{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:42:14Z","timestamp":1762508534408,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Fund of Hunan Provincial Education Departmen","award":["17A225"],"award-info":[{"award-number":["17A225"]}]},{"name":"Scientific Research Fund of Hunan Provincial Forestry Department","award":["XLK201986"],"award-info":[{"award-number":["XLK201986"]}]},{"name":"Training Fund of Young Professors from Hunan Provincial Education Department","award":["90102-7070220090001"],"award-info":[{"award-number":["90102-7070220090001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg\/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.<\/jats:p>","DOI":"10.3390\/rs13142792","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T10:52:58Z","timestamp":1626432778000},"page":"2792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1940-9952","authenticated-orcid":false,"given":"Fugen","family":"Jiang","sequence":"first","affiliation":[{"name":"Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"given":"Chuanshi","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"given":"Chengjie","family":"Li","sequence":"additional","affiliation":[{"name":"Forest Resources and Ecological Environment Monitoring Center of Guangxi Zhuang Autonomous Region, Nanning 530000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9996-2653","authenticated-orcid":false,"given":"Mykola","family":"Kutia","sequence":"additional","affiliation":[{"name":"Bangor College China, Bangor University, 498 Shaoshan Rd., Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5401-6783","authenticated-orcid":false,"given":"Hua","family":"Sun","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mauro, G. (2020). Rural\u2013Urban Transition of Hanoi (Vietnam): Using Landsat Imagery to Map Its Recent Peri-Urbanization. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9110669"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.landurbplan.2010.11.004","article-title":"A framework for developing urban forest ecosystem services and goods indicators","volume":"99","author":"Dobbs","year":"2011","journal-title":"Landsc. Urban. Plan."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1093\/forestry\/cpq027","article-title":"Pests and diseases threatening urban trees under a changing climate","volume":"83","author":"Tubby","year":"2010","journal-title":"Forestry"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1006\/jema.1993.1017","article-title":"Atmospheric Carbon Reduction by Urban Trees","volume":"37","author":"Nowak","year":"1993","journal-title":"J. Environ. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhao, X., Liu, J., Hao, H., and Yang, Y. (2020). Quantifying the Spatial Heterogeneity and Driving Factors of Aboveground Forest Biomass in the Urban Area of Xi\u2019an, China. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9120744"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1023\/A:1020512723753","article-title":"A gradient analysis of urban landscape pattern: A case study from the Phoenix metropolitan region, Arizona, USA","volume":"17","author":"Luck","year":"2002","journal-title":"Landsc. Ecol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1023\/A:1011190902041","article-title":"Resident bird species in urban forest remnants; landscape and habitat perspectives","volume":"16","year":"2001","journal-title":"Landsc. Ecol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ge, M., Fang, S., Gong, Y., Tao, P., Yang, G., and Gong, W. (2021). Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10010014"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jawarneh, R. (2021). Modeling Past, Present, and Future Urban Growth Impacts on Primary Agricultural Land in Greater Irbid Municipality, Jordan Using SLEUTH (1972\u20132050). ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10040212"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/j.foreco.2009.12.029","article-title":"Forest carbon storage in the northeastern United States: Net effects of harvesting frequency, post-harvest retention, and wood products","volume":"259","author":"Nunery","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_12","first-page":"467","article-title":"Development of monitoring and assessment of forest biomass and carbon storage in China","volume":"1","author":"Zeng","year":"2015","journal-title":"For. Ecosyst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15114","DOI":"10.3390\/rs71115114","article-title":"Increasing the Accuracy of Mapping Urban Forest Carbon Density by Combining Spatial Modeling and Spectral Unmixing Analysis","volume":"7","author":"Sun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s11427-014-4773-4","article-title":"Spatio-temporal change in forest cover and carbon storage considering actual and potential forest cover in South Korea","volume":"58","author":"Nam","year":"2015","journal-title":"Sci. China Life Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chi, D., Degerickx, J., Yu, K., and Somers, B. (2020). Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy. Remote Sens., 12.","DOI":"10.3390\/rs12152435"},{"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","first-page":"160","article-title":"Reprint of: Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area","volume":"28","author":"Tian","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2364","DOI":"10.1016\/j.foreco.2011.08.035","article-title":"Estimating forest carbon fluxes for large regions based on process-based modelling, NFI data and Landsat satellite images","volume":"262","author":"Harkonen","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/0034-4257(94)00073-V","article-title":"Optical remote sensing of vegetation: Modeling, caveats, and algorithms","volume":"51","author":"Myneni","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"697","DOI":"10.3390\/rs1040697","article-title":"Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: A Review of Methods for Visible and Near-Infrared Wavelengths","volume":"1","author":"Kay","year":"2009","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.rse.2006.10.011","article-title":"Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories","volume":"107","author":"Muukkonen","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1080\/01431160050021259","article-title":"An evaluation of the global 1-km AVHRR land dataset","volume":"21","author":"Teillet","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.agrformet.2011.01.019","article-title":"Classification method of mixed pixels does not affect canopy metrics from digital images of forest overstorey","volume":"151","author":"Macfarlane","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alvarez-Taboada, F., Paredes, C., and Juli\u00e1n-Pelaz, J. (2017). Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach. Remote Sens., 9.","DOI":"10.3390\/rs9090913"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, J., Feng, L., Pang, X., Gong, W., and Zhao, X. (2016). Radiometric cross Calibration of Gaofen-1 WFV Cameras Using Landsat-8 OLI Images: A Simple Image-Based Method. Remote Sens., 8.","DOI":"10.3390\/rs8050411"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"145910","DOI":"10.1016\/j.scitotenv.2021.145910","article-title":"Capturing dissolved organic carbon dynamics with Landsat-8 and Sentinel-2 in tidally influenced wetland\u2013estuarine systems","volume":"777","author":"Cao","year":"2021","journal-title":"Sci. Total. Environ."},{"key":"ref_28","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":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"033569","DOI":"10.1117\/1.3283904","article-title":"Gross forest cover loss in temperate forests: Biome-wide monitoring results using MODIS and Landsat data","volume":"3","author":"Potapov","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_30","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. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1191\/0309133303pp360ra","article-title":"LiDAR remote sensing of forest structure","volume":"27","author":"Lim","year":"2003","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2015.10.030","article-title":"The potential of ALOS PALSAR backscatter and InSAR coherence for forest growing stock volume estimation in Central Siberia","volume":"173","author":"Thiel","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1890\/070001","article-title":"Lidar: Shedding new light on habitat characterization and modeling","volume":"6","author":"Vierling","year":"2008","journal-title":"Front. Ecol. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"130","author":"Mahdianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Seydi, S., Akhoondzadeh, M., Amani, M., and Mahdavi, S. (2021). Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform. Remote Sens., 13.","DOI":"10.3390\/rs13020220"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_38","first-page":"102163","article-title":"High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data","volume":"92","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2015.04.013","article-title":"Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods\u2014A comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wei, C., Huang, J., Mansaray, L.R., Li, Z., Liu, W., and Han, J. (2017). Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method. Remote Sens., 9.","DOI":"10.3390\/rs9050488"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., Liu, L., Myint, S.W., Wang, S., Liu, H., and He, Z. (2017). Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms. Remote Sens., 9.","DOI":"10.3390\/rs9101060"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jiang, F., Smith, A.R., Kutia, M., Wang, G., Liu, H., and Sun, H. (2020). A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China. Remote Sens., 12.","DOI":"10.3390\/rs12111884"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1029\/WR023i009p01717","article-title":"Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity","volume":"23","author":"Ahmed","year":"1987","journal-title":"Water Resour. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1002\/(SICI)1097-0088(199807)18:9<1031::AID-JOC303>3.0.CO;2-U","article-title":"Comparison of geostatistical methods for estimating the areal average climatological rainfall mean using data on precipitation and topography","volume":"18","year":"1998","journal-title":"Int. J. Clim."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1139\/x11-015","article-title":"Geostatistical modeling of riparian forest microclimate and its implications for sampling","volume":"41","author":"Eskelson","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s10109-007-0051-3","article-title":"A systematic investigation of cross-validation in GWR model estimation: Empirical analysis and Monte Carlo simulations","volume":"9","author":"Farber","year":"2007","journal-title":"J. Geogr. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1111\/j.0002-9092.2004.600_2.x","article-title":"Geographically Weighted Regression: The Analysis of Spatially Varying Relationships","volume":"86","author":"McMillen","year":"2004","journal-title":"Am. J. Agric. Econ."},{"key":"ref_48","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":"2010","journal-title":"Geogr. Anal."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3923\/ajms.2012.132.141","article-title":"Land Price Model Considering Spatial Factors","volume":"5","author":"Saefuddin","year":"2012","journal-title":"Asian J. Math. Stat."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, Q., Wang, G., Lin, H., Luo, P., Li, J., Zeng, S., Xu, X., and Ren, L. (2018). Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images. Remote Sens., 10.","DOI":"10.3390\/rs10081248"},{"key":"ref_51","unstructured":"State Forestry Administration, P.R. China, SFAC (2010). Guidelines on Carbon Accounting and Monitoring for Afforestation Project, China Forestry Publishing House. (In Chinese)."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"402","DOI":"10.3724\/SP.J.1258.2011.00402","article-title":"Comparative analysis of three forest biomass estimation models","volume":"35","author":"Fan","year":"2011","journal-title":"Chin. J. Plant. Ecol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/0031-3203(91)90052-7","article-title":"Texture features based on texture spectrum","volume":"24","author":"He","year":"1991","journal-title":"Pattern Recognit."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jiang, F., Kutia, M., Sarkissian, A.J., Lin, H., Long, J., Sun, H., and Wang, G. (2020). Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method. Sensors, 20.","DOI":"10.3390\/s20247248"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1177\/0040517509104758","article-title":"Predicting Seam Performance of Commercial Woven Fabrics Using Multiple Logarithm Regression and Artificial Neural Networks","volume":"79","author":"Hui","year":"2009","journal-title":"Text. Res. J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/01431161.2013.860567","article-title":"Retrieval of forest growing stock volume by two different methods using Landsat TM images","volume":"35","author":"Zheng","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8995","DOI":"10.1029\/JC090iC05p08995","article-title":"Statistics for the evaluation and comparison of models","volume":"90","author":"Willmott","year":"1985","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"470","DOI":"10.21105\/joss.00470","article-title":"The psycho Package: An Efficient and Publishing-Oriented Workflow for Psychological Science","volume":"3","author":"Makowski","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_59","first-page":"82","article-title":"Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data","volume":"18","author":"Propastin","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s11258-010-9769-y","article-title":"Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe Catchment, China","volume":"209","author":"Zhao","year":"2010","journal-title":"China. Plant Ecol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/014311600210858","article-title":"Using Landsat TM data to estimate carbon release from burned biomass in an Alaskan spruce forest complex","volume":"21","author":"Michalek","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","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":"GIScience Remote Sens."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1016\/j.rse.2011.07.019","article-title":"Evaluating uncertainty in mapping forest carbon with airborne LiDAR","volume":"115","author":"Mascaro","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Neuenschwander, A.L., and Magruder, L.A. (2016). The Potential Impact of Vertical Sampling Uncertainty on ICESat-2\/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems. Remote Sens., 8.","DOI":"10.3390\/rs8121039"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lin, X., Xu, M., Cao, C., Dang, Y., Bashir, B., Xie, B., and Huang, Z. (2020). Estimates of Forest Canopy Height Using a Combination of ICESat-2\/ATLAS Data and Stereo-Photogrammetry. Remote Sens., 12.","DOI":"10.3390\/rs12213649"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2792\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:30:41Z","timestamp":1760164241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":66,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142792"],"URL":"https:\/\/doi.org\/10.3390\/rs13142792","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,7,16]]}}}