{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T23:06:29Z","timestamp":1769727989435,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Municipal Natural Science Foundation","award":["6232031"],"award-info":[{"award-number":["6232031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method\u2019s performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm\u2019s 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes.<\/jats:p>","DOI":"10.3390\/rs16020368","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T11:37:18Z","timestamp":1705405038000},"page":"368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Jie","family":"Xu","sequence":"first","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Minbin","family":"Su","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Yuxuan","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Wenbin","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Humanities and Social Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Hongchuan","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Shuo","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-4909","authenticated-orcid":false,"given":"Pei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"ref_1","first-page":"114","article-title":"Evaluation Method of Forest Management Models: A Case Study of Xiaolongshan Forest Area in Gansu Province","volume":"47","author":"Shiyun","year":"2011","journal-title":"Sci. Silvae Sin."},{"key":"ref_2","first-page":"102764","article-title":"Detecting and mapping tree crowns based on convolutional neural network and Google Earth images","volume":"108","author":"Yang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Quackenbush, L.J., and Zhang, L. (2016). Trends in Automatic Individual Tree Crown Detection and Delineation\u2014Evolution of LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8040333"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.07.028","article-title":"Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass","volume":"154","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wulder, M.A., and Franklin, S.E. (2003). Remote Sensing of Forest Environments: Concepts and Case Studies, Springer US.","DOI":"10.1007\/978-1-4615-0306-4"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, X., Xu, F., Yong, X., Chen, D., Xia, R., Ye, B., Gao, H., Chen, Z., and Lyu, X. (2023). SSCNet: A Spectrum-Space Collaborative Network for Semantic Segmentation of Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15235610"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1080\/01431161.2021.1876272","article-title":"Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images","volume":"42","author":"Li","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"118397","DOI":"10.1016\/j.foreco.2020.118397","article-title":"Individual tree detection and species classification of Amazonian palms using UAV images and deep learning","volume":"475","author":"Ferreira","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Korpela, I. (2004). Individual tree measurements by means of digital aerial photogrammetry. Silva Fennica. Monographs, 3.","DOI":"10.14214\/sf.sfm3"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1016\/j.rse.2011.01.026","article-title":"Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level","volume":"115","author":"Popescu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4225","DOI":"10.1109\/JSTARS.2017.2711482","article-title":"Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery","volume":"10","author":"Ma","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105213","DOI":"10.1016\/j.compag.2020.105213","article-title":"Multi-layered tree crown extraction from LiDAR data using graph-based segmentation","volume":"170","author":"Dong","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/TGRS.2003.811693","article-title":"Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003)","volume":"41","author":"Riano","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"1259","article-title":"Individual Tree Crown Extraction of High Resolution Image Based on Marker-controlled Watershed Segmentation Method","volume":"18","author":"Guo","year":"2016","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"923","DOI":"10.14358\/PERS.72.8.923","article-title":"Isolating individual trees in a savanna woodland using small footprint lidar data","volume":"72","author":"Chen","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2007.04.002","article-title":"Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery","volume":"111","author":"McRoberts","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, S., Kang, F., Li, W., Kan, J., Zheng, Y., and He, G. (2019). Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment. Sensors, 19.","DOI":"10.3390\/s19143212"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1175\/JTECH-D-11-00009.1","article-title":"A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images","volume":"28","author":"Li","year":"2011","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107849","DOI":"10.1016\/j.compag.2023.107849","article-title":"A novel solution for extracting individual tree crown parameters in high-density plantation considering inter-tree growth competition using terrestrial close-range scanning and photogrammetry technology","volume":"209","author":"Chai","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1007\/s11629-022-7563-7","article-title":"Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph","volume":"20","author":"Sun","year":"2023","journal-title":"J. Mt. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., and White, E. (2019). Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens., 11.","DOI":"10.1101\/532952"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2012.04.003","article-title":"An individual tree crown delineation method based on multi-scale segmentation of imagery","volume":"70","author":"Jing","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"914974","DOI":"10.3389\/fpls.2022.914974","article-title":"Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework","volume":"13","author":"Sun","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_25","first-page":"173","article-title":"Research on Crown Extraction Based on Improved Faster R-CNN Model","volume":"1","author":"Huang","year":"2021","journal-title":"For. Resour. Wanagement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112397","DOI":"10.1016\/j.rse.2021.112397","article-title":"Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering","volume":"258","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"G. Braga, J.R., Peripato, V., Dalagnol, R., P. Ferreira, M., Tarabalka, Y., O. C. Arag\u00e3o, L.E., F. de Campos Velho, H., Shiguemori, E.H., and Wagner, F.H. (2020). Tree Crown Delineation Algorithm Based on a Convolutional Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12081288"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104875","DOI":"10.1016\/j.compag.2019.104875","article-title":"Passive measurement method of tree diameter at breast height using a smartphone","volume":"163","author":"Wu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hao, Y., Widagdo, F.R.A., Liu, X., Quan, Y., Dong, L., and Li, F. (2021). Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning. Remote Sens., 13.","DOI":"10.3390\/rs13010024"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111323","DOI":"10.1016\/j.rse.2019.111323","article-title":"Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms","volume":"232","author":"Galvao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Adhikari, A., Montes, C.R., and Peduzzi, A. (2023). A Comparison of Modeling Methods for Predicting Forest Attributes Using Lidar Metrics. Remote Sens., 15.","DOI":"10.3390\/rs15051284"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s13595-020-00970-0","article-title":"Predicting individual tree growth using stand-level simulation, diameter distribution, and Bayesian calibration","volume":"77","author":"Tian","year":"2020","journal-title":"Ann. For. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gyawali, A., Aalto, M., Peuhkurinen, J., Villikka, M., and Ranta, T. (2022). Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests. Sustainability, 14.","DOI":"10.3390\/su14073720"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_35","unstructured":"Pereira, F., Burges, C., Bottou, L., and Weinberger, K. (2012). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_40","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks. [Ph.D. Thesis, Technical University of Munich]. Volume 385.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_42","first-page":"82","article-title":"Design and experiment of high precision forest resource investigation system based on UAV remote sensing images","volume":"33","author":"Shi","year":"2017","journal-title":"Nongye Gongcheng Xuebao\/Transactions Chin. Soc. Agric. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113180","DOI":"10.1016\/j.rse.2022.113180","article-title":"Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning","volume":"280","author":"Brede","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/LGRS.2013.2285471","article-title":"Breast Height Diameter Estimation From High-Density Airborne LiDAR Data","volume":"11","author":"Bucksch","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s11355-019-00379-6","article-title":"Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR","volume":"15","author":"Heo","year":"2019","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, B., Yuan, J., Shi, B., Chen, T., Li, Y., and Qiao, Y. (2023, January 17\u201324). Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00893"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., and Yan, Y. (2020, January 13\u201319). BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1109\/TGRS.2013.2264548","article-title":"Bayesian Approach to Tree Detection Based on Airborne Laser Scanning Data","volume":"52","author":"Lahivaara","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1109\/JSTARS.2018.2830410","article-title":"Individual Tree Crown Detection and Delineation From Very-High-Resolution UAV Images Based on Bias Field and Marker-Controlled Watershed Segmentation Algorithms","volume":"11","author":"Huang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/368\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:21Z","timestamp":1760104101000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":49,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020368"],"URL":"https:\/\/doi.org\/10.3390\/rs16020368","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]}}}