Historical trends of degradation, loss, and recovery in the tropical forest reserves of Ghana
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis Group
Abstract
The Upper Guinean Forest region of West Africa, a globally significant
biodiversity hotspot, is among the driest and most human-impacted
tropical ecosystems. We used Landsat to study forest degradation, loss,
and recovery in the forest reserves of Ghana from 2003 to 2019. Annual
canopy cover maps were generated using random forests and results
were temporally segmented using the LandTrendr algorithm. Canopy
cover was predicted with a predicted-observed r2 of 0.76, mean
absolute error of 12.8%, and mean error of 1.3%. Forest degradation,
loss, and recovery were identified as transitions between closed (>60%
cover), open (15–60% cover) and low tree cover (< 15% cover) classes.
Change was relatively slow from 2003 to 2015, but there was more
disturbance than recovery resulting in a gradual decline in closed
canopy forests. In 2016, widespread fires associated with El Niño
drought caused forest loss and degradation across more than 12% of
the moist semi-deciduous and upland evergreen forest types. The
workflow was implemented in Google Earth Engine, allowing
stakeholders to visualize the results and download summaries.
Information about historical disturbances will help to prioritize locations
for future studies and target forest protection and restoration activities
aimed at increasing resilience.
Description
Research Article
Keywords
Landsat, random forests, machine learning, time series, LandTrendr, drought, wildfire