To　achieve　the　effective　monitoring　of　plateau　forests　in　the　Sanjiangyuan　National　Nature　Reserve　in　Qinghai,　a　fusing　multi-scale　features　remote　sensing　image　segmentation　algorithm　based　on　deep　learning　technology　was　proposed.　First,　the　first　2　m　spatial　resolution　plateau　forest　dataset　in　the　region　was　constructed.　Second,　to　solve　the　problem　of　insufficient　ground-truth　label　of　remote　sensing　images　which　affects　the　training　of　network　models,　a　data　augmentation　method　of　shuffling　and　reorganizing　images　was　proposed　according　to　the　characteristics　of　forest　remote　sensing　image　segmentation,　and　the　training　data　was　expanded　to　1　600　images.　To　address　the　problem　of　mainstream　segmentation　networks　that　cannot　focus　on　details　in　processing　large-scale　remote　sensing　images,　a　fusing　multi-scale　features　high-resolution　forest　remote　sensing　image　segmentation　network　model　based　on　encoding　and　decoding　structures　was　proposed.　The　model　incorporated　the　designed　convolution　block,　multi-scale　feature　fusion　block　and　feature　amplification　extraction　block.　Results　show　that　the　data　augmentation　algorithm　proposed　improves　the　segmentation　accuracy　of　the　model,　while　the　proposed　model　trained　by　the　proposed　data　augmentation　achieves　an　intersection　over　union　(IoU)　of　89.　64%,　and　the　results　are　better　than　that　of　the　current　mainstream　image　segmentation　models.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.