Drivers of forest loss in a megadiverse hotspot on the pacific Coast of Colombia

Tropical forests are disappearing at unprecedented rates, but the drivers behind this transformation are not always clear. This limits the decision-making processes and the effectiveness of forest management policies. In this paper, we address the extent and drivers of deforestation of the Choco biodiversity hotspot, which has not received much scientific attention despite its high levels of plant diversity and endemism. The climate is characterized by persistent cloud cover which is a challenge for land cover mapping from optical satellite imagery. By using Google Earth Engine to select pixels with minimal cloud content and applying a random forest classifier to Landsat and Sentinel data, we produced a wall-to-wall land cover map, enabling a diagnosis of the status and drivers of forest loss in the region. Analyses of these new maps together with information from illicit crops and alluvial mining uncovered the pressure over intact forests. According to Global Forest Change (GFC) data, 2324 km2 were deforested in this area from 2001 to 2018, reaching a maximum in 2016 and 2017. We found that 68% of the area is covered by broadleaf forests (67,473 km2) and 15% by shrublands (14,483 km2), the latter with enormous potential to promote restoration projects. This paper provides a new insight into the conservation of this exceptional forest with a discussion of the drivers of forest loss, where illicit crops and alluvial mining were found to be responsible for 60% of forest loss. © 2020 by the authors.

• Fecha de publicación: 2020
• Institución: Universidad de Medellín
• Repositorio: Repositorio UDEM
• Idioma: eng
• OAI Identifier: oai:repository.udem.edu.co:11407/5945
• Acceso en línea: http://hdl.handle.net/11407/5945
• Palabra clave: Biodiversity hotspot; Deforestation; Google earth engine; Landsat; Sentinel; Tropical humid forests; Biodiversity; Crops; Decision making; Decision trees; Deforestation; Satellite imagery; Broadleaf forest; Decision making process; Forest management policies; Land cover mapping; Optical satellite imagery; Random forest classifier; Restoration project; Tropical forest; Conservation
• License: http://purl.org/coar/access_right/c_16ec