Heavy　metal　mining　activities　have　caused　the　complex　influence　on　the　ecological　environment　of　the　mining　regions.　For　example,　a　large　amount　of　acidic　waste　water　containing　heavy　metal　ions　have　be　produced　in　the　process　of　copper　mining　which　can　bring　serious　pollution　to　the　ecological　environment　of　the　region.　In　the　previous　research　work,　bare　soil　is　mainly　taken　as　the　research　target　when　monitoring　environmental　pollution,　and　thus　the　effects　of　land　surface　vegetation　have　been　ignored.　It　is　well　known　that　vegetation　condition　is　one　of　the　most　important　indictors　to　reflect　the　ecological　change　in　a　certain　region　and　there　is　a　significant　linkage　between　the　vegetation　spectral　characteristics　and　the　heavy　metal　when　the　vegetation　is　effected　by　the　heavy　metal　pollution.　It　means　the　vegetation　is　sensitive　to　heavy　metal　pollution　by　their　physiological　behaviors　in　response　to　the　physiological　ecology　change　of　their　growing　environment.　The　conventional　methods,　which　often　rely　on　large　amounts　of　field　survey　data　and　laboratorial　chemical　analysis,　are　time　consuming　and　costing　a　lot　of　material　resources.　The　spectrum　analysis　method　using　remote　sensing　technology　can　acquire　the　information　of　the　heavy　mental　content　in　the　vegetation　without　touching　it.　However,　the　retrieval　of　that　information　from　the　hyperspectral　data　is　not　an　easy　job　due　to　the　difficulty　in　figuring　out　the　specific　band,　which　is　sensitive　to　the　specific　heavy　metal,　from　a　huge　number　of　hyperspectral　bands.　Thus　the　selection　of　the　sensitive　band　is　the　key　of　the　spectrum　analysis　method.　This　paper　proposed　a　statistical　analysis　method　to　find　the　feature　band　sensitive　to　heavy　metal　ion　from　the　hyperspectral　data　and　to　then　retrieve　the　metal　content　using　the　field　survey　data　and　the　hyperspectral　images　from　China　Environment　Satellite　HJ-1.　This　method　selected　copper　ion　content　in　the　leaves　as　the　indicator　of　copper　pollution　level,　using　stepwise　multiple　linear　regression　and　cross　validation　on　the　dataset　which　is　consisting　of　44　groups　of　copper　ion　content　information　in　the　polluted　vegetation　leaves　from　Dexing　Copper　Mine　in　Jiangxi　Province　to　build　up　a　statistical　model　by　also　incorporating　the　HJ-1　satellite　images.　This　model　was　then　used　to　estimate　the　copper　content　distribution　over　the　whole　research　area　at　Dexing　Copper　Mine.　The　result　has　shown　that　there　is　strong　statistical　significance　of　the　model　which　revealed　the　most　sensitive　waveband　to　copper　ion　is　located　at　516　nm.　The　distribution　map　illustrated　that　the　copper　ion　content　is　generally　in　the　range　of　0~130　mg·kg-1　in　the　vegetation　covering　area　at　Dexing　Copper　Mine　and　the　most　seriously　polluted　area　is　located　at　the　South-east　corner　of　Dexing　City　as　well　as　the　mining　spots　with　a　higher　value　between　80　and　100　mg·kg-1.　This　result　is　consistent　with　the　ground　observation　experiment　data.　The　distribution　map　can　certainly　provide　some　important　basic　data　on　the　copper　pollution　monitoring　and　treatment.　©　2015,　Science　Press.　All　right　reserved.