Advertisement

Journal of the Indian Society of Remote Sensing

, Volume 45, Issue 5, pp 837–845 | Cite as

Monitoring Forest Disturbance in Lesser Khingan Mountains Using MODIS and Landsat TM Time Series from 2000 to 2011

  • Lingxue Yu
  • Tingxiang Liu
  • Kun Bu
  • Jiuchun Yang
  • Shuwen ZhangEmail author
Research Article

Abstract

The widespread changes in forest cover caused by climatological and anthropogenic factors can influence the forest ecosystem and climate system to a great extent. With the increasing availability of remote sensing data, monitoring of forest changes at high temporal resolution and on various scales is becoming more realistic. Though several methods based on time series data have been used to detect forest disturbance, there are few studies paying attention to boreal areas where the forest is significant in regulating the global carbon cycle and biogeophysical processes. In this paper, we present a robust method of Breaks Detection Based On Polynomial Model (BDPM) to track boreal (e.g. Lesser Khingan Mountains) deforestation and forest fires based on the MODIS and Landsat TM time series data. Compared with the previous methods, the BDPM offers the following advantages: (1) Fitting of the polynomial model using the seasonal variation of forests in the whole region instead of a single pixel to avoid error accumulation; (2) to avoid confusion between vegetation change due to climate changes and abrupt forest disturbances, we segmented the long-time NDVI series data into 12 seasonal cycles and simulated the temporal variations in each seasonal cycle.

Keywords

Monitoring forest disturbances BDPM MODIS time series Remote sensing Processing of long-term data 

Notes

Acknowledgements

This study was supported by the “Study on the digital reconstruction of land use change and its vulnerability in the agriculture and forestry ecotone in northeast China over the past century” of the National Natural Science Foundation of China (No. 41271416). We thank the reviewers for their valuable and constructive comments.

Supplementary material

12524_2016_645_MOESM1_ESM.tif (72 kb)
Supplementary material 1 (TIFF 73 kb)

References

  1. Chen, J., Jonsson, P., Tamura, M., Gu, Z. H., Matsushita, B., & Eklundh, L. (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment, 91(3–4), 332–344. doi: 10.1016/j.rse.2004.03.014.CrossRefGoogle Scholar
  2. Cihlar, J., Latifovic, R., Chen, J., Trishchenko, A., Du, Y., Fedosejevs, G., et al. (2004). Systematic corrections of AVHRR image composites for temporal studies. Remote Sensing of Environment, 89(2), 217–233. doi: 10.1016/j.rse.2002.06.007.CrossRefGoogle Scholar
  3. Cohen, W. B., Yang, Z. G., & Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—tools for calibration and validation. Remote Sensing of Environment, 114(12), 2911–2924. doi: 10.1016/j.rse.2010.07.010.CrossRefGoogle Scholar
  4. De Jong, R., Verbesselt, J., Zeileis, A., & Schaepman, M. E. (2013). Shifts in global vegetation activity trends. Remote Sensing, 5(3), 1117–1133. doi: 10.3390/rs5031117.CrossRefGoogle Scholar
  5. DeVries, B., Verbesselt, J., Kooistra, L., & Herold, M. (2015). Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sensing of Environment, 161, 107–121. doi: 10.1016/j.rse.2015.02.012.CrossRefGoogle Scholar
  6. Eklundh, L., & Olsson, L. (2003). Vegetation index trends for the African Sahel 1982–1999. Geophysical Research Letters. doi: 10.1029/2002gl016772.Google Scholar
  7. Fuller, D. O. (1998). Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987–1993. International Journal of Remote Sensing, 19(10), 2013–2018. doi: 10.1080/014311698215135.CrossRefGoogle Scholar
  8. Goodwin, N. R., Coops, N. C., Wulder, M. A., Gillanders, S., Schroeder, T. A., & Nelson, T. (2008). Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment, 112(9), 3680–3689. doi: 10.1016/j.rse.2008.05.005.CrossRefGoogle Scholar
  9. Gullison, R. E., Frumhoff, P. C., Canadell, J. G., Field, C. B., Nepstad, D. C., Hayhoe, K., et al. (2007). Tropical forests and climate policy. Science, 316(5827), 985–986. doi: 10.1126/science.1136163.CrossRefGoogle Scholar
  10. Healey, S. P., Yang, Z. Q., Cohen, W. B., & Pierce, D. J. (2006). Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sensing of Environment, 101(1), 115–126. doi: 10.1016/j.rse.2005.12.006.CrossRefGoogle Scholar
  11. Hirsch, A. I., Little, W. S., Houghton, R. A., Scott, N. A., & White, J. D. (2004). The net carbon flux due to deforestation and forest re-growth in the Brazilian Amazon: Analysis using a process-based model. Global Change Biology, 10(5), 908–924. doi: 10.1111/j.1529-8817.2003.00765.x.CrossRefGoogle Scholar
  12. Houghton, R. A. (2003). Why are estimates of the terrestrial carbon balance so different? Global Change Biology, 9(4), 500–509. doi: 10.1046/j.1365-2486.2003.00620.x.CrossRefGoogle Scholar
  13. Huang, C. Q., Coward, S. N., Masek, J. G., Thomas, N., Zhu, Z. L., & Vogelmann, J. E. (2010). An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114(1), 183–198. doi: 10.1016/j.rse.2009.08.017.CrossRefGoogle Scholar
  14. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. doi: 10.1016/S0034-4257(02)00096-2.CrossRefGoogle Scholar
  15. Jamali, S., Jonsson, P., Eklundh, L., Ardo, J., & Seaquist, J. (2015). Detecting changes in vegetation trends using time series segmentation. Remote Sensing of Environment, 156, 182–195. doi: 10.1016/j.rse.2014.09.010.CrossRefGoogle Scholar
  16. Kennedy, R. E., Cohen, W. B., & Schroeder, T. A. (2007). Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110(3), 370–386. doi: 10.1016/j.rse.2007.03.010.CrossRefGoogle Scholar
  17. Law, B. E., Turner, D., Campbell, J., Sun, O. J., Van Tuyl, S., Ritts, W. D., et al. (2004). Disturbance and climate effects on carbon stocks and fluxes across Western Oregon USA. Global Change Biology, 10(9), 1429–1444. doi: 10.1111/j.1365-2486.2004.00822.x.CrossRefGoogle Scholar
  18. Lee, K., & Kang, S. (2013). Mobile cloud service of geo-based image processing functions: A test iPad implementation. Remote Sensing Letters, 4(9), 910–919. doi: 10.1080/2150704X.2013.810821.CrossRefGoogle Scholar
  19. Liu, Z. Y., Notaro, M., & Kutzbach, J. (2006). Assessing global vegetation-climate feedbacks from observations. Journal of Climate, 5(19), 787–814. doi: 10.1175/JCLI3658.1.CrossRefGoogle Scholar
  20. Overpeck, J. T., Rind, D., & Goldberg, R. (1990). Climate-induced changes in forest disturbance and vegetation. Nature, 343(6253), 51–53. doi: 10.1038/343051a0.CrossRefGoogle Scholar
  21. Peng, J., Liu, Z. H., Liu, Y. H., Wu, J. S., & Han, Y. A. (2012). Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst Exponent. Ecological Indicators, 14(1), 28–39. doi: 10.1016/j.ecolind.2011.08.011.CrossRefGoogle Scholar
  22. Schmidt, M., Lucas, R., Bunting, P., Verbesselt, J., & Armston, J. (2015). Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Remote Sensing of Environment, 158, 156–168. doi: 10.1016/j.rse.2014.11.015.CrossRefGoogle Scholar
  23. Turner, M. G. (2010). Disturbance and landscape dynamics in a changing world. Ecology, 91(10), 2833–2849. doi: 10.1890/10-0097.1.CrossRefGoogle Scholar
  24. van der Werf, G. R., Morton, D. C., DeFries, R. S., Olivier, J. G. J., Kasibhatla, P. S., Jackson, R. B., et al. (2009). CO2 emissions from forest loss. Nature Geoscience, 2(11), 737–738. doi: 10.1038/ngeo671.CrossRefGoogle Scholar
  25. Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010a). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1), 106–115. doi: 10.1016/j.rse.2009.08.014.CrossRefGoogle Scholar
  26. Verbesselt, J., Hyndman, R., Zeileis, A., & Culvenor, D. (2010b). Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114(12), 2970–2980. doi: 10.1016/j.rse.2010.08.003.CrossRefGoogle Scholar
  27. Verbesselt, J., Zeileis, A., & Herold, M. (2012). Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123, 98–108. doi: 10.1016/j.rse.2012.02.022.CrossRefGoogle Scholar
  28. Zhang, S. W., Xu, X. L., Li, Y., Chang, L. P., Zhang, Y. Z., & Gao, Z. Q. (2004). Digital Northeast China for about 100 years: RS dynamic observations and GIS frame design and example analysis regarding resources and the environment in Northeast China. Paper presented at the Annual Meeting for the Remote Sensing and Modeling of Ecosystems for Sustainability, Colorado, August 2–4.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2016

Authors and Affiliations

  • Lingxue Yu
    • 1
  • Tingxiang Liu
    • 1
  • Kun Bu
    • 1
  • Jiuchun Yang
    • 1
  • Shuwen Zhang
    • 1
    Email author
  1. 1.Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunPeople’s Republic of China

Personalised recommendations