Reducing emissions from deforestation and forest degradation (REDD+) is a global climate change mitigation initiative under negotiation by the United Nations Framework Convention on Climate Change (UNFCCC), aimed at providing financial incentives to developing countries for enhancing carbon stocks in their forests by abstaining from deforestation which would lead to emission of co2 into the atmosphere. To get REDD+ payment, developing countries are required to measure and monitor (M&M) anthropogenic changes in forest (deforestation and forest degradation) and forest carbon to account for co2 emissions and removals from such changes. Bangladesh is steadily progressing through its REDD+ roadmap, however, several issues like feasibility of using remote sensing technology (which is very highly encouraged) for detecting deforestation, forest degradation and changes in forest carbon at local environmental setting remains totally unexplored. This study will develop approaches to detect deforestation and forest degradation (objective 2) and to measure forest carbon stock to account for emissions and removals of co2 (objective 3) in north-eastern Bangladesh using a combination of satellite and field measured data, employing various remote sensing, GIS, statistical and modelling software. It will also assess the driving forces and risk of deforestation and forest degradation in spatial modelling environment (objective 4) along with an in-depth analysis of the rationales and realities of integrating community forest users in REDD+ project implementation in Bangladesh (objective 5) using field survey data. As a pioneer REDD+ study in Bangladesh, outcome of this research are expected to create an opening for academics, researchers, forest and environmental institutions in Bangladesh, especially state owned Bangladesh Forest Department, responsible for negotiating REDD+ projects in the country.

Funding: IPRS, UQ Centennial Scholarship

Advisors: Prof Stuart Phinn, Dr Chris Roelfsema


Project members

Mohammad Redowan

Mohammad REDOWAN

PhD candidate - Awarded June 2019