The Importance of High–Quality Data for REDD+ Monitoring and Reporting

Abstract

This article discusses the importance of quality deforestation area estimates for reliable and credible REDD+ monitoring and reporting. It discusses how countries can make use of global spatial tree cover change assessments, but how considerable additional efforts are required to translate these into national deforestation estimates. The article illustrates the relevance of countries’ continued efforts on improving data quality for REDD+ monitoring by looking at Mexico, Cambodia, and Ghana. The experience in these countries show differences between deforestation areas assessed directly from maps and improved sample-based deforestation area estimates, highlighting significant changes in both magnitude and trend of assessed deforestation from both methods. Forests play an important role in achieving the goals of the Paris Agreement, and therefore the ability of countries to accurately measure greenhouse gases from forests is critical. Continued efforts by countries are needed to produce credible and reliable data. Supporting countries to continually increase the quality of deforestation area estimates will also support more efficient allocation of finance that rewards REDD+ results-based payments.

Are deforestation and degradation in the Congo Basin on the rise? An analysis of recent trends and associated direct drivers.

“…These datasets provide unprecedented, up-to-date information on various aspects of forest change for global or tropical forests. While there is a great scientific interest in providing wall-to-wall global forest data products and analysis, these data alone are often not sufficient for robust, accurate deforestation estimates and trends 14 and are often not relevant to decision making at national or subnational scales. Countries are more likely to use, understand and value data they produce and update themselves, and can apply to national forest monitoring systems for decision making and implementation of policies.…”

“…Global data have not been statistically validated, and the omission and commission errors are simply unknown, making direct comparisons impossible. Additionally, areas of forest loss reported by Global Forest Watch (GFW) from the Hansen dataset may overestimate forest disturbances relative to other datasets, as this product identifies tree cover loss, which is not necessarily deforestation 14,78 .…”

Editorial: REDD+: Protecting Climate, Forests and Livelihoods

“…a Sandker et al [2] describe the activity data associated with REDD+ accounting and discuss the importance of high-quality remote sensing analysis. b…”

Assessing land use following tropical deforestation : Combining remote sensing and deep learning

“…Currently, there are several global initiatives for the assessment and monitoring of deforestation and its proximate drivers (Curtis et al, 2018;Hansen et al, 2014;Hansen et al, 2013). However, these global assessments often differ from national assessments in terms of reported forest extent, drivers and trends of deforestation (Nomura et al, 2019;Sandker et al, 2021). The difference is due to the fact that these initiatives often require a similar definition of forest and method to ensure consistency on large area , which usually entails a choice in precision and accuracy at local level (Hansen et al, 2014;Latawiec and Agol, 2015;Lu, 2007;Yanai et al, 2020).…”

“…In the present work we aim at a method that is locally suited for developing a national forest monitoring system for REDD+ reporting, and thus informing the local and national decision-making processes (CIFOR, 2021). Having an open, accessible, transparent, reliable, credible, and relevant national forest monitoring system can result in better decision making for forests and can contribute to driving down deforestation and attain nationally determined contributions (NDCs) (Sandker et al, 2021;UNFCCC, 2021).…”