Abstract

Understanding dynamics of forest cover is important to monitor change in forest area. The objective of the present study is to develop an approach for assessing forest cover changes in landscapes with high spatial complexity and temporal variation that can allow the generation of robust monitoring information. The forest-cover change maps were produced using time-series of Landsat images, high resolution images from Google Earth, free software R and QGIS. A complete map of forest cover change at 30 m spatial resolution was produced over 603'972 ha. The result was validated by photo-interpretation of 5000 randomly sampled points and on the basis of high-resolution images available in Google Earth (Quickbird) for the year 2018 and Landsat satellite images for the year 2018, 1991 and 2003. The estimated overall accuracy of the forest cover change map is 88.7%. In the study area, the forest area was estimated at 246’915 ha in 1991, 232’741 ha in 2003 and 230’390 ha in 2018. The gross forest loss has increased from 182.5 ha/year in the first period 1991-2003 to 187.47 ha/year in the second period 2003-2018. The corresponding net annual forest loss (incl. regeneration) rates are 0.5% in the first period and 0.1% in the second period. The decrease of the net annual forest loss rate in the second period is attributed to an increase in forest regeneration. This study can be considered as a reproducible approach to map forest-cover change and can support policy approaches towards reducing emissions from deforestation and degradation (REDD+).

Key words: Forest loss, forest gain, multi-date, Landsat, random forest, Togo.