Detection of land-use change and rapid recovery of vegetation after deforestation in the Congo Basin

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The Congo Basin is severely understudied compared to other tropical regions; this is partly due to the lack of meteorological stations and the ubiquitous cloudiness hampering the use of remote-sensing products. Clustering of small-scale agricultural deforestation events within the Basin may result in deforestation on scales that are atmospherically important. This study uses 500 m MODIS data and the Global Forest Change dataset (GFC) to detect deforestation at a monthly and sub-km scale and to quantify how deforestation impacts vegetation proxies (VPs) within the Basin, the timescales over which these changes persist, and how they’re affected by the deforestation driver.

Missing MODIS data has meant that a new method, based on two-date image differencing, was developed to detect deforestation at a monthly scale. Evaluation against the yearly GFC data shows that the highest detection rate was 79% for clearing sizes larger than 500 m2. Recovery to pre-deforestation levels occurred faster than expected; analysis of post-deforestation evolution of the VPs found 66% of locations recovered within a year. Separation by land-cover type also showed unexpected regrowth as over 50% of rural complex and plantation land recovered within a year. The fallow period in the study region was typically short; by the 6th year after the initial deforestation event, ~88% of the locations underwent a further considerable drop. These results show the importance of fine spatial and temporal information to assess Congo Basin deforestation and highlight the large differences in the impacts of land-use change compared to other rainforests.
Original languageEnglish
Number of pages25
JournalEarth Interactions
Early online date27 Mar 2023
Publication statusE-pub ahead of print - 27 Mar 2023


  • Africa
  • Tropics
  • Algorithms
  • Remote sensing
  • Satellite observations
  • Changepoint analysis


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