Accurate　measurement　of　three⁃dimensional　green　biomass　is　essential　for　assessing　urban　greening　levels　and　conducting　quantitative　ecological　benefit　studies.　Current　remote　sensing⁃based　green　biomass　measurement　methods　require　time⁃consuming　field　surveys　to　collect　tree　species　data　and　establish　species⁃specific　crown　morphological　parameter　models.　This　study　develops　an　efficient　green　biomass　estimation　approach　by　simulating　individual⁃tree　green　biomass　using　directly　quantifiable　parameters　from　high⁃resolution　remote　sensing　imagery.　First,　centimeter⁃level　UAV　imagery　was　acquired　across　Fuzhou′s　urban　areas,　and　remote　sensing　methods　were　employed　to　measure　the　individual　and　total　green　biomass　in　different　areas　(streets,　neighborhoods,　and　urban　areas).　Next,　the　green　biomass　characteristics　of　different　areas　were　compared　and　analyzed.　Finally,　regression　models　were　constructed　using　directly　quantifiable　factors　from　remote　sensing　imagery　to　estimate　the　green　biomass　in　various　regions.　Key　findings　reveal:　(1)　Street　trees　in　Fuzhou　show　low　species　diversity　(61.3%　Ficus　spp.),　while　neighborhoods　maintain　balanced　species　distributions.　Significant　green　biomass　variations　exist　across　spatial　scales　(streets:　351.6　m3;　neighborhoods:　143.7　m3;　urban　areas:　161.4　m3).　(2)　Regression　models　using　canopy　projection　area　and　perimeter　achieved　high　precision　(adjusted　R2:　0.921　for　streets,　0.873　for　neighborhoods,　0.　882　for　urban　areas),　demonstrating　an　accurate,　efficient　solution　for　multi⁃scale　green　biomass　assessment.　©　2025　Science　Press.　All　rights　reserved.