{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T19:13:41Z","timestamp":1779909221878,"version":"3.53.1"},"reference-count":75,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Department of Technology","award":["2021YFD2200405"],"award-info":[{"award-number":["2021YFD2200405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Research on the inversion of forest aboveground biomass based on airborne light detection and ranging (LiDAR) data focuses on finding the relationship between the two, such as established linear or nonlinear models. However, these models may have poorer estimation accuracy for tree-components biomass and cannot guarantee the additivity of each component. Therefore, we aimed to develop an error-in-variable biomass model system that ensures both the compatibility of the individual tree component biomass with the diameter at breast height and the additivity of component biomass. The system we developed used the airborne LiDAR data and field-measured data of principis-rupprechtii (Larix gmelinii var.) trees, collected from north China. Our model system not only ensured the additivity of nonlinear biomass models, it also accounted for the impact of measurement errors. We first selected the airborne LiDAR-derived variable with the highest contribution to the biomass of each component and then developed an inversion model system with that variable as an independent variable and with the biomass of each component as the dependent variable using allometric functions. Moreover, two model estimation methods, two-stage error (TSEM) and nonlinear seemingly unrelated regression (NSUR) with one-step, two-step, and summation methods, were also applied, and their performances were compared. The results showed that both NSUR-one-step and TSEM-one-step led to similar parameter estimates and performance for a system, and the fitting accuracy of a model system was not very attractive. The variance function included in a model system reduced the heteroscedasticity effectively and improved the model accuracy. Overall, this study successfully combined the error-in-variable modeling with the airborne LiDAR data, proposed methods that can be used for the extension of component biomass from an individual tree to a stand and that might improve the estimation accuracy of carbon storage. A compatible model system can be further improved if various sources of error in the variables are identified, and their impacts on the system are effectively accounted for.<\/jats:p>","DOI":"10.3390\/rs15143546","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T08:40:06Z","timestamp":1689324006000},"page":"3546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Compatible Biomass Model with Measurement Error Using Airborne LiDAR Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Xingjing","family":"Chen","sequence":"first","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongbo","family":"Xie","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8623-6211","authenticated-orcid":false,"given":"Ram P.","family":"Sharma","sequence":"additional","affiliation":[{"name":"Institute of Forestry, Tribhuwan Univeristy, Kritipur 44600, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6458-8742","authenticated-orcid":false,"given":"Qiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2339-6223","authenticated-orcid":false,"given":"Qingwang","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyong","family":"Fu","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1139\/a11-015","article-title":"Carbon Sequestration by Chinese Bamboo Forests and Their Ecological Benefits: Assessment of Potential, Problems, and Future Challenges","volume":"19","author":"Song","year":"2011","journal-title":"Environ. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.apgeog.2015.09.003","article-title":"Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forest types, structural attributes and age in sub-tropical coastal region of KwaZulu Natal, South Africa using remote sensing","volume":"64","author":"Dube","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149958","DOI":"10.1016\/j.scitotenv.2021.149958","article-title":"Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data","volume":"802","author":"Mngadi","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.compag.2005.04.002","article-title":"Artificial Neural Networks as an Alternative Tool in Pine Bark Volume Estimation","volume":"48","author":"Diamantopoulou","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.compag.2018.06.039","article-title":"Tree-Bark Volume Prediction via Machine Learning: A Case Study Based on Black Alder\u2019s Tree-Bark Production","volume":"151","author":"Diamantopoulou","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","unstructured":"Pi\u00e9gay, H., Pautou, G., and Ruffinoni, C. (2003). Les For\u00eats Riveraines Des Cours D\u2019eau: \u00c9cologie, Fonctions et Gestion, IDF."},{"key":"ref_7","first-page":"163","article-title":"Review of the Characteristics of Black Alder (Alnus glutinosa (L.) Gaertn.) and Their Implications for Silvicultural Practices","volume":"83","author":"Claessens","year":"2010","journal-title":"For. Int. J. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/0034-4257(84)90031-2","article-title":"Determining Forest Canopy Characteristics Using Airborne Laser Data","volume":"15","author":"Nelson","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0034-4257(88)90028-4","article-title":"Estimating Forest Biomass and Volume Using Airborne Laser Data","volume":"24","author":"Nelson","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1080\/07038992.1986.10855092","article-title":"Gross-Merchantable Timber Volume Estimation Using an Airborne Lidar System","volume":"12","author":"Maclean","year":"1986","journal-title":"Can. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0924-2716(97)83000-6","article-title":"Determination of Mean Tree Height of Forest Stands Using Airborne Laser Scanner Data","volume":"52","year":"1997","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0034-4257(96)00213-1","article-title":"Separating the Ground and Airborne Laser Sampling Phases to Estimate Tropical Forest Basal Area, Volume, and Biomass","volume":"60","author":"Nelson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.rse.2004.01.006","article-title":"Estimation of Timber Volume and Stem Density Based on Scanning Laser Altimetry and Expected Tree Size Distribution Functions","volume":"90","author":"Maltamo","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.biombioe.2007.06.022","article-title":"Estimating Biomass of Individual Pine Trees Using Airborne Lidar","volume":"31","author":"Popescu","year":"2007","journal-title":"Biomass Bioenergy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.3724\/SP.J.1258.2012.01095","article-title":"Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing\u2019an Mountains, Northeastern of China: Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing\u2019an Mountains, Northeastern of China","volume":"36","author":"Pang","year":"2013","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2012.02.023","article-title":"Quantifying Aboveground Forest Carbon Pools and Fluxes from Repeat LiDAR Surveys","volume":"123","author":"Hudak","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.rse.2009.11.021","article-title":"Estimating Biomass Carbon Stocks for a Mediterranean Forest in Central Spain Using LiDAR Height and Intensity Data","volume":"114","author":"Chuvieco","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1109\/JSTARS.2016.2522960","article-title":"When Big Data Are Too Much: Effects of LiDAR Returns and Point Density on Estimation of Forest Biomass","volume":"9","author":"Singh","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, M., Liu, Q., Fu, L., Wang, G., and Zhang, X. (2019). Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach. Remote Sens., 11.","DOI":"10.3390\/rs11091050"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, K., Chen, Z., Fu, L., Tian, W., Jiang, F., Yi, J., Du, Z., and Sun, H. (2022). Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types. Remote Sens., 14.","DOI":"10.3390\/rs14020298"},{"key":"ref_21","unstructured":"(2023, February 22). Evaluation of Nonlinear Equations for Predicting Diameter from Tree Height. Available online: https:\/\/cdnsciencepub.com\/doi\/10.1139\/x2012-019."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.rse.2012.07.006","article-title":"Forest Biomass Estimation from Airborne LiDAR Data Using Machine Learning Approaches","volume":"125","author":"Gleason","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1016\/j.foreco.2008.01.044","article-title":"Spatial Partitioning of Biomass and Diversity in a Lowland Bolivian Forest: Linking Field and Remote Sensing Measurements","volume":"255","author":"Broadbent","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2416","DOI":"10.1016\/j.foreco.2008.01.022","article-title":"Automatic Recognition and Measurement of Single Trees Based on Data from Airborne Laser Scanning over the Richly Structured Natural Forests of the Bavarian Forest National Park","volume":"255","author":"Heurich","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1139\/X10-073","article-title":"Using Error-in-Variable Regression to Predict Tree Diameter and Crown Width from Remotely Sensed Imagery","volume":"40","author":"Zhang","year":"2010","journal-title":"Can. J. For. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2013.02.002","article-title":"A Simulation Approach for Accuracy Assessment of Two-Phase Post-Stratified Estimation in Large-Area LiDAR Biomass Surveys","volume":"133","author":"Ene","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rencher, A.C., and Schaalje, G.B. (2008). Linear Models in Statistics, Wiley. [2nd ed.].","DOI":"10.1002\/9780470192610"},{"key":"ref_28","unstructured":"(1987). Measurement Error Models; Wiley Series in Probability and Statistics, John Wiley & Sons, Inc.. Available online: https:\/\/onlinelibrary.wiley.com\/doi\/book\/10.1002\/9780470316665."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0378-1127(98)00562-3","article-title":"Biomass Estimation Procedures in Short Rotation Forestry","volume":"121","author":"Verwijst","year":"1999","journal-title":"For. Ecol. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1080\/01621459.1962.10480664","article-title":"An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias","volume":"57","author":"Zellner","year":"1962","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_31","first-page":"298","article-title":"Converting stem volume to biomass with additivity, bias corrections and confidence bands for two Australian tree species","volume":"31","author":"Bi","year":"2001","journal-title":"N. Zealand J. For. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s00468-004-0333-z","article-title":"Additive Biomass Equations for Native Eucalypt Forest Trees of Temperate Australia","volume":"18","author":"Bi","year":"2004","journal-title":"Trees"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1016\/j.foreco.2010.03.003","article-title":"Additive Prediction of Aboveground Biomass for Pinus Radiata (D. Don) Plantations","volume":"259","author":"Bi","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s00468-015-1325-x","article-title":"Comparison of Seemingly Unrelated Regressions with Error-in-Variable Models for Developing a System of Nonlinear Additive Biomass Equations","volume":"30","author":"Fu","year":"2016","journal-title":"Trees"},{"key":"ref_35","first-page":"573","article-title":"Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons","volume":"45","author":"Parresol","year":"1999","journal-title":"For. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lei, X., Zhang, H., and Bi, H. (November, January 31). Additive Aboveground Biomass Equations for Major Tree Species in Over-Logged Forest Region in Northeast China. Proceedings of the 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Shanghai, China.","DOI":"10.1109\/PMA.2012.6524837"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fu, L., Liu, Q., Sun, H., Wang, Q., Li, Z., Chen, E., Pang, Y., Song, X., and Wang, G. (2018). Development of a System of Compatible Individual Tree Diameter and Aboveground Biomass Prediction Models Using Error-In-Variable Regression and Airborne LiDAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10020325"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0378-1127(97)00161-8","article-title":"Effect of Errors-in-Variables on Coefficients of a Growth Model and on Prediction of Growth","volume":"102","author":"Kangas","year":"1998","journal-title":"For. Ecol. Manag."},{"key":"ref_39","unstructured":"(2023, February 22). A Study on Impact of Measurement Error on Whole Stand Model. Available online: http:\/\/www.linyekexue.net\/EN\/10.11707\/j.1001-7488.20050629."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1051\/forest\/2010042","article-title":"Allometric Equations to Predict the Total Above-Ground Biomass of Radiata Pine Trees","volume":"67","author":"Moore","year":"2010","journal-title":"Ann. For. Sci."},{"key":"ref_41","unstructured":"(2010). Measurement Error: Models, Methods, and Applications, CRC Press. [1st ed.]. Available online: https:\/\/www.routledge.com\/Measurement-Error-Models-Methods-and-Applications\/Buonaccorsi\/p\/book\/9781032477688."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"357","DOI":"10.14358\/PERS.72.4.357","article-title":"Detection of Individual Tree Crowns in Airborne Lidar Data","volume":"72","author":"Koch","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2014.06.009","article-title":"Assessing the Potential for Leaf-off LiDAR Data to Model Canopy Closure in Temperate Deciduous Forests","volume":"95","author":"Parent","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"MA, K., Xiong, Y., Jiang, F., Chen, S., and Sun, H. (2021). A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR. Remote Sens., 13.","DOI":"10.3390\/rs13081442"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.mcm.2011.10.071","article-title":"Extraction of Individual Tree Crowns from Airborne LiDAR Data in Human Settlements","volume":"58","author":"Liu","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.rse.2015.11.029","article-title":"Comparing Generalized Linear Models and Random Forest to Model Vascular Plant Species Richness Using LiDAR Data in a Natural Forest in Central Chile","volume":"173","author":"Lopatin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_48","first-page":"113","article-title":"Study on biomass of Larixprincipis rupprechtiiin Saihanba Mechanized Forestry Centre","volume":"30","author":"Fu","year":"2015","journal-title":"Hebei J. For. Orchard. Res."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (1999). Modern Applied Statistics with S-PLUS, Springer. Statistics and Computing.","DOI":"10.1007\/978-1-4757-3121-7"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1139\/x11-071","article-title":"Comparison of Different Methods for Fitting Nonlinear Mixed Forest Models and for Making Predictions","volume":"41","author":"Yang","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_51","first-page":"797","article-title":"Using Measurement Error Modeling Method to Establish Compatible Single-Tree Biomass Equations System","volume":"23","author":"Weisheng","year":"2010","journal-title":"For. Res. Beijing"},{"key":"ref_52","first-page":"23","article-title":"An algorithm for estimating multivariatenon-linear error-in-measure models","volume":"11","author":"Tang","year":"1996","journal-title":"J. Biomath."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s11676-012-0299-4","article-title":"Modeling Compatible Single-Tree Aboveground Biomass Equations for Masson Pine (Pinus massoniana) in Southern China","volume":"23","author":"Zeng","year":"2012","journal-title":"J. For. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0304-3800(01)00326-X","article-title":"Simultaneous Equations, Error-in-Variable Models, and Model Integration in Systems Ecology","volume":"142","author":"Tang","year":"2001","journal-title":"Ecol. Model."},{"key":"ref_55","unstructured":"Tang, S., Lang, K.J., and Li, H.K. (2008). Statistics and Computation of Biomathematical Models (ForStat Course), Science Press. (In Chinese)."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1139\/x03-022","article-title":"Does Cross Validation Provide Additional Information in the Evaluation of Regression Models?","volume":"33","author":"Kozak","year":"2003","journal-title":"Can. J. For. Res."},{"key":"ref_57","first-page":"106","article-title":"Goodness Evaluation and Precision Analysis of Tree Biomass Equations","volume":"47","author":"Zeng","year":"2011","journal-title":"Sci. Silvae Sin."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/00401706.1984.10487985","article-title":"Validating Regression Procedures With New Data","volume":"26","author":"Berk","year":"1984","journal-title":"Technometrics"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.jspi.2003.10.004","article-title":"Linear Model Selection by Cross-Validation","volume":"128","author":"Rao","year":"2005","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_60","first-page":"5","article-title":"Research on weighting regression and modeling","volume":"35","author":"Zeng","year":"1999","journal-title":"Sci. Silvae Sin."},{"key":"ref_61","unstructured":"Field, C., and Raupach, M. (2004). The Global Carbon Cycle: Integrating Humans, Climate and the Natural World, Island Press."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"984","DOI":"10.3390\/f4040984","article-title":"Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest","volume":"4","author":"He","year":"2013","journal-title":"Forests"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2005.01.005","article-title":"Parametric (modified least squares) and non-parametric (Thei-Sen) linear regression for predicting biophysical parameter in the presence of measurement errors","volume":"95","author":"Fernandes","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"111251","DOI":"10.1016\/j.rse.2019.111251","article-title":"Estimating Tree Height from TanDEM-X Data at the Northwestern Canadian Treeline","volume":"231","author":"Antonova","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.foreco.2014.04.003","article-title":"An Allometric Model for Estimating DBH of Isolated and Clustered Eucalyptus Trees from Measurements of Crown Projection Area","volume":"326","author":"Verma","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"114","DOI":"10.5558\/tfc65114-2","article-title":"A Comparison of Existing Models for DBH Estimation from Large-Scale Photos","volume":"65","author":"Hall","year":"1989","journal-title":"For. Chron."},{"key":"ref_67","first-page":"177","article-title":"The Relationship of Diameter at Breast Height and Crown Diameter for Four Species Groups in Hardin County, Tennessee","volume":"19","author":"Gering","year":"1995","journal-title":"S. J. Appl. For."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1007\/s13595-011-0067-1","article-title":"Comparison of Conventional Eight-Point Crown Projections with LIDAR-Based Virtual Crown Projections in a Temperate Old-Growth Forest","volume":"68","author":"Fleck","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0034-4257(96)00214-3","article-title":"Modeling Forest Canopy Heights: The Effects of Canopy Shape","volume":"60","author":"Nelson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1051\/forest:2003031","article-title":"Estimation of Crown Radii and Crown Projection Area from Stem Size and Tree Position","volume":"60","author":"Grote","year":"2003","journal-title":"Ann. For. Sci."},{"key":"ref_71","unstructured":"Tang, S.Z., Li, Y., and Fu, L.Y. (2015). Statistical Foundation for Biomathematical Models, Higher Education Press. [2nd ed.]."},{"key":"ref_72","first-page":"1","article-title":"Systemfit: A Package for Estimating Systems of Simultaneous Equations in R","volume":"23","author":"Henningsen","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/LGRS.2013.2260719","article-title":"Impacts of Plot Location Errors on Accuracy of Mapping and Scaling Up Aboveground Forest Carbon Using Sample Plot and Landsat TM Data","volume":"10","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_74","first-page":"25","article-title":"The influence of Heteroscedasticity on the Establishment of Biomass Model","volume":"30","author":"Yang","year":"2014","journal-title":"For. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/02827581.2011.564204","article-title":"Uncertainties of Mapping Aboveground Forest Carbon Due to Plot Locations Using National Forest Inventory Plot and Remotely Sensed Data","volume":"26","author":"Wang","year":"2011","journal-title":"Scand. J. For. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3546\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:13Z","timestamp":1760127133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3546"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":75,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143546"],"URL":"https:\/\/doi.org\/10.3390\/rs15143546","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,14]]}}}