Under　the　influence　of　human　activities　and　climate　change,　pine　wilt　disease　(PWD)　has　caused　significant　damage　to　Masson＇s　pine　(Pinus　massoniana　Lamb.)　forests　in　subtropical　China.　Existing　research　has　struggled　to　accurately　capture　the　large-scale　spatial　distribution　of　the　PWD,　particularly　for　precise　extraction　at　provincial　level.　This　study　focuses　on　Fujian　province　and　proposes　a　novel　method　for　extracting　PWD　information　at　the　sub-stand　level.　This　approach　uses　forest　age,　canopy　height,　and　temporal　vegetation　indices　(VIs)　data　for　deadwood　distribution　sub-stands　to　identify　the　suspected　outbreak　areas.　In　key　counties　and　cities,　High-resolution　satellite　imagery　(GF-2　and　GF-7)　was　used　to　construct　a　bi-level　scale-set　model　(BSM)　for　efficient　image　segmentation,　followed　by　selection　of　the　best　classification　algorithm　for　data　extraction.　For　non-key　counties,　sentinel　imagery　with　10-meter　resolution　was　used　on　the　GEE　cloud　platform　with　RF　classification.　The　results　showed　an　overall　annual　extraction　accuracy　exceeding　90%,　and　statistical　analysis　revealed　a　significant　reduction　in　the　number　of　dead　trees　from　2021　to　2022,　indicating　effective　control　measures.　This　study　demonstrated　that　multi-source　remote　sensing　data　can　efficiently　extract　PWD　distribution　information,　fill　data　gaps　for　provincial-level　monitoring,　and　support　forest　pest　management.