Integrating albedo offsets in reforestation decisions for climate change mitigation outcomes in 2050: A case study in the USA

Reforestation is generally regarded as having the most substantial climate mitigation potential among a suite of available natural climate solutions which have focused almost exclusively on the benefits of carbon sequestration and storage. However, these reforestation studies have not accounted for the adverse warming impacts resulting from corresponding surface albedo change. A newly available dataset developed with albedo imagery from the Landsat 8 satellite analyzed at field plots from the United States (US) Forest Inventory and Analysis (FIA) program provides non-soil carbon stocks and corresponding carbon-equivalent albedo offsets for 30 distinct forest-type groups indexed by 10-year age bins. In this case study we leverage this new dataset in concert with FIA species distribution data to investigate reforestation scenario planning based on joint carbon-albedo estimates (non-soil carbon stock less a carbon-equivalent albedo offset) instead of just carbon storage estimates alone. Specifically, our analysis informs managers interested in planting optimal forest-type groups for climate change mitigation outcomes approaching the year 2050. We assist in one of the most fundamental steps in any reforestation project: deciding which forest type or tree species mix to plant. We present our results as forest-type group recommendations within 64,000 hectare hexagons as a means to offer localized guidance and to examine the spatial patterns of albedo impacts across the conterminous US. We found that albedo offsets were most impactful on decisions in the Northeastern regions of the US, where optimizing for joint carbon-albedo in the next 25-years implies planting deciduous forest-type groups (Maple/beech/birch) instead of otherwise carbon-optimal coniferous forest-type groups (White/red/jack pine). Although the consideration of albedo did not alter 25-year tree planting decisions in most of the US, it did reduce the expected climate benefit of reforestation in general. We provide a standalone application that ranks all forest-type groups detected by FIA within a given hexagon, allowing managers to evaluate alternatives in light of site-specific constraints. This paper describes a replicable case study for incorporating albedo offsets in reforestation plans. Similar analyses may be performed anywhere Landsat albedo data are available over adequate measurements of forest carbon stocks. Recommendations: • Albedo impacts on 25-year tree planting decisions are concentrated in the Northeastern regions of the United States, where considering albedo offsets together with carbon stocks implies planting the Maple/beech/birch forest-type group in place of the otherwise carbon-optimal White/red/jack pine group. • Our reforestation support application allows managers to explore localized forest-type group rankings on the basis of joint carbon-albedo benefits. • Fine-resolution albedo data, which is not currently a standard data product, provides more comprehensive support for reforestation projects intended to mitigate global climate change. • Forest-type group recommendations for maximum climate change mitigation potential. • Albedo impacts on 25-year tree planting decisions concentrated in Northeastern US. • Albedo offsets impact decisions in > 50% of hexagons within the Eastern NFS region. • Downloadable application supporting planting decisions based on carbon and albedo. • Fine-resolution albedo data enhances support for reforestation projects.