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Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests

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  • Antje Ahrends
  • Mark Bulling
  • Ruth D. Swetnam
  • Casey Ryan
  • Nike Doggart
  • Peter M Hollingsworth
  • Andrew Balmford
  • David J. Harris
  • Nicole Gross-Camp
  • Peter Sumbi
  • Pantaleon K T Munishi
  • Seif Madoffe
  • Boniface E. Mhoro
  • Charles Leonard
  • Claire Bracebridge
  • Kathryn Doody
  • Victoria Wilkins
  • Nisha Owen
  • Marije Schaafsma
  • Kerstin Pfliegner
  • Trevor Jones
  • James Robinson
  • Elmer J Topp-Jorgensen
  • Henry Brink
  • Neil D. Burgess


Publication details

JournalPlants, People, Planet
DateAccepted/In press - 18 Jan 2021
DatePublished (current) - 27 Apr 2021
Number of pages14
Pages (from-to)268-281
Original languageEnglish


• Tropical forest degradation is widely recognised as a driver of biodiversity loss and a major source of carbon emissions. However, in contrast to deforestation, more gradual changes from degradation are challenging to detect, quantify and monitor. Here, we present a field protocol for rapid, area-standardised quantifications of forest condition, which can also be implemented by non-specialists. Using the example of threatened high-biodiversity forests in Tanzania, we analyse and predict degradation based on this method. We also compare the field data to optical and radar remote-sensing datasets, thereby conducting a large-scale, independent test of the ability of these products to map degradation in East Africa from space.• Our field data consist of 551 ‘degradation’ transects collected between 1996 and 2010, covering >600 ha across 86 forests in the Eastern Arc Mountains and coastal forests.• Degradation was widespread, with over one-third of the study forests—mostly protected areas—having more than 10% of their trees cut. Commonly used optical remote-sensing maps of complete tree cover loss only detected severe impacts (≥25% of trees cut), that is, a focus on remotely-sensed deforestation would have significantly underestimated carbon emissions and declines in forest quality. Radar-based maps detected even low impacts (<5% of trees cut) in ~90% of cases. The field data additionally differentiated types and drivers of harvesting, with spatial patterns suggesting that logging and charcoal production were mainly driven by demand from major cities.• Rapid degradation surveys and radar remote sensing can provide an early warning and guide appropriate conservation and policy responses. This is particularly important in areas where forest degradation is more widespread than deforestation, such as in eastern and southern Africa.

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