Three-dimensional　(3D)　branch　structures　provide　vital　information　for　understanding　tree　phenotypic　characteristics　and　for　ecological　studies　related　to　carbon　sequestration.　Light　detection　and　ranging　(LiDAR)　has　been　widely　applied　to　capture　the　3D　structural　information　of　individual　trees.　Wood–leaf　separation　and　tree　skeleton　extraction　are　essential　prerequisites　for　accurately　estimating　tree　attributes　(e.g.,　stem　volume,　aboveground　biomass,　and　crown　characteristics)　and　representing　the　tree　branch　network.　Owing　to　the　complex　internal　branch　morphology　and　intercanopy　component　occlusion,　precise　extraction　of　the　tree　skeleton　from　point　clouds　remains　a　challenging　issue.　In　this　study,　we　propose　an　improved　approach　for　extracting　tree　skeletons　on　the　basis　of　the　geometric　features　of　point　clouds.　The　approach　consists　of　two　steps:　separation　of　the　wood　and　leaves,　followed　by　extraction　of　the　tree　skeleton.　In　the　first　step,　the　point　clouds　of　the　trees　are　sliced　horizontally.　The　density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　algorithm　is　then　employed　to　cluster　each　layer　of　the　point　clouds　and　detect　the　main　trunk.　Subsequently,　random　sample　consensus　(RANSAC)　circle　feature　detection　and　linear　feature　constraints　are　applied　to　achieve　wood–leaf　separation.　In　the　second　step,　the　wood　point　clouds　are　used　to　extract　the　initial　tree　skeleton　via　a　minimum　spanning　tree　(MST),　and　the　initial　tree　skeleton　is　further　optimized.　Various　comparative　experiments　are　conducted　on　terrestrial-LiDAR-scanned　data　from　nine　trees　across　six　species.　The　results　show　that　the　proposed　method　performs　effectively,　with　overall　wood–leaf　separation　accuracies　ranging　from　86%　to　93%.　Additionally,　the　extracted　branch　skeleton　accurately　reflects　the　natural　geometric　structure　of　the　trees.　The　wood　points　and　tree　skeletons　are　further　used　to　estimate　tree　attributes,　demonstrating　the　potential　of　our　method　for　the　quantitative　representation　of　trees　and　their　ecological　characteristics　(e.g.,　carbon　sequestration).　©　2024