{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:13:26Z","timestamp":1776226406861,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T00:00:00Z","timestamp":1711756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA\u2019s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy (R2 = 0.58) is higher than that of using only optical images (R2 = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg\/ha, and it is the highest in the coniferous forest at 46.60 Mg\/ha. And the highest average AGB occurs in October\u2013December at 59.55 Mg\/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.<\/jats:p>","DOI":"10.3390\/rs16071229","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:28:00Z","timestamp":1711891680000},"page":"1229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Xuedi","family":"Zhao","sequence":"first","affiliation":[{"name":"The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0018-3436","authenticated-orcid":false,"given":"Wenmin","family":"Hu","sequence":"additional","affiliation":[{"name":"The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Jiang","family":"Han","sequence":"additional","affiliation":[{"name":"The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0845-2645","authenticated-orcid":false,"given":"Jiaxing","family":"Xu","sequence":"additional","affiliation":[{"name":"The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(03)00084-1","article-title":"Evaluating environmental influences of zoning in urban ecosystems with remote sensing","volume":"86","author":"Wilson","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1038\/502624d","article-title":"Urban greening needs better data","volume":"502","author":"Pataki","year":"2013","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1038\/468765b","article-title":"Sustainable cities: Seeing past the trees","volume":"468","author":"MacKenzie","year":"2010","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"635365","DOI":"10.3389\/fenvs.2021.635365","article-title":"Estimation of Above-Ground Carbon-Stocks for Urban Greeneries in Arid Areas: Case Study for Doha and FIFA World Cup Qatar 2022","volume":"9","author":"Habib","year":"2021","journal-title":"Front. Environ. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Henn, K.A., and Peduzzi, A. (2023). Biomass Estimation of Urban Forests Using LiDAR and High-Resolution Aerial Imagery in Athens\u2013Clarke County, GA. Forests, 14.","DOI":"10.3390\/f14051064"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/S0269-7491(01)00214-7","article-title":"Carbon storage and sequestration by urban trees in the USA","volume":"116","author":"Nowak","year":"2002","journal-title":"Environ. Pollut."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, X., Chen, L., Chen, L., Zhang, Y., and Liu, Y. (2020). Estimating Urban Vegetation Biomass from Sentinel-2A Image Data. Forests, 11.","DOI":"10.3390\/f11020125"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sousa J\u00fanior, V.d.P., Sparacino, J., Espindola, G.M.D., and Assis, R.J.S.D. (2023). Carbon Biomass Estimation Using Vegetation Indices in Agriculture\u2013Pasture Mosaics in the Brazilian Caatinga Dry Tropical Forest. ISPRS Int. J. Geoinf., 12.","DOI":"10.3390\/ijgi12090354"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.envpol.2013.06.005","article-title":"Identifying potential sources of variability between vegetation carbon storage estimates for urban areas","volume":"183","author":"Davies","year":"2013","journal-title":"Environ. Pollut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, J., Wang, M., Ma, M., and Lin, Y. (2018). Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography. Remote Sens., 10.","DOI":"10.3390\/rs10111849"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s13595-011-0040-z","article-title":"Review of ground-based methods to measure the distribution of biomass in forest canopies","volume":"68","author":"Seidel","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"012053","DOI":"10.1088\/1755-1315\/569\/1\/012053","article-title":"Estimating aboveground biomass of urban trees by high resolution remote sensing image: A case study in Hengqin, Zhuhai, China","volume":"569","author":"Bai","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4239","DOI":"10.1080\/01431161.2023.2234093","article-title":"Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems","volume":"44","author":"Pang","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xiao, J., Chen, L., Zhang, T., Li, L., Yu, Z., Wu, R., Bai, L., Xiao, J., and Chen, L. (2022). Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data. Forests, 13.","DOI":"10.3390\/f13071077"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mngadi, M., Odindi, J., and Mutanga, O. (2021). The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape. Remote Sens., 13.","DOI":"10.3390\/rs13214281"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"810","DOI":"10.3390\/rs4040810","article-title":"Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data","volume":"4","author":"Eckert","year":"2012","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.rse.2010.11.010","article-title":"Improved forest biomass estimates using ALOS AVNIR-2 texture indices","volume":"115","author":"Sarker","year":"2011","journal-title":"Remote Sens Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Stratoulias, D., Nuthammachot, N., Suepa, T., and Phoungthong, K. (2022). Assessing the Spectral Information of Sentinel-1 and Sentinel-2 Satellites for Above-Ground Biomass Retrieval of a Tropical Forest. ISPRS Int. J. Geoinf., 11.","DOI":"10.3390\/ijgi11030199"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1016\/j.rse.2011.01.026","article-title":"Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level","volume":"115","author":"Popescu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127728","DOI":"10.1016\/j.ufug.2022.127728","article-title":"Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data","volume":"76","author":"Qin","year":"2022","journal-title":"Urban For. Urban Green."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sun, M., Cui, L., Park, J., Garc\u00eda, M., Zhou, Y., Silva, C.A., He, L., Zhang, H., and Zhao, K. (2022). Evaluation of NASA\u2019s GEDI Lidar Observations for Estimating Biomass in Temperate and Tropical Forests. Forests, 13.","DOI":"10.3390\/f13101686"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","article-title":"Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_26","first-page":"159","article-title":"Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery","volume":"66","author":"Saatchi","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, M., Sun, R., and Xiao, Z. (2018). Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland. Remote Sens., 10.","DOI":"10.3390\/rs10020344"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112234","DOI":"10.1016\/j.rse.2020.112234","article-title":"Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping","volume":"253","author":"Silva","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Fayad, I., Vieilledent, G., Bailly, J.S., and Ho Tong Minh, D. (2017). Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas. Remote Sens., 9.","DOI":"10.3390\/rs9030213"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., Bailly, J.-S., Barbier, N., Gond, V., H\u00e9rault, B., El Hajj, M., Fabre, F., and Perrin, J. (2016). Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana. Remote Sens., 8.","DOI":"10.3390\/rs8030240"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/JSTARS.2013.2256883","article-title":"Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chi, H., Sun, G., Huang, J., Li, R., Ren, X., Ni, W., and Fu, A. (2017). Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat\/GLAS and Landsat\/TM Data. Remote Sens., 9.","DOI":"10.3390\/rs9070707"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, K., Wang, J., Zeng, W., and Song, J. (2017). Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data. Remote Sens., 9.","DOI":"10.3390\/rs9040341"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xi, X., Han, T., Wang, C., Luo, S., Xia, S., and Pan, F. (2016). Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data. ISPRS Int. J. Geoinf., 5.","DOI":"10.3390\/ijgi5040045"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.ecolind.2017.02.045","article-title":"Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest","volume":"78","author":"Nie","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dorado-Roda, I., Pascual, A., Godinho, S., Silva, C.A., Botequim, B., Rodr\u00edguez-Gonz\u00e1lvez, P., Gonz\u00e1lez-Ferreiro, E., and Guerra-Hern\u00e1ndez, J. (2021). Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sens., 13.","DOI":"10.3390\/rs13122279"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111283","DOI":"10.1016\/j.rse.2019.111283","article-title":"Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data","volume":"232","author":"Qi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13021-018-0098-0","article-title":"Estimating urban above ground biomass with multi-scale LiDAR","volume":"13","author":"Wilkes","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/JSTARS.2019.2961634","article-title":"Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network","volume":"13","author":"Shao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","first-page":"666","article-title":"Modelling LiDAR derived tree canopy height from Landsat TM, ETM+ and OLI satellite imagery\u2014A machine learning approach","volume":"73","author":"Staben","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1007\/s11769-013-0615-8","article-title":"Spatial pattern and regional types of rural settlements in Xuzhou City, Jiangsu Province, China","volume":"23","author":"Ma","year":"2013","journal-title":"Chin. Geogr. Sci."},{"key":"ref_42","unstructured":"(2023, December 18). GEDI User Guide, Available online: https:\/\/lpdaac.usgs.gov\/documents\/986\/GEDI02_UserGuide_V2.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1029\/2018EA000506","article-title":"The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions","volume":"6","author":"Hancock","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112845","DOI":"10.1016\/j.rse.2021.112845","article-title":"Aboveground biomass density models for NASA\u2019s Global Ecosystem Dynamics Investigation (GEDI) lidar mission","volume":"270","author":"Duncanson","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ren, C., Jiang, H., Xi, Y., Liu, P., and Li, H. (2023). Quantifying Temperate Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15020375"},{"key":"ref_46","unstructured":"(2023, December 18). Landsat 8-9 Collection 2 (C2) Level 2 Science Product (L2SP) Guide. Available online: https:\/\/d9-wret.s3.us-west-2.amazonaws.com\/assets\/palladium\/production\/s3fs-public\/media\/files\/LSDS-1619_Landsat8-9-Collection2-Level2-Science-Product-Guide-v5.pdf."},{"key":"ref_47","unstructured":"(2023, December 18). Landsat 8 (L8) Data Users Handbook. Available online: https:\/\/d9-wret.s3.us-west-2.amazonaws.com\/assets\/palladium\/production\/s3fs-public\/atoms\/files\/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf."},{"key":"ref_48","unstructured":"Michelle, H., and Blair, J.B. (2019). Algorithm Theoretical Basis Document (ATBD) for GEDI Transmit and Receive Waveform Processing for L1 and L2 Products, Goddard Space Flight Center."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Adam, M., Urbazaev, M., Dubois, C., and Schmullius, C. (2020). Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters. Remote Sens., 12.","DOI":"10.3390\/rs12233948"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wu, X., Shen, X., Zhang, Z., Cao, F., She, G., and Cao, L. (2022). An Advanced Framework for Multi-Scale Forest Structural Parameter Estimations Based on UAS-LiDAR and Sentinel-2 Satellite Imagery in Forest Plantations of Northern China. Remote Sens., 14.","DOI":"10.3390\/rs14133023"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1080\/01431161.2020.1820618","article-title":"Assessing of Urban Vegetation Biomass in Combination with LiDAR and High-resolution Remote Sensing Images","volume":"42","author":"Zhang","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Alcaras, E., Costantino, D., Guastaferro, F., Parente, C., and Pepe, M. (2022). Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14071727"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xi, Z., Xu, H., Xing, Y., Gong, W., Chen, G., and Yang, S. (2022). Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods. Remote Sens., 14.","DOI":"10.3390\/rs14020364"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1284235","DOI":"10.3389\/fpls.2023.1284235","article-title":"Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery","volume":"14","author":"Liu","year":"2023","journal-title":"Front. Plant Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Xu, L., Zhang, M., Wang, Z., Gu, Z., Wu, Y., Shi, Y., and Lu, Z. (2020). Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+. ISPRS Int. J. Geoinf., 9.","DOI":"10.3390\/ijgi9010048"},{"key":"ref_56","unstructured":"Zhou, W. (2012). Study on Forest Vegetation Carbon Stock and Its Influencing Factors in Xuzhou City, Nanjing Forestry University."},{"key":"ref_57","unstructured":"Li, J., Li, C., and Peng, S. (2007). Methods and Applications of Biomass Estimation in Poplar Plantation Forests, Nanjing Forestry University."},{"key":"ref_58","unstructured":"Zhu, Y. (2016). Compositional Structure of Trees and Their Carbon Storage Characteristics in the Campus Green Space of Anhui Agricultural University, China, Anhui University of Agriculture."},{"key":"ref_59","first-page":"12","article-title":"Ginkgo biomass allocation pattern and heterogeneous growth modeling","volume":"39","author":"Kun","year":"2017","journal-title":"J. Beijing For. Univ."},{"key":"ref_60","first-page":"35","article-title":"Modeling of single wood biomass of larch in North China","volume":"46","author":"Chun","year":"2017","journal-title":"Shanxi For. Sci. Technol."},{"key":"ref_61","first-page":"141","article-title":"Aboveground biomass modeling of four common greening tree species in Shanghai","volume":"42","author":"Zhang","year":"2018","journal-title":"Nanjing For. Univ. Nanjing China"},{"key":"ref_62","first-page":"16","article-title":"Quantitative study on the biomass accumulation pattern of Lankao paulownia trees","volume":"02","author":"Yang","year":"1999","journal-title":"J. Appl. Ecol."},{"key":"ref_63","unstructured":"Institute of Forest Ecology and Conservation, Chinese Academy of Forestry, The Nature Conservancy, State Forestry Administration Survey Planning and Design Institute, and China Green Carbon Foundation (2014). Guidelines for Measuring and Monitoring Carbon Sinks in Afforestation Projects, State Forestry Administration China."},{"key":"ref_64","unstructured":"Yao, Z. (2015). Estimation of Aboveground Carbon Stocks in Xi\u2019an\u2019s Urban Greenlands, North West Agriculture and Forestry University."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"15348","DOI":"10.3390\/s140815348","article-title":"Intra-and-inter species biomass prediction in a plantation forest: Testing the utility of high spatial resolution spaceborne multispectral RapidEye sensor and advanced machine learning algorithms","volume":"14","author":"Dube","year":"2014","journal-title":"Sensors"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.rse.2015.12.002","article-title":"Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data","volume":"173","author":"Su","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Yang, M., Zhou, X., Liu, Z., Li, P., Tang, J., Xie, B., and Peng, C. (2022). A Review of General Methods for Quantifying and Estimating Urban Trees and Biomass. Forests, 13.","DOI":"10.3390\/f13040616"},{"key":"ref_68","first-page":"101931","article-title":"Local validation of global biomass maps","volume":"83","author":"McRoberts","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s13021-016-0060-y","article-title":"Comparison of national level biomass maps for conterminous US: Understanding pattern and causes of differences","volume":"11","author":"Neeti","year":"2016","journal-title":"Carbon Balance Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:21:38Z","timestamp":1760106098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,30]]},"references-count":69,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16071229"],"URL":"https:\/\/doi.org\/10.3390\/rs16071229","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,30]]}}}