Advanced　geographic　technologies　provide　an　opportunity　to　understand　the　urban　forest　landscape　and　guide　the　governance　of　the　urban　ecosystem.　However,　only　few　studies　stressed　the　importance　of　data　techniques　in　understanding　urban　sustainability,　especially　urban　forest　landscape.　Therefore,　this　study　makes　an　analysis　of　urban　forest　resources　in　a　city　of　Yangtze　River　Delta　with　the　help　of　multi-source　data　techniques　and　further　data　analysis　of　different　forest　landscape　pattern　indices　in　the　study　area　with　the　help　of　SPSS　(Statistical　Product　and　Service　Solutions).　The　following　conclusions　are　drawn:　1)　According　to　the　visual　analysis,　the　spatial　distribution　of　forest　patches　in　the　study　area　has　a　great　difference.　2)　All　the　seven　landscape　pattern　indices　are　positively　correlated　with　the　distribution　density　of　POI　(Point　of　Interest),　which　represents　the　urban　economic　vitality.　The　correlation　coefficients　are　NP　(R-2　=　0.3063),　PD　(R-2　=　0.0079),　ED　(R-2　=　0.3955),　AREA　(R-2　=　0.5408),　CONTIG　(R-2　=　0.0323),　PAFRAC　(R-2　=　0.3662)　and　AI　(R-2　=　0.2014),　respectively.　This　indicates　that　the　higher　the　economic　vitality　is,　the　more　fragmented　and　complex　the　urban　forest　patches　are.　3)　According　to　the　geographically　weighted　regression　model,　the　goodness　of　fit　between　the　spatial　distribution　density　of　POI　and　NP,　PD,　ED,　and　AI　reaches　0.804,　0.771,　0.634,　and　0.619,　respectively,　and　the　explanatory　power　of　the　model　is　more　than　twice　that　of　the　corresponding　linear　regression　model.　The　data　illustrates　that　the　correlation　between　economic　vitality　and　urban　forest　landscape　pattern　indices　has　significant　spatial　heterogeneity.