{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:50:05Z","timestamp":1774659005765,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"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","award":["2022YFF0606402"],"award-info":[{"award-number":["2022YFF0606402"]}]},{"name":"National Key R&amp;D Program of China","award":["41471430"],"award-info":[{"award-number":["41471430"]}]},{"name":"National Key R&amp;D Program of China","award":["2021T3065"],"award-info":[{"award-number":["2021T3065"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFF0606402"],"award-info":[{"award-number":["2022YFF0606402"]}],"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":["41471430"],"award-info":[{"award-number":["41471430"]}],"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":["2021T3065"],"award-info":[{"award-number":["2021T3065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian Provincial Science and Technology Plan Project","award":["2022YFF0606402"],"award-info":[{"award-number":["2022YFF0606402"]}]},{"name":"Fujian Provincial Science and Technology Plan Project","award":["41471430"],"award-info":[{"award-number":["41471430"]}]},{"name":"Fujian Provincial Science and Technology Plan Project","award":["2021T3065"],"award-info":[{"award-number":["2021T3065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forests offer significant climate mitigation benefits, but existing emissions reduction assessment methodologies in forest-based mitigation activities are not scalable, which limits the development of carbon offset markets. In this study, we propose a measurement method using optical satellite imagery and space LiDAR data fusion to assess forest emissions reduction. Compared with the ALS-based carbon stock density estimation method, our approach presented a strong scalability for mapping 10 m-resolution carbon stock at a large scale. It was observed that dense canopy top height estimated by combining GEDI and Sentinel-2 could accurately predict forest carbon stock measurements estimated by the ALS-based method (R2 = 0.72). By conducting an on-site experiment of an ongoing forest carbon project in China, we found the consistency between the emissions reduction assessed by the data fusion measurement method (589,169 tCO2e) and the official ex post-monitored emissions reduction in the monitoring report (598,442 tCO2e). Our results demonstrated that forest carton stock estimation using optical satellite imagery and space LiDAR data fusion is efficient and economical for forest emissions reduction assessment. The acquisition of the data was more efficient over large areas with high frequencies using space-based technology. We further discussed the challenge of building a near-real-time monitoring system for forest-based mitigation activities by utilizing optical satellite imagery and space LiDAR data and pointed out that a quality control framework should be established to help us understand the sources of uncertainty in LiDAR-based models and improve carbon stock estimation from individual trees to forest carbon projects to meet the requirements of carbon standards better.<\/jats:p>","DOI":"10.3390\/rs15051410","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T01:43:00Z","timestamp":1677807780000},"page":"1410","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5902-6722","authenticated-orcid":false,"given":"Yue","family":"Jiao","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"},{"name":"Fujian Space Carbon Co., Ltd., No. 19 Tianxiang Road, Yanping District, Nanping 353000, China"}]},{"given":"Dacheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"},{"name":"Fujian Space Carbon Co., Ltd., No. 19 Tianxiang Road, Yanping District, Nanping 353000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9745-3150","authenticated-orcid":false,"given":"Xiaojing","family":"Yao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Shudong","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Tianhe","family":"Chi","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Yu","family":"Meng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1038\/s41586-020-2686-x","article-title":"Mapping carbon accumulation potential from global natural forest regrowth","volume":"585","author":"Leavitt","year":"2020","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1080\/14693062.2016.1277682","article-title":"Characteristics of forest carbon credit transactions in the voluntary carbon market","volume":"18","author":"Lee","year":"2018","journal-title":"Clim. Policy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106224","DOI":"10.1016\/j.resconrec.2022.106224","article-title":"Forest emissions reduction assessment using airborne LiDAR for biomass estimation","volume":"181","author":"Qin","year":"2022","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/S0034-4257(02)00013-5","article-title":"Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest","volume":"81","author":"Drake","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.biombioe.2007.06.022","article-title":"Estimating biomass of individual pine trees using airborne lidar","volume":"31","author":"Popescu","year":"2007","journal-title":"Biomass Bioenergy"},{"key":"ref_7","first-page":"399","article-title":"Lidar remote sensing of vegetation biomass","volume":"399","author":"Chen","year":"2013","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40725-017-0052-5","article-title":"Quantifying forest biomass carbon stocks from space","volume":"3","author":"Wheeler","year":"2017","journal-title":"Curr. For. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1073\/pnas.1019576108","article-title":"Benchmark map of forest carbon stocks in tropical regions across three continents","volume":"108","author":"Saatchi","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/nclimate1354","article-title":"Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps","volume":"2","author":"Baccini","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2018.11.017","article-title":"Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China","volume":"221","author":"Huang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111341","DOI":"10.1016\/j.rse.2019.111341","article-title":"Estimating aboveground biomass in subtropical forests of China by integrating multisource remote sensing and ground data","volume":"232","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112511","DOI":"10.1016\/j.rse.2021.112511","article-title":"Scaled biomass estimation in woodland ecosystems: Testing the individual and combined capacities of satellite multispectral and lidar data","volume":"262","author":"Campbell","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","unstructured":"Lang, N., Jetz, W., Schindler, K., and Wegner, J.D. (2022). A high-resolution canopy height model of the Earth. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1080\/15481603.2022.2115599","article-title":"Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2\/PALSAR2, and topographic information in Mediterranean forests","volume":"59","author":"Narine","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","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 Balance Manag."},{"key":"ref_19","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. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113114","DOI":"10.1016\/j.rse.2022.113114","article-title":"Global estimation of aboveground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure","volume":"279","author":"Santoro","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s10712-019-09532-0","article-title":"Upscaling forest biomass from field to satellite measurements: Sources of errors and ways to reduce them","volume":"40","author":"Barbier","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree allometry and improved estimation of carbon stocks and balance in tropical forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_23","unstructured":"Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry, IPCC."},{"key":"ref_24","unstructured":"Dubayah, R., Armston, J., Kellner, J., Duncanson, L., Healey, S., Patterson, P., Hancock, S., Tang, H., Bruening, J., and Hofton, M. (2022). GEDI L4A Footprint Level Aboveground Biomass Density, ORNL DAAC. Version 2.1."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1029\/1999GL010484","article-title":"Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data","volume":"26","author":"Blair","year":"1999","journal-title":"Geophys. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s10712-019-09528-w","article-title":"Ground data are essential for biomass remote sensing missions","volume":"40","author":"Chave","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TGRS.2003.810682","article-title":"A progressive morphological filter for removing nonground measurements from airborne LIDAR data","volume":"41","author":"Zhang","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/36.921414","article-title":"A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners","volume":"39","author":"Hyyppa","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_31","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_32","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.rse.2013.09.023","article-title":"Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric","volume":"140","author":"Asner","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Sothe, C., Gonsamo, A., Louren\u00e7o, R.B., Kurz, W.A., and Snider, J. (2022). Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel. Remote Sens., 14.","DOI":"10.3390\/rs14205158"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Torres de Almeida, C., Gerente, J., Rodrigo dos Prazeres Campos, J., Caruso Gomes Junior, F., Providelo, L.A., Marchiori, G., and Chen, X. (2022). Canopy Height Mapping by Sentinel 1 and 2 Satellite Images, Airborne LiDAR Data, and Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14164112"},{"key":"ref_37","first-page":"100817","article-title":"Mixed tropical forests canopy height mapping from spaceborne LiDAR GEDI and multisensor imagery using machine learning models","volume":"27","author":"Gupta","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111347","DOI":"10.1016\/j.rse.2019.111347","article-title":"Country-wide high-resolution vegetation height mapping with Sentinel-2","volume":"233","author":"Lang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"e01797","DOI":"10.1002\/ecs2.1797","article-title":"Topographically driven differences in energy and water constrain climatic control on forest carbon sequestration","volume":"8","author":"Swetnam","year":"2017","journal-title":"Ecosphere"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Torresan, C., Berton, A., Carotenuto, F., Chiavetta, U., Miglietta, F., Zaldei, A., and Gioli, B. (2018). Development and performance assessment of a low-cost UAV laser scanner system (LasUAV). Remote Sens., 10.","DOI":"10.3390\/rs10071094"},{"key":"ref_42","unstructured":"Schwartz, M., Ciais, P., Ottl\u00e9, C., De Truchis, A., Vega, C., Fayad, I., Brandt, M., Fensholt, R., Baghdadi, N., and Morneau, F. (2022). High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kanmegne Tamga, D., Latifi, H., Ullmann, T., Baumhauer, R., Bayala, J., and Thiel, M. (2022). Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data. Sensors, 23.","DOI":"10.3390\/s23010349"},{"key":"ref_44","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"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2416","DOI":"10.1016\/j.foreco.2008.01.022","article-title":"Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the Bavarian Forest National Park","volume":"255","author":"Heurich","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Aubry-Kientz, M., Dutrieux, R., Ferraz, A., Saatchi, S., Hamraz, H., Williams, J., Coomes, D., Piboule, A., and Vincent, G. (2019). A comparative assessment of the performance of individual tree crowns delineation algorithms from ALS data in tropical forests. Remote Sens., 11.","DOI":"10.3390\/rs11091086"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xu, W., Deng, S., Liang, D., and Cheng, X. (2021). A crown morphology-based approach to individual tree detection in subtropical mixed broadleaf urban forests using UAV LiDAR data. Remote Sens., 13.","DOI":"10.3390\/rs13071278"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7892","DOI":"10.3390\/rs70607892","article-title":"Individual tree segmentation from LiDAR point clouds for urban forest inventory","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_49","first-page":"5580","article-title":"What uncertainties do we need in bayesian deep learning for computer vision?","volume":"30","author":"Kendall","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","unstructured":"Duncanson, L., Disney, M., Armston, J., Nickeson, J., Minor, D., and Camacho, F. (2021). Good Practices for Satellite Derived Land Product Validation, Land Product Validation Subgroup (WGCV\/CEOS)."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40663-020-00245-0","article-title":"Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors","volume":"7","author":"Saarela","year":"2020","journal-title":"For. Ecosyst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1139\/cjfr-2020-0518","article-title":"Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data","volume":"51","author":"Breidenbach","year":"2021","journal-title":"Can. J. For. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1410\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:46:12Z","timestamp":1760121972000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,2]]},"references-count":52,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051410"],"URL":"https:\/\/doi.org\/10.3390\/rs15051410","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,2]]}}}