Picture credit © Stora Enso

RESTORE+: Addressing Landscape Restoration for Degraded Land in Indonesia and BrazilDr. Florian Kraxner | Laxenburg, 18 April 2017

Picture credit © Stora Enso

IMPORTANCE OF RESTORATION

Bonn Challenge and Global Partnership on Forest Landscape Restoration aim at restoring 150 million hectares of deforested and degraded land by 2020,

and additional 200 million hectares by 2030.

Means to achieve the CBD Aichi Target 15, the UNFCCC REDD+ goal, the Rio+20 land

degradation neutrality goal, the Sustainable Development Goal 15

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Picture credit © Stora Enso

WHAT TO RESTORE?

UNEP Global Assessment of Soil Degradation (GLASOD): 1,2 billion ha

FAO's Global Assessment of Lands Degradation and Improvement project (GLADA): 2,7 billion ha

FAO Terrastat (Bot et al, 2000): 6 billion ha

“Global estimates of total degraded area vary from less than 1 billion ha to over 6 billion ha, with equally wide

disagreement in their spatial distribution.”

(Gibbs and Salmon, 2015)

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FOREST LANDSCAPE RESTORATION (FLR)

• is an ongoing process of regaining ecological functionality and enhancing human well-being

• the majority of restoration opportunities are found on or adjacent to agricultural or pastoral land. In these situations, restoration must complement and not displace existing land uses.

• cross-sectoral, multi-stakeholders and cross-jurisdiction

• requires a bottom-up approach e.g. Restoration Opportunities Assessment Methodology (ROAM) by IUCN and WRI

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Picture credit © Stora Enso

How do we assess large scale (i.e. national or regional) landscape restoration potential?

Are current targets realistically ambitious (together with the expected co/multiple

benefits)?

How should we formulate operational restoration policies that ensure

environmental integrity and social benefits?

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THE RESTORE+ PROJECT: QUICK FACTS

• Countries of ImplementationIndonesia and Brazil (with limited activities in the Congo Basin area)

• Project duration5 years (2017-2022)

• Type of activitiesCapacity Building (enhancement of methods, datasets and institutional capacity)

• Partner institutions in countries of implementation

Indonesia:Ministry of National Development Planning/BAPPENAS, Ministry of Environment and Forestry

Brazil:Brazilian Cooperation Agency (Foreign Office), Ministry for the Environment

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• Funding support

• Project partners

RESTORE+ METHODOLOGY: A SYSTEMS PERSPECTIVE

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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

REMOTE SENSING LIMITATION IN IDENTIFYING DEGRADED LAND RESOURCES

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“Detecting degradation from remotely sensed data, especially with the most commonly used forms of medium spatial resolution data, is difficult because the scale at which the degradation takes place is often at sub-pixel resolution.” (FAO, 2016)

Biomass map by Saatchi et al. (2011) better corresponds to land cover in Cameron 4°00’N 9°50’E (right side)

Biomass map by Baccini et al. (2012) better recognizes water bodies in Congo 3°41’S 10°59’E (left side)

ENGAGING GRASS ROOT ACTORS

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Citizen-empowered

Scientific Assessment

Visualization of land cover data sets, suitability maps,

land use information, biomass, etc.

Crowdsourcing ofland cover analysis

Creation of Hybrid Land Cover Maps

Validation of LandCover Maps

In-situ Data validation using

mobile apps

Serious Games(Cropland Capture)

REALISTIC ESTIMATES OF LAND AVAILABILITY USING CROWDSOURCING

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Cai et al., 20111107 mil. ha

Fritz et al., 2013375 mil. ha

Fritz et al, 2013, Environmental Science and technology

BIOPHYSICAL VEGETATION MODELLING (1)

• Biophysical forest modelling

• Provides annual harvestable wood (for sawn wood and other wood)

• Forest management (rot/spec)

• Forest Carbon stock

• Harvesting costs

• Forest area change

• Spatially explicit

• Input Data Sets

• NPP

• Population Density

• Land cover

• Agricultural suitability

• Forest Biomass

• Price level

• Discount rate

• Money efficiency

• Product use

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Age / Max Age

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BIOPHYSICAL VEGETATION MODELLING (2)

• Biophysical agriculture model

• Weather

• Hydrology

• Erosion

• Carbon sequestration

• Crop growth

• Crop rotations

• Fertilization

• Tillage

• Irrigation

• Drainage

• Pesticide

• Grazing

• Manure

• Major outputs

• Crop yields, Environmental effects (e.g. soil carbon, )

• 20 crops (>75% of harvested area)

• 4 management systems

• High input, Low input, Irrigated, Subsistence

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EPIC

Rain, Snow, Chemicals

Subsurface Flow

Surface Flow

Below Root Zone

Evaporation and

Transpiration

ECONOMIC LAND USE DECISIONS

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EXAMPLE: FOREST PROJECTIONS

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Business as usual (400 Mha) Forest Code (430 Mha)

ACCESSIBILITY AND SPATIAL OPTIMIZATION

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Power plants

Bioenergy contribution in an optimized energy system

LAND-ENERGY NEXUS

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TWh

Geothermal contribution in 23% renewable energy target scenario

Geothermal contribution when excluding primary forests

TWh

LAND-ENERGY NEXUS

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MWe

MWe

Biomass harvesting in 23% renewable energy target scenario

Biomass harvesting when excluding primary forests

Picture credit © Stora Enso

CONTRIBUTION TO POLICY FORMULATION

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Brazil INDC presented at COP-Paris 2016

On 28 September 2015 Brazil announced an unconditional target of a 37% reduction of

greenhouse gas emissions by 2025 below 2005 levels including LULUCF. The INDC also contains a “subsequent indicative contribution” of 43%

below 2005 levels by 2030.

Potential contribution to Indonesia’s National Development Plan

RESTORE+ METHODOLOGY:FROM INDONESIA AND BRAZIL TO THE TROPICS

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Identifying degraded land Assess implication of using different degraded land

definitions

Assess sectoral interaction of Food-Land-Energy nexus

Picture credit © Stora Enso

RESTORE+: Addressing Landscape Restoration for Degraded Land in Indonesia and BrazilDr. Florian Kraxner | Laxenburg, 18 April 2017