# se.plan#

## Introduction#

### Overview#

se.plan is a spatially explicit online tool designed to support forest restoration planning decisions by restoration stakeholders. It is part of SEPAL (System for Earth Observation Data Access, Processing and Analysis for Land Monitoring), a component of UN FAO’s free, open-source software suite, Open Foris. It aims to identify locations where the benefits of forest restoration are high relative to restoration costs, subject to biophysical and socioeconomic constraints that users impose to define the areas where restoration is allowable. The computation is performed using cloud-based supercomputing and geospatial datasets from Google Earth Engine available through SEPAL. As a decision-support tool, it is intended to be used in combination with other information users may have that provides greater detail on planning areas and features of those areas that se.plan might not adequately include. It offers users the option to replace its built-in data layers, which are based on publicly available global datasets, with users’ own customized layers. Please see appendix_a for a list of se.plan’s built-in data layers and their sources.

The sections below highlight key features of se.plan. In brief, following the three steps below, se.plan can be used to generate information on forest restoration potential.

Users start by (i) selecting their geographical planning area, (ii) rating the relative importance of different restoration benefits from their perspective, and (iii) imposing constraints that limit restoration to only those sites they view as suitable, in view of ecological and socioeconomic risks. se.plan then generates maps and related information on restoration’s benefits, costs, and risks for all suitable sites within the planning area.

### Geographical resolution and scope#

se.plan divides the Earth’s surface into grid cells with 30 arc-second resolution (≈1km at the equator). It includes only grid cells that satisfy the following four criteria (view in gee)):

• They are in countries or territories of Africa and the Near East, Asia and the Pacific, and Latin America and the Caribbean that the World Bank classified as low or middle-income countries or territories (LMICs) during most years during 2000–2020. These countries and territories number 139 and are listed in appendix_b.

• They include areas where tree cover can potentially occur under current climatic conditions, as determined by Bastin et al. (2019).

• Their current tree cover, as measured by the European Space Agency’s Copernicus Programme (Buchhorn et al. 2020), is less than their potential tree cover.

• They are not in urban use.

se.plan labels grid cells that satisfy these criteria potential restoration sites. It treats each grid cell as an independent restoration planning unit, with its own potential to provide restoration benefits and to entail restoration costs and risks.

## Methodology#

### Selection of planning area#

se.plan offers users multiple ways to select their planning area, which se.plan labels as Area Of Interest (AOI) as described in the usage.

### Restoration indicators#

Restoration offers many potential benefits. In its current form, se.plan provides information on four benefit categories:

• Biodiversity conservation

• Carbon sequestration

• Local livelihoods

• Wood production

se.plan includes two indicators each for biodiversity conservation and local livelihoods and one indicator each for carbon sequestration and wood production. Each indicator is associated with a data layer that estimates each grid cell’s relative potential to provide each benefit if the grid cell is restored; the relative potential is measured on a scale of 1 (low) to 5 (high). Please see appendix_c for more detail on the interpretation and generation of the data layers for the benefit indicators.

Users rate the relative importance of these benefits from their standpoint (or the standpoint of stakeholders they represent), and se.plan then calculates an index that indicates each grid cell’s relative restoration value aggregated across all four benefit categories. This restoration value index is a weighted average of the benefits, with user ratings serving as the weights. It therefore accounts for not only the potential of a grid cell to provide each benefit but also the relative importance that a user assigns to each benefit. It is scaled from 1 (low restoration value) to 5 (high restoration value). Please see appendix_d for more detail on the generation of the index.

### Restoration cost#

Forest restoration incurs two broad categories of costs, opportunity cost and implementation costs.

Opportunity cost refers to the value of land if it is not restored to forest. se.plan assumes that the alternative land use would be some form of agriculture, either cropland or pasture. It sets the opportunity cost of potential restoration sites equal to the value of cropland for all sites where crops can be grown, with the opportunity cost for any remaining sites set equal to the value of pasture. Sites that cannot be used as either cropland or pasture are assigned an opportunity cost of zero.

Implementation costs refer to the expense of activities required to regenerate forests on cleared land. They include both: (i) initial expenses incurred in the first year of restoration (establishment costs), which are associated with such activities as site preparation, planting, and fencing; and (ii) expenses associated with monitoring, protection, and other activities during the subsequent 3–5 years that are required to enable the regenerated stand to reach the “free to grow” stage (operating costs).

se.plan assumes that implementation costs include planting expenses on all sites. This assumption might not be valid on sites where natural regeneration is feasible. To account for this possibility, se.plan includes a data layer that predicts the variability of natural regeneration success.

se.plan calculates the overall restoration cost of each site by summing the corresponding estimates of the opportunity cost and implementation costs. Please see appendix_e for more detail on the interpretation and generation of the data layers for opportunity and implementation costs.

### Benefit-cost ratio#

se.plan calculates an approximate benefit-cost ratio for each site by dividing the restoration value index by the restoration cost and converting the resulting number to a scale from 1 (small ratio) to 5 (large ratio). Sites with a higher ratio are the ones that se.plan predicts are more suitable for restoration, subject to additional investigation that draws on other information users have on the sites. Please see appendix_d for more detail on the generation and interpretation of this ratio. A key limitation is that the ratio does not account interdependencies across sites related to either benefits, such as the impact of habitat scale on species extinction risk, or costs, such as scale economies in planting trees. This limitation stems from se.plan’s treatment of each potential restoration site as an independent restoration planning unit.

### Constraint#

se.plan allows users to impose constraints that limit restoration to only those sites they view as suitable, in view of ecological and socioeconomic risks. It groups the constraints into four categories:

• Biophysical (5 constraints): elevation, slope, annual rainfall, baseline water stress, terrestrial ecoregion

• Current land cover (5 constraints): Shrub land, Herbaceous vegetation, Agricultural land, Urban / built up, Bare / sparse vegetation, Snow and ice, Herbaceous wetland, Moss and lichen

• Forest change (3 constraints): deforestation rate, climate risk, natural regeneration variability

• Socio-economic constraints (6 constraints): protected areas, population density, declining population, property rights protection, accessibility to cities

se.plan enables the user to adjust the values that will be masked from the analysis for most of these constraints. Some of the constraints are binary variables, with a value of 1 if a site has the characteristic associated with the variable and 0 if it does not. For these constraints, users can choose if they want to keep zeros or ones.

Please see appendix_f for more detail on the interpretation and generation of the data layers for the constraints.

### Customization#

Every Constraints, Costs and Indicators are based on layers provided within the tools. These layer may not be covering the AOI selected by the user or provide less accurate/updated data than the National datasets available. To allow user to improve the quality of the analysis se.plan provides the possiblity of replacing these datasets by any layer available with Google Earth Engine.

Please see usage section for more details on the customization process.

### Output#

se.plan provides two outputs:

• A map of the Restoration suitability index scaled from 1 (low suitability) to 5 (high suitability). This map, generated within the Google Earth Engine API can be displayed in the app but also exported as a GEE asset or a .tif file in your SEPAL folders.

• A dashboard gathering informations on the AOI and sub-AOIs defined by the users. The suitability index is thus presented as surfaces in Mha but se.plan also displays the mean values of the benefits and the sum of all the used constraints and cost over the AOIs.

## Usage#

In this section, we will exaustively describe how to use the se.plan application.

### Open the app#

Then click on the purple wrench on the right side of your screen to access the dashboard of application (https://sepal.io/app-launch-pad). On this page all the available applications of SEPAL are displayed.

In the app dashboard, type “se.plan” in the search bar. The list of application should be reduce to one single application.

Click on it and wait until the loading is finished. The application will display the about page.

Note

You might need to manually start an instance that is more powerful than the default t1 instance. Refer to Module <../module/index.html>__ section to see how to start instances.

Use the left side drawers to navigate through the application panels.

The next sections will guide you through each step of the se.plan process.

### Select AOI#

The restoration suitability index (hereinafter referred to as index) will be calculated based on the user inputs. The first mandatory input is the Area Of Interest (AOI). In this step you’ll have the possibility to choose from a predefined list of administrative layers or use your own datasets, the available options are:

Predefined layers

• Country/province

Custom layers

• Vector file

• Drawn shapes on map

After selecting the desired area, click over the Select these inputs button and the map shows up your selection. Once you see the confirmation green message, click on the “Questionnaire” panel to move to the next step.

Note

You can only select one area of interest. In some cases, depending on the input data you could run out of resources in GEE.

Warning

As described in the first section of this manual, the layers provided in this application are covering the 139 countries defined as LMIC by the World Bank. If the selected AOI is out of these boundaries, then the provided layers cannot be used to compute the index. A warning message will remind the user that every used layer will thus need to be replaced by a custom one that will conver the missing area.

### Questionnaire#

The questionnaire is split in 2 steps, the constraints that will narrow the spatial extend of the computation and the indicators that will allow the user to customize the priorities of its restoration analysis.

#### Select constraints#

Warning

This panel cannot be used prior to select an AOI

se.plan allows users to set constraints limiting restoration to only those sites they view as suitable, in view of ecological and socioeconomic risks. It groups the constraints into four categories:

• Biophysical (5 constraints): elevation, slope, annual rainfall, baseline water stress, terrestrial ecoregion

• Current land cover (8 constraints): Shrubs, Herbaceous vegetation, Cultivated and managed vegetation/agriculture, Urban / built up, Bare / sparse vegetation, Snow and ice, Herbaceous wetland, Moss and lichen

• Forest change (3 constraints): deforestation rate, climate risk, natural regeneration variability

• Socio-economic constraints (6 constraints): protected areas, population density, declining population, property rights protection, accessibility to cities

These categories are displayed to the user in expandable panels. Simply click on it to open its panel and select the appropriate constraint name in the dropdown menu labeled “criteria”. The constraints customization will appear underneath.

Some constraints are numerical or categorical, for which se.plan enables the user to adjust the values that will be masked from the analysis.

Tip

The values provided in the slider are computed on the fly over your AOI preventing the user from selecting a filter that would remove all pixels in your Area.

Other constraints are binary variables, with a value of 1 if a site has the characteristic associated with the variable and 0 if it does not. On the application it displays as a switch. For these constraints, users can choose if they want to keep zeros (switch off) or ones (switch on)..

Once the selection is finished, the selected constraints will be displayed as small chips in the expandable panel title, allowing the user to see all the selected constraints at a glance.

Every selected constraints is corresponding to a layer provided by se.plan listed in appendix_f. These layers can be customized in this panel to use national data or to provide information on areas that are not covered by the tool default layers. You do not need to add constraints if there isn’t any. In this case, default values will be used and you can simply proceed to the next steps.

Note

To use a customized dataset, it need to be uploaded as a ee.Image in Google Earth Engine.

Click on the pencil on the left side of the layer name and a popup will rise on the screen. It includes multiple information:

• The layer name as it can be found in GEE

• The unit of the provided layer

• A map displaying the layer over the AOI using a linear viridis color scale (the legend is in the bottom left corner)

The user can change the layer to any other image from GEE. The map will update automatically to display this new layer and change the legend. If the provided layer uses another unit please change it. This unit will be used in the final report of se.plan.

Warning

The user needs to have access to the provided custom layer to use it. if the asset cannot be accessed the application will fallback to the default one.

Once the modifications are finished click on save to apply the changes to the layer. If the constraint is non binary, the slider values will be updated to the customized dataset.

Warning

Don’t forget to change the slider values after a layer customization. If your layer uses a different unit, all the pixels might be included in your filtering parameters.

#### Select Indicators#

Users rate the relative importance of benefits from their standpoint (or the standpoint of stakeholders they represent), and se.plan then calculates an index that indicates each grid cell’s relative restoration value aggregated across all four benefit categories. To rate each indicator, the user simply ticks the corresponding checkbox.

Warning

This step is mandatory if you would like to perform an analysis. If every indicator is set to low (0), then the final output will be 0 everywhere.

Tip

Using the pencil icon next to the indicator name, the user can customize the layer used by se.plan to compute its index. The editing popup panel is the same as the one presented in the previous section.

#### Select costs#

User can customize the layers that will be used as costs in the weighted sum approach. To change it the user will go to the third tab of the questionnaire panel (“COSTS”) and click on the to open the modification dialog interface. The editing popup panel is the same as the one presented in the previous section.

### Recipe#

Next go to the Recipe panel. Recipe is the base information use by se.plan to compute the restoration suitability index. It’s a .json serialized version of all the inputs the user provided in the previous steps. It can be shared and reused by other users. You need to validate your recipe before proceed to the results. By clicking the “Save your recipe” button, all your customization in previous steps are recorded and validated.

#### Validate recipe#

Warning

The AOI and Questionnaire steps need to be completed to validate the recipe.

First the user should provide a name for its recipe. By default se.plan will use the current date but this can be specified to anything else.

Note

If unauthorized folder characters (", , :code:/, :code: ) are used they will be automatically replaced by :code:_.

Once all the required inputs are provided, the user can validate the recipe by clicking on the validate recipe button.

A .json file will be created in the module_result/restoration_planning_module/ directory of your SEPAL workspace and a sum-up of your inputs wil be displayed in expandable panels.

In the benefits section of the expandable panels, the user will find the list of indicators sets in the questionnaire with the selected wheights. If they are not matching its restoration priorities, they can still be modified in the questionnaire section.

Note

Don’t forget to validate again the recipe every time a change is made in the prior sections (AOI selector and/or Quetionnaire).

In the Constraints section of the expandable panels, the user will find the complete list of available constraints in the tool. The activated one will be displayed in blue. The red one will be ignored in the computation of the restoration suitability index.

#### Use existing recipe#

Tip

The recipe is a simple .json file. it’s meant to be shared and reused. To to so simply use the file selector of the recipe panel and select a recipe from your SEPAL workspace folder.

Note

• Only the .json files will be available.

• If you’ve just uploaded the file, hit the reload button to reload the file list of the menu.

Tip

By default the file selector is pointing where se.plan is saving recipes and results. If the user wants to access the rest of its SEPAL workspace, simply click on the parent link in the popup menu (on top of the list).

Once the user will click on apply the selected recipe, se.plan will reload the AOI specified in the recipe and changed all the questionnaire answers according to the loaded recipe. It’s then automatically validated.

### Result map#

Warning

the recipe needs to be validated

Once the recipe is validated, the compute the restoration map button is released and the restoration suitability index can be computed. Click the button to view the results map.

The map will be centered on the selected AOI and the value of the index will be displayed from 1 to 5 using a color blind friendly color ramp, red being “not suitable” and blue “very suitable”.

Note

The map can be downloaded as an asset to GEE or as a .tif file. Click on the button on the top left corner and follow the exportation instructions.

### Compute dashboard#

The compute dashboard button is initially deactivated, and will be activated after the results map correctly returned. Click on this button to view the dashboard where results will be displayed (see next section “Restoration dashboard”). The dashboard is a report of all the restoration information gathered by se.plan during the computation. It is run from the map and displayed in the “dasboard” page.

#### Select sub-AOI#

The Results from se.plan are given for the initial AOI. users can also provide sub-AOIs to the tool to provide extra information on smaller areas. The sub-area are not mandatory to compute the dashboard.

Important

Using sub-AOI is the only way to compare results for different zones as the normalization have been performed on the full extend of the initial AOI.

The sub-AOIs can be selected using a shapefile. The sub-AOIs names will be the one set in the selected property.

They can also be directly drawn on the map. There are three buttons under the cloud icon where you can choose to draw a polygon, a rectangle or a circle. Click any of them based on your needs. Each time a new geometry is drawned, a popup dialogue will ask the user to name it. This name will be used in the final report. You will need to click the compute dashboard button again to include all the sub-AOIs in the report.

Note

The user still have the possiblity to remove some geometry by clicking on the button on the map but editing is not possible.

Danger

Once the dashboard have been computed, sub-AOIs will be validated (a different color for each one of them) and it will be impossible to remove them. New geometries can still be added.

#### Restoration dashboard#

After clicking on compute dashboard button, The report generated from the previous step is displayed in this panel.

Warning

This action can take time as GEE needs to export and reduce information on the full extend of the user’s initial AOI. Wait until the button stop spinning before changing page.

Th dasboard has 2 sections:

1. Summary of restoration suitability by region

2. Area of interest - summary by subthemes

In the first one, the restoration suitability index is given as proportion of the AOI and the sub-AOIs. ISO3 codes rather than country names are used. Click on the details panel to get the surfaces of each restoration value in MHa.

The names use for AOIs are the one selected in the map.

In the second section, the summary is given by subtheme:

Benefits

The mean value of each benefits is displayed in a bar chart. These charts use the unit corresponding to each layer and display the value for each sub-AOI. Value will be using the SI prefixes if the value is not readable in the original unit. The main AOI is first displayed in gold and the sub-AOIs are displayed using the color attributed when the dashboard was computed (i.e. the same as the one used on the map).

Costs

The sum of each cost over the AOI is displayed in bar charts in the same fashion as the benefits.

Tip

If the surface difference between the main AOI and sub-AOIs is important as in this example, the summed value will also be vastly different.

Constraints

The constraints are displayed in percentages. Each value represents the percentage of surface affected by the filter applied by this constraint over the AOI. each color represent an AOI: gold for the main AOI and the automatically attributed colors of the sub-AOIs.

Note

THe dashboard is also exported in .csv format to be easily interpreted in any spreadsheet software. It is stored at the same place as the recipe in module_results/se.plan/.

## Primary data sources#

The se.plan team obtained data for the default spatial layers in the tool from various sources. It determined potential tree cover using data from:

J.F. Bastin, Y. Finegold, C. Garcia, et al., 2019, The global tree restoration potential, Science 365(6448), pp. 76–79, doi:10.1126/science.aax084

It determined current tree cover using data from:

M. Buchhorn, M. Lesiv, N.E. Tsendbazar, M. Herold, L. Bertels, B. Smets, 2020, Copernicus Global Land Cover Layers—Collection 2. Remote Sensing, 12 Volume 108, 1044. doi:10.3390/rs12061044

It drew data for remaining spatial layers primarily from the following sources. For additional detail, see appendix_c (benefits), appendix_e (costs), and appendix_f (constraints).

### Costs#

Spatial layer

Data sources

Land opportunity cost

International Food Policy Research Institute, 2019, Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V4

UN FAO, 2020, FAOSTAT: Crops, http://www.fao.org/faostat/en/#data/QC

UN FAO, 2007, Occurrence of Pasture and Browse (FGGD), https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/913e79a0-7591-11db-b9b2-000d939bc5d8

UN FAO, 2018, Gridded Livestock of the World – Latest – 2010 (GLW 3), https://dataverse.harvard.edu/dataverse/glw_3, Harvard Dataverse, V3

UN FAO, 2020, FAOSTAT: Livestock Primary, http://www.fao.org/faostat/en/#data/QL

UN FAO, 2020, RuLIS - Rural Livelihoods Information System, http://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en/

World Bank, 2020, World Development Indicators, https://databank.worldbank.org/source/world-development-indicators

CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW

M. Kummu, M. Taka, & J. Guillaume, 2018, Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015, Scientific Data 5, 180004, https://doi.org/10.1038/sdata.2018.4

Establishment cost

World Bank, various years, Projects & Operations [project appraisal documents and implementation completion reports for selected projects], https://projects.worldbank.org/en/projects-operations/projects-home

### Benefits#

Spatial layer

subtheme

Data sources

Biodiversity intactness index

Biodiversity conservation

T. Newbold, L. Hudson, A. Arnell, et al., 2016, Dataset: Global map of the Biodiversity Intactness Index, from Newbold et al., 2016, Science, Natural History Museum Data Portal (data.nhm.ac.uk), https://doi.org/10.5519/0009936

Endangered species

Biodiversity conservation

Layer obtained from World Bank, which processed species range maps from: (i) IUCN, The IUCN Red List of Threatened Species, https://www.iucnredlist.org; and (ii) BirdLife International, Data Zone, http://datazone.birdlife.org/species/requestdis

Aboveground carbon accumulation

Carbon sequestration

S.C. Cook-Patton, S.M. Leavitt, D. Gibbs, et al., 2020, Mapping carbon accumulation potential from global natural forest regrowth, Nature 585, pp. 545–550, https://doi.org/10.1038/s41586-020-2686-x

Forest employment

Local livelihoods

Downscaled estimates generated using national data from: International Labour Organization, 2020, Employment by sex and economic activity - ISIC level 2 (thousands) | Annual, ILOSTAT database, https://ilostat.ilo.org/data

Woodfuel harvest

Local livelihoods

Downscaled estimates generated using national data from: UN FAO, 2020, Forestry Production and Trade, FAOSTAT, http://www.fao.org/faostat/en/#data/FO

Plantation growth rate

Wood production

F. Albanito, T. Beringer, R. Corstanje, et al., 2016, Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment, GCB Bioenergy 8, pp. 81–95, https://doi.org/10.1111/gcbb.12242

### Constraints#

#### biophysical#

Spatial layer

Data sources

Annual rainfall

Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.68d2bb3

Baseline water stress

World Resources Institute, 2021, Aqueduct Global Maps 3.0 Data, https://www.wri.org/data/aqueduct-global-maps-30-data

Elevation

T.G. Farr, P.A. Rosen, E. Caro, et al., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183.

Slope

T.G. Farr, P.A. Rosen, E. Caro, et al., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183.

Terrestrial ecoregion

UN FAO, 2012 Global ecological zones for fao forest reporting: 2010 Update, http://www.fao.org/3/ap861e/ap861e.pdf

#### forest change#

Spatial layer

Data sources

Climate risk

J.F. Bastin, Y. Finegold, C. Garcia, et al., 2019, The global tree restoration potential, Science 365(6448), pp. 76–79, DOI: 10.1126/science.aax0848; data downloaded from: https://www.research-collection.ethz.ch/handle/20.500.11850/350258

Deforestation rate

Natural regeneration variability

Model from R. Crouzeilles, F.S. Barros, P.G. Molin, et al., 2019, A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes, J Appl Ecol. 56, pp. 2675– 2686, https://doi.org/10.1111/1365-2664.13501, applied to data from: ESA, 2017, Land Cover CCI Product User Guide, Version 2, maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

#### socio-economic#

Spatial layer

Data sources

Accessibility to cities

D.J. Weiss, A. Nelson, H.S. Gibson, et al., 2018, A global map of travel time to cities to assess inequalities in accessibility in 2015, Nature, doi:10.1038/nature25181; data downloaded from: https://malariaatlas.org/research-project/accessibility-to-cities/

A. Damodaran, 2020, Damodaran Online, http://pages.stern.nyu.edu/~adamodar/

Current land cover

Declining population

CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW

Governance index

World Bank, 2020, Worldwide Governance Indicators, https://info.worldbank.org/governance/wgi/

Land designated for or owned by IP and LC

Rights and Resources Initiative, 2015, Who Owns the World’s Land? A global baseline of formally recognized indigenous and community land rights, Washington, DC

Net imports of forest products

UN FAO, 2020, Forestry Production and Trade, FAOSTAT, http://www.fao.org/faostat/en/#data/FO

Population density

CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW

Perceived property security

Prindex, 2020, https://www.prindex.net/

Property rights protection

Downscaled estimates generated using national data from: World Bank, 2020, Worldwide Governance Indicators, https://info.worldbank.org/governance/wgi/

Protected area

IUCN, World Database on Protected Areas, https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas

Real interest rate

World Bank, 2020, World Development Indicators, https://databank.worldbank.org/source/world-development-indicators

## Countries#

Countries and territories in se.plan, by World Bank region.

### East Asia & Pacific#

Country

Official name

ISO3

ISO2

UNI

UNDP

FAOSTAT

GAUL

Cambodia

the Kingdom of Cambodia

KHM

KH

116

KHM

115

44

China

the People’s Republic of China

CHN

CN

156

CHN

41

147295

Cook Islands

the Cook Islands

COK

CK

184

COK

47

60

Democratic People’s Republic of Korea

the Democratic People’s Republic of Korea

PRK

KP

408

PRK

116

67

Fiji

the Republic of Fiji

FJI

FJ

242

FJI

66

83

Indonesia

the Republic of Indonesia

IDN

ID

360

IDN

101

116

Kiribati

the Republic of Kiribati

KIR

KI

296

KIR

83

135

Lao PDR

the Lao People’s Democratic Republic

LAO

LA

418

LAO

120

139

Malaysia

Malaysia

MYS

MY

458

MYS

131

153

Marshall Islands

the Republic of the Marshall Islands

MHL

MH

584

MHL

127

157

Micronesia

the Federated States of Micronesia

FSM

FM

583

FSM

145

163

Mongolia

Mongolia

MNG

MN

496

MNG

141

167

Myanmar

the Republic of the Union of Myanmar

MMR

MM

104

MMR

28

171

Nauru

the Republic of Nauru

NRU

NR

520

NRU

148

173

Palau

the Republic of Palau

PLW

PW

585

PLW

180

189

Papua New Guinea

Independent State of Papua New Guinea

PNG

PG

598

PNG

168

192

Philippines

the Republic of the Philippines

PHL

PH

608

PHL

171

196

Samoa

the Independent State of Samoa

WSM

WS

882

WSM

244

212

Solomon Islands

Solomon Islands

SLB

SB

90

SLB

25

225

Thailand

the Kingdom of Thailand

THA

TH

764

THA

216

240

Timor-Leste

the Democratic Republic of Timor-Leste

TLS

TL

626

TLS

176

242

Tokelau

Tokelau

TKL

TK

772

TKL

218

244

Tonga

the Kingdom of Tonga

TON

TO

776

TON

219

245

Tuvalu

Tuvalu

TUV

TV

798

TUV

227

252

Vanuatu

the Republic of Vanuatu

VUT

VU

548

VUT

155

262

Viet Nam

the Socialist Republic of Viet Nam

VNM

VN

704

VNM

237

264

### Central Asia#

Country

Official name

ISO3

ISO2

UNI

UNDP

FAOSTAT

GAUL

Armenia

the Republic of Armenia

ARM

AM

51

ARM

1

13

Azerbaijan

the Republic of Azerbaijan

AZE

AZ

31

AZE

52

19

Georgia

Georgia

GEO

GE

268

GEO

73

92

Kazakhstan

the Republic of Kazakhstan

KAZ

KZ

398

KAZ

108

132

Kyrgyzstan

the Kyrgyz Republic

KGZ

KG

417

KGZ

113

138

Tajikistan

the Republic of Tajikistan

TJK

TJ

762

TJK

208

239

Turkey

the Republic of Turkey

TUR

TR

792

TUR

223

249

Turkmenistan

Turkmenistan

TKM

TM

795

TKM

213

250

Uzbekistan

the Republic of Uzbekistan

UZB

UZ

860

UZB

235

261

### Latin America & Caribbean#

Country

Official name

ISO3

ISO2

UNI

UNDP

FAOSTAT

GAUL

Antigua and Barbuda

Antigua and Barbuda

ATG

AG

28

ATG

8

11

Argentina

the Argentine Republic

ARG

AR

32

ARG

9

12

BRB

BB

52

BRB

14

24

Belize

Belize

BLZ

BZ

84

BLZ

23

28

Bolivia

the Plurinational State of Bolivia

BOL

BO

68

BOL

19

33

Brazil

the Federative Republic of Brazil

BRA

BR

76

BRA

21

37

Chile

the Republic of Chile

CHL

CL

152

CHL

40

51

Colombia

the Republic of Colombia

COL

CO

170

COL

44

57

Costa Rica

the Republic of Costa Rica

CRI

CR

188

CRI

48

61

Cuba

the Republic of Cuba

CUB

CU

192

CUB

49

63

Dominica

the Commonwealth of Dominica

DMA

DM

212

DMA

55

71

Dominican Republic

the Dominican Republic

DOM

DO

214

DOM

56

72

ECU

EC

218

ECU

58

73

SLV

SV

222

SLV

60

75

French Guiana

GUF

86

GRD

GD

308

GRD

86

99

Guatemala

the Republic of Guatemala

GTM

GT

320

GTM

89

103

Guyana

the Co-operative Republic of Guyana

GUY

GY

328

GUY

91

107

Haiti

the Republic of Haiti

HTI

HT

332

HTI

93

108

Honduras

the Republic of Honduras

HND

HN

340

HND

95

111

Jamaica

Jamaica

JAM

JM

388

JAM

109

123

Mexico

the United Mexican States

MEX

MX

484

MEX

138

162

Nicaragua

the Republic of Nicaragua

NIC

NI

558

NIC

157

180

Panama

the Republic of Panama

PAN

PA

591

PAN

166

191

Paraguay

the Republic of Paraguay

PRY

PY

600

PRY

169

194

Peru

the Republic of Peru

PER

PE

604

PER

170

195

Saint Kitts and Nevis

Saint Kitts and Nevis

KNA

KN

659

KNA

188

208

Saint Lucia

Saint Lucia

LCA

LC

662

LCA

189

209

VCT

VC

670

VCT

191

211

Suriname

the Republic of Suriname

SUR

SR

740

SUR

207

233

the Republic of Trinidad and Tobago

TTO

TT

780

TTO

220

246

Uruguay

the Eastern Republic of Uruguay

URY

UY

858

URY

234

260

Venezuela

the Bolivarian Republic of Venezuela

VEN

VE

862

VEN

236

263

### Middle East & North Africa#

Country

Official name

ISO3

ISO2

UNI

UNDP

FAOSTAT

GAUL

Algeria

the People’s Democratic Republic of Algeria

DZA

DZ

12

DZA

4

4

Djibouti

the Republic of Djibouti

DJI

DJ

262

DJI

72

70

Egypt

the Arab Republic of Egypt

EGY

EG

818

EGY

59

40765

Iran

the Islamic Republic of Iran

IRN

IR

364

IRN

102

117

Iraq

the Republic of Iraq

IRQ

IQ

368

IRQ

103

118

Jordan

the Hashemite Kingdom of Jordan

JOR

JO

400

JOR

112

130

Lebanon

the Lebanese Republic

LBN

LB

422

LBN

121

141

Libya

State of Libya

LBY

LY

434

LBY

124

145

Morocco

the Kingdom of Morocco

MAR

MA

504

MAR

143

169

Oman

the Sultanate of Oman

OMN

OM

512

OMN

221

187

Palestine

[Often called West Bank and Gaza]

PSE

267

Syria

the Syrian Arab Republic

SYR

SY

760

SYR

212

238

Tunisia

the Republic of Tunisia

TUN

TN

788

TUN

222

248

Western Sahara

ESH

268

Yemen

the Republic of Yemen

YEM

YE

887

YEM

249

269

### South Asia#

Country

Official name

ISO3

ISO2

UNI

UNDP

FAOSTAT

GAUL

Afghanistan

the Islamic Republic of Afghanistan

AFG

AF

4

AFG

2

1

BGD

BD

50

BGD

16

23

Bhutan

the Kingdom of Bhutan

BTN

BT

64

BTN

18

31

India

the Republic of India

IND

IN

356

IND

100

115

Maldives

the Republic of Maldives

MDV

MV

462

MDV

132

154

Nepal

the Federal Democratic Republic of Nepal

NPL

NP

524

NPL

149

175

Pakistan

the Islamic Republic of Pakistan

PAK

PK

586

PAK

165

188

Sri Lanka

the Democratic Socialist Republic of Sri Lanka

LKA

LK

144

LKA

38

231

### Sub-Saharan Africa#

Country

Official name

ISO3

ISO2

UNI

UNDP

FAOSTAT

GAUL

Angola

the Republic of Angola

AGO

AO

24

AGO

7

8

Benin

the Republic of Benin

BEN

BJ

204

BEN

53

29

Botswana

the Republic of Botswana

BWA

BW

72

BWA

20

35

Burkina Faso

Burkina Faso

BFA

BF

854

BFA

233

42

Burundi

the Republic of Burundi

BDI

BI

108

BDI

29

43

Cabo Verde

Republic of Cabo Verde

CPV

CV

132

CPV

35

47

Cameroon

the Republic of Cameroon

CMR

CM

120

CMR

32

45

Central African Republic

the Central African Republic

CAF

CF

140

CAF

37

49

TCD

TD

148

TCD

39

50

Comoros

the Union of the Comoros

COM

KM

174

COM

45

58

Congo

the Republic of the Congo

COG

CG

178

COG

46

59

Côte d’Ivoire

the Republic of Côte d’Ivoire

CIV

CI

384

CIV

107

66

Democratic Republic of the Congo

the Democratic Republic of the Congo

COD

CD

180

COD

250

68

Equatorial Guinea

the Republic of Equatorial Guinea

GNQ

GQ

226

GNQ

61

76

Eritrea

the State of Eritrea

ERI

ER

232

ERI

178

77

Eswatini

the Kingdom of Eswatini

SWZ

SZ

748

SWZ

209

235

Ethiopia

the Federal Democratic Republic of Ethiopia

ETH

ET

231

ETH

238

79

Gabon

the Gabonese Republic

GAB

GA

266

GAB

74

89

Gambia

the Republic of the Gambia

GMB

GM

270

GMB

75

90

Ghana

the Republic of Ghana

GHA

GH

288

GHA

81

94

Guinea

the Republic of Guinea

GIN

GN

324

GIN

90

106

Guinea-Bissau

the Republic of Guinea-Bissau

GNB

GW

624

GNB

175

105

Kenya

the Republic of Kenya

KEN

KE

404

KEN

114

133

Lesotho

the Kingdom of Lesotho

LSO

LS

426

LSO

122

142

Liberia

the Republic of Liberia

LBR

LR

430

LBR

123

144

MDG

MG

450

MDG

129

150

Malawi

the Republic of Malawi

MWI

MW

454

MWI

130

152

Mali

the Republic of Mali

MLI

ML

466

MLI

133

155

Mauritania

the Islamic Republic of Mauritania

MRT

MR

478

MRT

136

159

Mauritius

the Republic of Mauritius

MUS

MU

480

MUS

137

160

Mozambique

the Republic of Mozambique

MOZ

MZ

508

MOZ

144

170

Namibia

the Republic of Namibia

NAM

NA

516

NAM

147

172

Niger

the Republic of the Niger

NER

NE

562

NER

158

181

Nigeria

the Federal Republic of Nigeria

NGA

NG

566

NGA

159

182

Rwanda

the Republic of Rwanda

RWA

RW

646

RWA

184

205

Sao Tome and Principe

the Democratic Republic of Sao Tome and Principe

STP

ST

678

STP

193

214

Senegal

the Republic of Senegal

SEN

SN

686

SEN

195

217

Seychelles

the Republic of Seychelles

SYC

SC

690

SYC

196

220

Sierra Leone

the Republic of Sierra Leone

SLE

SL

694

SLE

197

221

Somalia

the Federal Republic of Somalia

SOM

SO

706

SOM

201

226

South Africa

the Republic of South Africa

ZAF

ZA

710

ZAF

202

227

South Sudan

the Republic of South Sudan

SSD

SS

728

SSD

277

74

Sudan

the Republic of the Sudan

SDN

SD

736

SDN

276

6

Tanzania

the United Republic of Tanzania

TZA

TZ

834

TZA

215

257

Togo

the Togolese Republic

TGO

TG

768

TGO

217

243

Uganda

the Republic of Uganda

UGA

UG

800

UGA

226

253

Zambia

the Republic of Zambia

ZMB

ZM

894

ZMB

251

270

Zimbabwe

the Republic of Zimbabwe

ZWE

ZW

716

ZWE

181

271

## Benefits data layers#

Note

Every data layer presented in the following document can be displayed in Google Earth Engine as an overview of our datasets. Click on the provided link in the description, you’ll be redirected to the GEE code editor panel. The selected layer will be displayed over Uganda. To modify the country change the fao_gaul variable line 7 by your country number (listed in the Country list section in the rightmost column). If you want to export this layer, please set the value of to_export (line 10) and to_drive (line 13) according to your need. Hit the run button again to relaunch the computation. Code used for this display can be found here.

In its current form, se.plan provides information on four categories of potential benefits of forest restoration:

• Biodiversity conservation

• Carbon sequestration

• Local livelihoods

• Wood production

se.plan does not predict the levels of benefits that will occur if forests are restored. Instead, it uses data on benefit-related site characteristics to quantify the potential of a site to provide benefits if it is restored. To clarify this distinction, consider the case of species extinctions. A predictive tool might, for example, estimate the number of extinctions avoided if restoration occurs. To do so, it would need to account for restoration scale and interdependencies across sites associated with distances and corridors between restored sites. se.plan instead takes a simpler approach: it includes information on the total number of critically endangered and endangered amphibians, reptiles, birds, and mammals at each site. Sites with a larger number of critically endangered and endangered species are ones where the potential number of avoided extinctions is greater. Realizing the benefit of reduced extinctions depends on factors beyond simply restoring an individual site, including the type of forest that is restored (native tree species or introduced tree species, single tree species or multiple tree species, etc.) and the pattern of restoration in the rest of the landscape. Interpreting se.plan output in the context of additional, location-specific information available to a user is therefore important.

Quantitative measures of potential benefits in se.plan should be viewed as averages for a grid cell. Potential benefits could be higher at some locations within a given grid cell and lower at others.

Variable

Description

Source

Endangered species (Biodiversity conservation) in count

Total number of critically endangered and endangered amphibians, reptiles, birds, and mammals whose ranges overlap a site. Rationale for including in se.plan: sites with a larger number of critically endangered and endangered species are ones where successful forest restoration can potentially contribute to reducing a larger number of extinctions. (view in gee)

World Bank, which processed over 25,000 species range maps from: (i) IUCN, The IUCN Red List of Threatened Species, https://www.iucnredlist.org; and (ii) BirdLife International, Data Zone, http://datazone.birdlife.org/species/requestdis. Resolution of World Bank layer: 1 kilometer. More information may be found at https://datacatalog.worldbank.org/dataset/terrestrial-biodiversity-indicators, and data may be downloaded at http://wbg-terre-biodiv.s3.amazonaws.com/listing.html. See also: (i) Dasgupta, Susmita; Wheeler, David. 2016. Minimizing Ecological Damage from Road Improvement in Tropical Forests. Policy Research Working Paper: No. 7826. World Bank, Washington, DC. (ii) Danyo Stephen, Susmita Dasgupta and David Wheeler. 2018. Potential Forest Loss and Biodiversity Risks from Road Improvement in Lao PDR. World Bank Policy Research Working Paper 8569. World Bank, Washington, DC. (iii) Damania Richard, Jason Russ, David Wheeler and Alvaro Federico Barra. 2018. The Road to Growth: Measuring the Tradeoffs between Economic Growth and Ecological Destruction, World Development, Elsevier, vol. 101(C), pp. 351-376.

BII gap (Biodiversity conservation) in percent

The biodiversity intactness index (BII) describes the average abundance of a large and diverse set of organisms in a given geographical area, relative to the set of originally present species. se.plan subtracts the BII from 100, to measure the gap between full intactness and current intactness. Rationale for including in se.plan: sites with a larger BII gap are ones where successful forest restoration can potentially contribute to reducing a larger gap. (view in gee)

T. Newbold, L. Hudson, A. Arnell, et al., 2016, Dataset: Global map of the Biodiversity Intactness Index, from Newbold et al., 2016, Science, Natural History Museum Data Portal (data.nhm.ac.uk), https://doi.org/10.5519/0009936. Resolution of Newbold et al. layer: 1 km. See also: (i) Scholes, R.J. and Biggs, R., 2005. A biodiversity intactness index. Nature, 434(7029), pp.45-49. (ii) Newbold, T., Hudson, L.N., Arnell, A.P., Contu, S., De Palma, A., Ferrier, S., Hill, S.L., Hoskins, A.J., Lysenko, I., Phillips, H.R. and Burton, V.J., 2016. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science, 353(6296), pp.288-291.

Aboveground carbon accumulation (Carbon sequestration) in metric tons C/hectare/year

Projected potential mean annual aboveground carbon accumulation rates for natural forest regeneration during 2020-2050. Accounts for variation in such factors as climate and soil. Rationale for including in se.plan: climate mitigation benefits of forest restoration are greater where forests regenerate more rapidly. Although the layer refers to natural regeneration, it might also reflect relative spatial differences in aboveground carbon sequestration in planted forests, given that climate and soil also affect growth of those forests. Can also be viewed as complementing the plantation growth rate layer (see below). (view in gee)

S.C. Cook-Patton, S.M. Leavitt, D. Gibbs, et al., 2020, Mapping carbon accumulation potential from global natural forest regrowth, Nature 585(7826), pp. 545–550, https://doi.org/10.1038/s41586-020-2686-x. Resolution of Cook-Patton et al. layer: 1 km.

Forest employment (Local livelihoods) in count

Number of forest-related jobs per ha of forest in 2015, summed across three economic activities: forestry, logging, and related service activities; manufacture of wood and of products of wood and cork, except furniture; and manufacture of paper and paper products. Varies by country and, when data are sufficient for downscaling, first-level administrative subdivision (e.g., state or province). Rationale for including in se.plan: a higher level of forest employment implies the existence of attractive business conditions for labor-intensive wood harvesting and processing industries, which tends to make forest restoration more feasible when income for local households is a desired benefit. (view in gee)

Developed by se.plan team, by downscaling national data from: International Labour Organization, 2020, Employment by sex and economic activity - ISIC level 2 (thousands) | Annual, ILOSTAT database, https://ilostat.ilo.org/data

Woodfuel harvest (Local livelihoods) in m3/hectare

Harvest of wood fuel per hectare of forest in 2015. Rationale for including in se.plan: a higher level of wood fuel harvest implies greater demand for wood fuel as an energy source, which tends to make forest restoration more feasible when supply of wood to meet local demands is a desired benefit. (view in gee)

Developed by se.plan team, by downscaling national data from: UN FAO, 2020, Forestry Production and Trade, FAOSTAT, http://www.fao.org/faostat/en/#data/FO

Plantation growth rate (Wood production) in dry metric tons of woody biomass/hectare/year

Potential annual production of woody biomass by fast-growing trees such as eucalypts, poplars, and willows. Rationale for including in se.plan: faster growth of plantation trees tends to make forest restoration more feasible when desired benefits include income for landholders and wood supply to meet local and export demands. (view in gee)

F. Albanito, T. Beringer, R. Corstanje, et al., 2016, Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment, GCB Bioenergy 8, pp. 81–95, https://doi.org/10.1111/gcbb.12242. Resolution of Albanito et al. layer: 55 km.

## Benefit-cost ratio#

In its current form, se.plan includes numerical estimates of four categories of potential restoration benefits for each potential restoration site:

• Biodiversity conservation

• Carbon sequestration

• Local livelihoods

• Wood production.

Denote these benefits, respectively, by $$B_1$$, $$B_2$$, $$B_3$$, and $$B_4$$. The data on which the benefit estimates are based have different units. To enable the benefit estimates to be compared to each other, se.plan converts them to the same, relative scale, which ranges from 1 (low) to 5 (high). se.plan includes two indicators each for $$B_1$$ and $$B_3$$ and a single indicator for $$B_2$$ and $$B_4$$. We return to this difference in number of indicators below.

se.plan users rate the relative importance of each benefit on a scale of 1 (low) to 5 (high). se.plan treats these ratings as weights and calculates a restoration value index for each site by the weighted-average formula:

$Restoration\_value\_index = (w_1B_1 + w_2B_2 + w_3B_3 + w_4B_4.) / (w_1 + w_2 + w_3 + w_4)$

Where $$w_1$$, $$w_2$$, $$w_3$$, and $$w_4$$ are the user ratings for the four corresponding benefits.

se.plan also includes numerical estimates of restoration cost, defined as the sum of opportunity cost and implementation cost in 2017 US dollars per hectare, for each potential restoration site. se.plan calculates an approximate benefit-cost ratio by dividing the restoration value index by the estimate of restoration cost:

$Benefit\_cost\_ratio = Restoration\_value\_index / Restoration\_cost.$

The benefit-cost ratio in se.plan is approximate in several ways. In particular, se.plan does not value potential restoration benefits in monetary terms, and it does not calculate the discounted sum of benefits over a multi-year time period that extends into the future. Its cost estimates account for the future to a greater degree, however; see appendix_e. As a final step, se.plan converts the benefit-cost ratio across all sites in the user’s area of interest to a scale from 1 (low) to 5 (high). It reports this value as the restoration suitability index on the map and dashboard.

As noted above, se.plan includes two indicators for benefits $$B_1$$ (biodiversity conservation) and $$B_3$$ (local livelihoods). For $$B_1$$, the two indicators are the biodiversity intactness index and number of endangered species. Denote these two indicators by $$B_1a$$ and $$B_1b$$. se.plan converts each of these indicators to a 1-5 scale and then calculates the overall biodiversity benefit, $$B_1$$, as their simple average:

$B_1 = (B_1a + B_1b) / 2$

se.plan calculates the overall local livelihoods benefit in the same way from its two constituent indicators, forest employment and woodfuel harvest.

## Cost data layers#

In the cases of benefits (appendix_c) and constraints (appendix_f), the se.plan team adopted the tool’s data layers primarily from existing sources, with little or no modification of the original layers. In contrast, it developed wholly new data layers for both the opportunity cost and the implementation cost of forest restoration. Developing these layers involved multiple steps, which are described below.

Note

Every data layer presented in the following document can be displayed in Google Earth Engine as an overview of our datasets. Click on the provided link in the description, you’ll be redirected to the GEE code editor panel. The selected layer will be displayed over Uganda. To modify the country change the fao_gaul variable line 7 by your country number (listed in the Country list section). If you want to export this layer, please set the value of to_export (line 10) and to_drive (line 13) according to your need. Hit the run button again to relaunch the computation. Code used for this display can be found here.

### Opportunity cost#

pportunity cost in se.plan refers to the value of land if it is not restored to forest: i.e., the value of land in its current use. A higher opportunity cost tends to make restoration less feasible, although restoration can nevertheless be feasible on land with a high opportunity cost if it generates sufficiently large benefits. se.plan assumes that the alternative land use would be some form of agriculture, either cropland or pastureland. It sets the opportunity cost of potential restoration sites equal to the value of cropland for all sites where crops can be grown, with the opportunity cost for any remaining sites set equal to the value of pastureland.

The value of land in agricultural use is defined as the portion of agricultural profit that is attributable to land as a production input. Economists label this portion “land rent”. Agricultural profit is the difference between the gross revenue a farmer receives from selling agricultural products (= product price × quantity sold) and the expenditures the farmer makes on variable inputs, such as seeds and fertilizer, used in production. It is the return earned by fixed inputs, which include labor and capital (e.g., equipment, structures) in addition to land. These relationships imply that the se.plan team needed to sequentially estimate gross revenue, profit, and land rent.

The se.plan team assumed that forest restoration is intended to be permanent, and so it estimated land rent in perpetuity: the opportunity cost of forgoing agricultural use of a restored site forever, not just for a single year. The estimates of this long-run opportunity cost in se.plan are expressed in US dollars per hectare for reference year 2017.

#### Cropland#

The workflow to develop cropland opportunity cost can be summarized as follows:

1. The se.plan team obtained gridded data on 2010 value of crop production per hectare (i.e., gross revenue per hectare) from the International Food Policy Research Institute’s MapSPAM project (International Food Policy Research Institute, 2019; Yu et al., 2020). The resolution of this layer was 5 arc-minutes (~10 km at the equator).

2. The team updated the MapSPAM data to 2017 using country-specific data on total cereal yield from FAOSTAT (UN FAO, 2020a) and the global producer price index for total cereals, also from FAOSTAT. The MapSPAM data reflect gross revenue from a much wider range of crops than cereals, but cereals are the dominant crops in most countries.

3. The team multiplied the data from step 2 by an estimate of the share of crop revenue that was attributable to land, i.e., the land-rent share. The rent-share estimates differed across countries and, where data permitted, by first-level administrative subdivisions (e.g., states, provinces) within countries. The team developed the rent-share estimates through a two-step procedure:

1. It used 229,859 annual survey observations spanning 2004–2017 from 196,327 unique farm households (UN FAO, 2020c) in 32 low- and middle-income countries (LMICs) to statistically estimate a model that related profit from growing crops to fixed inputs. Table E1 shows the distribution of observations by country in the statistical model, and Table E2 shows the estimation results for the model. The dependent variable in the model was the natural logarithm of profit (lnQuasiRent in the table), and fixed inputs were represented by the natural logarithms of cultivated area (lncultivated) and family labor (lnfamlabor) and a binary (“dummy”) variable that indicated whether the farm was mechanized (dmechuse). The model also included year dummies and fixed effects for regions (countries or first-level subdivisions, depending on the survey), which controlled for unobserved factors that varied across time but not regions (the year dummies) and unobserved factors that varied across regions but not time (the region fixed effects). Post-estimation, the team calculated land rent for each observation by multiplying profit by 0.325, the estimated coefficient on the log cultivated area variable. This procedure assumes that the coefficients on inputs in the log-log profit model can be interpreted as profit shares. This assumption is valid if production has constant returns to scale: i.e., if the coefficients sum to 1, which they approximately do in the model.

2. The team used sampling weights from the surveys to calculate mean values of crop revenue and land rent for each region in the sample. It then calculated the ratio of mean land rent to mean crop revenue—i.e., the land-rent share—for each region, and it statistically related the rent shares to a set of spatial variables, which included the region’s gross domestic product per capita in 2015 (Kummu et al., 2018), its population density in 2015 (CIESIN, 2018), the strength of property rights in it (see discussion of this variable in Appendix F), area shares of terrestrial ecoregions in it (Olson and Dinerstein, 2002), and its classification by World Bank region. Table E3 shows the estimation results for the rent-share model. The team used this model to predict rent shares for the LMICs spanned by se.plan and, where possible, first-level subdivisions within them.

4. The team estimated the value of cropland in perpetuity by dividing the annual land rent estimates from step 3 by 0.07, under the assumption that the financial discount rate is 7%. It based this assumption on the mean value of real interest rates across the LMICs in the tool (World Bank, 2020).

#### Pastureland#

The se.plan team used similar procedures to estimate the value of pastureland. In place of cropland steps 1 and 2, it:

1. Predicted pastureland area in 2015 by first statistically relating pastureland percentage in 2000 (UN FAO, 2007, van Velthuizen et al., 2007) to a set of land-cover variables for 2000 at 300m resolution from the European Space Agency (ESA, 2017), and then using the resulting statistical model and 2015 values of the land-cover variables to predict 2015 pastureland area within each 300m grid cell.

2. Calculated gross revenue from livestock in ~2017 by multiplying gridded data on livestock numbers (buffaloes, cattle, goats, horses, sheep) in 2010 at 10km resolution (UN FAO, 2018) by 2017 estimates of production value per animal, calculated by using country-specific data on stocks of animals and production value of livestock products from FAOSTAT (UN FAO, 2020b). It adjusted the resulting estimates of gross revenue per grid cell to include production only from grazing lands, not from feedlots, by using FAO estimates of national shares of meat production from grazing lands provided by the World Bank.

3. Calculated gross revenue per hectare in ~2017 by dividing gross revenue from step b by pastureland area from step a.

Compared to cropland step 3, household survey data on livestock production on pastureland (UN FAO, 2020c) were too limited to estimate land-rent shares that varied across countries or first-level subdivisions. Instead, the statistical rent-share estimate used in the tool, 6.1% of gross revenue, is identical across all countries and first-level subdivisions.

Step 4 was the same as for cropland.

### Implementation costs#

Implementation costs refer to the expense of activities required to regenerate forests. They include both: (i) initial expenses incurred in the first year of restoration (establishment costs), which are associated with such activities as site preparation, planting, and fencing; and (ii) expenses associated with monitoring, protection, and other activities in years following establishment (operating costs), which are required to enable the regenerated stand to reach the “free to grow” stage. se.plan does not report these two components of implementation costs separately. Instead, it reports the aggregate cost of restoring a site, in 2017 US dollars per hectare, by summing the estimates of opportunity cost and implementation costs. This aggregate cost is the cost variable that it includes in the benefit-cost ratio (Appendix D). The estimates of implementation costs vary by country and, for countries with sufficient data, by first-level subdivision.

As discussed above, se.plan assumes that current land use is some form of agriculture. It therefore also assumes that regeneration requires planting, as sources of propagules for natural regeneration are often not adequate on land that has been cleared for agriculture. se.plan does not ignore natural regeneration as a restoration option, however, as it includes a constraint layer that predicts the variability of natural regeneration success (see appendix_e).

The se.plan team estimated implementation costs in three steps:

1. It extracted data on implementation costs from project appraisal reports and implementation completion reports for 50 World Bank afforestation and reforestation projects spanning 24 LMICs during the past 2-3 decades. Afforestation refers to regeneration of sites where the most recent land use was not forest, e.g., agriculture, while reforestation refers to regeneration of sites that only recently lost their forest cover, e.g., due to harvesting or wildfire. Whenever possible, the team extracted data on operating costs in addition to data on establishment costs, with operating costs typically extending up to 3–5 years after establishment (depending on project and site). It converted all estimates to a per-hectare basis, expressed in constant 2011 US dollars. It classified the estimates by country and, where possible, first-level subdivision.

2. It statistically related the natural logarithm of implementation cost per hectare to a set of variables hypothesized to explain it, including: (i) GDP per capita, also natural log transformed (Kummu et al., 2018); (ii) a dummy variable distinguishing reforestation from afforestation (regeneration of sites where the most recent land use was not forest, e.g., agriculture); (iii) a dummy variable distinguishing natural regeneration from planting; (iv) the total regenerated area (natural log transformed); (v) dummy variables giving the dominant biome in the region (tropical or subtropical, vs. temperate/boreal; (UN FAO, 2013); (vi) a dummy variable indicating whether the project began pre- or post-2010; (vii) a dummy variable that can be interpreted as indicating whether the cost estimate accounted for project overhead costs or not (“UnitArea”); and (viii) a set of dummy variables that indicated projects that included special types of regeneration that did not commonly occur in the dataset, which mainly referred to regeneration of small to large stands of trees on interior sites. Table E4 shows estimation results for the model.

3. The team predicted spatial estimates of implementation costs by region (country or first-level subdivision) by inserting into the model gridded GDP estimates for 2011, the mean of project area in the estimation sample, and the biome variables. All of the other binary variables were set to 0. As a final step, the team converted the predicted implementation costs to constant 2017 US dollars using annual inflation rates between 2012 and 2017.

## Constraints data layers#

se.plan includes various constraints that enable users to restrict restoration to sites that satisfy specific criteria. Many of the constraints can be viewed as indicators of risk, which allows users to avoid sites where the risk of failure, or the risk of undesirable impacts, might be unacceptable. Values of the constraints should be viewed as average values for a site, with some locations within a site likely having higher or lower values. The constraints are grouped into four categories: biophysical, current land cover, forest change, and socio-economic.

Note

Every data layer presented in the following document can be displayed in Google Earth Engine as an overview of our datasets. Click on the provided link in the description, you’ll be redirected to the GEE code editor panel. The selected layer will be displayed over Uganda. To modify the country change the fao_gaul variable line 7 by your country number (listed in the Country list section). If you want to export this layer, please set the value of to_export (line 10) and to_drive (line 13) according to your need. Hit the run` button again to relaunch the computation. Code used for this display can be found here.

### Potential constraint#

Warning

This contraint is hard coded in the tool, the user cannot customize it. It covers the entire world meaning that it will not mask all your analysis if se.plan is run outside of the LMIC.

Variable

Units/measure

Description

Source

Potential for restoration

Binary

Sites that have the potential for restoration. Their tree-cover fraction is less its potential and they are not in urban areas. (view in gee)

Bastin, Jean-François & Finegold, Yelena & Garcia, Claude & Mollicone, Danilo & Rezende, Marcelo & Routh, Devin & Zohner, Constantin & Crowther, Thomas. (2019). The global tree restoration potential. Science. 365. 76-79. https://doi.org/10.1126/science.aax0848. Buchhorn M, Lesiv M, Tsendbazar N-E, Herold M, Bertels L, Smets B. Copernicus Global Land Cover Layers—Collection 2. Remote Sensing. 2020; 12(6):1044. https://doi.org/10.3390/rs12061044

### Biophysical constraints#

Variable

Units/measure

Description

Source

Elevation

meters

Void-filled digital elevation dataset from Shuttle Radar Topography Mission (SRTM). (view in gee)

T.G. Farr, P.A. Rosen, E. Caro, et al., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183.

Slope

degrees

The elevation dataset (see above) was used to calculate slope in units of degrees from horizontal, with greater values indicating steeper inclines. (view in gee)

T.G. Farr, P.A. Rosen, E. Caro, et al., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183.

Annual rainfall

mm/yr

High-resolution estimates of total annual rainfall based on mean value from past 30 year measurements. (view in gee)

“Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.68d2bb3

Baseline water stress

scale (0 to 5)

Ratio of total water withdrawals (for consumptive and nonconsumptive domestic, industrial, irrigation, and livestock uses) to available renewable supplies of surface water and groundwater, averaged across months of the year and converted to a numerical scale. Higher values of the scale indicate greater water stress. (view in gee)

World Resources Institute, 2021, Aqueduct Global Maps 3.0 Data, https://www.wri.org/data/aqueduct-global-maps-30-data

### Current land cover#

Variable

Units/measure

Description

Source

Terrestrial ecoregion

ecological zone labels

Classification of Earth’s land surface into 20 ecological zones, which have relatively homogeneous vegetation formations under natural conditions and similar physical features (e.g., climate). (view in gee)

UN FAO, 2012 Global ecological zones for fao forest reporting: 2010 Update, http://www.fao.org/3/ap861e/ap861e.pdf

### Forest change constraints#

Variable

Units/measure

Description

Source

Deforestation rate

%/yr

Annual rate of tree-cover loss within a 5 km buffer around a site during 2005–2015, expressed as a positive percentage of total tree cover. Higher values indicate higher rates of loss. The value is zero in areas without deforestation (i.e., areas with expanding tree cover). (view in gee)

Developed by se.plan team, using data from: ESA, 2017, Land Cover CCI Product User Guide, Version 2, maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

Climate risk

% of area

Difference between potential tree cover in 2050 if climate trends continue, and potential tree cover under current climatic conditions. Positive values indicate increases in potential tree cover, while negative values indicate decreases. (view in gee)

J.F. Bastin, Y. Finegold, C. Garcia, et al., 2019, The global tree restoration potential, Science 365(6448), pp. 76–79, DOI: 10.1126/science.aax0848; data downloaded from: https://www.research-collection.ethz.ch/handle/20.500.11850/350258

Natural regeneration variability

scale (0 to 1)

Measure of variability of forest restoration in fostering recovery of biodiversity to typical levels in natural native forests. Higher values indicate that biodiversity recovery is more variable (i.e., less predictable). (view in gee)

Developed by se.plan team, using model from: R. Crouzeilles, F.S. Barros, P.G. Molin, et al., 2019, A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes, J Appl Ecol. 56, pp. 2675– 2686, https://doi.org/10.1111/1365-2664.13501; and data from: ESA, 2017, Land Cover CCI Product User Guide, Version 2, maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

### Socio-economic constraints#

Variable

Units/measure

Description

Source

Protected areas

binary (0 or 1)

Value of 1 indicates that a site is located in a protected area, while a value of 0 indicates it is not. (view in gee)

IUCN, World Database on Protected Areas, https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas

Population density

persons per km2

Modeled distribution of human population for 2020, based on census data for the most disaggregated administrative units available. (view in gee)

CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW

Declining population

binary (0 or 1)

Value of 1 indicates that human population in a 5 km buffer around a site declined during 2010 – 2020, while a value of 0 indicates it rose or did not change. (view in gee)

Developed by se.plan team, using 2.5 arc-minute data from: CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW

Property rights protection

index (−2.5 to +2.5)

Downscaled version of the World Bank’s Rule of Law governance indicator, which is often interpreted as an indicator of property rights protection. Values range from −2.5 (very weak property rights) to +2.5 (very strong property rights). Varies by country and, when data are sufficient for downscaling, first-level administrative subdivision (e.g., state or province). (view in gee)

Developed by se.plan team, by downscaling national data from: World Bank, 2020, Worldwide Governance Indicators, https://info.worldbank.org/governance/wgi/

Accessibility to cities

minutes

Travel time from a site to the nearest city in 2015. (view in gee)

D.J. Weiss, A. Nelson, H.S. Gibson, et al., 2018, A global map of travel time to cities to assess inequalities in accessibility in 2015, Nature, doi:10.1038/nature25181; data downloaded from: https://malariaatlas.org/research-project/accessibility-to-cities/

## Acknowledgement#

This tool has been developed by UN FAO in close collaboration with Spatial Informatics Group (SIG), SilvaCarbon and researchers at Peking University and Duke University, with financial support from the Government of Japan.