and recovery (LandTrendr) algorithm) by assessing forest cover dynamics for an area in Romania, Eastern Europe. Our results illustrate that trajectory-based time series approaches can successfully be applied in relatively data scarce regions. Annual disturbance patterns allow for improved process understanding, and provide valuable inputs to a range of applications, including resource management, climate modelling or socio-ecological systems understanding, as in the case of Romania.


Forest Cover Change Time Series Approach Change Detection Method Disturbance Class Time Series Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was funded by the Belgian Science Policy, Research Program for Earth Observation Stereo II, contract SR/00/133, as part of the FOMO project (Remote sensing of the forest transition and its ecosystem impacts in mountain environments). Support is gratefully acknowledged by Humboldt-University Berlin.


  1. Abrudan IV, Marinescu V, Ignea G, Codreanu C (2005) Present situation and trends in Romanian forestry. In: Abrudan IV, Schmitthüsen FJ, Herbst P (eds) Legal aspects of European forest sustainable development, Proceedings of the 6th international symposium, Poiana Brasov, Romania, pp 157–171Google Scholar
  2. Canty MJ, Nielsen AA (2008) Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sens Environ 112:1025–1036CrossRefGoogle Scholar
  3. Chavez PS (1996) Image-based atmospheric corrections revisited and improved. Photogramm Eng Remote Sens 62:1025–1036Google Scholar
  4. Cohen WB, Yang Z, Kennedy R (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync – tools for calibration and validation. Remote Sens Environ 114:2911–2924CrossRefGoogle Scholar
  5. Coppin P, Bauer ME (1996) Digital change detection in forest ecosystems with remote sensing imagery. Remote Sens Rev 13:207–234CrossRefGoogle Scholar
  6. Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:1565–1596CrossRefGoogle Scholar
  7. Crist EP, Cicone RC (1984) A physically-based transformation of Thematic Mapper data – the TM Tasseled Cap. IEEE Trans Geosci Remote Sens 22:256–263CrossRefGoogle Scholar
  8. Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25–36CrossRefGoogle Scholar
  9. Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C, Patz JA, Prentice IC, Ramankutty N, Snyder PK (2005) Global consequences of land use. Science 309:570–574CrossRefGoogle Scholar
  10. Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83:287–302CrossRefGoogle Scholar
  11. GLP (2005) Science plan and implementation strategy, IGBP report no. 53/IHDP report no. 19. IGBP Secretariat, StockholmGoogle Scholar
  12. Griffiths P, Kuemmerle T, Kennedy RE, Abrudan IV, Knorn J, Hostert P (2012) Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania. Remote Sens Environ 118:199–214CrossRefGoogle Scholar
  13. Griffiths P, Kuemmerle T, Baumann M, Radeloff VC, Abrudan IV, Lieskovsky J, Munteanu C, Ostapowicz K, Hostert P (2014) Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sens Environ 151:72–88CrossRefGoogle Scholar
  14. Huang C, Coward SN, Masek JG, Thomas N, Zhu Z, Vogelmann JE (2010) An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ 114:183–198CrossRefGoogle Scholar
  15. Ioras F, Abrudan IV (2006) The Romanian forestry sector: privatisation facts. Int For Rev 8:361–367Google Scholar
  16. Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens Environ 110:370–386CrossRefGoogle Scholar
  17. Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – temporal segmentation algorithms. Remote Sens Environ 114:2897–2910CrossRefGoogle Scholar
  18. Kennedy RE, Andrefouet S, Cohen WB, Gomez C, Griffiths P, Hais M, Healey SP, Helmer EH, Hostert P, Lyons MB, Meigs GW, Pflugmacher D, Phinn SR, Powell SL, Scarth P, Sen S, Schroeder TA, Schneider A, Sonnenschein R, Vogelmann JE, Wulder MA, Zhu Z (2014) Bringing an ecological view of change to Landsat-based remote sensing. Front Ecol Environ 12:339–346CrossRefGoogle Scholar
  19. Kuemmerle T, Hostert P, Perzanowski K, Radeloff VC (2006) Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique. Remote Sens Environ 103:449–464CrossRefGoogle Scholar
  20. Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2407CrossRefGoogle Scholar
  21. Main-Knorn M, Cohen WB, Kennedy RE, Grodzki W, Pflugmacher D, Griffiths P, Hostert P (2013) Monitoring coniferous forest biomass change using a Landsat trajectory-based approach. Remote Sens Environ 139:277–290CrossRefGoogle Scholar
  22. Mihalciuc V, Simionescu A, Mircioiu L (1999) Sanitary state of coniferous stands calamited by wind and snow on 5./6. November 1995 in Eastern carpathians from Romania. In: Forster B, Knízek M, Grodzki W (eds) Methodology of forest insect and disease survey in Central Europe. Second workshop of the IUFRO WP 7.03.10. Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Sion-Châteauneuf, pp 238–241Google Scholar
  23. Olofsson P, Foody GM, Stehman SV, Woodcock C (2013) Making better use of accuracy data in land change studies: estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens Environ 129:122–131CrossRefGoogle Scholar
  24. Reed BC, Brown JF, Vanderzee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5:703–714CrossRefGoogle Scholar
  25. Roy DP, Wulder MA, Loveland TR, Woodcock CE, Allen RG, Anderson MC, Helder D, Irons JR, Johnson DM, Kennedy R, Scambos TA, Schaaf CB, Schott JR, Sheng Y, Vermote EF, Belward AS, Bindschadler R, Cohen WB, Gao F, Hipple JD, Hostert P, Huntington J, Justice CO, Kilic A, Kovalskyy V, Lee ZP, Lymburner L, Masek JG, McCorkel J, Shuai Y, Trezza R, Vogelmann J, Wynne RH, Zhu Z (2014) Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ 145:154–172CrossRefGoogle Scholar
  26. Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, El Saleous N (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26:4485–4498CrossRefGoogle Scholar
  27. Wulder MA, Franklin SE (2003) Remote sensing of forest environments: concepts and case studies. Kluwer, BostonCrossRefGoogle Scholar
  28. Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122:2–10CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Geography DepartmentHumboldt-Universität zu BerlinBerlinGermany
  2. 2.IRI THESysHumboldt-Universität zu BerlinBerlinGermany

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