Assessing Impacts of Forest Landscape Restoration: An Evaluation of Three Projects in Latin America and the Caribbean Using the Collect Earth Land Monitoring Tool

Organised by Laboratory of Geo-information Science and Remote Sensing

Tue 28 May 2019 10:00 to 10:30

Venue Gaia, gebouwnummer 101
Room 1

By Marcus Betts

Forests landscapes are of huge importance in terms of the resources they provide, the biodiversity they support and the role they play in regulating climate. Today, however, they are under threat from deforestation, resulting in loss of biodiversity, increased CO2 emissions and habitat fragmentation, particularly across Latin America with its expanse of tropical rainforest. To address deforestation, international commitments such as the Bonn Challenge have been established, committing millions of hectares of deforested land for restoration. Forest landscape restoration (FLR) projects have been implemented across Latin America and the Caribbean in order to meet these targets. The objective of this research is to analyse the impact of 3 FLR initiatives in Argentina, Haiti and Peru using the augmented visual interpretation tool Collect Earth. For each project, land use change and change in canopy cover percentage were recorded in order to determine whether restoration or degradation had occurred. In terms of restored land area, the Argentina project had 2697.5 hectares, the Haiti project 594.1 hectares and the Peru project 39.3 hectares. Also, 3168.5 hectares of land was degraded in the Haiti project area and 6.4 hectares in the Peru area. Comparison with project goals, shows evidence that the Argentina project is achieving its primary goal of establishing forest through plantation. However, evidence for the Haiti and Peru projects meeting their goals is less clear, due to the different FLR approaches they take. It was concluded that Collect Earth is a suitable tool for monitoring more obvious FLR impacts such as forest regrowth due to reforestation. More subtle changes related to different FLR approaches such as agroforestry or sustainable forest management are less easily detected.