4.6 Article

Spatiotemporal pattern of forest degradation and loss of ecosystem function associated with Rohingya influx: A geospatial approach

Journal

LAND DEGRADATION & DEVELOPMENT
Volume 32, Issue 13, Pages 3666-3683

Publisher

WILEY
DOI: 10.1002/ldr.3821

Keywords

CA Markov; ecosystem function; forest degradation; LULC; multi‐ criteria evaluation; Rohingya influx

Funding

  1. Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19030302]
  2. CAS-TWAS President's PhD Fellowship-2017 [2017CTF099]

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This study used satellite images and data to monitor forest cover degradation and ecosystem function loss in Cox's Bazar-Teknaf Peninsula, predicting that the presence of Rohingya refugees will continue to impact forest cover, with 3130 hectares of forest land transformed between 2017 and 2019. The projection suggests that around 5115 hectares of forest cover may experience loss from 2019 to 2027, with aboveground biomass and carbon stock estimation indicating significant and consistent loss during the study period. The findings have important implications for conservation decisions and policies in Bangladesh's ecologically critical areas.
Violence in Rakhine State of Myanmar forcibly displaced nearly one million Rohingya. They took refuge, from August 25, 2017 to the time of writing, in Cox's Bazar-Teknaf Peninsula of Bangladesh. Initially, nearly 2,000 ha of forested lands had to be cleared to accommodate them in one of the most ecologically critical areas (ECA) in the Peninsula. To support Rohingyas livelihoods, fuelwood collection and illegal logging have become widespread since their arrival, causing severe environmental degradation, including loss of a vast amount of forest cover. To devise conservation and protection strategies for a highly sensitive ecosystem, it is imperative to understand the degree of forest cover deterioration and associated impacts related to Rohingya emigration. This study employed satellite images and collateral data to monitor and model spatiotemporal patterns of forest cover degradation and loss of ecosystem function in Cox's Bazar-Teknaf Peninsula. Supervised classification method was used to derive multi-date land use/cover data which was then utilized to monitor spatiotemporal pattern of forest cover change from 2017 to 2019. A projection of forest cover loss was also carried out using the Markov chain with cellular automata technique. Dynamic modeling was performed to predict changes in forest covers, assuming that displaced Rohingya continues to reside in this environmentally sensitive location. The result revealed that 3,130 ha of forested lands of different categories were transformed into either refugee camps or Rohingya influenced degraded forests between 2017 and 2019. Prediction showed that around 5,115 ha of forest cover may experience loss from 2019 to 2027. Furthermore, aboveground biomass and carbon stock estimation indicated a consistent and substantial loss during the study period, which is likely to swell if present deforestation rate continues. The findings have considerable implications in developing conservation decisions, priority interventions and public policies to save the ECA of Bangladesh.

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