Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation Module developers: Erika Romijn, Wageningen University Martin Herold, Wageningen University Country examples: National analysis of drivers of deforestation in 1.Democratic Republic of the Congo 2.Indonesia Photo credit: AFP V1, May 2015 Creative Commons License

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 2 1. Democratic Republic of the Congo (DRC): National analysis of drivers of deforestation DRC is actively participating in REDD+ and performed a national analysis of drivers of deforestation and forest degradation that included:  Qualitative assessments ● By civil society ● By UN Environment Programme (UNEP)  Quantitative assessment ● By Université catholique de Louvain (UCL)  Synthesis of all studies

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 3 Qualitative analysis by civil society  Extensive literature review  Interviews with four to seven experts per province, to list the 10 most important direct and indirect drivers per province  Direct drivers consolidated at national level: 1.Slash-and-burn agriculture practiced by farmers 2.Local wood consumption 3.Charcoal and fuel wood / firewood  Indirect drivers consolidated at national level: 1.Population growth 2.Poverty of local population (farmers) 3.Administrative deficit

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Qualitative analysis by UNEP  Selection of 40 deforestation sites: literature review, field observations, and interviews with experts and different actors  Direct drivers consolidated at national level: 1.Slash-and-burn agriculture 2.Charcoal production 3.Causes related to demographics and phenomena such as wildfire  Indirect drivers consolidated at national level: 1.Population fluctuation 2.Institutional aspects (successive wars) 3.Economic aspects (poverty, youth employment)

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5 Quantitative study on the relation between direct activities and explanatory variables  Object-based segmentation combined with nonsupervised classification of Landsat satellite images produced land cover maps for 1990, 2000, and 2005  Analysis of land cover changes for 1990–2000 and 2000–2005  Thirty-five explanatory variables were grouped into eight categories of underlying causes: infrastructure, agriculture, forest exploitation, economic factors, transport axes, demographic factors, sociopolitical factors, and biophysical factors

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 Quantitative study on the relation between direct activities and explanatory variables  Linking land-cover changes (deforestation and degradation) to explanatory factors: ● Univariate statistical analysis to quantify the influence of the different explanatory variables during the two different time periods: 1990–2000 and 2000–2005 ● Multivariate statistical analysis to create explanatory models for combinations of underlying causes of deforestation

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 7 Direct drivers, explanatory factors, and underlying causes of deforestation Direct driversExplanatory variablesIndirect drivers 1 Slash-and-burn agriculture Biophysical factors: degraded forests Population growth 2Local wood consumption Biophysical factors: fragmentation Institutional aspects (political decisions, mismanagement, civil wars) 3 Charcoal and fuel wood / firewood Agriculture: rural complex Infrastructure and urbanization 4MiningTransport roads Economic aspects: economic crisis, unemployment, poverty 5BushfireDemographic factors: population growth

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 2. Indonesia: Drivers of deforestation analysis using national data  How to derive quantitative driver information based on land cover maps?

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Step 1. Starting point: time series of land-cover maps

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 10 Land-cover classification Primary forest 1Primary upland forest 2Primary mangrove forest 3Primary swamp forest Degraded forest 4Secondary upland forest 5Secondary mangrove forest 6Secondary swamp forest 7 Forest plantation (i.e., Eucalypt, Acacia, Teak) 8Bushes / shrubland 9Bushy swamp Nonforest 10Plantation (oil palm) 11Savanna 12Upland agriculture 13 Upland agriculture mixed with bush 14Rice field 15Cultured fisheries / fishpond 16Settlement / developed land 17Transmigration 18Open land 19Mining / mines 20Water body 21Swamp 22Airport Using the FAO forest definition: Forest > 0.5 ha Tree canopy cover > 10% Tree height > 5m forest being the predominant land use in the area

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 11 Step 2. Map deforestation areas for subsequent time periods ● Forest, defined following the FAO forest definition ● Deforestation, degradation, reforestation/regeneration: Primary Forest Degraded Forest Nonforest Classes Forest Nonforest degradation deforestation reforestation/ regeneration

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 12 Deforestation and forest degradation in Indonesia between 2000 and 2009

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 13 Step 3. Link deforested areas to postforest land use Mapping direct drivers: land cover following deforestation Postforest land useLand cover class Commercial agriculturePlantation (oil palm) Upland agriculture Rice field Subsistence agricultureUpland agriculture mixed with bush Urban and Infrastructure Settlement / developed land Transmigration Airport / harbor MiningMining / mines AquacultureCultured fisheries / fish pond Open land

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 14 Drivers of deforestation 2000–2009: Postforest land use per the FAO forest definition

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 15 Step 4. Quantify the different drivers of deforestation (for the time period 2000–2009) DriverArea (km 2 ) Commercial agriculture Subsistence agriculture Urban and infrastructure Mining Aquaculture Open land Source: MOFOR Distribution of different drivers in terms of area change

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 16 Step 5. Possibility of linking different drivers to GHG emissions  Reporting of carbon and other GHG emissions for each driver is encouraged  Following information required: ● Carbon density (emission factor) for each different forest type, to quantify total emissions (= area of deforestation X emission factor) (See modules 2.3 and 2.5.) ● Additional emissions related to specific drivers, these may occur after the deforestation and depend on the type of driver

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 17 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

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 18 References  Defourny, P., C. Delhage, J-P. Kibambe Lubamba Analyse quantitative des causes de la deforestation et de la degradation des forets en République Démocratique du Congo. Kinshasa: FAO- RDC Coordination nationale REDD N°UNJP/DRC /041/01/ redd.org/Newsletter35/DRC_Drivers_of_Deforestation/tabid/105802/Default.aspx.  Mahonghol, Denis Analyse qualitative des causes et agents de la déforestation et de la dégradation des terres forestières dans une RDC post-conflit. Evaluation environnementale post-conflict (EEPC). Kinshasa : Division Post-Conflit et Gestion des Désastres Programme Pays de la RDC.  MECNT (Ministère de l’Environnement, Conservation de la Nature et Tourisme). 2012a. Étude qualitative sur les causes de la déforestation et de la dégradation des forêts en République Démocratique du Congo. Kinshasa: Groupe de Travail Climat REDD. redd.org/Newsletter35/DRC_Drivers_of_Deforestation/tabid/105802/Default.aspx.  MECNT. 2012b. Synthèse des études sur les causes de la déforestation et de la dégradation des forêts en République Démocratique du Congo. Kinshasa. redd.org/Newsletter35/DRC_Drivers_of_Deforestation/tabid/105802/Default.aspx.

Module 1.3 Assessing and analyzing drivers of deforestation and forest degradation REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 19  Ministry of Forestry (MOFOR), Digital Land Cover Map Ministry of Forestry, Jakarta, Indonesia (unpublished).  Romijn E., J. H. Ainembabazi, A. Wijaya, M. Herold, A. Angelsen, L. Verchot, and D. Murdiyarso “Exploring Different Forest Definitions and Their Impact on developing REDD+ Reference Emission Levels: A Case Study for Indonesia. Environmental Science and Policy 33: 246–259.