Forests　are　critical　to　mitigating　global　climate　change　and　regulating　climate　through　their　role　in　the　global　carbon　and　water　cycles.　Accurate　monitoring　of　forest　cover　is,　therefore,　essential.　Image　segmentation　networks　based　on　convolutional　neural　networks　have　shown　significant　advantages　in　remote　sensing　image　analysis　with　the　development　of　deep　learning.　However,　deep　learning　networks　typically　require　a　large　amount　of　manual　ground　truth　labels　for　training,　and　existing　widely　used　image　segmentation　networks　struggle　to　extract　details　from　large-scale　high　resolution　satellite　imagery.　Improving　the　accuracy　of　forest　image　segmentation　remains　a　challenge.　To　reduce　the　cost　of　manual　labelling,　this　paper　proposed　a　data　augmentation　method　that　expands　the　training　data　by　modifying　the　spatial　distribution　of　forest　remote　sensing　images.　In　addition,　to　improve　the　ability　of　the　network　to　extract　multi-scale　detailed　features　and　the　feature　information　from　the　NIR　band　of　satellite　images,　we　proposed　a　high-resolution　forest　remote　sensing　image　segmentation　network　by　fusing　multi-scale　features　based　on　double　input.　The　experimental　results　using　the　Sanjiangyuan　plateau　forest　dataset　show　that　our　method　achieves　an　IoU　of　90.19%,　which　outperforms　prevalent　image　segmentation　networks.　These　results　demonstrate　that　the　proposed　approaches　can　extract　forests　from　remote　sensing　images　more　effectively　and　accurately.