Paper Info
Title
UNDER-estimation of biomass loss with REDD+ standard reporting method
Abstract
Countries that have ratified and/or are signatories to the United Nations Framework Convention on Climate Change (UNFCCC) and its related Kyoto Protocol have been actively involved in developing relevant policy processes that aim at mitigating the effects of climate change, collectively known as Reducing Emissions from Deforestation and Degradation (REDD+). A key question for REDD+ measurement reporting and verification (MRV), is how much aboveground biomass (AGB) or carbon has been released. The standard method for REDD+ MRV consists in using earth observation optical data to assess change in forest cover, together with a priori knowledge of values of forest carbon per unit area. However, current results of mapping forest cover and estimating forest cover changes using optical remote sensing depend on the forest definition. Each country can select their forest definition using recommended values. In this paper, the focus is on the assessment of African tropical forests AGB that are not taken into account into REDD+ processes. To do so, the AGB map from [1] over Africa has been improved and AGB from this map has been assessed in UN-REDD partner countries over areas from the Landsat tree cover benchmark map [2] that are not considered as forests (the so-called non forest class). The results show that biomass loss estimation is clearly under-estimated when using standard methods such as changes in forest cover using spaceborne optical data, in countries mostly covered by savannas.
Year
DOI
Venue
2015
10.1109/IGARSS.2015.7326672
Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
Carbon loss,Forest definition,REDD+,SAR,Savannas
Biomass,Vegetation,Forest cover,Kyoto Protocol,Computer science,Remote sensing,Effects of global warming,Earth observation,Deforestation,United Nations Framework Convention on Climate Change
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Stephane Mermoz1174.95
Alexandre Bouvet27410.34
Le Toan, T.323395.19
Renaud Mathieu412517.29