{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T06:22:19Z","timestamp":1772605339968,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Zhejiang Province, China","award":["LQ22D010001"],"award-info":[{"award-number":["LQ22D010001"]}]},{"name":"Natural Science Foundation of Zhejiang Province, China","award":["42101323"],"award-info":[{"award-number":["42101323"]}]},{"name":"Natural Science Foundation of Zhejiang Province, China","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"Natural Science Foundation of Zhejiang Province, China","award":["4085C50220204092"],"award-info":[{"award-number":["4085C50220204092"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LQ22D010001"],"award-info":[{"award-number":["LQ22D010001"]}],"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":["42101323"],"award-info":[{"award-number":["42101323"]}],"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":["42171367"],"award-info":[{"award-number":["42171367"]}],"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":["4085C50220204092"],"award-info":[{"award-number":["4085C50220204092"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["LQ22D010001"],"award-info":[{"award-number":["LQ22D010001"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["42101323"],"award-info":[{"award-number":["42101323"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["4085C50220204092"],"award-info":[{"award-number":["4085C50220204092"]}]},{"name":"National Earth System Science Data Center of China","award":["LQ22D010001"],"award-info":[{"award-number":["LQ22D010001"]}]},{"name":"National Earth System Science Data Center of China","award":["42101323"],"award-info":[{"award-number":["42101323"]}]},{"name":"National Earth System Science Data Center of China","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"National Earth System Science Data Center of China","award":["4085C50220204092"],"award-info":[{"award-number":["4085C50220204092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aboveground biomass (AGB) mapping using spaceborne LiDAR data and multi-sensor images is essential for efficient carbon monitoring and climate change mitigation actions in heterogeneous forests. The optimal predictors of remote sensing-based AGB vary greatly with geographic stratification, such as topography and forest type, while the way in which geographic stratification influences the contributions of predictor variables in object-based AGB mapping is insufficiently studied. To address the improvement of mapping forest AGB by geographic stratification in heterogeneous forests, satellite multisensory data from global ecosystem dynamics investigation (GEDI) and series of advanced land observing satellite (ALOS) and Sentinel were integrated. Multi-sensor predictors for the AGB modeling of different types of forests were selected using a correlation analysis of variables calculated from topographically stratified objects. Random forests models were built with GEDI-based AGB and geographically stratified predictors to acquire wall-to-wall biomass values. It was illustrated that the mapped biomass had a similar distribution and was approximate to the sampled forest AGB. Through an accuracy comparison using independent validation samples, it was determined that the geographic stratification approach improved the accuracy by 34.79% compared to the unstratified process. Stratification of forest type further increased the mapped AGB accuracy compared to that of topography. Topographical stratification greatly influenced the predictors\u2019 contributions to AGB mapping in mixed broadleaf\u2013conifer and broad-leaved forests, but only slightly impacted coniferous forests. Optical variables were predominant for deciduous forests, while for evergreen forests, SAR indices outweighed the other predictors. As a pioneering estimation of forest AGB with geographic stratification using satellite multisensory data, this study offers optimal predictors and an advanced method for obtaining carbon maps in heterogeneous regional landscapes.<\/jats:p>","DOI":"10.3390\/rs15102625","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T06:32:58Z","timestamp":1684391578000},"page":"2625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Improved Object-Based Mapping of Aboveground Biomass Using Geographic Stratification with GEDI Data and Multi-Sensor Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9270-1626","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Earth Sciences, School of Information Science and Technology, Zhejiang Provincial Key Laboratory of Urban Wetlands and Regional Change, Hangzhou Normal University, Hangzhou 311121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8798-3449","authenticated-orcid":false,"given":"Chunying","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Wuyishan 354300, China"}]},{"given":"Bai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9865-8235","authenticated-orcid":false,"given":"Zongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"National Earth System Science Data Center, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1960-1976","authenticated-orcid":false,"given":"Weidong","family":"Man","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of Mining Development and Security Technology, Hebei Industrial Technology Institute of Mine Ecological Remediation, College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China"}]},{"given":"Mingyue","family":"Liu","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of Mining Development and Security Technology, Hebei Industrial Technology Institute of Mine Ecological Remediation, College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s11676-021-01421-w","article-title":"Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau","volume":"33","author":"Luo","year":"2022","journal-title":"J. Forestry Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1038\/s41558-020-00976-6","article-title":"Global maps of twenty-first century forest carbon fluxes","volume":"11","author":"Harris","year":"2021","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112964","DOI":"10.1016\/j.rse.2022.112964","article-title":"Forest aboveground biomass in the southwestern United States from a MISR multi-angle index, 2000\u20132015","volume":"275","author":"Chopping","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"113367","DOI":"10.1016\/j.rse.2022.113367","article-title":"Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat","volume":"284","author":"Liang","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"1","article-title":"Large loss and rapid recovery of vegetation cover and aboveground biomass over forest areas in Australia during 2019\u20132020","volume":"224","author":"Qin","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2012.05.029","article-title":"Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band","volume":"124","author":"Cartus","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112235","DOI":"10.1016\/j.rse.2020.112235","article-title":"Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden","volume":"253","author":"Santoro","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2012.03.011","article-title":"Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions","volume":"70","author":"Cutler","year":"2012","journal-title":"ISPRS J. Photogramm."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111669","DOI":"10.1016\/j.rse.2020.111669","article-title":"Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran","volume":"240","author":"Poorazimy","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1080\/15481603.2022.2103069","article-title":"Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes","volume":"59","author":"Mahoney","year":"2022","journal-title":"GISci. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"e2021GL093799","DOI":"10.1029\/2021GL093799","article-title":"Mapping forest height and aboveground biomass by integrating ICESat-2, Sentinel-1 and Sentinel-2 data using random forest algorithm in northwest Himalayan foothills of India","volume":"48","author":"Nandy","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","first-page":"103108","article-title":"Fusing GEDI with earth observation data for large area aboveground biomass mapping","volume":"115","author":"Shendryk","year":"2022","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113391","DOI":"10.1016\/j.rse.2022.113391","article-title":"Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery","volume":"285","author":"Silveira","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tamiminia, H., Salehi, B., Mahdianpari, M., Beier, C.M., and Johnson, L. (2022). Mapping two decades of New York State forest aboveground biomass change using remote sensing. Remote Sens., 14.","DOI":"10.3390\/rs14164097"},{"key":"ref_17","first-page":"175","article-title":"Object-based random forest modeling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment","volume":"78","author":"Silveira","year":"2019","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.07.028","article-title":"Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass","volume":"154","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.01.037","article-title":"Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data","volume":"224","author":"Narine","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","unstructured":"Zhou, G., Yi, G., Tang, X., Wen, Z., Liu, C., Kuang, Y., and Wang, W. (2018). Carbon Stock of Forest Ecosystems in China\u2014Biomass Equations, Science Press."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"095002","DOI":"10.1088\/1748-9326\/ab2917","article-title":"High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA","volume":"14","author":"Huang","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13021-022-00212-y","article-title":"Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China","volume":"17","author":"Jiang","year":"2022","journal-title":"Carbon Bal. Manag."},{"key":"ref_23","unstructured":"Silva, C.A., Hamamura, C., Valbuena, R., Hancock, S., Cardil, A., Broadbent, E.N., Almeida, D.R.A., Silva Junior, C.H.L., and Klauberg, C. (2020, April 01). rGEDI: NASA\u2019s Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing. 2020. version 0.1.2. Available online: https:\/\/CRAN.R-project.org\/package=rGEDI."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hird, J.N., DeLancey, E.R., McDermid, G.J., and Kariyeva, J. (2017). Google Earth Engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote Sens., 9.","DOI":"10.3390\/rs9121315"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2019.02.005","article-title":"Estimating prescribed fire impacts and post-fire tree survival in eucalyptus forests of Western Australia with L-band SAR data","volume":"224","author":"McCaw","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery","volume":"151","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113040","DOI":"10.1016\/j.rse.2022.113040","article-title":"Site-specific scaling of remote sensing-based estimates of woody cover and aboveground biomass for mapping long-term tropical dry forest degradation status","volume":"276","author":"Fremout","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111496","DOI":"10.1016\/j.rse.2019.111496","article-title":"Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets\u2014A case study","volume":"236","author":"Forkuor","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112907","DOI":"10.1016\/j.rse.2022.112907","article-title":"Monitoring standing herbaceous biomass and thresholds in semiarid rangelands from harmonized Landsat 8 and Sentinel-2 imagery to support within-season adaptive management","volume":"271","author":"Kearney","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107645","DOI":"10.1016\/j.ecolind.2021.107645","article-title":"Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data","volume":"126","author":"Wang","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13021-018-0104-6","article-title":"Multiscale divergence between Landsat- and lidar-based biomass mapping is related to regional variation in canopy cover and composition","volume":"13","author":"Bell","year":"2018","journal-title":"Carbon Bal. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100059","DOI":"10.1016\/j.fecs.2022.100059","article-title":"Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes","volume":"9","author":"Yang","year":"2022","journal-title":"For. Ecosyst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1538-4632.1996.tb00936.x","article-title":"Geographically weighted regression: A method for exploring spatial nonstationarity","volume":"28","author":"Brunsdon","year":"1996","journal-title":"Geogr. Anal."},{"key":"ref_35","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M.E. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Wiley."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1080\/07038992.2016.1252908","article-title":"Comparing modeling methods for predicting forest attributes using LiDAR metrics and ground measurements","volume":"42","author":"Shin","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_37","unstructured":"Nakaya, T., Charlton, M., Lewis, P., Brunsdon, C., Yao, J., and Fotheringham, S. (2014). GWR4 User Manual, Windows Application for Geographically Weighted Regression Modelling, Ritsumeikan University."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2017.10.018","article-title":"Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data","volume":"204","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8489","DOI":"10.3390\/rs70708489","article-title":"On the importance of training data sample selection in RF classification: A case study in peatland ecosystem mapping","volume":"7","author":"Millard","year":"2015","journal-title":"Remote Sens."},{"key":"ref_40","first-page":"386","article-title":"Evaluation of modeling approaches in predicting forest volume and stand age for small-scale plantation forests in New Zealand with RapidEye and LiDAR","volume":"73","author":"Xu","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.foreco.2018.12.019","article-title":"Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments","volume":"434","author":"Zhao","year":"2019","journal-title":"Forest Ecol. Manag."},{"key":"ref_43","first-page":"167","article-title":"Comparison of two-dimensional multitemporal Sentinel-2 data with three dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest","volume":"76","author":"Wittke","year":"2019","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_44","first-page":"102414","article-title":"Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm","volume":"102","author":"Cao","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.isprsjprs.2015.04.007","article-title":"Savannah woody structure modeling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data","volume":"105","author":"Naidoo","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1080\/15481603.2021.2023842","article-title":"A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets","volume":"59","author":"Zhang","year":"2022","journal-title":"GISci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyypp\u00e4, J., Saari, H., P\u00f6l\u00f6nen, I., and Imai, N.N. (2017). Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens., 9.","DOI":"10.3390\/rs9030185"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"14153","DOI":"10.1038\/s41598-020-71055-1","article-title":"Increasing the broad-leaved tree fraction in European forests mitigates hot temperature extremes","volume":"10","author":"Schwaab","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_49","first-page":"53","article-title":"Forest biomass retrieval approaches from earth observation in different biomes","volume":"77","author":"Quegan","year":"2019","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_50","first-page":"102326","article-title":"Improved estimation of forest stand volume by the integration of GEDI LiDAR data and multi-sensor imagery in the Changbai Mountains Mixed Forests Ecoregion (CMMFE), Northeast China","volume":"100","author":"Chen","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1016\/j.rse.2008.06.005","article-title":"Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass","volume":"112","author":"Soudani","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107494","DOI":"10.1016\/j.ecolind.2021.107494","article-title":"An improved approach to estimate above-ground volume and biomass of desert shrub communities based on UAV RGB images","volume":"125","author":"Mao","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_53","first-page":"179","article-title":"Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach","volume":"30","author":"Couturier","year":"2014","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_54","unstructured":"MOF (Ministry of Forestry) (1982). Standards for Forestry Resource Survey. China, Forestry Publisher."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Bao, G., Zhang, B., Wang, Z., Liu, M., Man, W., and Liu, J. (2022). Improved object-based estimation of forest aboveground biomass by integrating LiDAR data from GEDI and ICESat-2 with multi-sensor images in a heterogeneous mountainous region. Remote Sens., 14.","DOI":"10.3390\/rs14122743"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2625\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:37:34Z","timestamp":1760125054000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"references-count":55,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102625"],"URL":"https:\/\/doi.org\/10.3390\/rs15102625","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}