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


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.


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



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)


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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

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