Cervid browsing influences forest ecosystems worldwide, stressing the need for wildlife management founded in accurate estimates of available forage. In this study, we developed the first national-scale models for Sweden to estimate the abundance of cervid forage by combining data from the National Forest Inventory (NFI) and different remote sensing (RS) datasets. We focused on six key forage tree species for cervids in Sweden: Scots pine (Pinus sylvestris), birch (Betula spp.), European aspen (Populus tremula), rowan (Sorbus aucuparia), oak (Quercus spp.), and goat willow (Salix caprea). We combined airborne laser scanning and other auxiliary RS data with NFI data from 2016 to 2022 to model small tree abundance from 19 461 plots across Sweden in an area-based approach. We fitted generalized linear mixed models using likelihood-ratio tests to predict species-specific forage availability. Models were validated using an independent dataset of NFI data collected in 2023. Our models demonstrated moderate to strong predictive performance, with marginal R2 values ranging from 0.226 to 0.973. Model validation suggested higher RMSE and rRMSE values for tree species that are scarce throughout the country than for more abundant species. We provide maps for all six modelled tree species, both at a 1 ha and a 1 km2 spatial scale, with the aim for them to be used in wildlife management, forestry planning, and ecological research. Our map products can for example help stakeholders assess a region’s spatial distribution of cervid forage and thus inform habitat management and potentially mitigate browsing-related economic losses in forestry. The goal of running this project on a HPC is to futher provide the estimated upper and lower confidence limits for every pixel in the map products as well. The main advisor for the project is Annika Maria Felton from the Southern Swedish Forest Research Centre (annika.felton@slu.se).