restore+: addressing landscape restoration on degraded
TRANSCRIPT
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© Neil Palmer
RESTORE+: Addressing Landscape Restoration on Degraded Land in Indonesia, the Congo Basin, and Brazil
EUBCE, Lisbon, 28 May 2019
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RESTORE+Quick facts
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• Project duration5 years (2017‐2022)
• Type of activitiesEnhancement of methods, tools, datasets and institutional capacity
• Partner institutions
Indonesia: Ministry of National Development Planning/BAPPENAS
Brazil: Brazilian Cooperation Agency (Foreign Office), Ministry for the Environment
• Funding support
• Project partners
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RESTORE+Approach
Identifying degraded land:
• Exploring possible definitions of degraded land including social and biophysical consideration
• Assess land degradation through analysis of high resolution (satellite) imagery
• Big earth observation data analysis• Crowdsourcing and grass‐root engagement
Assess implication of using different degraded land definitions in:
• Vegetation modelling to project carbon stock, potential yield under different restoration measures etc.
• Biodiversity assessment (priority areas, species, biodiversity modelling)
Assess sectoral interaction of Food‐Land‐Energy nexus:
• Projection scenarios for production and trade of forestry and agriculture (food) commodities
• Land use/cover projection scenarios based on spatially explicit bottom‐up informed economic models
• Assess bioenergy supply chain in its interaction with the overall energy system
• Assess market support for sustainability safeguards
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Importance of fuel wood in Sub‐Sahara Africa
• 80% of all Sub‐Saharan African households, relied on fuel wood as their main source of energy – mostly open fires
• Two out of three of SSA households—585 million people—live without electricity
• Fuel wood collection & charcoal production are the most important drivers of forest degradation in large parts of Africa (Herold et al., 2012)
• For Cameroon, 9.8 million m3 of fuel wood are collected annually in Cameroon (Topa et al., 2010) (for comparison, total harvest of Austria is 16 million m3)
Source: IEA, 2014 if not stated otherwise
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Primary energy demand in SSA
Source: IEA, 2014
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Fuelwood vs. Charcoal/Energy Density (16–21:30 MJ/kg)
Source: Mosnier et al., 2016
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Cameroon
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Cameroon Megatrends
Deforestation
Sourc: GFW
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Cameroon Megatrends
Population growth
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5
10
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25
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35
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2000 2005 2010 2015 2020 2025 2030 2035
Millions of inh
abita
nts
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Cameroon Megatrends
Urbanization
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Cameroon Megatrends
Urbanisation
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GLOBIOM model results for Cameroon
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Possible solutions
• Portable natural gas/biogas for cities • Improved cookstoves instead of open fires to reduce wood demand• Short rotation plantations
• WHAT? • WHERE? • HOW?
• combined restoration and bioenergy generation
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Incorporating biophysical productivity in spatially explicit partial equilibrium model
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1 Gridded representation with detailed agroecological information (topography, climate, soil type etc.)
2 Each pixel contains information on potential productivity for main agriculture and forest commodities based on biophysical modelling
Matching potential of supply with demand (both spatially explicit) for all modelled commodities
Optimization model with the objective function of maximizing producer and consumer surplus to calculate
production of all commodities in every pixel34
EPIC & WaNuLCAS RUMINANT G4MAgronomic Model
Global: annual crops; low/high input
Indonesia:Tree crops; Intensification
CattleSheep & goat
PoultrySwine
Forest Growth Model
AreaCarbon stock
AgeSpecies
Rotation time
Population, GDP, Diet
Food Energy Fiber Industry
Market/trade Prices
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Degradation assessmentUse of radar based remote sensing products
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ADVANTAGES:• Multiple data sources• Easy to interpret• Long legacy of usage
DISADVANTAGES:• Infrequent data, affected by cloud/ haze/smoke• Difficult to detect subtle changes (e.g. degradation)
ADVANTAGES:• Independent of cloud/smoke/haze and day‐light conditions• Possible to detect subtle changes (e.g. degradation). • Dense time‐series
DISADVANTAGES:• Affected by ground/vegetation moisture conditions• Difficult to interpret
Optical‐based satellite data (e.g. Landsat, Sentinel 2) Radar‐based satellite data (e.g. Sentinel 1, ALOS PALSAR)
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Potential to better capture degradationRadar based remote sensing product
3010 areas of change
400,000 ha in South Sumatra
954
547 834 1624Fire Events Canal LUC
areas of other/mixed/unknown causes of change
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Crowdsourcing of data collectionCitizen science approach
Mobile application for in‐situ data collection to promote community‐based LULC awareness and monitoring.
Campaign launched in Austria
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Crowdsourcing platform tailored for IndonesiaIndonesia’s own citizen‐science platform
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Average increment (over rotation time of 6 years)
SPATIAL RESULTS/MODEL VALIDATION/LAND USE MODELING
Source: RESTORE+, IIASA (2019)
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Harvested wood (including destroyed biomass and residuals) in the secondary forest with rotation time of 30 years and disturbance intensity 30%. Commercial wood is 50% of the indicated amount
Source: RESTORE+, IIASA (2019)
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Accessibility and spatial optimization
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Power plants
Bioenergy contribution in an optimized energy system
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Land‐energy nexus
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TWh
Geothermal contribution in 23% renewable energy target scenario
Geothermal contribution when excluding primary forests
TWh
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Land‐energy nexus
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Biomass harvesting in 23% renewable energy target scenario
Biomass harvesting when excluding primary forests
MWe
MWe
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Spatial suppression efficiency (at 25 km2 resolution) calibrated in FLAM using burned area reported in GFED for wildfire in Indonesia, accumulated over 2000‐2009
FLAM‐IDENTIFIED FIRE HOT SPOTS IN INDONESIA
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Contact
Dr. Florian Kraxner
Deputy Director | Senior Research Scholar |Ecosystem Services and Management Program, ESMHead |Center for Landscape Resilience & Management, CLRInternational Institute for Applied Systems Analysis, IIASA Laxenburg, [email protected]://www.iiasa.ac.at
G4MEPIC