{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T16:48:50Z","timestamp":1768322930964,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The development and management of green open spaces are essential in overcoming environmental problems such as air pollution and urban warming. 3D modeling and biomass calculation are the example efforts in managing green open spaces. In this study, 3D modeling was carried out on point clouds data acquired by the UAV photogrammetry and UAV LiDAR methods. 3D modeling is done explicitly using the point clouds fitting method. This study uses three fitting methods: the spherical fitting method, the ellipsoid fitting method, and the spherical harmonics fitting method. The spherical harmonics fitting method provides the best results and produces an R2 value between 0.324 to 0.945. In this study, Above-Ground Biomass (AGB) calculations were also carried out from the modeling results using three methods with UAV LiDAR and Photogrammetry data. AGB calculation using UAV LiDAR data gives better results than using photogrammetric data. AGB calculation using UAV LiDAR data gives an accuracy of 78% of the field validation results. However, for visualization purposes with a not-too-wide area, a 3D model of photogrammetric data using the spherical harmonics method can be used.<\/jats:p>","DOI":"10.3390\/ijgi11030174","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:38:16Z","timestamp":1646599096000},"page":"174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["3D Modeling of Individual Trees from LiDAR and Photogrammetric Point Clouds by Explicit Parametric Representations for Green Open Space (GOS) Management"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0440-2253","authenticated-orcid":false,"given":"Deni","family":"Suwardhi","sequence":"first","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Kamal Nur","family":"Fauzan","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Agung Budi","family":"Harto","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Budhy","family":"Soeksmantono","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Riantini","family":"Virtriana","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocab":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-8937","authenticated-orcid":false,"given":"Arnadi","family":"Murtiyoso","sequence":"additional","affiliation":[{"name":"Forest Resources Management Group, Department of Environmental Systems Science, Institute of Terrestrial Ecosystems, ETH Z\u00fcrich, 8092 Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocab":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.2307\/1941591","article-title":"Beyond global warming: Ecology and global change","volume":"75","author":"Vitousek","year":"1994","journal-title":"Ecology"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ribeiro, H.V., Rybski, D., and Kropp, J.P. (2019). Effects of changing population or density on urban carbon dioxide emissions. Nat. Commun., 10.","DOI":"10.1038\/s41467-019-11184-y"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s12205-015-0524-8","article-title":"Climate change\u2014Is it the cause or the effect?","volume":"19","author":"Jayawardena","year":"2015","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_4","unstructured":"Shi, A. (2001). Population growth and global carbon dioxide emissions. IUSSP Conference in Brazil\/Session-s09, The World Bank."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.envpol.2007.06.012","article-title":"Human health effects of air pollution","volume":"151","author":"Kampa","year":"2008","journal-title":"Environ. Pollut."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Heidt, V., and Neef, M. (2008). Benefits of urban green space for improving urban climate. Ecology, Planning, and Management of Urban Forests, Springer.","DOI":"10.1007\/978-0-387-71425-7_6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.20886\/jsek.2013.10.1.1-20","article-title":"Analisis cadangan karbon pohon pada lanskap hutan kota di DKI Jakarta","volume":"10","author":"Lubis","year":"2013","journal-title":"J. Penelit. Sos. Ekon. Kehutan."},{"key":"ref_8","unstructured":"Lessie, O.-C. (2018). Urban Vegetation Modeling 3D Levels of Detail. [Master\u2019s Thesis, Delft University of Technology]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40965-018-0046-7","article-title":"3DCityDB\u2014A 3D Geodatabase Solution for the Management, Analysis, and Visualization of Semantic 3D City Models Based on CityGML","volume":"3","author":"Yao","year":"2018","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2842","DOI":"10.3390\/ijgi4042842","article-title":"Applications of 3D City Models: State of the Art Review","volume":"4","author":"Biljecki","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_11","first-page":"248","article-title":"Web enabled spatio-temporal semantic analysis of traffic noise using CityGML","volume":"11","author":"Konde","year":"2017","journal-title":"J. Geomat."},{"key":"ref_12","unstructured":"Kavisha, K., Ledoux, H., Commandeur, T.J.F., Stoter, J.E., and Kavisha, K. (2017, January 26\u201327). Modeling urban noise in CityGML ADE: Case of the Netherlands. Proceedings of the 12th 3D Geoinfo Conference, Melbourne, Australia."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hajji, R., Yaagoubi, R., Meliana, I., Laafou, I., and Gholabzouri, A.E. (2021). Development of an Integrated BIM-3D GIS Approach for 3D Cadastre in Morocco. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10050351"},{"key":"ref_14","unstructured":"Stojanovic, V., Trapp, M., Richter, R., Hagedorn, B., and D\u00f6llner, J. (2018, January 3\u20135). Towards the generation of digital twins for facility management based on 3D point clouds. In Proceeding of the 34th Annual ARCOM Conference, Belfast, UK."},{"key":"ref_15","unstructured":"Singh, S., Shrivastava, V., and Sharma, V. (2019, January 6\u20137). CityGML based 3D modeling of urban area using UAV dataset for estimation of solar potential. Proceedings of the International Conference on Unmanned Aerial System in Geomatics, Roorkee, India."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rosser, J.F., Long, G., Zakhary, S., Boyd, D.S., Mao, Y., and Robinson, D. (2019). Modeling urban housing stocks for building energy simulation using CityGML EnergyADE. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040163"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bao, K., Padsala, R., Thr\u00e4n, D., and Schr\u00f6ter, B. (2020). Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9110642"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"519","DOI":"10.5194\/isprs-archives-XLII-1-W1-519-2017","article-title":"Oblique photogrammetry supporting 3D urban reconstruction of complex scenarios","volume":"XLII-1\/W1","author":"Toschi","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"175","DOI":"10.5194\/isprs-archives-XLII-5-175-2018","article-title":"3D Citygml Building Modeling from Lidar Point Cloud Data","volume":"XLII-5","author":"Jayaraj","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Popovic, D., Govedarica, M., Jovanovic, D., Radulovic, A., and Simeunovic, V. (2017). 3D Visualization of Urban Area Using Lidar Technology and CityGML. IOP Conference Series: Earth and Environmental Science, IOP Publishing.","DOI":"10.1088\/1755-1315\/95\/4\/042006"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ortega, S., Santana, J.M., Wendel, J., Trujillo, A., and Murshed, S.M. (2021). Generating 3D city models from open LiDAR point clouds: Advancing towards smart city applications. Open Source Geospatial Science for Urban Studies, Springer.","DOI":"10.1007\/978-3-030-58232-6_6"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ledoux, H., Biljecki, F., Dukai, B., Kumar, K., Peters, R., Stoter, J., and Commandeur, T. (2021). 3dfier: Automatic reconstruction of 3D city models. J. Open Source Softw., 6.","DOI":"10.21105\/joss.02866"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1080\/01431161.2020.1851062","article-title":"DBSCAN-based point cloud extraction for Tomographic synthetic aperture radar (TomoSAR) three-dimensional (3D) building reconstruction","volume":"42","author":"Guo","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1007\/s12524-018-0827-6","article-title":"3D Modeling of Urban Area Using Synthetic Aperture Radar (SAR)","volume":"46","author":"Sharafzadeh","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kolbe, T.H. (2009). Representing and exchanging 3D city models with CityGML. 3D Geo-Information Sciences, Springer.","DOI":"10.1007\/978-3-540-87395-2_2"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compenvurbsys.2016.04.005","article-title":"An improved LOD specification for 3D building models","volume":"59","author":"Biljecki","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Arroyo Ohori, K., Biljecki, F., Kumar, K., Ledoux, H., and Stoter, J. (2018). Modeling Cities and Landscapes in 3D with CityGML. Building Information Modeling, Springer.","DOI":"10.1007\/978-3-319-92862-3_11"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"185","DOI":"10.4308\/hjb.26.4.185","article-title":"3D Landscape Recording and Modeling of Individual Trees","volume":"26","author":"Trisyanti","year":"2019","journal-title":"Hayati J. Biosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"42","DOI":"10.5194\/isprs-archives-XLII-4-W10-55-2018","article-title":"Modeling Trees for Virtual Singapore: From Data Acquisition to Citygml Models","volume":"XLII-4\/W10","author":"Gobeawan","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Agus, M., Veloz Castillo, M., Garnica Molina, J.F., Gobbetti, E., Lehv\u00e4slaiho, H., Morales Tapia, A., and Cal\u00ed, C. (2019). Shape analysis of 3D nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations. Comput. Graph. X, 1.","DOI":"10.1016\/j.cagx.2019.100004"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fan, G., Nan, L., Dong, Y., Su, X., and Chen, F. (2020). AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point clouds. Remote Sens., 12.","DOI":"10.3390\/rs12183089"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wagers, S., Castilla, G., Filiatrault, M., and Sanchez-Azofeifa, G.A. (2021). Using TLS-Measured Tree Attributes to Estimate above Ground Biomass in Small Black Spruce Trees. Forests, 12.","DOI":"10.3390\/f12111521"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ten Harkel, J., Bartholomeus, H., and Kooistra, L. (2020). Biomass and crop height estimation of different crops using UAV-based LiDAR. Remote Sens., 12.","DOI":"10.3390\/rs12010017"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1111\/2041-210X.12904","article-title":"Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR","volume":"9","author":"Lau","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.fcr.2017.11.024","article-title":"Photogrammetry for the estimation of wheat biomass and harvest index","volume":"216","author":"Walter","year":"2018","journal-title":"Field Crops Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1080\/10106049.2018.1552322","article-title":"Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry","volume":"35","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0378-1127(00)00460-6","article-title":"Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests","volume":"146","author":"Ketterings","year":"2001","journal-title":"For. Ecol. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yan, Z., Liu, R., Cheng, L., Zhou, X., Ruan, X., and Xiao, Y. (2019). A concave hull methodology for calculating the crown volume of individual trees based on vehicle-borne LiDAR data. Remote Sens., 11.","DOI":"10.3390\/rs11060623"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Trochta, J., Kr\u016f\u010dek, M., Vr\u0161ka, T., and Kr\u00e1l, 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_40","doi-asserted-by":"crossref","unstructured":"Rachakonda, P., Muralikrishnan, B., Cournoyer, L., Cheok, G., Lee, V., Shilling, M., and Sawyer, D. (2017). Methods and considerations to determine sphere center from terrestrial laser scanner point clouds data. Meas. Sci. Technol., 28.","DOI":"10.1088\/1361-6501\/aa8011"},{"key":"ref_41","unstructured":"Brown, S. (1997). Estimating Biomass and Biomass Change of Tropical Forests: A Primer, Food and Agriculture Organization."},{"key":"ref_42","unstructured":"Sutaryo, D. (2009). Penghitungan Biomassa Sebuah Pengantar untuk Studi Karbon dan Perdagangan Karbon, Wetlands International Indonesia Programme."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s10457-012-9529-1","article-title":"Crown area allometries for estimation of aboveground tree biomass in agricultural landscapes of western Kenya","volume":"86","author":"Kuyah","year":"2012","journal-title":"Agrofor. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1016\/j.foreco.2009.01.027","article-title":"Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests","volume":"257","author":"Basuki","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.isprsjprs.2018.11.001","article-title":"Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations","volume":"146","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Iizuka, K., Yonehara, T., Itoh, M., and Kosugi, Y. (2018). Estimating tree height and diameter at breast height (DBH) from digital surface models and orthophotos obtained with an unmanned aerial system for a Japanese cypress (Chamaecyparis obtusa) forest. Remote Sens., 10.","DOI":"10.3390\/rs10010013"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ku\u017eelka, K., Slav\u00edk, M., and Surov\u00fd, P. (2020). Very High Density Point cloudss from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement. Remote Sens., 12.","DOI":"10.3390\/rs12081236"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mokro\u0161, M., Liang, X., Surov\u00fd, P., Valent, P., \u010cer\u0148ava, J., Chud\u00fd, F., Tun\u00e1k, D., Salo\u0148, \u0160., and Mergani\u010d, J. (2018). Evaluation of close-range photogrammetry image collection methods for estimating tree diameters. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030093"},{"key":"ref_49","first-page":"61","article-title":"Surface reconstruction from unorganized points using self-organizing neural networks","volume":"99","author":"Yu","year":"1999","journal-title":"IEEE Vis."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Fryskowska, A. (2019). Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis. Sensors, 19.","DOI":"10.3390\/s19030700"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"39","DOI":"10.25077\/jbioua.9.1.39-46.2021","article-title":"Above Ground Biomass Estimation of Syzygium aromaticum using structure from motion (SfM) derived from Unmanned Aerial Vehicle in Paninggahan Agroforest Area, West Sumatra","volume":"9","author":"Harapan","year":"2021","journal-title":"J. Biol. UNAND"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3882","DOI":"10.3390\/f6113882","article-title":"Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest","volume":"6","author":"Ota","year":"2015","journal-title":"Forests"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Maesano, M., Khoury, S., Nakhle, F., Firrincieli, A., Gay, A., Tauro, F., and Harfouche, A. (2020). UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax. Remote Sens., 12.","DOI":"10.3390\/rs12203464"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/3\/174\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:32:24Z","timestamp":1760135544000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/3\/174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["ijgi11030174"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11030174","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,4]]}}}