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Service Description: This map of reforestation potential is a more recent, modified version of the Atlas of Forest Landscape Restoration Opportunities. It identifies global areas that may be an opportunity for restorative activities, and the authors address the reforestation component of this larger opportunity map in the Reforestation Potential data displayed here. In PIE, these areas would represent an activity mask for potential reforestation actions as a selected intervention. Broadly they should align with areas where forests used to grow and where reforestation may be an effective restoration or Natural Climate Solution.

Griscom, B. W., J. Adams, P. W. Ellis, R. A. Houghton, G. Lomax, D. A. Miteva, W. H. Schlesinger, D. Shoch, J. V. Siikamäki, P. Smith, P. Woodbury, C. Zganjar, A. Blackman, J. Campari, R. T. Conant, C. Delgado, P. Elias, T. Gopalakrishna, M. R. Hamsik, M. Herrero, J. Kiesecker, E. Landis, L. Laestadius, S. M. Leavitt, S. Minnemeyer, S. Polasky, P. Potapov, F. E. Putz, J. Sanderman, M. Silvius, E. Wollenberg, and J. Fargione. 2017. Natural climate solutions. Proceedings of the National Academy of Sciences 114:11645–11650. https://doi.org/10.1073/pnas.1710465114


Supplementary information: https://www.pnas.org/doi/suppl/10.1073/pnas.1710465114/suppl_file/pnas.1710465114.sapp.pdf
Data source: https://zenodo.org/records/883444

To calculate the extent of reforestation potential, we modify a 1 km resolution map from the Atlas of Forest Landscape Restoration Opportunities (FLRO) (82). This map uses ecoregional data and bioclimatic modeling of the following criteria to identify areas with opportunities for forest landscape restoration: potential forest cover (230, 231) minus existing forests (232) minus areas incompatible with returning to forests (233–235). The potential forest cover map combined data on climate, soils, elevation, current and historical forest extent, and potential forest composition and density (5, 40, 231, 236, 237). The existing forest map was derived from MODIS 250m data from 2000 to 2009, which maps forest extent, and MODIS vegetation continuous fields data, which maps tree canopy density (232, 238). We excluded areas incompatible with returning to forests, included locations with dense rural population (>100 person km-2), agricultural and other intensively used areas (233, 235, 239). Note that the FLRO map classifies forests as either closed forest (canopy density >45%), open forest (canopy density between 25-45%), and woodlands (canopy density between 10-25%). We applied additional spatially explicit filters to avoid double-counting among pathways, avoid overlap with wetlands, exclude boreal ecoregions, remove native non-forest ecosystems, and improve estimates of additionality as follows: • Deductions to avoid double-counting with forest management pathways: To adjust the estimated restoration opportunity area to our definition of reforestation opportunities (where non-forests can be converted to forests >25% tree cover), we removed (i) areas identified by FLRO based on Olson ecoregions (231) as having potential forest cover with <25% tree canopy Page 49 cover, and (ii) Hansen pixels (6) identifying existing forest cover >25% tree canopy cover. This modification avoids double-counting between reforestation and other forest restoration pathways (e.g. natural forest management), and reduces the substantial remote sensing error associated with more subtle changes in vegetation in forests remaining forests and non-forests remaining non-forests (240). • Boreal albedo exclusion: We excluded boreal forest ecoregions (10%) given biophysical effects of forest cover that may offset carbon sequestration (i.e. albedo warming (197)). • Biodiversity safeguard: To avoid negative impacts to biodiversity, we excluded areas in grassy biomes where forests naturally transition to grassland and savannah ecosystems. As indicated in the literature (241, 242) , the potential vegetation cover data used by the FLRO map (231) does not accurately delimit grass-dominated ecosystems. We make use of a new study (NESCent grasslands working group, unpublished) to map the extent of grassy biomes globally, which excludes 47% of the 2.5 Gha FLRO area identified by WRI. • Baseline adjustment: To account for baseline reforestation between the FLRO base year 2000 and present, we apply the mean forest “gain” rate for 2000-2012 from the UMD dataset (6) to the intervening period. To appropriately account for additionality, we use the same rate to exclude baseline reforestation during the 2016-2030 period. We also applied a non-spatial deduction to eliminate double counting: we deducted the unmapped area of forested peatlands and mangrove forests (66, 73, 76, 77) (see wetland restoration methods below). We note that our definition of agroforestry in this analysis (use of trees in cropping systems where tree cover <25%), excludes agroforestry interventions as defined here from the reforestation pathway, thereby eliminating double-counting. We assumed that potential reforestation areas do not compete with future areas of cropland expansion, as croplands were already excluded from the FLRO map, and we assume that the current extent of agricultural land can effectively feed projected future populations ((191) “yield growth” scenario). Page 50 To calculate rates of forest carbon sequestration, we conducted a literature review of plantation and natural forest growth studies in different climate domains (Table S9). Our analysis indicates that the majority of potential reforestation area is located in the tropics (70%), where growth rates are higher, thereby representing an even greater proportion of the mitigation potential (79%).

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Layers: Description: This map of reforestation potential is a more recent, modified version of the Atlas of Forest Landscape Restoration Opportunities. It identifies global areas that may be an opportunity for restorative activities, and the authors address the reforestation component of this larger opportunity map in the Reforestation Potential data displayed here. In PIE, these areas would represent an activity mask for potential reforestation actions as a selected intervention. Broadly they should align with areas where forests used to grow and where reforestation may be an effective restoration or Natural Climate Solution.

Griscom, B. W., J. Adams, P. W. Ellis, R. A. Houghton, G. Lomax, D. A. Miteva, W. H. Schlesinger, D. Shoch, J. V. Siikamäki, P. Smith, P. Woodbury, C. Zganjar, A. Blackman, J. Campari, R. T. Conant, C. Delgado, P. Elias, T. Gopalakrishna, M. R. Hamsik, M. Herrero, J. Kiesecker, E. Landis, L. Laestadius, S. M. Leavitt, S. Minnemeyer, S. Polasky, P. Potapov, F. E. Putz, J. Sanderman, M. Silvius, E. Wollenberg, and J. Fargione. 2017. Natural climate solutions. Proceedings of the National Academy of Sciences 114:11645–11650. https://doi.org/10.1073/pnas.1710465114


Supplementary information: https://www.pnas.org/doi/suppl/10.1073/pnas.1710465114/suppl_file/pnas.1710465114.sapp.pdf
Data source: https://zenodo.org/records/883444

To calculate the extent of reforestation potential, we modify a 1 km resolution map from the Atlas of Forest Landscape Restoration Opportunities (FLRO) (82). This map uses ecoregional data and bioclimatic modeling of the following criteria to identify areas with opportunities for forest landscape restoration: potential forest cover (230, 231) minus existing forests (232) minus areas incompatible with returning to forests (233–235). The potential forest cover map combined data on climate, soils, elevation, current and historical forest extent, and potential forest composition and density (5, 40, 231, 236, 237). The existing forest map was derived from MODIS 250m data from 2000 to 2009, which maps forest extent, and MODIS vegetation continuous fields data, which maps tree canopy density (232, 238). We excluded areas incompatible with returning to forests, included locations with dense rural population (>100 person km-2), agricultural and other intensively used areas (233, 235, 239). Note that the FLRO map classifies forests as either closed forest (canopy density >45%), open forest (canopy density between 25-45%), and woodlands (canopy density between 10-25%). We applied additional spatially explicit filters to avoid double-counting among pathways, avoid overlap with wetlands, exclude boreal ecoregions, remove native non-forest ecosystems, and improve estimates of additionality as follows: • Deductions to avoid double-counting with forest management pathways: To adjust the estimated restoration opportunity area to our definition of reforestation opportunities (where non-forests can be converted to forests >25% tree cover), we removed (i) areas identified by FLRO based on Olson ecoregions (231) as having potential forest cover with <25% tree canopy Page 49 cover, and (ii) Hansen pixels (6) identifying existing forest cover >25% tree canopy cover. This modification avoids double-counting between reforestation and other forest restoration pathways (e.g. natural forest management), and reduces the substantial remote sensing error associated with more subtle changes in vegetation in forests remaining forests and non-forests remaining non-forests (240). • Boreal albedo exclusion: We excluded boreal forest ecoregions (10%) given biophysical effects of forest cover that may offset carbon sequestration (i.e. albedo warming (197)). • Biodiversity safeguard: To avoid negative impacts to biodiversity, we excluded areas in grassy biomes where forests naturally transition to grassland and savannah ecosystems. As indicated in the literature (241, 242) , the potential vegetation cover data used by the FLRO map (231) does not accurately delimit grass-dominated ecosystems. We make use of a new study (NESCent grasslands working group, unpublished) to map the extent of grassy biomes globally, which excludes 47% of the 2.5 Gha FLRO area identified by WRI. • Baseline adjustment: To account for baseline reforestation between the FLRO base year 2000 and present, we apply the mean forest “gain” rate for 2000-2012 from the UMD dataset (6) to the intervening period. To appropriately account for additionality, we use the same rate to exclude baseline reforestation during the 2016-2030 period. We also applied a non-spatial deduction to eliminate double counting: we deducted the unmapped area of forested peatlands and mangrove forests (66, 73, 76, 77) (see wetland restoration methods below). We note that our definition of agroforestry in this analysis (use of trees in cropping systems where tree cover <25%), excludes agroforestry interventions as defined here from the reforestation pathway, thereby eliminating double-counting. We assumed that potential reforestation areas do not compete with future areas of cropland expansion, as croplands were already excluded from the FLRO map, and we assume that the current extent of agricultural land can effectively feed projected future populations ((191) “yield growth” scenario). Page 50 To calculate rates of forest carbon sequestration, we conducted a literature review of plantation and natural forest growth studies in different climate domains (Table S9). Our analysis indicates that the majority of potential reforestation area is located in the tropics (70%), where growth rates are higher, thereby representing an even greater proportion of the mitigation potential (79%).

Copyright Text: Griscom, B. W., J. Adams, P. W. Ellis, R. A. Houghton, G. Lomax, D. A. Miteva, W. H. Schlesinger, D. Shoch, J. V. Siikamäki, P. Smith, P. Woodbury, C. Zganjar, A. Blackman, J. Campari, R. T. Conant, C. Delgado, P. Elias, T. Gopalakrishna, M. R. Hamsik, M. Herrero, J. Kiesecker, E. Landis, L. Laestadius, S. M. Leavitt, S. Minnemeyer, S. Polasky, P. Potapov, F. E. Putz, J. Sanderman, M. Silvius, E. Wollenberg, and J. Fargione. 2017. Natural climate solutions. Proceedings of the National Academy of Sciences 114:11645–11650. https://doi.org/10.1073/pnas.1710465114

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