{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T02:26:43Z","timestamp":1768789603956,"version":"3.49.0"},"reference-count":105,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFD0600904"],"award-info":[{"award-number":["2017YFD0600904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key Research and Development Program","doi-asserted-by":"publisher","award":["31922055"],"award-info":[{"award-number":["31922055"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2017YFD0600904"],"award-info":[{"award-number":["2017YFD0600904"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31922055"],"award-info":[{"award-number":["31922055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["2017YFD0600904"],"award-info":[{"award-number":["2017YFD0600904"]}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["31922055"],"award-info":[{"award-number":["31922055"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimation and extrapolation of forest structural parameters in planted forests are essential for monitoring forest resources, investigating their ecosystem services (e.g., forest structure and functions), as well as supporting decisions for precision silviculture. Advances in unmanned aerial vehicle (UAV)-borne Light Detection and Ranging (LiDAR) technology have enhanced our ability to precisely characterize the 3-D structure of the forest canopy with high flexibility, usually within forest plots and stands. For wall-to-wall forest structure mapping in broader landscapes, samples (transects) of UAV-LiDAR datasets are a cost-efficient solution as an intermediate layer for extrapolation from field plots to full-coverage multispectral satellite imageries. In this study, an advanced two-stage extrapolation approach was established to estimate and map large area forest structural parameters (i.e., mean DBH, dominant height, volume, and stem density), in synergy with field plots and UAV-LiDAR and GF-6 satellite imagery, in a typical planted forest of southern China. First, estimation models were built and used to extrapolate field plots to UAV-LiDAR transects; then, the maps of UAV-LiDAR transects were extrapolated to the whole study area using the wall-to-wall grid indices that were calculated from GF-6 satellite imagery. By comparing with direct prediction models that were fitted by field plots and GF-6-derived spectral indices, the results indicated that the two-stage extrapolation models (R2 = 0.64\u20130.85, rRMSE = 7.49\u201326.85%) obtained higher accuracy than direct prediction models (R2 = 0.58\u20130.75, rRMSE = 21.31\u201338.43%). In addition, the effect of UAV-LiDAR point density and sampling intensity for estimation accuracy was studied by sensitivity analysis as well. The results showed a stable level of accuracy for approximately 10% of point density (34 pts\u00b7m\u22122) and 20% of sampling intensity. To understand the error propagation through the extrapolation procedure, a modified U-statistics uncertainty analysis was proposed to characterize pixel-level estimates of uncertainty and the results demonstrated that the uncertainty was 0.75 cm for mean DBH, 1.23 m for dominant height, 14.77 m3\u00b7ha\u22121 for volume and 102.72 n\u00b7ha\u22121 for stem density, respectively.<\/jats:p>","DOI":"10.3390\/rs14112677","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T08:01:18Z","timestamp":1654243278000},"page":"2677","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Extrapolation Assessment for Forest Structural Parameters in Planted Forests of Southern China by UAV-LiDAR Samples and Multispectral Satellite Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Hao","family":"Liu","sequence":"first","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Fuliang","family":"Cao","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Guanghui","family":"She","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5195-0477","authenticated-orcid":false,"given":"Lin","family":"Cao","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"ref_1","first-page":"6","article-title":"Wood from Planted Forests","volume":"58","author":"Carle","year":"2008","journal-title":"For. Prod. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1038\/459906a","article-title":"Forestry: Planting the Forest of the Future","volume":"459","author":"Marris","year":"2009","journal-title":"Nat. News"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1007\/s10531-013-0458-8","article-title":"Plantation Forests, Climate Change and Biodiversity","volume":"22","author":"Pawson","year":"2013","journal-title":"Biodivers. Conserv."},{"key":"ref_4","first-page":"65","article-title":"Planted Forests and Biodiversity","volume":"104","author":"Carnus","year":"2006","journal-title":"J. For."},{"key":"ref_5","unstructured":"FAO (2020). Global Forest Resources Assessment 2020: Main Report, FAO."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1007\/s11056-019-09708-x","article-title":"An Analysis of Potential Investment Returns of Planted Forests in South China","volume":"50","author":"Zhang","year":"2019","journal-title":"New For."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.3390\/f5071682","article-title":"Outlook for the Next Generation\u2019s Precision Forestry in Finland","volume":"5","author":"Holopainen","year":"2014","journal-title":"Forests"},{"key":"ref_8","first-page":"15","article-title":"Remote Sensing for Precision Forestry","volume":"60","author":"Dash","year":"2016","journal-title":"N. Z. J. For."},{"key":"ref_9","unstructured":"Choudhry, H., and O\u2019Kelly, G. (2018). Precision Forestry: A Revolution in the Woods, McKinsey Co."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.forpol.2003.09.003","article-title":"Sustainable Forest Management: Global Trends and Opportunities","volume":"7","author":"Siry","year":"2005","journal-title":"For. Policy Econ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"807","DOI":"10.5558\/tfc84807-6","article-title":"The Role of LiDAR in Sustainable Forest Management","volume":"84","author":"Wulder","year":"2008","journal-title":"For. Chron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D., and Woods, M. (2013). A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach, Information Report FI-X-10.","DOI":"10.5558\/tfc2013-132"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.foreco.2007.06.033","article-title":"Accuracy of Forest Inventory Mapping: Some Implications for Boreal Forest Management","volume":"252","author":"Thompson","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Franklin, S.E. (2001). Remote Sensing for Sustainable Forest Management, CRC Press.","DOI":"10.1201\/9781420032857"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The Potential and Challenge of Remote Sensing-based Biomass Estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.1016\/j.rse.2010.05.022","article-title":"Segment-Constrained Regression Tree Estimation of Forest Stand Height from Very High Spatial Resolution Panchromatic Imagery over a Boreal Environment","volume":"114","author":"Mora","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1080\/01431161.2013.779041","article-title":"Forest Inventory Stand Height Estimates from Very High Spatial Resolution Satellite Imagery Calibrated with Lidar Plots","volume":"34","author":"Mora","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1080\/01431160500142145","article-title":"Aboveground Biomass Estimation Using Landsat TM Data in the Brazilian Amazon","volume":"26","author":"Lu","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/s40663-020-00276-7","article-title":"Machine Learning and Geostatistical Approaches for Estimating Aboveground Biomass in Chinese Subtropical Forests","volume":"7","author":"Su","year":"2020","journal-title":"For. Ecosyst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.3390\/s90402719","article-title":"Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors","volume":"9","author":"Zheng","year":"2009","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S0034-4257(01)00300-5","article-title":"Derivation and Validation of Canada-Wide Coarse-Resolution Leaf Area Index Maps Using High-Resolution Satellite Imagery and Ground Measurements","volume":"80","author":"Chen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.foreco.2005.10.056","article-title":"Estimation of Tree Canopy Cover in Evergreen Oak Woodlands Using Remote Sensing","volume":"223","author":"Carreiras","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"96010","DOI":"10.1117\/1.JRS.9.096010","article-title":"Improved Model for Estimating the Biomass of Populus Euphratica Forest Using the Integration of Spectral and Textural Features from the Chinese High-Resolution Remote Sensing Satellite GaoFen-1","volume":"9","author":"Zhang","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8766","DOI":"10.1080\/01431161.2018.1492176","article-title":"Aboveground Forest Biomass Derived Using Multiple Dates of WorldView-2 Stereo-Imagery: Quantifying the Improvement in Estimation Accuracy","volume":"39","author":"Vastaranta","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2007.01.009","article-title":"Predicting and Mapping Mangrove Biomass from Canopy Grain Analysis Using Fourier-Based Textural Ordination of IKONOS Images","volume":"109","author":"Proisy","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2017.01.026","article-title":"Multi-Feature Combined Cloud and Cloud Shadow Detection in GaoFen-1 Wide Field of View Imagery","volume":"191","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tong, X.-Y., Lu, Q., Xia, G.-S., and Zhang, L. (2018, January 22\u201327). Large-Scale Land Cover Classification in Gaofen-2 Satellite Imagery. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518389"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shao, W., Sheng, Y., and Sun, J. (2017). Preliminary Assessment of Wind and Wave Retrieval from Chinese Gaofen-3 SAR Imagery. Sensors, 17.","DOI":"10.3390\/s17081705"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xu, J., Liang, Y., Liu, J., and Huang, Z. (2017). Multi-Frame Super-Resolution of Gaofen-4 Remote Sensing Images. Sensors, 17.","DOI":"10.3390\/s17092142"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/MGRS.2019.2927687","article-title":"The Advanced Hyperspectral Imager: Aboard China\u2019s GaoFen-5 Satellite","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, A., Zhong, B., Hu, L., Wu, S., Xu, Z., Wu, H., Wu, J., Gong, X., Wang, H., and Liu, Q. (2020). Radiometric Cross-Calibration of the Wide Field View Camera Onboard Gaofen-6 in Multispectral Bands. Remote Sens., 12.","DOI":"10.3390\/rs12061037"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, J., Dian, Y., Wang, X., Yao, C., Jian, Y., Li, Y., and Han, Z. (2020). Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China. Forests, 11.","DOI":"10.3390\/f11040407"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, C., Zhang, H., Wang, P., Tang, J., Tian, Y., and Zhang, Q. (2021). Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040801"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2006.09.034","article-title":"Remote Sensing Support for National Forest Inventories","volume":"110","author":"McRoberts","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s00468-006-0119-6","article-title":"Estimating Canopy Structure of Douglas-Fir Forest Stands from Discrete-Return LiDAR","volume":"21","author":"Coops","year":"2007","journal-title":"Trees-Struct. Funct."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dubayah, R.O., Sheldon, S.L., Clark, D.B., Hofton, M.A., Blair, J.B., Hurtt, G.C., and Chazdon, R.L. (2010). Estimation of Tropical Forest Height and Biomass Dynamics Using Lidar Remote Sensing at La Selva, Costa Rica. J. Geophys. Res. Biogeosci., 115.","DOI":"10.1029\/2009JG000933"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0034-4257(01)00243-7","article-title":"Estimating Tree Height and Tree Crown Properties Using Airborne Scanning Laser in a Boreal Nature Reserve","volume":"79","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shen, X., Cao, L., Chen, D., Sun, Y., Wang, G., and Ruan, H. (2018). Prediction of Forest Structural Parameters Using Airborne Full-Waveform LiDAR and Hyperspectral Data in Subtropical Forests. Remote Sens., 10.","DOI":"10.3390\/rs10111729"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.rse.2011.11.015","article-title":"Estimation of 3D Vegetation Structure from Waveform and Discrete Return Airborne Laser Scanning Data","volume":"118","author":"Lindberg","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.rse.2012.01.015","article-title":"Forest Structure Modeling with Combined Airborne Hyperspectral and LiDAR Data","volume":"121","author":"Latifi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2016.07.023","article-title":"Forest Aboveground Biomass Mapping and Estimation across Multiple Spatial Scales Using Model-Based Inference","volume":"184","author":"Chen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"382","DOI":"10.5589\/m13-046","article-title":"Airborne Laser Scanning and Digital Stereo Imagery Measures of Forest Structure: Comparative Results and Implications to Forest Mapping and Inventory Update","volume":"39","author":"Vastaranta","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2014.10.004","article-title":"Generalizing Predictive Models of Forest Inventory Attributes Using an Area-Based Approach with Airborne LiDAR Data","volume":"156","author":"Bouvier","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2012.02.001","article-title":"Lidar Sampling for Large-Area Forest Characterization: A Review","volume":"121","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2017.12.020","article-title":"Large-Area Mapping of Canadian Boreal Forest Cover, Height, Biomass and Other Structural Attributes Using Landsat Composites and Lidar Plots","volume":"209","author":"Matasci","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2007.02.002","article-title":"Integrating Profiling LIDAR with Landsat Data for Regional Boreal Forest Canopy Attribute Estimation and Change Characterization","volume":"110","author":"Wulder","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"Hopkinson, C., Wulder, M.A., Coops, N.C., Milne, T., Fox, A., and Bater, C.W. (2011, January 16\u201320). Airborne Lidar Sampling of the Canadian Boreal Forest: Planning, Execution, and Initial Processing. Proceedings of the SilviLaser 2011 Conference, Hobart, Australia."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2017.03.019","article-title":"Use of Partial-Coverage UAV Data in Sampling for Large Scale Forest Inventories","volume":"194","author":"Puliti","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1080\/01431161.2016.1252477","article-title":"Forestry Applications of UAVs in Europe: A Review","volume":"38","author":"Torresan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.3390\/rs4061519","article-title":"Development of a UAV-LiDAR System with Application to Forest Inventory","volume":"4","author":"Wallace","year":"2012","journal-title":"Remote Sens."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"Peng, X., Zhao, A., Chen, Y., Chen, Q., Liu, H., Wang, J., and Li, H. (2020). Comparison of Modeling Algorithms for Forest Canopy Structures Based on UAV-LiDAR: A Case Study in Tropical China. Forests, 11.","DOI":"10.3390\/f11121324"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s10712-019-09532-0","article-title":"Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them","volume":"40","author":"Barbier","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_55","first-page":"101986","article-title":"Estimating Aboveground Biomass of the Mangrove Forests on Northeast Hainan Island in China Using an Upscaling Method from Field Plots, UAV-LiDAR Data and Sentinel-2 Imagery","volume":"85","author":"Wang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2016.10.038","article-title":"Lidar-Based Estimates of Aboveground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations","volume":"188","author":"Nelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1029\/2010GL043622","article-title":"A Global Forest Canopy Height Map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System","volume":"37","author":"Lefsky","year":"2010","journal-title":"Geophys. Res. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1139\/cjfr-2013-0401","article-title":"Mapping Attributes of Canada\u2019s Forests at Moderate Resolution through k NN and MODIS Imagery","volume":"44","author":"Beaudoin","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.rse.2012.05.026","article-title":"Lidar Calibration and Validation for Geometric-Optical Modeling with Landsat Imagery","volume":"124","author":"Chen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2016.01.015","article-title":"Integrating Landsat Pixel Composites and Change Metrics with Lidar Plots to Predictively Map Forest Structure and Aboveground Biomass in Saskatchewan, Canada","volume":"176","author":"Zald","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Zuo, Z., Wang, R., and Wu, X. (2019). Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling. Remote Sens., 11.","DOI":"10.3390\/rs11182156"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2018.11.017","article-title":"Integration of Multi-Resource Remotely Sensed Data and Allometric Models for Forest Aboveground Biomass Estimation in China","volume":"221","author":"Huang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1016\/j.foreco.2009.06.056","article-title":"Mapping and Spatial Uncertainty Analysis of Forest Vegetation Carbon by Combining National Forest Inventory Data and Satellite Images","volume":"258","author":"Wang","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1098\/rstb.2003.1425","article-title":"Error Propagation and Scaling for Tropical Forest Biomass Estimates","volume":"359","author":"Chave","year":"2004","journal-title":"Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.01.009","article-title":"Uncertainty of Remotely Sensed Aboveground Biomass over an African Tropical Forest: Propagating Errors from Trees to Plots to Pixels","volume":"160","author":"Chen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"75","DOI":"10.14358\/PERS.78.1.75","article-title":"A New Method for Segmenting Individual Trees from the Lidar Point Cloud","volume":"78","author":"Li","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.10.013","article-title":"Estimating Forest Canopy Fuel Parameters Using LIDAR Data","volume":"94","author":"Andersen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_68","first-page":"1367","article-title":"Predicting Forest Stand Characteristics with Airborne Scanning Lidar","volume":"66","author":"Means","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.3390\/rs4051190","article-title":"Advances in Forest Inventory Using Airborne Laser Scanning","volume":"4","author":"Yu","year":"2012","journal-title":"Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Shen, W., Li, M., Huang, C., and Wei, A. (2016). Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data. Remote Sens., 8.","DOI":"10.3390\/rs8070595"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2017.10.016","article-title":"Estimation and Mapping of Above-Ground Biomass of Mangrove Forests and Their Replacement Land Uses in the Philippines Using Sentinel Imagery","volume":"134","author":"Castillo","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.2134\/agronj2010.0395","article-title":"Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index","volume":"103","author":"Hunt","year":"2011","journal-title":"Agron. J."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.3390\/s8042480","article-title":"Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring","volume":"8","author":"Miura","year":"2008","journal-title":"Sensors"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs6021211","article-title":"The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization","volume":"6","author":"Wu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_78","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/02757259409532252","article-title":"Influences of Canopy Architecture on Relationships between Various Vegetation Indices and LAI and FPAR: A Computer Simulation","volume":"10","author":"Goel","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_80","unstructured":"Pearson, R.L., and Miller, L.D. (1972, January 2\u20136). Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Shortgrass Prairie. Proceedings of the English International Symposiumon on Remote Sensing of Enviroment, Ann Arbor, MI, USA."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Desboulets, L.D.D. (2018). A Review on Variable Selection in Regression Analysis. Econometrics, 6.","DOI":"10.3390\/econometrics6040045"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature Selection with the Boruta Package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ecoinf.2019.05.008","article-title":"Estimating Leaf Area Index and Light Extinction Coefficient Using Random Forest Regression Algorithm in a Tropical Moist Deciduous Forest, India","volume":"52","author":"Srinet","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.isprsjprs.2014.11.007","article-title":"Characterizing Stand-Level Forest Canopy Cover and Height Using Landsat Time Series, Samples of Airborne LiDAR, and the Random Forest Algorithm","volume":"101","author":"Ahmed","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Silva, C.A., Klauberg, C., Hudak, A.T., Vierling, L.A., Jaafar, W.S.W.M., Mohan, M., Garcia, M., Ferraz, A., Cardil, A., and Saatchi, S. (2017). Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest. Forests, 8.","DOI":"10.3390\/f8070254"},{"key":"ref_89","first-page":"1625","article-title":"Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife","volume":"15","author":"Wager","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_90","first-page":"841","article-title":"Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests","volume":"17","author":"Mentch","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"189","DOI":"10.14358\/PERS.82.3.189","article-title":"Approximating Prediction Uncertainty for Random Forest Regression Models","volume":"82","author":"Coulston","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_92","first-page":"983","article-title":"Quantile Regression Forests","volume":"7","author":"Meinshausen","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s13595-015-0473-x","article-title":"Propagating Uncertainty through Individual Tree Volume Model Predictions to Large-Area Volume Estimates","volume":"73","author":"McRoberts","year":"2016","journal-title":"Ann. For. Sci."},{"key":"ref_94","unstructured":"Lee, A.J. (2019). U-Statistics: Theory and Practice, Routledge."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1214\/aoms\/1177730196","article-title":"A Class of Statistics with Asymptotically Normal Distributions","volume":"19","author":"Hoeffiding","year":"1948","journal-title":"Ann. Math. Stat."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.foreco.2004.07.077","article-title":"Simulation Study for Finding Optimal Lidar Acquisition Parameters for Forest Height Retrieval","volume":"214","author":"Lovell","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.rse.2012.11.024","article-title":"Tradeoffs between Lidar Pulse Density and Forest Measurement Accuracy","volume":"130","author":"Jakubowski","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2012.10.017","article-title":"A Meta-Analysis of Terrestrial Aboveground Biomass Estimation Using Lidar Remote Sensing","volume":"128","author":"Zolkos","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Wallace, L., Lucieer, A., Turner, D., and Vop\u011bnka, P. (2016). Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7.","DOI":"10.3390\/f7030062"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2017.10.007","article-title":"Combining UAV and Sentinel-2 Auxiliary Data for Forest Growing Stock Volume Estimation through Hierarchical Model-Based Inference","volume":"204","author":"Puliti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2954","DOI":"10.1080\/01431161.2017.1285083","article-title":"An Integrated UAV-Borne Lidar System for 3D Habitat Mapping in Three Forest Ecosystems across China","volume":"38","author":"Guo","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.isprsjprs.2015.02.007","article-title":"Lidar with Multi-Temporal MODIS Provide a Means to Upscale Predictions of Forest Biomass","volume":"102","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1080\/17538947.2016.1227380","article-title":"Fine-Resolution Forest Tree Height Estimation across the Sierra Nevada through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery","volume":"10","author":"Su","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ecoinf.2018.12.010","article-title":"Forest Aboveground Biomass Estimation Using Machine Learning Regression Algorithm in Yok Don National Park, Vietnam","volume":"50","author":"Dang","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_105","unstructured":"Yadav, K.R. (2019). Coupling Airborne LiDar and High Resolution Optical Sensor Parameters for Biomass Estimation Using Machine Learning, University of Twente."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2677\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:24:04Z","timestamp":1760138644000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,2]]},"references-count":105,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112677"],"URL":"https:\/\/doi.org\/10.3390\/rs14112677","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,2]]}}}