{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:26:59Z","timestamp":1769725619141,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,14]],"date-time":"2023-05-14T00:00:00Z","timestamp":1684022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Leading Goose Project of Science Technology Department of Zhejiang Province","award":["2023C02035"],"award-info":[{"award-number":["2023C02035"]}]},{"name":"Leading Goose Project of Science Technology Department of Zhejiang Province","award":["32171785"],"award-info":[{"award-number":["32171785"]}]},{"name":"National Natural Science Foundation","award":["2023C02035"],"award-info":[{"award-number":["2023C02035"]}]},{"name":"National Natural Science Foundation","award":["32171785"],"award-info":[{"award-number":["32171785"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer\u2019s and user\u2019s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R2) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage.<\/jats:p>","DOI":"10.3390\/rs15102566","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T02:02:11Z","timestamp":1684116131000},"page":"2566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Yulong","family":"Lv","sequence":"first","affiliation":[{"name":"Anji Forestry Bureau, Anji 313300, China"}]},{"given":"Ning","family":"Han","sequence":"additional","affiliation":[{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6765-2279","authenticated-orcid":false,"given":"Huaqiang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113232","DOI":"10.1016\/j.rse.2022.113232","article-title":"Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery","volume":"282","author":"Ruusa","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113462","DOI":"10.1016\/j.rse.2023.113462","article-title":"Object-based continuous monitoring of land disturbances from dense Landsat time series","volume":"287","author":"Ye","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of live aboveground forest biomass dynamics with landsat time-series and field inventory data: A comparison of empirical modeling approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, J., Du, H., Li, X., Mao, F., Zhang, M., Liu, E., Ji, J., and Kang, F. (2021). Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression. Remote Sens. Environ., 13.","DOI":"10.3390\/rs13152962"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103137","DOI":"10.1016\/j.jag.2022.103137","article-title":"Using object-oriented coupled deep learning approach for typical object inspection of transmission channel","volume":"116","author":"Wei","year":"2023","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"ref_6","first-page":"229","article-title":"Stratified aboveground forest biomass estimation by remote sensing data","volume":"38","author":"Latifi","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","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_8","unstructured":"Kang, X. (2001). Forest Resource Management, China Forestry Publishing House."},{"key":"ref_9","first-page":"80","article-title":"Multi-scale segmentation, object-based extraction of moso bamboo forest from spot5 imagery","volume":"49","author":"Sun","year":"2013","journal-title":"Sci. Silv. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isprsjprs.2023.01.001","article-title":"Task interleaving and orientation estimation for high-precision oriented object detection in aerial images","volume":"196","author":"Ming","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.patrec.2020.08.028","article-title":"Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm","volume":"141","author":"Tan","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1080\/01431161.2011.605084","article-title":"Integration of texture and landscape features into object-based classification for delineating torreya using ikonos imagery","volume":"33","author":"Han","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1080\/01431160601047771","article-title":"Development of an object-oriented classification model using very high resolution satellite imagery for monitoring diamond mining activity","volume":"29","author":"Pagot","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1016\/j.rse.2009.04.007","article-title":"Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study","volume":"113","author":"Zhou","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.isprsjprs.2007.08.007","article-title":"Object-based classification using quickbird imagery for delineating forest vegetation polygons in a mediterranean test site","volume":"63","author":"Mallinis","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1080\/01431161.2013.875634","article-title":"Object-based classification using spot-5 imagery for moso bamboo forest mapping","volume":"35","author":"Han","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","first-page":"126","article-title":"Desertification land information extraction based on object-oriented classification method","volume":"49","author":"Yiming","year":"2013","journal-title":"Sci. Silv. Sin."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1016\/j.rse.2010.01.002","article-title":"Synergistic use of quickbird multispectral imagery and lidar data for object-based forest species classification","volume":"114","author":"Ke","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1080\/01431161.2017.1421793","article-title":"Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland dipterocarp forest: Integration of field-based and remotely sensed estimates","volume":"39","author":"Zaki","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"383","DOI":"10.14358\/PERS.72.4.383","article-title":"Object-based analysis of ikonos-2 imagery for extraction of forest inventory parameters","volume":"72","author":"Chubey","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","first-page":"650","article-title":"The basic principle of random forest and its applications in ecology: A case study of pinus yunnanensis","volume":"34","author":"Zhang","year":"2014","journal-title":"Acta Ecol. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.rse.2015.11.029","article-title":"Comparing generalized linear models and random forest to model vascular plant species richness using lidar data in a natural forest in central chile","volume":"173","author":"Lopatin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/JSTARS.2019.2953234","article-title":"Very High Resolution Remote Sensing Imagery Classification Using a Fusion of Random Forest and Deep Learning Technique-Subtropical Area for Example","volume":"13","author":"Dong","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3143","DOI":"10.1109\/JSTARS.2014.2304642","article-title":"Forest biomass and carbon stock quantification using airborne lidar data: A case study over huntington wildlife forest in the adirondack park","volume":"7","author":"Li","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2017.03.013","article-title":"Monitoring mangrove biomass change in vietnam using spot images and an object-based approach combined with machine learning algorithms","volume":"128","author":"Pham","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.ecolind.2015.08.036","article-title":"Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem","volume":"60","author":"Yang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Song, L., Langfelder, P., and Horvath, S. (2013). Random generalized linear model: A highly accurate and interpretable ensemble predictor. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-5"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1111\/j.1749-8198.2012.00507.x","article-title":"Vegetation Indices, Remote Sensing and Forest Monitoring","volume":"6","author":"Huete","year":"2012","journal-title":"Geogr. Compass"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Serrano, P.M., Corral-Rivas, J.J., D\u00edaz-Varela, R.A., \u00c1lvarez-Gonz\u00e1lez, J.G., and L\u00f3pez-S\u00e1nchez, C.A. (2016). Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data. Remote Sens., 8.","DOI":"10.3390\/rs8050369"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.agrformet.2018.04.002","article-title":"Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms","volume":"256\u2013257","author":"Li","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_32","first-page":"309","article-title":"1974. Monitoring Vegetation Systems in the Great Plains with Erts","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_33","first-page":"1541","article-title":"Distinguishing Vegetation from Soil Background Information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_35","unstructured":"Lou, Y.P., Li, Y.X., Buckingham, K., Henley, G., and Zhou, G.M. (2010). Bamboo and Climate Change Mitigation, INBAR."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jenvman.2015.03.030","article-title":"Current and potential carbon stocks in moso bamboo forests in china","volume":"156","author":"Li","year":"2015","journal-title":"J. Env. Manag."},{"key":"ref_37","unstructured":"Henley, G., and Lou, Y. (2009). The Climate Change Challenge and Bamboo: Mitigation and Adaptation, INBAR."},{"key":"ref_38","unstructured":"Zhou, G., Shi, Y., Lou, Y., Li, J., Yannick, K., Chen, J., Ma, G., He, Y., Wang, X., and Yu, T. (2013). Methodology for Carbon Accounting and Monitoring of Bamboo Afforestation Projects in China, INBAR."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dong, L., Du, H., Han, N., Li, X., Zhu, D., Mao, F., Zhang, M., Zheng, J., Liu, H., and Huang, Z. (2020). Application of Convolutional neural network on lei bamboo above-ground-biomass (AGB) estimation using Worldview-2. Remote Sens., 12.","DOI":"10.3390\/rs12060958"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.14358\/PERS.77.11.1123","article-title":"Estimating aboveground carbon of moso bamboo forests using the k nearest neighbors technique and satellite imagery","volume":"77","author":"Zhou","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5351","DOI":"10.1080\/01431161.2013.788260","article-title":"Moso bamboo forest extraction and aboveground carbon storage estimation based on multi-source remotely sensed images","volume":"34","author":"Shang","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4917","DOI":"10.1080\/01431161.2013.782115","article-title":"Spatiotemporal heterogeneity of moso bamboo aboveground carbon storage with landsat thematic mapper images: A case study from anji county, china","volume":"34","author":"Han","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","first-page":"9","article-title":"Carbon sequestration potential of Moso bamboo forest in Zhejiang Province based on the non-spatial structure","volume":"48","author":"Liu","year":"2012","journal-title":"Sci. Silv. Sin."},{"key":"ref_44","first-page":"51","article-title":"Effects of different management models on carbon storage in phyllostachys pubescens forests","volume":"28","author":"Zhou","year":"2006","journal-title":"J. Beijing Univ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3544","DOI":"10.1080\/01431161.2015.1065357","article-title":"Exploring the synergistic use of multi-scale image object metrics for land-use\/land-cover mapping using an object-based approach","volume":"36","author":"Han","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tavallali, P., Razavi, M., and Brady, S. (2017). A non-linear data mining parameter selection algorithm for continuous variables. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0187676"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"45","author":"Lin","year":"1989","journal-title":"Biometrics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1080\/01431160310001654978","article-title":"Mapping mediterranean scrub with satellite imagery: Biomass estimation and spectral behaviour","volume":"25","author":"Palmeirim","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"102086","DOI":"10.1016\/j.jag.2020.102086","article-title":"A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images","volume":"88","author":"Zhang","year":"2020","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"ref_51","first-page":"744","article-title":"A hybrid model of object-oriented and pixel based classification of remotely sensed data","volume":"15","author":"Li","year":"2013","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1080\/01431160701408337","article-title":"Object-based land-cover classification for the phoenix metropolitan area: Optimization vs. Transportability","volume":"29","author":"Walker","year":"2008","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2566\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:47Z","timestamp":1760124887000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,14]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102566"],"URL":"https:\/\/doi.org\/10.3390\/rs15102566","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,14]]}}}