Remote　sensing　is　a　cost-effective　tool　for　assessing　vegetation　damage　by　typhoon　events　at　various　scales.　Taking　Xiamen　Island,　southeastern　China,　as　a　study　case,　this　paper　aimed　to　assess　and　analyze　the　vegetation　damage　caused　by　Typhoon　Meranti　landfalling　on　September　15,　2016,　using　two　high　spatial　resolution　remote　sensing　images　before　and　after　the　typhoon　event.　Seven　severely　damaged　vegetation　regions　were　selected　based　on　the　classification　of　vegetation　types　and　visual　interpretation　of　the　images.　Regression　analysis　was　used　to　correct　seasonal　variation　of　the　two　high-solution　images　before　and　after　typhoon.　The　vegetation　area　of　the　whole　of　Xiamen　Island　and　the　selected　seven　regions　before　and　after　typhoon　were　then　calculated,　respectively.　Two　spectral　vegetation　indicators,　normalized　difference　vegetation　index　(NDVI)　and　fractional　vegetation　coverage　(FVC),　were　also　retrieved　for　the　whole　island　and　the　seven　regions.　By　comparing　the　difference　in　NDVI　values　before　and　after　the　typhoon　of　the　two　high　spatial　resolution　images,　we　analyzed　the　most　affected　vegetation　areas,　as　well　as　the　most　seriously　damaged　vegetation　species.　The　typhoon　has　caused　a　decrease　in　vegetation　area　by　95.1 ha　across　the　whole　Xiamen　Island.　The　mean　NDVI　and　FVC　decreased　by　0.209　and　13　percentage　points,　respectively.　While,　in　the　seven　selected　severely　damaged　areas,　the　mean　NDVI　decreased　by　0.356–0.444　and　FVC　decreased　by　27–42　percentage　points.　The　visual　inspection　showed　that　the　tone　of　typhoon-damaged　vegetation　became　darker,　the　patches　of　damaged　vegetation　became　smaller　and　more　fragmented,　and　the　gap　between　vegetation　canopies　became　larger.　The　most　affected　vegetation　areas　occurred　in　the　southeastern　hilly　area,　Jinshang　and　Hubin　South　Roads,　as　well　as　the　Wuyuan　Bay　area.　The　most　seriously　damaged　vegetation　type　is　broad-leaved　trees,　especially　the　species,　Acacia　confusa,　Delonix　regia,　Bauhinia　variegata,　Chorisia　speciosa,　Ficus　benjamina　and　F.　Concinna.　©　2018,　Springer　Science+Business　Media　B.V.,　part　of　Springer　Nature.