{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T17:55:21Z","timestamp":1772819721973,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds of Chinese Academy of Forestry (CAF)","award":["CAFYBB2021ZE005"],"award-info":[{"award-number":["CAFYBB2021ZE005"]}]},{"name":"Fundamental Research Funds of Chinese Academy of Forestry (CAF)","award":["CAFYBB2019SZ004"],"award-info":[{"award-number":["CAFYBB2019SZ004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Diameter at breast height (DBH) is a critical metric for quantifying forest resources, and obtaining accurate, efficient measurements of DBH is crucial for effective forest management and inventory. A backpack LiDAR system (BLS) can provide high-resolution representations of forest trunk structures, making it a promising tool for DBH measurement. However, in practical applications, deep learning-based tree trunk detection and DBH estimation using BLS still faces numerous challenges, such as complex forest BLS data, low proportions of target point clouds leading to imbalanced class segmentation accuracy in deep learning models, and low fitting accuracy and robustness of trunk point cloud DBH methods. To address these issues, this study proposed a novel framework for BLS stratified-coupled tree trunk detection and DBH estimation in forests (BSTDF). This framework employed a stratified coupling approach to create a tree trunk detection deep learning dataset, introduced a weighted cross-entropy focal-loss function module (WCF) and a cosine annealing cyclic learning strategy (CACL) to enhance the WCF-CACL-RandLA-Net model for extracting trunk point clouds, and applied a (least squares adaptive random sample consensus) LSA-RANSAC cylindrical fitting method for DBH estimation. The findings reveal that the dataset based on the stratified-coupled approach effectively reduces the amount of data for deep learning tree trunk detection. To compare the accuracy of BSTDF, synchronous control experiments were conducted using the RandLA-Net model and the RANSAC algorithm. To benchmark the accuracy of BSTDF, we conducted synchronized control experiments utilizing a variety of mainstream tree trunk detection models and DBH fitting methodologies. Especially when juxtaposed with the RandLA-Net model, the WCF-CACL-RandLA-Net model employed by BSTDF demonstrated a 6% increase in trunk segmentation accuracy and a 3% improvement in the F1 score with the same training sample volume. This effectively mitigated class imbalance issues encountered during the segmentation process. Simultaneously, when compared to RANSAC, the LSA-RANCAC method adopted by BSTDF reduced the RMSE by 1.08 cm and boosted R2 by 14%, effectively tackling the inadequacies of RANSAC\u2019s filling. The optimal acquisition distance for BLS data is 20 m, at which BSTDF\u2019s overall tree trunk detection rate (ER) reaches 90.03%, with DBH estimation precision indicating an RMSE of 4.41 cm and R2 of 0.87. This study demonstrated the effectiveness of BSTDF in forest DBH estimation, offering a more efficient solution for forest resource monitoring and quantification, and possessing immense potential to replace field forest measurements.<\/jats:p>","DOI":"10.3390\/rs15143480","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T01:42:58Z","timestamp":1689039778000},"page":"3480","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm"],"prefix":"10.3390","volume":"15","author":[{"given":"Huacong","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Experimental Center of Subtropical Forestry, Chinese Academy of Forestry, Fenyi 336600, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3874-5326","authenticated-orcid":false,"given":"Huaiqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China"}]},{"given":"Keqin","family":"Xu","sequence":"additional","affiliation":[{"name":"Experimental Center of Subtropical Forestry, Chinese Academy of Forestry, Fenyi 336600, China"}]},{"given":"Yueqiao","family":"Li","sequence":"additional","affiliation":[{"name":"Experimental Center of Subtropical Forestry, Chinese Academy of Forestry, Fenyi 336600, China"}]},{"given":"Linlong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China"}]},{"given":"Ren","family":"Liu","sequence":"additional","affiliation":[{"name":"Experimental Center of Subtropical Forestry, Chinese Academy of Forestry, Fenyi 336600, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4800-1865","authenticated-orcid":false,"given":"Hanqing","family":"Qiu","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China"}]},{"given":"Longhua","family":"Yu","sequence":"additional","affiliation":[{"name":"Experimental Center of Subtropical Forestry, Chinese Academy of Forestry, Fenyi 336600, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, C., Xing, Y., Duanmu, J., and Tian, X. (2018). Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning. Remote Sens., 10.","DOI":"10.3390\/rs10040513"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Duanmu, J., and Xing, Y. (2020). Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation. Remote Sens., 12.","DOI":"10.3390\/rs12050808"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.3390\/rs6054323","article-title":"Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm","volume":"6","author":"Olofsson","year":"2014","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brede, B., Lau, A., Bartholomeus, H.M., and Kooistra, L. (2017). Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors, 17.","DOI":"10.3390\/s17102371"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Oveland, I., Hauglin, M., Giannetti, F., Schipper Kj\u00f8rsvik, N., and Gobakken, T. (2018). Comparing Three Different Ground Based Laser Scanning Methods for Tree Stem Detection. Remote Sens., 10.","DOI":"10.3390\/rs10040538"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lu, X., An, F., Zhou, L., Wang, X., Wang, Z., Zhang, H., and Yun, T. (2022). Integrating Real Tree Skeleton Reconstruction Based on Partial Computational Virtual Measurement (CVM) with Actual Forest Scenario Rendering: A Solid Step Forward for the Realization of the Digital Twins of Trees and Forests. Remote Sens., 14.","DOI":"10.3390\/rs14236041"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s10342-010-0381-4","article-title":"Retrieval of forest structural parameters using LiDAR remote sensing","volume":"129","author":"Nieuwenhuis","year":"2010","journal-title":"Eur. J. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.3390\/s140101228","article-title":"Possibilities of a Personal Laser Scanning System for Forest Mapping and Ecosystem Services","volume":"14","author":"Liang","year":"2014","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bauwens, S., Bartholomeus, H., Calders, K., and Lejeune, P. (2016). Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning. Forests, 7.","DOI":"10.3390\/f7060127"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, H., Feng, Z., Shen, C., and Chen, P. (2019). Applicability of personal laser scanning in forestry inventory. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211392"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2012.12.001","article-title":"The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans","volume":"77","author":"Pueschel","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive estimates of above-ground biomass using terrestrial laser scanning","volume":"6","author":"Calders","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Trochta, J., Krucek, M., Vrska, T., and Kral, K. (2017). 3D forest: An application for descriptions of three-dimensional forest structures using terrestrial lidar. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176871"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/LGRS.2015.2443553","article-title":"Retrieval and accuracy assessment of tree and stand parameters for chinese fir plantation using terrestrial laser scanning","volume":"12","author":"Sun","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Del Perugia, B., Giannetti, F., Chirici, G., and Travaglini, D. (2019). Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning. Forests, 10.","DOI":"10.3390\/f10030277"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"878","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. Syst. Biol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Olofsson, K., and Holmgren, J. (2016). Single Tree Stem Profile Detection Using Terrestrial Laser Scanner Data, Flatness Saliency Features and Curvature Properties. Forests, 7.","DOI":"10.3390\/f7090207"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.isprsjprs.2023.01.013","article-title":"GlobalMatch: Registration of forest terrestrial point clouds by global matching of relative stem positions","volume":"197","author":"Wang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110440","DOI":"10.1016\/j.measurement.2021.110440","article-title":"A branch-trunk-constrained hierarchical clustering method for street trees individual extraction from mobile laser scanning point clouds","volume":"189","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, P., Tang, Y., Liao, Z., Yan, Y., Dai, L., Liu, S., and Jiang, T. (2023). Road-Side Individual Tree Segmentation from Urban MLS Point Clouds Using Metric Learning. Remote Sens., 15.","DOI":"10.3390\/rs15081992"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, H., Cui, Z., Lei, K., Zuo, Y., Wang, J., Hu, X., and Qiu, H. (2023). Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection. Remote Sens., 15.","DOI":"10.3390\/rs15020519"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Armitage, S., Awty-Carroll, K., Clewley, D., and Martinez-Vicente, V. (2022). Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning. Remote Sens., 14.","DOI":"10.3390\/rs14143425"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ning, X., Ma, Y., Hou, Y., Lv, Z., Jin, H., and Wang, Y. (2022). Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion. Remote Sens., 14.","DOI":"10.3390\/rs14194926"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, X., Jiang, K., Zhu, Y., Wang, X., and Yun, T. (2021). Individual Tree Crown Segmentation Directly from UAV-Borne LiDAR Data Using the PointNet of Deep Learning. Forests, 12.","DOI":"10.3390\/f12020131"},{"key":"ref_25","unstructured":"Thomas, H., Qi, R., Deschaud, E., Marcotegui, B., Goulette, F., and Guibas, L. (November, January 27). Kpconv: Flexible and deformable convolution for point clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aloysius, N., and Geetha, M. (2017, January 6\u20138). A Review on Deep Convolutional Neural Networks. Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP 2017), Chennai, India.","DOI":"10.1109\/ICCSP.2017.8286426"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/01431160902882561","article-title":"Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests","volume":"31","author":"Lee","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 13\u201319). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_29","unstructured":"Ester, M., Kriegel, P., Sander, J., and Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, AAAI Press."},{"key":"ref_30","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep hierarchical feature learning on point sets in a metric space. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s00468-020-02067-7","article-title":"Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning","volume":"35","author":"Demol","year":"2021","journal-title":"Trees"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xu, D., Chen, G., and Jing, W. (2023). A Single-Tree Point Cloud Completion Approach of Feature Fusion for Agricultural Robots. Electronics, 12.","DOI":"10.3390\/electronics12061296"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.isprsjprs.2021.03.002","article-title":"Individual tree extraction from urban mobile laser scanning point clouds using deep pointwise direction embedding","volume":"175","author":"Luo","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Khalid, S., Xiao, W., Trigoni, N., and Markham, A. (2021, January 20\u201325). Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00494"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TGRS.2011.2161613","article-title":"Automatic stem mapping using single-scan terrestrial laser scanning","volume":"50","author":"Liang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s13595-011-0102-2","article-title":"The use of terrestrial LiDAR technology in forest science: Application fields, benefits and challenges","volume":"68","author":"Dassot","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100088","DOI":"10.1016\/j.fecs.2023.100088","article-title":"A tree detection method based on trunk point cloud section in dense plantation forest using drone LiDAR data","volume":"10","author":"Zhang","year":"2023","journal-title":"For. Ecosyst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., and Pollefeys, M. (2017). Semantic3D.Net: A New Large-Scale Point Cloud Classification Benchmark. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-W1-91-2017"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1080\/22797254.2018.1482733","article-title":"Integrating terrestrial and airborne laser scanning for the assessment of single-tree attributes in Mediterranean forest stands","volume":"51","author":"Giannetti","year":"2018","journal-title":"Eur. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3480\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:10:07Z","timestamp":1760127007000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3480"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":39,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143480"],"URL":"https:\/\/doi.org\/10.3390\/rs15143480","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,10]]}}}