{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:22:46Z","timestamp":1781763766759,"version":"3.54.5"},"reference-count":65,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T00:00:00Z","timestamp":1585526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China project \u201cResearch of Key Technologies for Monitoring Forest Plantation Resources\u201d","award":["(2017YFD0600900"],"award-info":[{"award-number":["(2017YFD0600900"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571411"],"award-info":[{"award-number":["41571411"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.<\/jats:p>","DOI":"10.3390\/rs12071101","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Xiandie","family":"Jiang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&amp;F University, Hangzhou 311300, China"},{"name":"School of Environmental &amp; Resource Sciences, Zhejiang A&amp;F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7198-4607","authenticated-orcid":false,"given":"Guiying","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 350007, China"},{"name":"School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dengsheng","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&amp;F University, Hangzhou 311300, China"},{"name":"School of Environmental &amp; Resource Sciences, Zhejiang A&amp;F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erxue","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinliang","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&amp;F University, Hangzhou 311300, China"},{"name":"School of Environmental &amp; Resource Sciences, Zhejiang A&amp;F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, X., Ge, H., Lu, D., Zhang, M., Lai, Z., and Yao, R. (2019). Comparative Study on Variable Selection Approaches in Establishment of Remote Sensing Model for Forest Biomass Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11121437"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1038\/nature07944","article-title":"The carbon balance of terrestrial ecosystems in China","volume":"458","author":"Piao","year":"2009","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.atmosenv.2010.12.041","article-title":"An old-growth subtropical Asian evergreen forest as a large carbon sink","volume":"45","author":"Tan","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_4","first-page":"75","article-title":"Spatio-temporal changes in forest fragmentation, disturbance patterns over the three giant forested regions of China","volume":"37","author":"Shen","year":"2013","journal-title":"J. Nanjing For. Univ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, S., Wei, X., Li, D., and Lu, D. (2017). Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data. Remote Sens., 9.","DOI":"10.3390\/rs9050479"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"357","DOI":"10.5194\/bg-7-357-2010","article-title":"Ecosystem carbon exchanges of a subtropical evergreen coniferous plantation subjected to seasonal drought, 2003\u20132007","volume":"7","author":"Wen","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.5194\/bg-8-1667-2011","article-title":"Underestimated effects of low temperature during early growing season on carbon sequestration of a subtropical coniferous plantation","volume":"8","author":"Zhang","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4910","DOI":"10.1073\/pnas.1317065111","article-title":"High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region","volume":"111","author":"Yu","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D. (2018). Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region. Remote Sens., 10.","DOI":"10.3390\/rs10040627"},{"key":"ref_10","first-page":"1","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.2747\/1548-1603.48.2.141","article-title":"A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small-Area Applications","volume":"48","author":"Gleason","year":"2011","journal-title":"GISci. Remote Sens."},{"key":"ref_12","first-page":"776","article-title":"A review on biomass estimation methods using synthetic aperture radar data","volume":"1","author":"Nafiseh","year":"2011","journal-title":"Int. J. Geomat. Geosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1177\/0309133312471367","article-title":"Optical remote sensing of forest leaf area index and biomass","volume":"37","author":"Song","year":"2012","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_14","first-page":"399","article-title":"LiDAR Remote Sensing of Vegetation Biomass","volume":"Volume 20135777","author":"Chen","year":"2013","journal-title":"Remote Sensing of Natural Resources"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2011.10.012","article-title":"Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda","volume":"117","author":"Avitabile","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2014.08.017","article-title":"Global, Landsat-based forest-cover change from 1990\u20132000","volume":"155","author":"Kim","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0034-4257(03)00039-7","article-title":"Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions","volume":"85","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"967","DOI":"10.14358\/PERS.71.8.967","article-title":"Satellite Estimation of Aboveground Biomass and Impacts of Forest Stand Structure","volume":"71","author":"Lu","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. (2016). Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060469"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.isprsjprs.2012.03.010","article-title":"A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region","volume":"70","author":"Li","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","first-page":"1092","article-title":"Prediction of subtropical forest parameters using airborne laser scanner","volume":"15","author":"Tian","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","first-page":"1","article-title":"Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon","volume":"50","author":"Feng","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dong, P., and Chen, Q. (2017). LiDAR Remote Sensing and Applications, Informa UK Limited.","DOI":"10.4324\/9781351233354"},{"key":"ref_26","unstructured":"Guo, Q., Su, Y., Hu, T., and Liu, J. (2018). LiDAR Principles, Processing and Applications in Forest Ecology, Higher Education Press. (In Chinese)."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7110","DOI":"10.3390\/rs6087110","article-title":"Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests","volume":"6","author":"Cao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"081598","DOI":"10.1117\/1.JRS.8.081598","article-title":"Light detection and ranging and hyperspectral data for estimation of forest biomass: A review","volume":"8","author":"Man","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"309","DOI":"10.17521\/cjpe.2015.0030","article-title":"Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data","volume":"39","author":"Xu","year":"2015","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_30","first-page":"60","article-title":"Algorithm comparative analysis with stepwise linear regression and neural network","volume":"5","author":"Tan","year":"2014","journal-title":"J. North China Inst. Sci. Technol."},{"key":"ref_31","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_32","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_33","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.rse.2004.07.016","article-title":"Quantifying forest above ground carbon content using LiDAR remote sensing","volume":"93","author":"Patenaude","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1139\/x05-230","article-title":"Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: An examination of scanning density","volume":"36","author":"Thomas","year":"2006","journal-title":"Can. J. For. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.rse.2011.10.009","article-title":"Airborne scanning LiDAR in a double sampling forest carbon inventory","volume":"117","author":"Stephens","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.foreco.2004.12.001","article-title":"Estimating stand structure using discrete-return lidar: An example from low density, fire prone ponderosa pine forests","volume":"208","author":"Hall","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1080\/01431160903474970","article-title":"Estimating above-ground biomass in young forests with airborne laser scanning","volume":"32","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"510","DOI":"10.5424\/fs\/2013223-03878","article-title":"Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities","volume":"22","author":"Miranda","year":"2013","journal-title":"For. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"733","DOI":"10.14358\/PERS.77.7.733","article-title":"A Support Vector Regression Approach to Estimate Forest Biophysical Parameters at the Object Level Using Airborne Lidar Transects and QuickBird Data","volume":"77","author":"Chen","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5569","DOI":"10.1109\/JSTARS.2017.2748341","article-title":"Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass","volume":"10","author":"Shao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1016\/j.rse.2007.09.009","article-title":"Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest","volume":"112","author":"Anderson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2006.01.021","article-title":"Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR\/InSAR, ETM+, Quickbird) synergy","volume":"102","author":"Hyde","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1016\/j.rse.2010.08.029","article-title":"Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors","volume":"115","author":"Clark","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s13595-011-0023-0","article-title":"Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests","volume":"68","author":"Banskota","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.02.009","article-title":"Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest","volume":"69","author":"Tsui","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2007.02.006","article-title":"Investigating RaDAR\u2013LiDAR synergy in a North Carolina pine forest","volume":"110","author":"Nelson","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_48","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","volume":"36","author":"Pang","year":"2013","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_49","first-page":"631","article-title":"General review on remote sensing-based biomass estimation","volume":"37","author":"Li","year":"2012","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.01.021","article-title":"Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass","volume":"121","author":"Chen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_51","first-page":"18","article-title":"Effects of sample plots stratification on estimation accuracy of aboveground carbon storage for Phyllostachys edulis Forests","volume":"49","author":"Xu","year":"2013","journal-title":"Sci. Silvae Sin."},{"key":"ref_52","first-page":"58","article-title":"Modeling of standing tree biomass for main species of trees in Guangxi province","volume":"4","author":"Cai","year":"2014","journal-title":"For. Resour. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, G., Xie, Z., Jiang, X., Lu, D., and Chen, E. (2019). Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China. Remote Sens., 11.","DOI":"10.3390\/rs11192328"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.rse.2005.10.019","article-title":"Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia","volume":"100","author":"Lucas","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1016\/j.ecolmodel.2011.02.007","article-title":"Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy","volume":"222","author":"Vincenzi","year":"2011","journal-title":"Ecol. Model."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.rse.2013.05.033","article-title":"Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics","volume":"151","author":"Pflugmacher","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2014.01.024","article-title":"Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests","volume":"145","author":"Tanase","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"1","article-title":"Biomass estimation of Sonneratia caseolaris (L.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks","volume":"54","author":"Pham","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D., and Bui, D.T. (2018). Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ecolmodel.2017.10.009","article-title":"Enhancing of accuracy assessment for forest above-ground biomass estimates obtained from remote sensing via hypothesis testing and overfitting evaluation","volume":"366","author":"Valbuena","year":"2017","journal-title":"Ecol. Model."},{"key":"ref_62","first-page":"267","article-title":"The elements of statistical learning","volume":"45","author":"Hastie","year":"2010","journal-title":"Technometrics"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1080\/01431160310001654978","article-title":"Mapping Mediterranean scrub with satellite imagery: Biomass estimation and spectral behaviour","volume":"25","author":"Palmeirim","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, L., Lu, D., and Li, D. (2018). Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11010007"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Chen, Q., Lu, D., Keller, M., Dos-Santos, M.N., Bolfe, E.L., Feng, Y., and Wang, C. (2015). Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. Remote Sens., 8.","DOI":"10.3390\/rs8010021"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:13:32Z","timestamp":1760174012000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,30]]},"references-count":65,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071101"],"URL":"https:\/\/doi.org\/10.3390\/rs12071101","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,30]]}}}