Projecting Future Unplanned Deforestation for a REDD Feasibility Study in Cameroon

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1 Projecting Future Unplanned Deforestation for a REDD Feasibility Study in Cameroon Robert Rose, Francis Okeke, Christian Burren, Olivier Sene, Marisa Arpels

2 Wildlife Conservation Society WCS Mission Statement The Wildlife Conservation Society saves wildlife and wild places worldwide. We do so through science, global conservation, education and the management of the world's largest system of urban wildlife parks, led by the flagship Bronx Zoo. Together these activities change attitudes towards nature and help people imagine wildlife and humans living in harmony. WCS is committed to this mission because it is essential to the integrity of life on Earth.

3 73 Land/Sea Scapes 43 Countries REDD will help protect critical habitat Help raise funds for conservation

4 WCS and REDD+ Worldwide CC threatens to destroy wild places and to accelerate species extinction 17% of global carbon emissions derive from land use changes Deforestation continues at an alarming rate, particularly in tropical countries REDD+ provides an opportunity to address and mitigate climate change by: Reducing CO 2 emissions Improving forest policies, governance and management Generating incentives for forest conservation to major stakeholders

5 WCS and REDD+ Worldwide WCS REDD Initiatives Present in 15 countries and 22 landscapes Two Flagship projects: Seima Cambodia Makira Madagascar Team of global and national experts

6 REDD+ Projects Principle Carbon Stock Project Scenario Baseline (BAU) Scenario Time

7 REDD+ Pilot Initiatives and Projects Project Development Main Steps Collect Data and information on: Carbon stocks in different land cover types and forest strata (carbon stock data) Areas affected by deforestation and forest degradation in the past (activity data) Direct causes and underlying drivers of deforestation and forest degradation Define main parameters of the project regarding: Spatial and temporal boundaries of a potential REDD project Carbon pools and emission sources to be considered in the project Develop a credible baseline scenario for future emissions: Analyse the extent and localization of historic deforestation Develop models projecting extent and localization of future deforestation Evaluate the impact on emissions of potential project scenarios: Develop potential REDD project options and combine them into projects Evaluate and compare the different projects and select the best

8 The Takamanda Mone Landscape

9 Takamanda Mone Context Land cover and management Total Area: Land cover (2008) and use 450,000 ha Takamanda National Park (TNP): Total: 62,258 ha Forest: 57,271 ha Ambelle Protection Forest (APF): Total: 4,642 ha Forest: 4,591 ha Kagwene Gorilla Sanctuary KGS): Total: 1,989 ha Forest: 1,635 ha Mone River Forest Reserve (MRFR): Total: 45,868 ha Forest: 45,674 ha FMU (TRC): Total: 15,233 ha Forest: 14,504 ha Non designated forests: Total: 216,240 ha Forest: 187,193 ha Source GIZ

10 Takamanda Mone Context Biodiversity Biodiversity Aspects Cross River Gorilla: Most endangered Gorilla species Fewer than 250 ind. thought to remain Credit: Nicky Lankester, Limbe Wildlife Centre 25 large mammal species: Nigeria-Cameroon Chimp (Pan trogl. ellioti) Drill (Mandrillus leucophaeus) Forest elephant (Loxodonta afric. cyclotis) Leopard (Panthera pardus) Preuss monkey (Cercopithecus preussi) Major threats: Habitat loss through deforestation Poaching

11 Takamanda Mone Context Communities Community Aspects Low population density (2001): 16,000 people in Mone and Mbulu area 12,000 people in 31 villages around TNP High population growth: Population expected to double by % between 0 and 20 years old Major activities: Small scale subsistence agriculture Poor infrastructure: Difficult road access to Takamanda and Mone areas Very limited access to safe drinking water, health care or education

12 Takamanda Mone Context Drivers of Deforestation and Forest Degradation Agriculture and Mining Small scale agriculture: Mainly subsistence driven, 1ha plots Cassava, yam, ground nut, sugarcane, corn, and sorghum Customary tenure systems but no formal land use designation or use rights Increase in land conflicts due to immigration Commercial Agriculture: Limited due to poor infrastructure Some commercial cocoa Rumor that lease for palm oil plantation Mining: Some small scale mining, few impact Exploration permit for SoftRock

13 Takamanda Mone Context Drivers of Deforestation and Forest Degradation Existing Roads: Infrastructure Low density of road network Seasonal, impracticable during wet season Planned projects: Project to upgrade and new part between Mamfe and Akwaya, studies conducted Project to tar national road linking Bameda to Ekok on Nigeria border Potential impact: Increased immigration and need for food production Better access to markets might increase commercial agriculture Former marginal lands may become profitable for agriculture mining, and logging

14 Takamanda Mone Context Drivers of Deforestation and Forest Degradation Commercial Logging Active logging concessions: None of the concessions use Reduced Impact Logging practices or are FSC Legislation excludes steep slopes and wetlands from harvesting Potential future legal logging: Mone designated as forest reserve and firm intention to open logging there Potentially community/council forests

15 REDD Project Parameters

16 REDD Project Parameters Estimating Emissions from Deforestation and Forest Degradation Activity Data Carbon Data Forest 1000 ha FOREST 400 tco 2 -e/ha Cropland Forest 700 ha Cropland 40 tco 2 -e/ha 300 ha Land Use Change = 300 ha X = Emission Factor = 360 tco 2 -e/ha 108,000 tco 2 -e Emissions

17 Project Geography

18 REDD Project Parameters Project Boundaries Spatial Boundaries: Project Area (green): Total Area: 336,230 ha Forest Area (2010): 310,868 ha Reference Area (blue): Total Area: 1,434,730 ha Forest Area (1986): 1,282,108 ha Leakage Belt: none (usually forests around project area) Temporal Bondaries: Hist. Reference Period: Project Crediting Period:

19 REDD Project Parameters Carbon Pools Above Ground Tree Biomass (inventory data) Below Ground Tree Biomass (proxy) Above Ground Non Tree Biomass (proxy) Litter Biomass (proxy) Standing/Lying Dead Wood (proxy) Soil Non Organic Carbon Emission Sources Carbon Stock Changes Biomass Burning (non-co 2 ) Fossil Fuel Consumption Nitrogen Fertilizer Application Cattle

20 REDD Project Parameters Carbon Stocks Intact Natural Forest (SUNDERLAND): tco 2 -e Conv. Logged Forest (GAF-AG): tco 2 -e FSC Logged Forest (GAF-AG): tco 2 -e Small-scale Agroforest (IPCC): tco 2 -e Industrial Agroforest (IPCC): tco 2 -e Shrub Savannah (IPCC): tco 2 -e Grassland (IPCC): tco 2 -e Cropland (IPCC): tco 2 -e Emission Factors Dense Forest -> Cropland: tco 2 -e Logged Forest/Agroforest -> Cropland: tco 2 -e Intact Forest -> S-s Agroforest: tco 2 -e Intact Forest -> Ind. Agroforest: tco 2 -e Intact Forest -> Conv. Logged Forest: tco 2 -e Intact Forest -> FSC Logged Forest: tco 2 -e Conv. Logged Forest -> FSC Logged Forest: tco 2 -e

21 Baseline Scenario

22 Baseline Scenario Analysis of Historic Deforestation 1. Acquire images 2. Classify images 3. Map Forest Cover 4. Map Deforestation Medium resolution satellite images for 3 points in time Classify land uses for 3 points in time Map Land Use Classes for 3 points in time Calculate and map land use change for 2 periods

23 Past Land Cover Analysis Land Cover LULC T1 LULC T2 LULC T3 (1990) (2000) (2008) Land Cover Change LULCC T1-T2 LULCC T2-T3

24 Baseline Scenario Analysis of Historic Deforestation Classification Process Landsat 5 for 1986, Landsat 7 for 2000/08 1 Scene covers entire reference area Semi-automated supervised classification with image enhancement 3 Classes: Forest Non-Forest Water

25 Baseline Scenario Analysis of Historic Deforestation Forest Cover 1986: RA: PA: 2000: RA: PA: 2008: RA: PA: 1,282,108 ha 325,594 ha 1,262,255 ha 321,859 ha 1,218,409 ha 315,415 ha

26 Baseline Scenario Analysis of Historic Deforestation Annual Deforestation : RA: 1,418 ha (0.11%) PA: 267 ha (0.08%) TNP: 11 ha (0.02%) MFR: 7 ha (0.01%) Roads: 970 ha (0.39%) : RA: 5,481 ha (0.44%) PA: 806 ha (0.25%) TNP: 79 ha (0.14%) MFR: 8 ha (0.02%) Roads: 1,411 ha (0.60%)

27 Baseline Scenario Analysis of Historic Deforestation Conclusions Increase of deforestation over the historic reference period: Increase of annual deforestation in all areas Lower deforestation rates in project area: Higher deforestation rates in the reference area than project area -> Indicates Frontier Deforestation Configuration Deforestation lowest in designated areas (MFR, TNP) Higher deforestation rates along roads: Higher deforestation inside one km buffer along roads -> Suggests that planned extension of road network will increase deforestation in the landscape if no REDD measures

28 Baseline Scenario Location of Unplanned Baseline Deforestation in the RA 1. Determine Factors 2. Calibrate Model 3. Validate Model 4. Project Deforestation Determine factors impacting on deforestation Develop deforestation model based on period Select the model with the best fit Apply best model to project future de-forestation in the reference area

29 Baseline Scenario Location of Unplanned Baseline Deforestation in the RA Factor Maps Slope Protection status Distance from Road Distance from Forest Edge Distance from villages

30 LULCC T1-T2 1Model Calibration Risk Map Model Of Change (p1, p2, p3,, pn) Threats (T1) LULC T1 LULC T2

31 2 Model Validation Model Of Change True LULC T3 (p1, p2, p3,, pn) Compare (Through Risk Map) Threats T2 Predicted LULC T3

32 3 Baseline Prediction Model Of Change LULCC T3-T4 (p1, p2, p3,, pn) Predicted LULC T4 LULC T3 Threats T3 Risk Map

33 Baseline Scenario Annual Areas of Unplanned Baseline Deforestation in the PA Baseline Deforestation Project Area: 2015: 2020: 2025: 2030: Average: 4,130 ha 8,660 ha 13,170 ha 17,875 ha 894 ha/y Baseline Emissions Project Area: 2015: 1,836,895 tco 2 -e 2020: 3,859,810 tco 2 -e 2025: 5,862,210 tco 2 -e 2030: 7,959,655 tco 2 -e Average: 397,983 tco 2 -e/y

34 Challenges Land use and land cover analysis in data poor areas o o o Difficult to find good satellite images cloud and error free Lack of good ground truth data many places are hard to reach on foot Lack of in country capacity to perform analysis Models of land cover change o o o Non-stationarity of variables through time Error assessments are challenging Don t care about pixel-to-pixel accuracy, just need right amount of defor predicted in each carbon strata Methods are difficult to learn and take a lot of time Hard to build capacity for this work

35 Conclusions Significant amount of projected deforestation and emissions REDD+ can be useful mechanism for raising conservation funds Methodologically o o Land cover change analysis relatively straightforward Modeling can be difficult and needs to be revisited as project moves forward

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