Remotely sensed tree canopy cover-based indicators for monitoring global sustainability and environmental initiatives

With the intensifying challenges of global environmental change, sustainability, and biodiversity conservation, the monitoring of the world's remaining forests has become more important than ever. Today, Earth observation technologies, particularly remote sensing, are at the forefront of forest cover monitoring worldwide. Given the current conceptual understanding of what a forest is, canopy cover threshold values are used to map forest cover from remote sensing imagery and produce categorical data products such as forest/non-forest (F/NF) maps. However, multi-temporal categorical map products have important limitations because they inadequately represent the actual status of forest landscapes and the trajectories of forest cover changes as a result of the thresholding effect. Here, we examined the potential of using remotely sensed tree canopy cover (TCC) datasets, which are continuous data products, to complement F/NF maps for forest cover monitoring. We developed a conceptual analytical framework for forest cover monitoring using both types of data products and applied it to the forests of Southeast Asia. We conclude that TCC datasets and the statistics derived from them can be used to complement the information provided by categorical F/NF maps. TCC-based indicators (i.e. losses, gains, and net changes) can help in monitoring not only deforestation but also forest degradation and forest cover enhancement, all of which are highly relevant to the 2030 Agenda for Sustainable Development and other global forest cover monitoring-related initiatives. We recommend that future research should focus on the production, application, and evaluation of TCC datasets to advance the current understanding of how accurately these products can capture changes in forest landscapes across space and time.

Automated summary

With the increasing challenges of global environmental change, sustainability, and biodiversity conservation, monitoring the world's remaining forests has become more crucial than ever. Earth observation technologies, particularly remote sensing, play a vital role in forest cover monitoring worldwide. However, multi-temporal categorical map products have limitations and may not accurately represent forest landscapes. Using remotely sensed tree canopy cover datasets can complement traditional forest/non-forest maps for more accurate forest cover monitoring.