National training workshop on measurement and monitoring of forests in the context of REDD+ MRV (Measurement Reporting and Verification)

   TwitCount

Background: 

The performance-based climate mitigation framework REDD+  encourages the reduction of emissions related to deforestation and forest degradation, and removals through enhanced forest carbon stocks and improved forest management. These activities should be measured and reported to the United Nations Framework Convention on Climate Change (UNFCCC); including a process of verification. This requires methodologies for estimating actual emissions and removals and for establishing the reference level. UNFCCC requests countries to build robust and transparent national forest monitoring systems to facilitate the measurement and reporting of forest related greenhouse gas (GHG) emissions, following the guidelines and guidance from the Intergovernmental Panel on Climate Change (IPCC).

Deforestation and forest degradation assessments are considered important elements of carbon mitigation strategies. While satellite based deforestation monitoring is largely technically proven, the operational establishment and application of forest cover monitoring systems at different scales has to go a long way in many countries of the Hindu Kush Himalaya (HKH) region. Forest degradation assessment and monitoring focusing on forest biomass is currently conceived through community and state owned ground level monitoring systems. The completeness, consistence and correctness of these components depend on spatial explicitness of the given observations, estimation methods and up scaling approaches put into practice. 

Remote sensing can provide precise stratification in terms of forest crown density, forest types, communities and species formations. However, forest structure (i.e. CPA, DBH, BA) and biomass often exhibit nonlinear variations and variable correlation across temporal and spatial scales. In this context remote sensing based spatial information, geo-statistical tools and non-parametric tools provide effective means to develop robust models. These approaches use spatial variability of forest spectral reflectance across each unit (pixel) of the remotely sensed image, relate with field based biomass and develop a model to associate a biomass value for each pixel. 

Rationale 

The International Centre for Integrated Mountain Development (ICIMOD) works on promoting applications of Geospatial and Earth Observation tools and technologies for mountain development in the HKH. The ICIMOD’s REDD+ Himalaya programme tends to facilitate the forest measurements and monitoring activities, development of a GIS database and models for biomass estimation in order to enhance capacities of its partners under the overall umbrella of REDD+ readiness in Myanmar. In this context, the three training course is designed with the following objectives:

Objectives: 

Under the REDD+ Himalaya programme, the overall objective of the training workshop is to enhance the capacity of forestry officials and stakeholders by providing advanced Remote Sensing (RS) knowledge and techniques to measure and monitor forest cover and associated biomass in Myanmar. 

The following are the more specific objectives:

  • Impart UNFCCC context and requirements and introduction to IPCC guidelines
  • Build capacity for building national forest-monitoring systems for REDD+
  • Impart RS based knowledge and techniques for forest monitoring and deforestation at medium level spatial resolution
  • Integration of forest inventory data impart RS based knowledge and techniques to assess biomass at very high spatial resolution level

Target audience

The participants of the workshop will be Myanmar’s professionals (15-20)  working in natural resources management nominated by the Ministry of Natural Resources and Environmental Conservation (MONREC). The participants are expected to have basic knowledge of computer sciences, remote sensing and GIS as well as forestry. 

Expected outcome

The participants will acquire knowledge on concepts of remote sensing for forest monitoring and techniques for biomass estimation based on allometric equations using different biomass and carbon estimating models. Participants can use this knowledge to develop REDD+ projects that rely on RS technology for MRV processes.