Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation Module developers: Erika Romijn, Wageningen University Martin Herold, Wageningen University After the course the participants should be able to: Explain the need for monitoring direct and indirect drivers of deforestation and forest degradation within the UNFCCC REDD+ context Summarize different approaches to monitor drivers of deforestation and forest degradation Assess likely direct drivers of deforestation and degradation in a country Photo credit: Agence France-Presse V1, May 2015 Creative Commons License

Background material GOFC-GOLD. 2014. Sourcebook. Section 2.8.1. GFOI. 2014. Integrating Remote-sensing and Ground-based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests: Methods and Guidance from the Global Forest Observation Initiative (MGD). Sections 2.2.1 (Deforestation) and 2.2.2 (Forest degradation). Kissinger, Herold, and De Sy. 2012. Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers. UNFCCC. 2013. Decision 15/CP.19. Addressing the drivers of deforestation and forest degradation. http://unfccc.int/resource/docs/2013/cop19/eng/10a01.pdf#page=43 UNFCCC. 2010. Decision 1/CP.16. The Cancun Agreements. http://unfccc.int/resource/docs/2010/cop16/eng/07a01.pdf#page=2

Outline of lecture Background and UNFCCC requirements on addressing drivers of deforestation and forest degradation Definitions and overview of drivers Different approaches for monitoring drivers of deforestation and forest degradation Role of drivers in developing national forest reference (emission) levels and designing policy interventions This lecture starts by explaining the need to monitor drivers of deforestation and forest degradation and the UNFCCC requirements on addressing drivers of deforestation and forest degradation. Then it proceeds with an overview and explanation of the different types and definitions of drivers: the direct (proximate) drivers and the indirect drivers (underlying causes). Different approaches to monitor direct drivers of deforestation and forest degradation are explained. Also, the approaches to monitor the indirect drivers are mentioned. Monitoring indirect drivers is much more complex. The lecture ends by explaining the role of drivers in developing national forest reference (emission) levels. Driver input reflects the national circumstances that have to be taken into account when establishing a forest reference level. Briefly, the relevance of drivers analysis for designing policy interventions is explained.

Outline of lecture Background and UNFCCC requirements on addressing drivers of deforestation and forest degradation Definitions and overview of drivers Different approaches for monitoring drivers of deforestation and forest degradation Role of drivers in developing national forest reference (emission) levels and designing policy interventions

UNFCCC requirements on addressing drivers of deforestation and forest degradation (UNFCCC. 2014. 15/CP.19; UNFCCC. 2011. 1/CP.16) Developing country parties are requested, when developing and implementing their national strategies or action plans to address the drivers of deforestation and forest degradation. Drivers of deforestation and forest degradation have many causes; actions to address these drivers are a function of a countries’ national circumstances, capacities, and capabilities. This slide and the next slide present the requirements on addressing the drivers of deforestation and forest degradation.

UNFCCC requirements on addressing drivers of deforestation and forest degradation Countries are encouraged to: Take action to reduce the drivers of deforestation and forest degradation Share the results on addressing drivers, including via the web platform on the UNFCCC website (http://unfccc.int/redd) Take note of the information from ongoing and existing work on addressing the drivers of deforestation and forest degradation by developing country parties and relevant organizations and stakeholders

Relevance of drivers in REDD+ policy development and implementation Addressing all direct and indirect drivers (see next section) is essential for effectively reducing emissions from deforestation and forest degradation and enhancing forest carbon stocks in every REDD+ country. Understanding of drivers is essential for: Designing interventions specifically to target the drivers, thereby increasing the likelihood of reducing emissions through REDD+ Assessing the impact of mitigation actions (track drivers locally, nationally, internationally) Climate change mitigation policies for REDD+ aim to address the drivers of deforestation and forest degradation and reduce the emissions that are associated with these drivers. Therefore it is important to understand and monitor drivers of deforestation and forest degradation in order to develop these mitigation policies and define effective strategies and interventions to reduce emissions.

Outline of lecture Background and UNFCCC requirements on addressing drivers of deforestation and forest degradation Definitions and overview of drivers Different approaches for monitoring drivers of deforestation and forest degradation Role of drivers in developing national forest reference (emission) levels and designing policy interventions

Direct and indirect drivers of deforestation and forest degradation Direct drivers (or proximate drivers): human activities or immediate actions that directly impact forest cover and loss of carbon: Deforestation drivers, mainly large-scale processes Forest degradation drivers, mostly small-scale processes For examples of direct drivers of deforestation and forest degradation, see next slides Indirect drivers (or underlying drivers): social, economic, political, cultural, and technological processes that give rise to the direct drivers: Examples of indirect drivers: population growth, migration, market growth, agro-technical change, weak governance and enforcement, individual and household behaviour, etc. There are two different paths of drivers of deforestation and forest degradation: direct causes and indirect causes. Direct drivers are direct human activities that affect forest cover. For deforestation, these are mainly large-scale processes; for forest degradation these are small-scale processes, or processes that take place underneath the canopy. Indirect drivers are the underlying processes of the direct human activities.

Examples of direct drivers of deforestation Category Commercial agriculture Forest clearing for cropland, pasture, and tree plantations For both international and domestic markets Usually large to medium scale Subsistence agriculture Includes both permanent subsistence and shifting cultivation Usually by (local) smallholders Mining All types of surface mining Infrastructure Roads, railroads, pipelines, hydroelectric dams Urban expansion Settlement expansion This table gives an overview of the direct drivers of deforestation: the processes that reduce forest cover Source: Hosonuma et al. 2012. Source: Hosonuma et al. 2012.

Examples of direct drivers of forest degradation These activities cause degradation (as indicated by lower carbon densities) if practiced in previously undisturbed or little disturbed forest ecosystems, and can continue to cause degradation depending on the intensity of subsequent impact Category Timber / logging Selective logging For both commercial and subsistence use Includes both legal and illegal logging Uncontrolled fires Includes all types of wildfire Livestock grazing in forest On both large and small scales Fuelwood / charcoal Fuelwood collection Charcoal production For both domestic and local markets This table gives an overview of direct drivers of degradation: the processes that degrade the forest and affect forest carbon. Note that timber/logging is categorized as degradation process; when a forest is logged, and after a while starts to regrow again, the land use does not change and it remains a forest. When a forest is logged and the cleared land is converted into agriculture, then the process is categorized as “commercial agriculture” (see previous slide, 10). Source: Hosonuma et al. 2012.

Direct deforestation drivers: An overview Distribution of deforestation drivers (% of area of deforested land) Agriculture is the largest driver of deforestation worldwide (~ 80%) Latin America: Commercial agriculture is biggest driver (2/3 of total deforested area) Africa and (sub)tropical Asia: Commercial agriculture is of similar importance as subsistence agriculture (both 1/3 of total deforested area) This bar chart gives an overview of the direct drivers of deforestation in tropical non-Annex I countries for three continents. The largest driver of deforestation worldwide is agriculture (+/-80% of all deforestation). In Latin America it concerns mostly commercial agriculture, while in Africa and (sub)tropical Asia there is an equal distribution of commercial and subsistence agriculture. Other drivers are mining (mostly in Africa), infrastructure, and urban expansion (mostly in (sub)tropical Asia). However, these drivers are of less impact (in terms of area change) than agriculture. Note: the data used in this study were derived from Readiness Preparation Proposals (R-PPs) and other country readiness plans. Not all data were based on quantitative figures, so the results are not always objective. Adapted from Hosonuma et al. 2012.

Direct degradation drivers: An overview Distribution forest degradation drivers (% of area of degraded forest) Latin America and (sub)tropical Asia: Commercial timber extraction and logging > 70% of total degradation Africa: Fuel wood collection, charcoal production, followed by timber logging This bar chart gives an overview of the direct drivers of forest degradation in tropical non-Annex I countries for three continents. The largest driver of degradation in Latin America and (sub)tropical Asia is timber logging. In in Africa it is fuel wood collection and charcoal production, followed by timber logging. Uncontrolled fires mostly occur in Latin America. Livestock grazing in forests occurs mostly in Africa. However, these drivers are of less impact that timber/logging activities (in terms of area change). Adapted from Hosonuma et al. 2012.

Drivers of deforestation: Relative importance of commercial versus subsistence agriculture Source: Kissinger et al. 2012. Agriculture is the most important driver of deforestation. There are differences in the importance of commercial versus subsistence agriculture. This figure shows the relative importance of commercial agriculture (orange color when the driver importance > 50%) and of subsistence agriculture (green color when the driver importance >50%). Commercial agriculture is more important than subsistence agriculture as a driver mainly in the Amazon region and South East Asia. Main drivers are cattle ranching, soybean farming, and oil palm plantations. Subsistence agriculture is more important mostly in Central African countries. Note: the data used in this study were derived from R-PP’s and other country readiness plans. Not all data were based on quantitative figures, so the results are not always objective. Commercial agriculture: Large scale Subsistence agriculture: Shifting cultivators and smallholders

Drivers of forest degradation: Relative importance of commercial vs subsistence drivers Source: Kissinger et al. 2012. This figure shows the relative importance of commercial types of degradation (orange color when the driver importance > 50%) and subsistence types of degradation (green color when the driver importance >50%). In African countries, subsistence degradation is more important than commercial degradation; this concerns degradation activities such as fuel wood collection, charcoal production and livestock grazing in forests. Commercial degradation is more important in the majority of countries on other continents; this concerns commercial wood extraction. Commercial degradation: Timber / logging Subsistence degradation: Fuel wood collection, charcoal production and livestock grazing in forest

Indirect drivers of deforestation Social processes that underpin the direct causes of deforestation Interplay of demographic, economic, technological, institutional, and sociocultural factors Main indirect drivers of tropical deforestation and degradation can include: Economic growth if this increases pressure on primary forest resources through increased demand for timber, mineral resources in forest areas, and agricultural products often in the context of a globalizing economy Population growth if this increases pressure on forest resources Insufficient regulatory arrangements—sustainability policies may exist, but not be adequately enforced (see next slide) Indirect drivers of deforestation are the fundamental social processes that explain the direct drivers of deforestation. They are an interplay of demographic, economic, technological, institutional, and sociocultural factors. Main indirect drivers of deforestation and forest degradation are related to economic growth and population growth and density.

Indirect drivers of deforestation (continued) Other indirect drivers of deforestation and forests degradation, related to enforcement of forest policies: Weak forest sector governance and institutions Conflicting policies Illegal activity (related to weak enforcement) Corruption Low capacity of public forestry agencies Land tenure uncertainties Inadequate natural resource planning and monitoring This slide lists other indirect drivers related to enforcement of forest policies.

Relationship between direct and indirect drivers of deforestation The indirect drivers are an interplay of demographic, economic, technological, institutional, and sociocultural factors and underpin the direct drivers of deforestation. Direct drivers Indirect drivers This figure shows the main indirect drivers (processes ) that underpin the direct drivers of deforestation, based on a study by Geist and Lambin (2002). The indirect drivers are an interplay of demographic, economic, technological, institutional, and sociocultural factors. Source: Geist & Lambin 2002.

Trends in indirect drivers, expected to increase pressures on forest in the future Increasingly meat-based diets Long-term population trends Growth in developing country regional markets for key commodities Climate change adaptation factors There are shifts in indirect drivers, and pressures on the forests are expected to increase in the future. The main shifts are listed on this slide: Increasing meat-based diets: growth is expected in demand from large economies in Asia, Latin America, and oil-exporting countries. Long-term population trends: global increase (largest increase in Africa and Asia & Pacific), expected stabilization of population after 2050. Growth in developing country regional markets for key commodities: will lead to increase in demand for food products. These shifts will redefine pressures on the forest and lead to trade-offs among different land uses. Source: Kissinger, Herold, and De Sy 2012.

Outline of lecture Background and UNFCCC requirements on addressing drivers of deforestation and forest degradation Definitions and overview of drivers Different approaches for monitoring drivers of deforestation and forest degradation Role of drivers in developing national forest reference (emission) levels and designing policy interventions

Carbon stock changes due to different deforestation and degradation processes Different forestry activities and processes have different impacts on the total forest carbon stock. This figure shows some of the activities and how they affects forest carbon.

Monitoring drivers of deforestation and forest degradation Drivers and REDD+ interventions are key to defining priorities and appropriate methods for MRV. Monitoring drivers important for: Tracking forest change activities over time Attributing emissions to specific causes Monitoring drivers requires resources and efforts additional to estimation and reporting of GHG emissions. Engagement with nonforest sectors is important in order to track drivers. The particular drivers in a country determine what kind of monitoring approaches are needed to track them and are key to defining monitoring priorities and appropriate methods for MRV. By tracking forest activities over time, a country can follow the impact of different drivers on deforestation (carbon and noncarbon impacts). Monitoring drivers of deforestation and forest degradation provide an essential data stream for countries in their REDD+ strategy and policy design and implementation. Actions to address these drivers are unique to a country’s national circumstances, capacities and capabilities. By monitoring drivers, the GHG emissions can be attributed to the specific causes. These need to be reported in the GHG inventory of a country. It provides information on the impact of different drivers on the carbon budget and shows the priorities for addressing drivers. Countries should consider and integrate information beyond the forest sector in order to track driver activity, social and environmental safeguards, and evaluation of trade-offs and livelihood implications. Engagement with nonforest sectors is important.

Approaches to monitor direct drivers of deforestation Integrate and combine capacity-development efforts for monitoring drivers with ongoing national forest monitoring for REDD+: Link activity data monitoring with monitoring drivers. Remote sensing analysis: Linking forest area changes to specific activities and follow-up land use (time series analysis) Spatial context and location and other features (e.g., roads, settlements) can help in interpretation and can be a useful input for stratification, as set out in the GDOI MGD Ground observations for interpretation of land-use patterns: gather national inventories or local and regional knowledge from experts and communities. As monitoring drivers requires resources and efforts additional to estimation and reporting of GHG emissions, it is recommended that countries integrate and combine capacity-development efforts for monitoring drivers as much as possible with ongoing national forest monitoring for REDD+. In particular, it is important to link activity data monitoring with driver monitoring. The size of the deforestation area gives a first indication about the driver and enables one to discriminate, for example, between commercial versus subsistence agricultural expansion. With time series analysis of remote sensing data, deforestation areas can be mapped and directly linked to the follow-up land use, which gives an indication of the activities (direct drivers) of deforestation. Spatial context and location can further help in the interpretation. In the country examples of Module 1.3, a practical example for assessing direct drivers in Indonesia is given. Further, regional and local knowledge from experts and communities, obtained through ground observations or national inventories, is needed to support the interpretation of land-use patterns to determine the exact drivers of deforestation.

Approaches to monitor direct drivers of forest degradation Drivers of forest degradation are more difficult to detect with use of remote sensing than drivers of deforestation. High spatial and temporal variation in forest carbon stock change due to degradation, so frequent ground surveying is required. Define, identify, and measure an appropriate benchmark or reference condition in order to assess degradation. Assess range of natural variation of carbon stocks and other forest attributes before establishing benchmarks. Assessing forest degradation means that a current state of the forest needs to be compared with a previous state of the forest. However, it is often difficult to get historical data on the forest condition and carbon stocks in the forest. In that case, a reference condition (benchmark) needs to be established. This is a challenging task, because it is difficult to define forest degradation (many different definitions exist, based on management goals, natural conditions, etc. (Morales-Barquero et al. 2014). It is important to assess the range of natural variation of carbon stocks and other forest attributes (RONV assessment) in a forest area, before establishing a reference condition or benchmark. A range of natural variation would occur without human disturbance. This information, together with information on land-use history and degradation agents, can be used to classify areas into different levels of degradation (Morales-Barquero et al. 2014). For monitoring forest degradation associated with local markets and subsistence, proxy data may be needed as historical field data sources are generally rare and remote sensing approaches have limited ability to provide information based on archived data (Skutsch et al. 2011). Monitoring of commercial types of degradation (see slide 14) Through a combination of satellite data, forest concession data, forest inventories Monitoring of subsistence types of degradation (see slide 14) Proxy data needed

Remote sensing for monitoring direct drivers Indicator of driver Method Sensors Deforestation Industrial agricultural clearing for cattle ranching, row crops, etc. Large-clearings (>25 ha); post-clearing land use Size of deforestation polygons (see section 2.1); map of land use following deforestation MODIS, Landsat-like sensors Small-scale agricultural clearing for pastures, shifting cultivation, smallholder farming Small clearings (<25 ha) Size of deforestation polygons (see section 2.1) Landsat-like sensors Infrastructure expansion (roads, mines etc.) Road networks, new mines Visual analysis or automated detection of infrastructure features Landsat-like and high resolution sensors Degradation Unsustainable logging Logging roads Spectral mixing (see section 2.1.3) Fuel wood and NTFP collection Footpaths, low biomass, ground data No accepted method High resolution Forest grazing Ground data Wildfire Burn scars Burn scar detection (see section 2.5) Landsat-like sensors, MODIS This table shows different remote sensing approaches to monitor direct drivers of deforestation and degradation. The size of the polygon is the first indicator to differentiate between large-scale and small-scale processes. Land use following deforestation gives an indication of the deforestation activities. Large deforestation areas can be observed with MODIS data. To observe the activities of deforestation, sensors with a spatial resolution comparable to Landsat need to be used. To observe infrastructure expansion (road networks or mines), Landsat-like sensors or sensors with a higher spatial resolution need to be used. Monitoring drivers of forest degradation is more complex. Usually, higher resolution sensors are needed to observe degradation. Logging roads and burn scars can be detected with remote sensing. It is more difficult to detect fuel wood collection and forest grazing. High resolution satellite data combined with ground data are required and there are no widely accepted methods for mapping these types of degradation. GOFC-GOLD (2014) Sourcebook, table 2.8.1, “Remote Sensing of Proximate Drivers of Deforestation and Degradation.” Sections mentioned in the methods column of the table refer to sections in the GOFC-GOLD Sourcebook. Source: GOFC-GOLD Sourcebook 2014, table 2.8.1.

National capacities for analyzing drivers Comparing country capacities for forest area change monitoring (derived from FAO 2010; see Romijn et al. 2012) with the quality of reported data on drivers from REDD+ readiness reports of 45 countries (i.e., R-PP, CIFOR reports; and see Hosonuma et al. 2012) Data on GHG emissions from drivers: commonly not available on national level This table gives an indication of the national capacities for analyzing drivers in 45 countries. Country capacities for monitoring forest area change where compared with the quality of reported driver data for the same countries. Driver quality is based on REDD+ readiness reports: whether countries just list important drivers (low), rank them according to importance (medium) or provide quantitative data (high). There is a tendency that countries with higher capacities for forest area change monitoring are able to provide higher quality driver data. However, there are some cases that deviate: In some cases, monitoring capacities are low, but countries are still able to provide good driver data. This is because they derive driver data from other national sources In some cases monitoring capacities are high, but countries provide low quality driver data. These countries produce good activity data, but still need to expand and integrate the efforts to monitor drivers GHG emissions are the result of deforestation, but in some cases there will be additional GHG emissions after the deforestation event, depending on the driver. Data on GHG emission from drivers are commonly not known / available on national level. Sources: FAO 2010; R-PP: Readiness Preparation Proposal submitted to the Forest Carbon Partnership Facility from the World Bank; Hosonuma et al. 2012; Romijn et al. 2012. Source: Kissinger et al. 2012.

Approaches to monitor indirect drivers of deforestation and forest degradation Relies on socioeconomic, statistical, and modelling analyses using economic, social, and demographic data and analysis of policy and governance aspects Must be sure to include assessment of factors outside the forest sector which affect forests Assessment / prediction of indirect drivers is complicated Important to address indirect drivers separately and examine at various scales for specific analysis and intervention strategies It is difficult to establish a clear link between indirect and direct drivers. The indirect drivers are an interplay of demographic, economic, technological, institutional, and sociocultural factors. Statistical analyses and modelling is needed to assess and predict their characteristics and behavior. Assessment of indirect drivers of deforestation is complicated and often it is not possible to detect the indirect drivers with use of remote sensing and ground data.

Approaches to monitor indirect drivers of deforestation and forest degradation It is difficult to establish clear correlation between indirect driver assessments and remote sensing and ground-level data: Both ex-poste (after) and ex-ante (before intervention) assessments are challenging and may need to be revised as new information becomes available Good subnational data is needed: these are often scattered among sources, sectors and ministries. To assess the indirect drivers, good subnational data are needed that describe the economic, social, and demographic conditions that are linked to deforestation patterns. These data are linked to different sectors and often scattered among different sources and need to be integrated and harmonized before they can be used.

Outline of lecture Background and UNFCCC requirements on addressing drivers of deforestation and forest degradation Definitions and overview of drivers Different approaches for monitoring drivers of deforestation and forest degradation Role of drivers in developing national forest reference (emission) levels and designing policy interventions

Role of drivers in developing national forest reference (emission) levels Scenarios of future deforestation and degradation are based on understanding of drivers and their future developments. Considering drivers is important for adjusting forest reference (emission) levels (FRL) based on historical data taking into account national circumstances. Availability and quality of driver data is fundamental in establishing FRLs. A stepwise approach may be used to improve the quality and accuracy of the FRL, with increasing capacities and improved data input. Data on drivers of deforestation are needed to predict future deforestation scenarios and to establish forest reference emission levels. Using a stepwise approach: For Step 1, FRLs rely only on historical deforestation data. For Step 2, the FRLs may be adjusted for national circumstances; in this case, data on drivers is needed as input to develop the FRL. The availability and quality of the driver data is then crucial. With increasing capacities, following a stepwise approach for developing the FRL, the driver input data quality can be improved over time, which will result in improved FRLs with better quality and accuracy.

REDD+ strategies and interventions In order to be successful, REDD+ strategies and interventions need to address direct drivers and indirect drivers of deforestation and degradation simultaneously. Interventions are related to decoupling economic growth from deforestation. Engagement with nonforest sectors is important. It can help integrate information in order to: Track driver activity Ensure social and environmental safeguards Evaluate trade-offs and livelihood implications Developing countries need to reduce emissions, while indirect drivers are expected to increase. Therefore it is important to address all direct drivers and indirect drivers. This can be done by decoupling economic growth from deforestation. Integration of different sources of information helps in addressing drivers, so engagement with nonforest sectors is important.

Examples of interventions and strategies Summary of national REDD+ readiness plan interventions and strategies to address drivers of deforestation and forest degradation This table shows a summary of national REDD+ readiness plan interventions and strategies to address drivers of deforestation and forest degradation. Note that the interventions / strategies mentioned in this table are mainly meant to address national and local scale drivers. The REDD+ readiness plans largely omitted strategies to address the biggest global driver of deforestation: commercial agriculture. Also note that countries face international drivers and market pressure (legal and illegal wood flows for fuel and fiber; particularly in Africa and Asia). This will likely increase in the future, but no strategies to address these pressures have been defined yet (in the REDD+ readiness plans). Of course each country and context is different. According to this study, most countries choose to sustainably manage their forests. The strategies include better inventories and management plans, improved silvicultural technologies, forest certification, community forest concessions and better management regimes for agro-sylvio-pastoral systems. Also 55% of the countries want to reduce emissions from fuel wood, by using fuel wood efficient cook stoves. Afforestation (19%) and reforestation (29%) are strategies to address fuel wood demand, demand for construction materials, to increase carbon stocks and to restore degraded lands. Source: Kissinger, Herold, and De Sy 2012. Source: Kissinger et al. 2012.

In summary Addressing drivers of deforestation and forest degradation part of national strategies or action plans to reduce emissions in the context of REDD+ Distinction between direct drivers (proximate causes) and indirect drivers (underlying causes) of deforestation and forest degradation Remote sensing analysis combined with ground observations important for assessing direct drivers Indirect drivers analysis more complex than direct, good subnational data from different sources and sectors needed Drivers input essential for developing REDD+ forest reference emission levels and for developing effective REDD+ policy strategies and interventions

Country examples and exercises National analysis of drivers of deforestation in: Democratic republic of the Congo Indonesia Exercises Assessing and analyzing drivers of deforestation in a tropical country: Exercise 1 – Strategy development on how to address drivers Exercise 2 – Qualitative assessment of drivers of deforestation

Recommended modules as follow-up Module 2.1 to proceed with REDD+ measuring and monitoring and focus on monitoring activity data for forests using remote sensing Modules 3.1 to 3.3 to learn more about REDD+ assessment and reporting

References FAO (Food and Agriculture Organization). 2010. FAO Forest Resources Assessment of 2010. Rome: FAO. http://www.fao.org/docrep/013/i1757e/i1757e.pdf. Geist, H., and E. Lambin. 2001. What Drives Tropical Deforestation? A Meta-analysis of Proximate and Underlying Causes of Deforestation Based on Subnational Case Study Evidence. Land-Use and Land- Cover Change (LUCC) Project, International Geosphere-Biosphere Programme (IGBP), LUCC Report Series: 4. Louvain-la-Neuve, Belgium: CIACO. GFOI (Global Forest Observations Initiative). 2014. Integrating Remote-sensing and Ground-based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests: Methods and Guidance from the Global Forest Observations Initiative. (Often GFOI MGD.) Geneva, Switzerland: Group on Earth Observations, version 1.0. http://www.gfoi.org/methods-guidance/. Sect. 5. http://www.gfoi.org/methods-guidance-documentation. GOFC-GOLD (Global Observation of Forest Cover and Land Dynamics). 2014. A Sourcebook of Methods and Procedures for Monitoring and Reporting Anthropogenic Greenhouse Gas Emissions and Removals Associated with Deforestation, Gains and Losses of Carbon Stocks in Forests Remaining Forests, and Forestation. (Often GOFC-GOLD Sourcebook.) Netherland: GOFC-GOLD Land Cover Project Office, Wageningen University. http://www.gofcgold.wur.nl/redd/index.php.

Hosonuma, N. , M. Herold, V. De Sy, R. De Fries, M. Brockhaus, L Hosonuma, N., M. Herold, V. De Sy, R. De Fries, M. Brockhaus, L. Verchot, A. Angelsen, and E. Romijn. 2012. ”An Assessment of Deforestation and Forest Degradation Drivers in Developing Countries.” Environmental Research Letters 7. doi:10.1088/1748-9326/7/4/044009. Kissinger, G., M. Herold, and V. De Sy. 2012. Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers. Lexeme Consulting, Vancouver Canada. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/65505/6316-drivers- deforestation-report.pdf. Morales-Barquero, L., M. Skutsch, E. J. Jardel-Peláez, A. Ghilardi, C. Kleinn, and J. R. Healey. 2014. “Operationalizing the Definition of Forest Degradation for REDD+, with Application to Mexico.” Forests 5: 1653–1681. doi:10.3390/f5071653. Romijn E., M. Herold, L. Kooistra, D. Murdiyarso, and L. Verchot. 2012. “Assessing Capacities of non- Annex I Countries for National Forest Monitoring in the Context of REDD+.” Environmental Science and Policy 19–20: 33-48. R-PP: Readiness Preparation Proposal submitted to the Forest Carbon Partnership Facility from the World Bank. For diverse countries, available from: https://www.forestcarbonpartnership.org/ Skutsch M., A. B. Torres, T. H. Mwampamba, A. Ghilardi, M. Herold 2011. “Dealing with Locally Driven Degradation: A Quick Start Option under REDD+. Carbon Balance and Management 6. doi:10.1186/1750-0680-6-16.