{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T03:50:58Z","timestamp":1785469858991,"version":"3.56.0"},"reference-count":48,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 31670644, 31901310"],"award-info":[{"award-number":["No. 31670644, 31901310"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the State Key Laboratory of Subtropical Silviculture","award":["No. ZY20180201"],"award-info":[{"award-number":["No. ZY20180201"]}]},{"name":"Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization","award":["Grant No. S2017011"],"award-info":[{"award-number":["Grant No. S2017011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)\u2014which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems\u2014is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.<\/jats:p>","DOI":"10.3390\/rs13152962","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T05:23:48Z","timestamp":1627449828000},"page":"2962","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression"],"prefix":"10.3390","volume":"13","author":[{"given":"Jingyi","family":"Wang","sequence":"first","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6765-2279","authenticated-orcid":false,"given":"Huaqiang","family":"Du","sequence":"additional","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejian","family":"Li","sequence":"additional","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangjie","family":"Mao","sequence":"additional","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enbin","family":"Liu","sequence":"additional","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Ji","sequence":"additional","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfang","family":"Kang","sequence":"additional","affiliation":[{"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"},{"name":"School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/TGRS.2010.2068574","article-title":"Improved biomass estimation using the texture parameters of two high-resolution optical sensors","volume":"49","author":"Nichol","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dong, L., Du, H., Han, N., Li, X., and He, S. (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_3","first-page":"239","article-title":"Review on Correlation Analysis of Independent Variables in Estimation Models of Vegetation Biomass Based on Remote Sensing","volume":"23","author":"Xu","year":"2008","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_4","first-page":"8","article-title":"Aboveground biomass estimates for tropical moist forests of Brazilian Amazon","volume":"17","author":"Brown","year":"1992","journal-title":"Interciencia"},{"key":"ref_5","first-page":"1084","article-title":"Forest Biomass of China: An estimate based on the Biomass-Volume Relationship","volume":"8","author":"Fang","year":"1998","journal-title":"Ecol. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1046\/j.1440-1703.2001.00419.x","article-title":"Forest biomass estimation at regional and global levels, with special reference to China\u2019s forest biomass","volume":"16","author":"Fang","year":"2010","journal-title":"Ecol. Res."},{"key":"ref_7","first-page":"631","article-title":"General Review on Remote Sensing-Based Biomass Estimation","volume":"37","author":"Li","year":"2012","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_8","first-page":"116","article-title":"Review of Methods on Estimation Forest Biomass","volume":"20","author":"Wang","year":"2019","journal-title":"J. Beihua Univ. (Nat. Sci.)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2017.05.026","article-title":"Modeling forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the qilian mountains","volume":"246","author":"Tian","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"172","DOI":"10.3390\/rs10020172","article-title":"Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)","volume":"10","author":"Sasan","year":"2018","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.1109\/JSTARS.2019.2944779","article-title":"Assessing the Uncertainty of Tree Height and Aboveground Biomass From Terrestrial Laser Scanner and Hypsometer Using Airborne LiDAR Data in Tropical Rainforests","volume":"12","author":"Ojoatre","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"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","unstructured":"Puliti, S., Breidenbach, J., Schumacher, J., Hauglin, M., and Astrup, R. (2020). Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat 8. arXiv.","DOI":"10.1016\/j.rse.2021.112644"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1080\/01431161.2011.603379","article-title":"Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique","volume":"33","author":"Huaqiang","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","first-page":"1400","article-title":"Estimation of the Forest Aboveground Biomass at Regional Scale Based on Remote Sensing","volume":"40","author":"Duan","year":"2015","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Y., Ning, H., Li, X., Du, H., and Xing, L. (2018). Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China. Remote Sens., 10.","DOI":"10.3390\/rs10060898"},{"key":"ref_18","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\u2014ScienceDirect","volume":"256\u2013257","author":"Li","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rasel, S., Chang, H.C., Ralph, T.J., Saintilan, N., and Diti, I.J. (2019). Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery. Geocarto Int., 1\u201325.","DOI":"10.1080\/10106049.2019.1624988"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, M., Du, H., Zhou, G., Li, X., and He, S. (2019). Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM\/OLI Data and Random Forest Model. Forests, 10.","DOI":"10.3390\/f10111004"},{"key":"ref_21","first-page":"101","article-title":"Estimation of Forest Above-Ground Biomass Based on Geostatistics","volume":"49","author":"He","year":"2013","journal-title":"Sci. Silvae Sin."},{"key":"ref_22","unstructured":"Guo, H. (2015). Geographically Weighted Regression based Estimation of Regional Forest Carbon Storage, Zhejiang A&F Universuty."},{"key":"ref_23","first-page":"38","article-title":"Light Saturation Point Determination and Biomass Remote Sensing Estimation of Pinus kesiya var. langbianensis Forest Based on Spatial Regression Models","volume":"56","author":"Zhou","year":"2020","journal-title":"Sci. Silvae Sin."},{"key":"ref_24","unstructured":"Izadi, s., and Sohrabi, H. (2020). Estimating the Spatial Distribution of Above-ground Carbon of Zagros Forests using Regression Kriging, Geographically Weighted Regression Kriging and Landsat 8 imagery. J. Environ. Sci. Technol."},{"key":"ref_25","first-page":"113","article-title":"Comparison of Geographically Weighted Regression and Regression Kriging to Estimate the Spatial Distribution of Aboveground Biomass of Zagros Forests","volume":"9","author":"Izadi","year":"2020","journal-title":"J. Geomat. Sci. Technol."},{"key":"ref_26","unstructured":"Zhang, B., and Ou, G. (2016). Application of Spatial Effect and Regression Model on Forestry Research. J. Southwest For. Univ., 144\u2013152."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1093\/forestscience\/50.2.225","article-title":"Local Modeling of Tree Growth by Geographically Weighted Regression","volume":"50","author":"Zhang","year":"2004","journal-title":"Forest Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.rse.2003.08.004","article-title":"Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI\u2013rainfall relationship","volume":"88","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s10980-006-9058-2","article-title":"Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models","volume":"22","author":"Kupfer","year":"2006","journal-title":"Landsc. Ecol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/13615930120032617","article-title":"Spatial Variations in School Performance: A Local Analysis Using Geographically Weighted Regression","volume":"5","author":"Fotheringham","year":"2001","journal-title":"Geogr. Environ. Model."},{"key":"ref_31","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\u2014Subtropical Area for Example","volume":"13","author":"Dong","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","first-page":"456","article-title":"Image-Based atmospheric radiation correction and reflectance retrieval methods","volume":"9","author":"Tian","year":"1998","journal-title":"Q. J. Appl. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/0034-4257(95)00193-X","article-title":"Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS","volume":"55","author":"Kaufman","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"100","article-title":"Geometric Correction of Remote Sensing Image","volume":"30","author":"Tang","year":"2007","journal-title":"Geomat. Spat. Inf. Technol."},{"key":"ref_35","unstructured":"Zhou, G. (2006). Carbon Storage, Fixation and Distribution in Mao Bamboo(Phyllostachys Pubescens) Stands Ecosystem. [Ph.D. Thesis, Zhejiang University]."},{"key":"ref_36","first-page":"327","article-title":"Advances in study on vegetation indices","volume":"13","author":"Tian","year":"1998","journal-title":"Adv. Earth Sci."},{"key":"ref_37","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_38","unstructured":"Kaufman, Y.J., Tanre, D., Holben, B.N., Markham, B., and Gitelson, A. (1992, January 26\u201329). Atmospheric effects on the NDVI\u2014Strategies for its removal. Proceedings of the International Geoscience & Remote Sensing Symposium, Houston, TX, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"2005","journal-title":"Proc. IEEE"},{"key":"ref_41","first-page":"1642","article-title":"Spatial distribution of forest carbon storage in Maoershan region, Northeast China based on geographically weighted regression kriging model","volume":"30","author":"Sun","year":"2019","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_42","unstructured":"Lv, Y., Li, C., Ou, G., Xiong, H., Wei, A., Zhang, B., and Xu, H. (2017). Remote Sensing Estimation of Biomass of Pinus kesiya var. langbianensis by Geographically Weighted Regression Models. For. Resour. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"Autom. Control IEEE Trans."},{"key":"ref_44","first-page":"2779","article-title":"Spatial Distribution of Forest Carbon Storage in Heilongjiang Province","volume":"25","author":"Liu","year":"2014","journal-title":"Ying Yong Sheng Tai Xue Bao"},{"key":"ref_45","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_46","first-page":"184","article-title":"Biomass Estimation of Arbor Forest in Subtropical Region Based on Geographically Weighted Regression Model","volume":"49","author":"Wang","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s11258-009-9659-3","article-title":"Spatial heterogeneity and carbon contribution of aboveground biomass of moso bamboo by using geostatistical theory","volume":"207","author":"Du","year":"2010","journal-title":"Plant Ecol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2962\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:36:02Z","timestamp":1760164562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,28]]},"references-count":48,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152962"],"URL":"https:\/\/doi.org\/10.3390\/rs13152962","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,28]]}}}