1. Module 3.1 National data organization and management
Module developers:
Erika Romijn, Wageningen University
Veronique De Sy, Wageningen University
After the course the participants should be able to:
Explain the importance of having a clear and stable institutional set-up
Describe the different types of roles and responsibilities that agencies may have in organizing and managing data
Understand the procedures on how to ensure IPCC reporting principles when collecting and managing data associated with MRV and carbon accounting
IPCC = Intergovernmental Panel on Climate Change; MRV = measuring, reporting, and verification; WRI = World Resources Institute; LULUCF = land use, land-use change, and forestry ; GHG = greenhouse gas.
Figure source: Bhattacharya 2012.
WRI analysis: Work allocation and implementation arrangements for developing the LULUCF GHG emissions inventory in India
V1, May 2015
Creative Commons License

2. Background material
Hewson, Steininger, and Pesmajoglou, eds REDD+ Measurement, Reporting and Verification (MRV) Manual.
Cheung et al “Building National Forest and Land-Use Information Systems.”
Crompvoets et al A Multi-View Framework to Assess SDIs.
EPA National System Templates: Building Sustainable National Inventory Management Systems.
Mora et al Capacity Development in National Forest Monitoring.
Herold and Skutsch “Measurement, Reporting and Verification for REDD+: Objectives, Capacities and Institutions.” In Realising REDD+: National strategy and policy options, ed. A. Angelsen.
Eelderink, Crompvoets, and de Man. 2008. “Towards Key Variables To Assess National Spatial Data Infrastructures (NSDIs) in Developing Countries.” In A Multi- view Framework to Assess SDI, ed. Crompvoets et al.

3. Outline of lecture
Institutional framework
Data management

4. Outline of lecture
Institutional framework
Data management

5. Importance of institutional framework for MRV and data management
To have different actors and sectors work together for efficiency and long-term sustainability
To clearly define roles and responsibilities of involved agencies and stakeholders
To improve coordination among ministries, subnational governments, and other agencies
To improve efficiency and long-term sustainability
To coordinate decision making and activities across political levels
Clear and stable institutional arrangements (including national coordination for MRV) and consistent political support are important enabling factors for continuous improvements in national forest monitoring (for REDD+) in non-Annex I countries.
Note: In the past in some countries (e.g., Peru), national communications have been done by external parties, including consulting companies.

6. Clear and stable institutional arrangements
Institutional framework is important for best practices such as documenting and archiving
Institutional memory requires documentation so other parties understand the nature of the data
Benefits of institutional arrangements:
The inventory team knows who is providing data
If responsibility is clearly communicated to data providers, sector leads can be confident that data is available
Institutional memory is created if continuity exists in the arrangements
Appropriate agencies and experts are identified early on in the process

7. Requirements for institutional framework for MRV
Coordination: High-level national coordination and cooperation mechanism to:
Link forest carbon MRV and national policy for REDD+
Specify and oversee roles and responsibilities
Measurement and monitoring protocols and technical units for acquiring and analysing data
Reporting unit for collecting data in central database -> national estimates, international reporting, etc.
Verification framework
The requirements for a national institutional framework for MRV are the following:
Coordination: A high-level national coordination and cooperation mechanism is needed to link forest carbon MRV and national policy for REDD+ and to specify and oversee roles, responsibilities and co-benefits and other monitoring efforts.
Measurement and monitoring: Protocols and technical units are needed for acquiring and analysing the data related to forest carbon at national and subnational levels.
Reporting: There needs to be a unit responsible for collecting all relevant data in a central database, for national estimates and international reporting according to IPCC Good Practice Guidance (GPG), and uncertainty assessments and improvement plans.
Verification: An independent framework is needed for verifying the long-term effectiveness of REDD+ actions at different levels and by different actors.
Reference: Herold and Skutsch 2009.

8. Recommended institutions for MRV
A national coordination and steering body or advisory board, including a national carbon registry
A central carbon monitoring, estimation, reporting, and verification authority, including forest carbon measurement units
Spatial data infrastructure and/or forest and land-use information system
 Actual institutional arrangements depend on national circumstances
 EPA template can be used to summarize existing institutional arrangements and identify gaps and ways of improvements (see next section)
As a minimum, a country should consider setting up the following institutions and clearly defining their roles and responsibilities:
A national coordination and steering body or advisory board, including a national carbon registry
A central carbon monitoring, estimation, reporting, and verification authority
Forest carbon measurement units
The resources required for setting up and maintaining institutional capacities depend on several factors. Some countries may acquire, process, and analyse most data through their own agencies or central units. Others may decide to work with partners outside government (e.g., contractors, local communities or regional centres) or involve communities.
Reference: Herold and Skutsch 2009.

9. Some options for institutional arrangements
Actual institutional arrangements depend on national circumstances:
Centralized vs. decentralized
In-sourced vs. out-sourced
Single agency vs. multiagency
Integrated vs. separate
Source: Hewson, Steininger, and Pesmajoglou 2013, ch. 2.4.

10. Example of institutional arrangement in India
Work allocation and implementation arrangements for developing the LULUCF GHG emissions inventory in India
Source: Bhatacharya (WRI MAPT case studies).
The ministry of environment and forests (MoEF) is responsible for the overall coordination of the preparation of GHG estimation for the LULUCF sector. Some of the other institutions that are involved in the process are the National Remote Sensing Centre (NRSC), Forest Survey of India (FSI), Indian Council of Forest Research and Education (ICFRE), and Indian Institute of Science (IISc). They meet regularly and have clearly defined roles and responsibilities.
Functions of the different institutes for LULUCF GHG emissions inventory preparation:
NRSC: Providing earth observation data; preparing land-use map for nonforest categories (cropland, grassland, settlements, and other land for the entire country); and land-use change estimation.
FSI: Forest and tree cover assessments, which includes preparing forest land-use map, estimating change in forest cover, estimating area of biomass burning; determination of aboveground biomass, belowground biomass, and soil carbon; and estimation of carbon stock change from land-use changes in forests and from biomass burning.
ICFRE: 11 research institutes are working under ICFRE across India. They were responsible for soil carbon measurements in 600 sample plots in different forest types and physiographic zones.
IISc: Providing overall guidance to the other institutions on development the GHG emissions inventory. Also, estimation of GHG emissions for nonforest categories with input from the NRSC.
More detailed info can be found in Bhattacharya (2012).

11. Recommendations for institutional arrangements: Data management system
Have high-level commitment from government
Involve all relevant agencies
Establish long-term financial support
Invest in maintaining human capital (technical staff, institutional memory)
Build subnational capacity and coordination
Establish policy framework: e.g., to formalize data sharing and communication, legal arrangements, partnership agreements, access rights, etc.
References:
Eelderink et al. 2008; Cheung 2014.

12. Outline of lecture
Institutional framework
Data management

13. Importance of a national data organization and building a data management system
Importance of national data organization and management:
Provide input to support policies and decisions
Improve coordination among ministries, subnational governments, and other agencies
Developing a data management system:
Data integration (spatial and nonspatial data from multiple sources)
Data access
Data sharing among ministries and agencies
In many countries, there is lack of coordination among ministries, subnational governments, and other agencies. Often, data are scattered among agencies and a good overview on all available data is missing. This keeps countries from being able to track the impacts of land-use-based climate change mitigation policies and identifying new policies.
Improving data integration, data access, and data sharing is very important. Developing data management systems that integrate spatial and nonspatial data from multiple sources will provide the necessary input to support policies and decisions.

14. Concept of spatial data infrastructure
Framework to facilitate and coordinate exchange and sharing of spatial data
Enabling platform that links people to data
National spatial data can be organized and managed through a spatial data infrastructure (SDI), which is a framework that facilitates and coordinates the exchange and sharing of spatial data.
An SDI is a dynamic, hierarchical, and multidisciplinary concept which can be seen as an infrastructure that allows data sharing between and within organisations, states, or countries. These infrastructures include policies, standards and access networks which connect the data with the people (see figure).
Improved availability of data supports better decision making. It also avoids duplication of datasets by different agencies.
Reference: Crompvoets et al
Source: Crompvoets et al

15. Forest and land-use information systems (FLUIS)
Data management system for storing, organizing, and integrating large amounts of forest and land-use data from multiple sources
Components of a FLUIS
Source: Cheung et al
Regarding REDD+ measurement, reporting and verification, there is the need for better coordination among the broad range of ministries and government agencies involved in forest and land-use policy implementation.
A national data management system (aka an SDI) helps to improve cross-sectoral coordination, communication, and data integration for better under­standing of drivers of forest and land-use change, which enables better informed decision making and land-use planning.
A forest and land-use information system (FLUIS) is a data management system, especially fit for this purpose, that stores, organizes and integrates large amounts of forest and land-use data from multiple sources. A number of components are required for a FLUIS (see figure). Important components are:
Data repository for storing spatial data, nonspatial data, and metadata
People who manage the data (system managers and technicians) and people who use the data (end users)
Policy framework for improving data management
Protocols and standards for data collection and protocols for data sharing
Technology for managing and maintaining the data management system
Reference: Cheung et al

16. FLUIS: Sample flow of data from data collectors to end-users
This is a generalized overview of how data can flow from data collectors to end-users. Data are collected by subnational governments and then transferred to national agencies for integration. These national agencies send data to the managing body of the FLUIS for aggregation. From the central platform the data can be accessed by the end-users, who in turn may contribute data to the FLUIS.
Reference: Cheung, et al
Source: Cheung et al

17. Examples of FLUIS: Cameroon & Indonesia
Cameroons Interactive Forest Atlas.
Living forest information system
Web-based tool built on GIS platform with map-viewing application
Indonesia’s One Map policy
One set of authoritative maps
Web-based portal with ArcGIS server for sharing of maps, geodatabases and tools
Interactive Forest Atlas of Cameroon (Version 3.0) is available at It helped Cameroon improve land-use planning and monitoring of forest resources.
With the One Map policy, Indonesia is developing a national spatial data infrastructure with one set of authoritative maps, which integrate available data from different sources. The aim is to improve coordination and data sharing across ministries and jurisdictions. Land-use decisions will be based on this map.
Reference: Cheung et al

18. Examples of national spatial data networks
Cameroon
Indonesia
In Cameroon, agencies responsible for implementing the FLUIS are the Ministry of Forestry and Wildlife (MINFOF) and the World Resources Institute. They develop and manage the Forest Atlas. Regional and local governments and national agencies provide the data for the FLUIS, and the map products are used by government agencies and the general public.
In Indonesia the FLUIS consists of the Geospatial Information Agency (BIG), the National Spatial Data Network (JDSN), and the REDD+ Task Force. BIG and the REDD+ Task Force are responsible for developing the National Spatial Data Network and implementing the One Map. Regional and local governments and national agencies provide the data for the FLUIS, and the map products are used by government agencies, the REDD+ Task Force, and the general public.
Please note that these initiatives focus on compiling activity data (AD) and producing maps, while a spatial data infrastructure for REDD+ also requires compiling data for estimating emission factors (EFs).
Reference: Cheung, et al

19. Data management needs for preparing a national GHG inventory (1/3)
Data collection and data management should comply with five IPCC principles: transparency, completeness, consistency, comparability, accuracy (See Module 1.1 and Module 3.3 for more details)
Data collection and assimilation
Collecting AD and collecting data to develop EF:
Ensure quality of data through planning, preparation, and management of inventory activities
Use national standards and best practice for measuring forest area and carbon stock (See Modules 2.1 and 2.2 on AD and 2.3 and 2.5 on EF)
Data management for preparing a national GHG inventory includes the following components:
Data collection, assimilation, and processing
Data management
Data archiving
This slide and the following three slides explain which steps should be considered within these three components, in order to ensure the quality of the data and to comply with the IPCC good practice principles.
Estimating and reporting emissions / removals should be consistent with IPCC (2003) good practice guidance and guidelines. Therefore, the data collection and management process (for preparing activity data and developing emission factors) should also follow the five IPCC principles.
It is very important to ensure the quality of the data in each phase of the inventory process (planning, preparation, and management). Archiving of all relevant data, information, methods, and explanations for the selection of data, methods, and procedures is essential in order to ensure transparency and to be able to respond to requests for clarification of information on the national inventory.

20. Data management needs for preparing a national GHG inventory (2/3)
Data assimilation and processing:
Processing of data into a suitable format for estimating anthropogenic GHG emissions and removals
Estimating anthropogenic GHG emissions by sources and removals by sinks
Implementing uncertainty assessments

21. Data management needs for preparing a national GHG inventory (3/3)
Data management in the context of MRV and carbon accounting:
Organization of MRV data into a reporting format
Implementing quality assurance / quality control procedures for national and subnational MRV (see next slide)
Verification of data
Data archiving:
Archive all relevant data, information, methods, explanations:
Database management process for archiving, storing, and retrieving information (using a FLUIS)
Reference: Hewson, Steininger, and Pesmajoglou 2013, ch. 2, “Institutional Arrangements.”

22. Developing capacities for data collection and management
Ensure sufficient capacity and train staff for all aspects of data collection, processing, and storing
Establish procedures and systems for collecting and archiving information: use of special software (e.g., ALU)
Develop standard operating procedures (SOP):
To include user friendly documentation for nontechnical users
To enable the entire process to perform forest-area change assessment (remote sensing image processing and analysis and mapping change using GIS) and to develop emission factors (e.g., using appropriate allometric equations)
For field data collection
For community measurement and monitoring (CMRV)
Software can be used to simplify the process of conducting the GHG inventory and collecting and archiving data. The Agriculture and Land Use Greenhouse Gas Inventory (ALU) Software has a user interface that guides an inventory compiler through the process of estimating GHG emissions and removals related to agricultural and forestry activities. Inventory analysis is divided into steps, which makes the compilation of activity data, assignment of emission factors, and estimation of emissions and removals easier. The software also has internal checks to ensure data integrity. The tool is based on IPCC guidance and guidelines.
For more on ALU see “Agriculture and Land Use National Greenhouse Gas Inventory Software,” Colorado State University,

23. Quality assurance / Quality control
QC procedures for key categories have a significant contribution to the total emissions/removals in terms of:
Absolute level (all inventory activities that account for 95% of the total GHG emissions)
Trend
Uncertainty in emissions and removals
QC for categories in which significant methodological and/or data revisions have occurred
In accordance with guidance provided by IPCC
 For more information, see section 5.4 of IPCC (2003) GPG-LULUCF.
QC procedures need to be implemented for checking the quality of the data included in the GHG inventory following the IPCC guidance. QC procedures need to be implemented for key categories and for individual categories in which significant methodological and/or data revisions have occurred. QA/QC helps improve transparency, consistency, comparability, completeness, and accuracy in national GHG inventories. Sending QA/QC data out for review allows other third parties to have input on other relevant data they may have for GHG inventories.
EPA (n.d.), National System Templates, template 3, “Description of QA/QC Procedures,” describes in detail how QA/QC can be performed. It helps in constructing the plan to perform QA/QC control and offers step-by-step guidance and checklists for performing QA/QC. QA is performed by people who are not involved in the inventory development process. QC is performed by the inventory development team itself.
Reference: Hewson, Steininger, and Pesmajoglou 2013.

24. National System Templates from the U. S
National System Templates from the U.S. Environmental Protection Agency (EPA)
EPA provides templates to document and organize different components of the national inventory system:
Template 1: Institutional arrangement
Template 2: Methods and Data Documentation (documenting and reporting origin of methodologies, activity datasets, and EFs)
Template 3: Description of QA/QC Procedures
Template 4: Description of Archiving System
Template 5: Key Category Analysis (indicates most important GHG emissions by sources and removals by sinks)
The templates can be found at

25. EPA Template 2: “Methods and Data Documentation”
Step-by-Step Instructions
STEP 1: Provide source/sink category information
STEP 2: Identify method choice and description
STEP 3: List activity data
STEP 4: List emission factors
STEP 5: List uncertainty estimates (optional)
STEP 6: Provide any additional information
STEP 7: Provide improvements to this analysis
Table templates to fill in for each of the steps
Table below: Example for Step 3: “List activity data”
Type of Activity Data
Activity Data
Value(s)
Activity Data Units
Year (s) of Data
Refe-rence
Other Information (e.g., date obtained and data source or contact information)
Category QA/QC Procedure Adequate / Inadequate / Unknown
Are all data entered correctly into models, spreadsheets, etc.? Yes / No (List Corrective Action)
Checks with Comparable Data (e.g., At international level, IPCC defaults). Explain and show results.
... 
 ...
...
This slide shows an example for an EPA template: Template 2, “Methods and Data Documentation.“ The material includes table templates, where data and methods and other information should be filled in for each of the steps.
The templates can help inventory teams in documenting and reporting the methodologies, datasets, and assumptions used to estimate emissions and removals from each category. They can also be used as supporting information for national communications to provide transparency on the data and methods used. They also help future inventory teams in preparing future inventory reports.
Source: EPA n.d.
Source: EPA Template 2, Methods and Data Documentation.

26. In summary
Clear and stable institutional set-up and clearly defined roles and responsibilities are important for preparation of GHG estimation for the LULUCF sector.
Recommended institutions include a national coordination and steering body or advisory board; a central carbon monitoring, estimation, reporting, and verification authority; and a spatial data infrastructure and/or forest and land-use information system (FLUIS).
A FLUIS is a data management system for storing, organizing, and integrating large amounts of forest and land-use data from multiple sources and connects data collectors with end-users.
Data management needs include collection, assimilation, processing, estimation, uncertainty assessment, QA/QC, verification, and archiving.

27. Recommended modules as follow-up
Module 3.2 to continue with guidance on developing REDD+ reference levels and Module 3.3 to learn more about reporting REDD+ performance using IPCC (2003) guidelines and guidance

28. References
Bhattacharya, Sumana “Producing a National GHG Inventory for the Land Use, Land-Use Change, and Forestry (LULUCF) Sector: Case Study from India.” Washington, DC: MAPT Partner Research, World Resources Institute. study-series/producing-a-national-ghg-inventory-for-the-land-use-land-use-change-and-forestry-lulucf- sector
Cheung, L., et al “Building National Forest and Land-Use Information Systems: Lessons from Cameroon, Indonesia, and Peru.” Working paper. Washington, DC: World Resources Institute.
Crompvoets, J. W. H. C., A. Rajabifard, B. van Loenen, and T. Delgado Fernández A Multi-View Framework to Assess SDIs. Melbourne: Published jointly by Space for Geo-Information (RGI), Wageningen University and Centre for SDIs and Land Administration, Department of Geomatics, The University of Melbourne. ISBN
Eelderink, L., J. W. H. C. Crompvoets, and W.H. E. de Man. 2008. “Towards Key Variables to Assess National Spatial Data Infrastructures (NSDIs) in Developing Countries.” In A Multi-view Framework to Assess SDIs. Ed. J. Crompvoets et al. 307–325. Melbourne: Melbourne University Press.

29. EPA (U. S. Environmental Protection Agency). n. d
EPA (U.S. Environmental Protection Agency). n.d. National System Templates. Research Triangle Park, NC: EPA. building.html
Hewson, J., M. Steininger, and S. Pesmajoglou, eds REDD+ Measurement, Reporting and Verification (MRV) Manual: USAID-supported Forest Carbon, Markets and Communities Program. Washington, DC: USAID.
Herold, M., and M. Skutsch “Measurement, Reporting and Verification for REDD+: Objectives, Capacities and Institutions.” In Realising REDD+: National strategy and policy options, ed. A. Angelsen. 85–100. Bogor, Indonesia: Center for International Forestry Research. library/browse/view-publication/publication/3835.html.
IPCC, Good Practice Guidance for Land Use, Land-Use Change and Forestry, Prepared by the National Greenhouse Gas Inventories Programme, Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Wagner, F. (eds.). Published: IGES, Japan. (Often referred to as IPCC GPG)
Mora, B., M. Herold, V. De Sy., A. Wijaya, L. Verchot, and J. Penman Capacity Development in National Forest Monitoring: Experiences and Progress for REDD+. Bogor, Indonesia: Joint report by Center for International Forestry Research and GOFC-GOLD. library/browse/view-publication/publication/3944.html.