Mangroves　are　among　the　richest　ecosystems　in　terms　of　natural　resources.　Because　of　the　similar　spectral　characteristics　and　complex　spatial　structure　among　different　mangrove　communities,　it　is　still　difficult　to　extract　accurate　mangrove　communities　covers　from　the　very　high　resolution　(VHR)　satellite　images,　based　on　the　conventional　pixel-based　classification　methods.　Object-oriented　classification　methods　are　proposed　to　process　VHR　images　because　they　can　incorporate　as　much　information　on　spatial　neighbourhood　properties　as　possible　into　the　classification　process.　On　the　basis　of　object-oriented　classification　method,　the　paper　extracted　the　Zhangjiangkou　mangrove　communities　using　Quickbird　image,　through　image　segmentation,　merge　segmentation,　computing　and　selecting　attributes,　K　Nearest　Neighbor　classification,　et　al.　Mangrove　communities　were　eventually　divided　into　six　types,　i.e.　Aegiceras　corniculatum,　Phragmites　karka,　Kandelia　candel,　Cyperus　malaccensis,　Avicennia　marin,　Aegiceras　corniculatum　&　Kandelia　candel.　The　extraction　accuracy　is　85.7%.　In　this　paper,　through　many　experiments,　the　scale　level　was　determined　to　50,　and　the　merge　threshold　was　chosen　as　90.　In　this　case,　the　resulting　segments　of　mangrove　communities　had　a　obvious　spatial　characteristics,　and　the　boundary　can　be　delineated　well,　which　would　be　help　for　later　information　extraction.　Considering　mangrove　communities　characters,　the　following　object　attributes　are　generated　and　added:　NDVI,　texture,　and　AVGBAND.　This　research　shows　very　encouraging　results　for　the　use　of　image　segmentation　and　Quickbird　data　for　mapping　mangrove　communities.　©　(2013)　Trans　Tech　Publications,　Switzerland.