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Stand: 11.09.2013

Q&A Carbon Map and Model Project

Q: What is the Carbon Map and Model Project?

With the support of the International Climate Initiative (ICI) of the Federal Ministry of the Environment, Conservation, and Nuclear Security, the implementation of the German Development Bank KfW, the "Carbon Map and Model (CO2M&M)" project by the World Wide Fund for Nature (WWF) Germany and local partners will produce a national scale biomass map for the entire forest coverage of the Democratic Republic of Congo (DRC) along with feasibility assessments of different forest protection measures within a framework of a REDD+ model project. 

The Carbon Map Component:
A national biomass map for the DRC will enable quantitative assessments of carbon stocks and emissions in the largest forest of the Congo Basin to support the national REDD (Reducing Emissions from Deforestation and Degradation) program in DRC, which plays a major role in sustainable development and poverty alleviation. This map will be developed from field data, complemented by airborne LiDAR (Light Detection and Ranging) and up-scaled to satellite images to accurately estimate carbon content in all forested areas. The second component of the project is to develop specific approaches for model REDD projects in key landscapes. 

The Carbon Model Component:
The carbon model component will assist the jurisdictional stakeholders to go ‘full circle’ with REDD+, by:

  • developing a REDD+ mitigation strategy following feasibility studies for conservation concessions, improved forest management and reduced impact logging, reduction of unsustainable shifting cultivation and improved grazing management;
  • developing a benefit sharing and distribution scheme for compensation;
  • providing technical support to the development of a jurisdictional programme in Mai Ndombe district / province;
  • lessons learned to feed into to the national / international REDD+ strategies.

Q: Who is responsible / involved in the Carbon Map component of the project?

WWF-DE: Aurélie Shapiro, Remote Sensing Specialist from WWF-DE is the technical lead for the carbon mapping.

Southern Mapping Company, based in South Africa is tasked with collecting the LiDAR.

Dr. Sassan Saatchi is leading the data processing and carbon map development along with his research team at University of California, Los Angeles (UCLA).

Additional partners include:
WWF-US: Offering Russell Train Education for Nature Program post-graduate fellowships to Congolese citizens performing research in forest conservation;

OSFAC: Local non-governmental organization tasked at space observation of central Africa Forests is supporting technical training, emissions calculations and data management

MECNT: DRC Ministry of Environment, Conservation of Nature and Tourism; integrating the carbon map into the online forest monitoring system and central database, building national capacity for REDD

Q: Who is responsible/involved in the REDD+ Model component of the project?

WWF-DE: Yougha von Laer, Forest & Climate Officer from WWF-DE, as well as Livia Wittiger, Coordinator Congo, have the technical and coordinating lead for the REDD+ Model component.

GFA Envest Consutling group: Jointly with WWF, GFA will be responsible for developing the carbon model component of the project including evaluation of options for the development of mitigation activities in the Lac Tumba region of DRC; supporting a JNR Approach in Mai Ndombe Distict as Model for a National REDD+; assessing different Reference Emission Level/Reference Level (REL/RL) approaches and contributing to the national National REDD+ Strategy, Monitoring Reporting and Verification (MRV) procedures.

Q: How does this project contribute to ongoing international and national REDD+ processes?

International Level – UNFCCC
According to the Durban decision, a country’s performance towards REDD+ will be measured in tonnes of carbon dioxide equivalent per year as a benchmark. (UNFCCC SBSTA 35,7) As the project will enable the DRC to calculate their biomass and emissions from deforestation as well as degradation (the second D in REDD+), it helps elevate the DRC into REDD+ phase 3.

At the same time, the resources and capacities of the national forest monitoring as another prerequisite to receive results-based finance will be strengthened

National Strategy
The project contributes to, and will directly feed into the working methodology of the National Forest Monitoring system (TERRA CONGO). Available data (National Forest Inventory) and data technology (TerraAmazon, GeoServer, Landsat) will be complemented through biomass data retrieved. The DIAF staff dedicated to establishing the national MRV system will be closely involved in this project and benefit from the trainings and capacity building provided. Overall, the project allows the development of emission factors associated with particular activities with enhanced accuracy through biomass calculations in the various forest types and land uses.

FCPF ER PIN – Subnational REDD+ program – Reference (emission) levels 
a) The “Model Component” of the Co2 M&M Project
In 2013 the national government of the DRC as well as the government of Bandundu province, have decided to develop a jurisdictional programme in what is to become the new Mai Ndombe Province. The main reason for developing a jurisdictional programme is to be able to participate in the Forest Partnership Facility (FCPF) Carbon Fund, which intends to purchase emission reductions from sub-national jurisdictions (or emission reduction programme areas). The government of the DRC has already submitted its first version of an Emission Reduction Programme Idea Note (ER-PIN) in June 2013 with the proposed programme area being the future province of Mai Ndombe. In support of this effort, the Verified Carbon Standard (VCS), with funds from NORAD, intends to pilot the implementation of the new VCS Jurisdictional and Nested REDD+ (JNR) requirements in Mai Ndombe province starting in 2013. The carbon mapping component will support updated, and potentially more accurate reference emissions levels. The Model Component of the project will focus exclusively on supporting the development and implementation of a jurisdictional REDD+ programme in Mai Ndombe province, to assist the jurisdictional proponents to go ‘full circle’ with REDD+, in order to:

  • provide technical support to the development of a jurisdictional programme in Mai Ndombe district / province;
  • achieve emission reductions through a set of implementing activities following feasibility studies;
  • To monitor and account for emission reductions;
  • To develop payment schemes and pay for performance;
  • To learn from this process and feed experiences to the national / (international) REDD+ development.
Levels of interventions (click to enlarge)
Levels of interventionsLupe

b) The Carbon Map Component
The data on forest degradation which will be retrieved through the Carbon Map component of the project will be a crucial value-added product for the REDD+ process in the DRC both on subnational and national level as it helps defining the forest reference (emission) level (RLs) according to which REDD+ finance will be granted. Current FACET data available does not take into account degradation of forests. Degradation represents a significant share of historical emissions in the Congo Basin (Asner et al 2005 Marklund & Schoene 2006, Lambin et al. 2003). Thus, information about emission from degradation of forest provided by the project, which will likely become available by end of 2014, will provide most needed information on degradation mapping, and proxies and at the same time avoid vague assumptions of degradation inside a potential adjustment factor.

Q: Is there already a carbon map for the DRC?

Existing maps for Central Africa and global Pan-tropical forests (Saatchi et al. 2011, and Baccini et al., 2012) have been developed using spaceborne (i.e. satellite) LiDAR data and various types of satellite imagery. These maps are generally 1km resolution, and can provide reasonable estimates of forest carbon stock at the national or regional scales, but they have large errors and inconsistencies at smaller scales as well as an unquantifiable bias – meaning they are based mostly on data from Asia, South America, and not specifically derived from data over the Congo Basin. The existing maps have either unknown accuracy (that has not been rigorously assessed), or when assessed have about 70% or higher accuracy, which can mean up to 30% error.

These existing maps have varying spatial characteristics and significantly large spatial differences between them, along with a low spatial resolution that together introduce errors in estimating carbon stock and emissions for developing national and regional scale REDD projects and implementation of MRV (Monitoring Reporting and Verification). These differences are particularly significant in DRC where ground measurements are very scarce, where optical satellite imagery is hindered by extensive cloud cover, and when the rates of deforestation and degradation are low – meaning greater precision in biomass is desired in order to assess small emissions due to degradation. This Carbon Map and Model project will use newly available optical data, as well as active radar datasets, which can penetrate clouds.

Q: How do you justify the cost – benefit, given a high percentage of REDD finance goes into MRV?

As a country with such a large extent of forest cover (over 1 million km2), and high threat from degradation, accurate, and unbiased maps of medium resolution derived from a combination of space and ground inventory techniques are needed to establish baselines for REDD projects, as well as to support forest conservation and management, and to inform sustainable land use plans for the in future. Existing maps that have been developed at global scale (explained above) are biased towards areas with abundant inventory data – for example, Asia, South America. A new map developed specifically for the DRC, using systematic sampling and forest inventory data collected throughout the country will provide the most accurate estimates of carbon stocks: upwards of 80% accuracy – which will provide the best available product to determine biomass stocks, emissions reductions, goals and compensations. While the proposed investments and efforts for developing the DRC carbon map are significant, they are relatively small in comparison to potential returns on carbon storage, provision of ecosystem services and benefits of climate change mitigation, and ultimately REDD benefits for residents in the second largest rainforest in the world. The cost of the development of an accurate carbon map from integrated field, aerial and satellite data is actually much lower, per hectare, than a map created through extensive field data alone. This is due to the fact that DRC is so large (over 2 million km2) with many areas that are largely inaccessible, difficult and costly to access.

This carbon mapping effort proposed for DRC is one of the largest airborne LiDAR efforts to date. In a country, with such vast forests that are inaccessible and/or dangerous, the use of airborne LiDAR will provide unprecedented insight into virtually unknown landscapes, which have rarely been visited by scientists in recent years. Flying over these areas with an airplane is significantly cheaper per hectare than deploying teams to multiple plots in forests throughout the country. Ground collection will cost about 3,000-5,000 € per hectare and will cover a maximum of 200 ha over the duration of the project. The ground data will provide estimates (not measurements) of biomass with more than 80% accuracy. In comparison, the LiDAR collection will cost about 3€/ha, and will provide more than 400,000 ha of biomass estimation with about the same accuracy as field measurements – this is 2000 times more area as can be covered by field inventories.

Q: What information does LiDAR tell you?

LiDAR sensors are Light Detection and Ranging active sensors using an altimetry approach (like sonar from a boat) to send pulses of light to the earth’s surface and record the accurate time they travel back to a sensor. This provides a very accurate estimate of the elevation of the land surface (<10cm), as well as the height of the forest. Over dense forested surfaces, the LiDAR pulse will provide both the estimated height of trees, information on the structure (canopy density), as well as the elevation of the underlying ground. LiDAR is considered the most accurate technology to provide forest height and structure over a variety of forest types and over areas of complex topography. The height estimates from LiDAR is acquired at a spatial resolution of 1 m, and when aggregated over a plot size of 1-ha, will provide three-dimensional structural information needed to estimate the forest biomass. There are existing models in the scientific literature that can covert LiDAR measurements to forest biomass, and when these models are calibrated with local field inventory plots, they can provide biomass estimates with approximately the same accuracy as the measurements made on the ground in plots at 1-ha scales. In addition to biomass, LiDAR will also provide information about forest disturbance such as logging in forest concessions, in order to provide information about the extent of degradation and regenerating capacity in tropical forests. We plan to use the LiDAR data for forest carbon estimation. However, this data can be used to for other research including forest health and integrity for various biodiversity and conservation applications. 

Q: Where will LiDAR data be collected?

The Carbon Map and Model project is employing a statistically random sampling approach that mimics field forest inventory techniques. A stratified, random LiDAR sampling strategy was agreed upon by stakeholders, including NGOs and local government ministries in March 2013 in order to create the most unbiased dataset possible (i.e. with data widely and evenly distributed throughout the country) while consistently representing the various forest types over the entire country. The DRC was divided into a series of 100km x 100km squares, inside which point locations inside each grid were selected randomly, and a 2,000 ha 10km x 2km rectangle was placed upon these points. In addition, sites where known forest plots, such as those being assessed for the National Forest Inventory were selected for airborne sampling or during transit flights for LiDAR calibration. A total of 212 locations, totaling over 400,000 ha will be flown. In addition, high resolution (10cm) color aerial photos will be collected simultaneously. Finally, there will be an additional 100,000 -300,000 ha of data collected during the transit flights as the plane travels over the country, providing one of the most comprehensive forest inventories from the air ever achieved in DRC. Existing and new field plots will be established to calibrate the LiDAR data regionally, also providing extensive data set for calibration and validation. In addition, we will use the existing research and national forest inventory plots to verify our approach. 

Q: What do you do with the data retrieved?

Data delivered from Southern Mapping will be processed by UCLA, while building local technical capacity and knowledge at OSFAC and MECNT by demonstrating and sharing processing techniques, software and algorithms. LiDAR data from existing sample sites are locally calibrated with information from field plots, and then converted to above ground biomass. These samples are then up-scaled to various existing satellite data, combined with information on soil, elevation, climate – which are all related to variations in forest carbon density. This statistical model then derives biomass for the entire country, and additional field data are used to validate the map and estimate accuracy.

All processed and intermediate data, including aerial photos will be delivered to the DRC for integration into the National Forest Inventory and additional research. Raw data will also be made available so that the map can be improved in the future as new methods are elaborated, or, for example allometric equations derived. The data will also provide information required by UNFCCC to link future REDD+ payments with biomass estimates.

Q: What forest cover data / maps are already available?

Several forest cover maps for the Congo Basin have been developed, with varying accuracy. The most well-known and used is the FACET map that was produced in 2000 by the University of Maryland and OSFAC, with gross changes in forest cover assessed in 2005 and 2010. The final product is a 60m resolution map for 2010 indicating forest areas divided into three classes: primary forest, secondary forest and savannah, and loss of each of these classes in the 2000-2005 and 2005-2010 time periods. These data are freely available on the website of the OSFAC (www.osfac.net) and are commonly used in the DRC. All forested areas with a canopy cover over 30% and mature tree height over 5 meters were classified as forests. The forest category was then further divided into the following sub-types:

  • Primary forest is defined as mature rainforest with canopy cover greater than 60%;
  • Secondary forest is defined as a regenerating forest with canopy cover greater than 60%; and
  • Savannah forest is defined as a forest with a canopy cover of 30 to 60%. All forested areas not classified as primary forest and secondary forests were classified by default as savannah forest.

Q: What are the limits of this map?

This map is NOT a carbon or biomass map – it defines where forests are, not how much biomass or carbon are stored. This map does not provide all the required information for assessing carbon stocks for REDD MRV.

This map only maps deforestation, and does not assess forest regeneration, afforestation, or degradation.

This map while extensively used for many projects has not been systematically assessed for accuracy though preliminary assessments have estimated about 87% accuracy of forest/non-forest mapping. Other assessments indicate that this data may be significantly underestimating forest and deforestation. This map also suffers from extensive cloud cover in some portions, including Bandundu and the new Mai Ndombe province, where most REDD projects in DRC are being implemented. The carbon map and model project will use new, cloud-independent radar data to fill data gaps and improve and update existing forest cover maps where needed.

Q: What is the value added of the carbon map in comparison to what is already available?

The new biomass map will be based on a new approach different from the previous maps with significant improvements in three areas:

  • The new forest carbon map will be based on the development of a combined field and space inventory using a spatially balanced and systematic sampling of forest biomass from airborne high resolution LiDAR. The project will acquire close to 400,000 ha of forest biomass sampled over the entire country, providing the first unbiased estimate of the forest biomass over old growth and degraded and secondary forests in DRC.
  • Data collected from the ground samples will be based on locally established and widely accepted methodologies, and will provide both calibration and validation data for the airborne LiDAR data and as independent set of data of forest carbon estimates for different forest types.
  • We will be able to solve the problem of mapping degraded forests and its carbon loss and potentially sequestration using a one time observation of forest structure and biomass with a combination of optical and active (radar) satellite data, which is especially important in the DRC where deforestation rate is low, and degradation is potentially very high.
  • Our field data includes a large number of permanent plots for future monitoring spread over old growth, secondary and degraded forests and will allow accurate and detailed information on potential carbon gain of forests after REDD implementations at the national or project scales.
  • The LiDAR integrated map will be derived, in part from very high resolution optical data (<1cm resolution) from Digital Globe, new medium optical data from Landsat (USA), coarse resolution MODIS (USA), as well as old and new active radar data (ALSO PALSAR from Japan), which is cloud independent, i.e. not affected by cloud cover.

Q: How do you ensure the carbon map is updated – do you need to fly LiDAR again after 5 years?

The carbon map provides an estimate of carbon at one instance in time. Old growth and undisturbed forests are often in equilibrium at the landscape scale and will not change rapidly in carbon stock over time unless a major disturbance occurs. This is the main reason that the forest inventory from ground generally uses a cycle of 5-10 years in temperate and boreal zones, which have rates of change that are even faster than the tropical forests. For areas which are disturbed and losing carbon, or regenerating from past disturbance (secondary forests), there are several monitoring systems in place in DRC, which include OSFAC and DIAF activities including TerraCongo and FACET (http://www.rdc-snsf.org/) along with support from FAO and international space agencies. These systems use a combination of spaceborne optical and radar imagery to monitor forest loss and regeneration and provide the necessary data for quantifying the national scale deforestation.

Ideally, a LiDAR-derived map with known accuracy would be derived every 10 years to provide adequate baselines. However, there are existing complementary efforts within our project and by other stakeholders and partners, including significant support from FAO’s UN-REDD program to establish permanent field plots, and secure funding to collect repeated inventory data to quantify the forest biomass changes over time. This LiDAR data will provide an extensive capacity building resource in the country for the next 10 years and even longer, for a variety of applications including national and regional land use planning, establishment of conservation areas based on detailed space and ground inventory information, data for scientific research for academic institutions, and resources for many local and international groups for land use planning, and establishing REDD projects in DRC without requiring investments or the difficulties of individual data collections. In fact, the data will provide an unprecedented asset for the country for both environmental and economical developments in future.

In near future (circa 2020), we expect to have a new spaceborne Biomass mission from the European Space Agency to provide monitoring capability to map biomass stock and stock change at comparable accuracy and resolution. In the meantime, the proposed LiDAR and ground measurements for carbon mapping in DRC are the state-of-the-art approach for developing national baselines and technical local capacities in carbon measurement and monitoring.

Q: Who will benefit from the data?

With an open data policy, anyone can access this exceptional dataset for non-commercial purposes and REDD activities approved by the DRC. DRC government, local NGOs and all stakeholders in the REDD process will be able to use and integrate the carbon map into their activities, including land use planning, low carbon development activities and academic research. As explained above this innovative dataset will provide many derived research and economic potential for DRC, including sustainable land use planning and forestry, biodiversity studies.

Q: How do you ensure the sustainability of the project and capacities around carbon accounting are being built?

The project has a strong capacity building component which will enable local stakeholders to manipulate and use the carbon map and its various elements to enhance existing activities around REDD+, land use planning, forest monitoring and more. As the carbon map integrates into existing forest monitoring activities, it allows accurate carbon estimates to complement ongoing deforestation observations.

In addition, the project is supporting a number of graduate fellowships through the WWF-US Education for Nature (EFN) Russell E. Train fellowships (http://worldwildlife.org/initiatives/russell-e-train-education-for-nature) for Congolese citizens who wish to pursue advanced graduate degrees in themes related to the project. This will ensure additional technical capacity and trained scientists in DRC to advance REDD and sustainable development and conservation.

Local capacities in emissions reductions will also be ensured through the development of a REDD+ mitigation strategy. The local staff of WWF in DRC, along with local stakeholders will be closely involved in the activities related to the development of a Jurisdictional and Nested REDD+ (JNR) approach for DRC. This will be achieved through the elaboration and provision of criteria concerning the development of procedures for monitoring of 'safeguards' for avoiding negative project impacts in order to ensure positive social and ecologic impacts, transparency and stakeholder involvement. Furthermore, regular trainings and workshops will be conducted to support local stakeholders (including WWF, district and regional managers) for the dissemination of innovative project results.

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