Date

14:00 - 15:30, Thursday 11 May 2023

Room/Zoom

Iran Room

https://fao.zoom.us/j/95757352379

Meeting ID: 957 5735 2379
Passcode: 33419974

Background
Accurate and precise information on land cover and land use change is critical for benchmarking and measuring the effect of policies and activities meant to mitigate, adapt, or prevent the effect of climate change through forest based solutions.
For forest change, recent standards adopted by the international community require high quality information obtained through Sample-Based Area Estimation (SBAE).
Current recommendations for area estimation are achieved through a combination of sample-based data and maps (Stehman 1997, Olofsson 2013, 2014). In practice, stratified area estimates (SAE) using a random distribution of optimally allocated sample units can generate misleading results due to errors of omission of mapped deforestation (Olofsson 2020).
To meet the needs of practitioners and satisfy reporting requirements, interpretation of a large sample is necessary. Thus, in order to ease the burden of reference sample interpretation and decrease the time required to produce results, we propose to improve methodological guidance on SBAE, combining visual interpretation and machine learning for sample labelling through an ensemble approach (eSBAE).
Beyond area estimation, sample based interpretation of very high spatial resolution imagery is also mandatory for assessing drivers of deforestation and degradation. The side event will provide an opportunity to present results from a global sample based assessment of forest change (FRA RSS 2020), focusing on quantifying the share of agriculture-driven deforestation linked to small-scale and large-scale farming, both for cropping and livestock systems.

Objectives

This side event will serve to:
-    review the current state of measurement, reporting, and verification (MRV) on SBAE
-    present and discuss an updated methodology eSBAE
-    provide feedback from concrete country experience applying eSBAE
-    present results from a global sampled based assessment of deforestation drivers

Outcomes: An improved method and associated guidance for area estimation of activity data which builds on existing GFOI guidelines and previous GFOI area estimation activities.
The ‘Ensemble Sample-Based Area Estimation’ methods will be critiqued and ‘the way forward’ will be planned to implement the ‘eSBAE’ methods at country level to improve MRV under various carbon accounting standards.
Finally, updated information on agriculture driven deforestation will be released.

Agenda