Monitoring　the　extent　of　plateau　forests　has　drawn　much　attention　from　governments　given　the　fact　that　the　plateau　forests　play　a　key　role　in　global　carbon　circulation.　Despite　the　recent　advances　in　the　remote-sensing　applications　of　satellite　imagery　over　large　regions,　accurate　mapping　of　plateau　forest　remains　challenging　due　to　limited　ground　truth　information　and　high　uncertainties　in　their　spatial　distribution.　In　this　paper,　we　aim　to　generate　a　better　segmentation　map　for　plateau　forests　using　high-resolution　satellite　imagery　with　limited　ground-truth　data.　We　present　the　first　2　m　spatial　resolution　large-scale　plateau　forest　dataset　of　Sanjiangyuan　National　Nature　Reserve,　including　38,708　plateau　forest　imagery　samples　and　1187　handmade　accurate　plateau　forest　ground　truth　masks.　We　then　propose　an　few-shot　learning　method　for　mapping　plateau　forests.　The　proposed　method　is　conducted　in　two　stages,　including　unsupervised　feature　extraction　by　leveraging　domain　knowledge,　and　model　fine-tuning　using　limited　ground　truth　data.　The　proposed　few-shot　learning　method　reached　an　F1-score　of　84.23%,　and　outperformed　the　state-of-the-art　object　segmentation　methods.　The　result　proves　the　proposed　few-shot　learning　model　could　help　large-scale　plateau　forest　monitoring.　The　dataset　proposed　in　this　paper　will　soon　be　available　online　for　the　public.