{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:07:38Z","timestamp":1761491258094,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010083","name":"Hunan Provincial Innovation Foundation For Postgraduate","doi-asserted-by":"publisher","award":["CX20200694"],"award-info":[{"award-number":["CX20200694"]}],"id":[{"id":"10.13039\/501100010083","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China project \u201cResearch of Key Technologies for Monitoring Forest Plantation Resources\u201d","award":["2017YFD0600900"],"award-info":[{"award-number":["2017YFD0600900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate measurement of forest growing stem volume (GSV) is important for forest resource management and ecosystem dynamics monitoring. Optical remote sensing imagery has great application prospects in forest GSV estimation on regional and global scales as it is easily accessible, has a wide coverage, and mature technology. However, their application is limited by cloud coverage, data stripes, atmospheric effects, and satellite sensor errors. Combining multi-sensor data can reduce such limitations as it increases the data availability, but also causes the multi-dimensional problem that increases the difficulty of feature selection. In this study, GaoFen-2 (GF-2) and Sentinel-2 images were integrated, and feature variables and data scenarios were derived by a proposed adaptive feature variable combination optimization (AFCO) program for estimating the GSV of coniferous plantations. The AFCO algorithm was compared to four traditional feature variable selection methods, namely, random forest (RF), stepwise random forest (SRF), fast iterative feature selection method for k-nearest neighbors (KNN-FIFS), and the feature variable screening and combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (DC-FSCK). The comparison indicated that the AFCO program not only considered the combination effect of feature variables, but also optimized the selection of the first feature variable, error threshold, and selection of the estimation model. Furthermore, we selected feature variables from three datasets (GF-2, Sentinel-2, and the integrated data) following the AFCO and four other feature selection methods and used the k-nearest neighbors (KNN) and random forest regression (RFR) to estimate the GSV of coniferous plantations in northern China. The results indicated that the integrated data improved the GSV estimation accuracy of coniferous plantations, with relative root mean square errors (RMSErs) of 15.0% and 19.6%, which were lower than those of GF-2 and Sentinel-2 data, respectively. In particular, the texture feature variables derived from GF-2 red band image have a significant impact on GSV estimation performance of the integrated dataset. For most data scenarios, the AFCO algorithm gained more accurate GSV estimates, as the RMSErs were 30.0%, 23.7%, 17.7%, and 17.5% lower than those of RF, SRF, KNN-FIFS, and DC-FSCK, respectively. The GSV distribution map obtained by the AFCO method and RFR model matched the field observations well. This study provides some insight into the application of optical images, optimization of the feature variable combination, and modeling algorithm selection for estimating the GSV of coniferous plantations.<\/jats:p>","DOI":"10.3390\/rs13142740","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T21:56:37Z","timestamp":1626126997000},"page":"2740","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Mapping the Growing Stem Volume of the Coniferous Plantations in North China Using Multispectral Data from Integrated GF-2 and Sentinel-2 Images and an Optimized Feature Variable Selection Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6678-8660","authenticated-orcid":false,"given":"Xinyu","family":"Li","sequence":"first","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & 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":"Hui","family":"Lin","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9971-5505","authenticated-orcid":false,"given":"Jiangping","family":"Long","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & 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":"Xiaodong","family":"Xu","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & 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":[[2021,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.foreco.2015.06.013","article-title":"Forest resources assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries","volume":"352","author":"Sean","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_2","unstructured":"UNFCCC (2021, April 17). Report of the Conference of the Parties on Its Twenty-First Session, Held in Paris from 30 November to 13 December 2015. Addendum. Part Two: Action Taken by the Conference of the Parties at Its Twenty-First Session. Available online: http:\/\/unfccc.int\/resource\/docs\/2015\/cop21\/eng\/10a01.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2019.01.019","article-title":"Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region","volume":"223","author":"Astola","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1080\/2150704X.2017.1295479","article-title":"Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem","volume":"8","author":"Chrysafis","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.3390\/rs4041004","article-title":"Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest","volume":"4","author":"Lindberg","year":"2012","journal-title":"Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"Improved strategy for estimating stem volume and forest biomass using moderate resolution remote sensing data and GIS","volume":"21","author":"Wijaya","year":"2010","journal-title":"J. For. Res. Jpn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16738","DOI":"10.1073\/pnas.1004875107","article-title":"High-resolution forest carbon stocks and emissions in the Amazon","volume":"107","author":"Asner","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2017.10.007","article-title":"Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference","volume":"204","author":"Puliti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105001","DOI":"10.1088\/1748-9326\/aa8352","article-title":"Mapping growing stock volume and forest live biomass: A case study of the Polissya region of Ukraine","volume":"12","author":"Bilous","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2017.07.018","article-title":"Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method","volume":"199","author":"Chrysafis","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2019.03.031","article-title":"Imaging spectrometry-derived estimates of regional ecosystem composition for the Sierra Nevada, California","volume":"228","author":"Bogan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, Z., Lin, H., Wang, G., Sun, H., Long, J., and Zhang, M. (2020). Estimating the growing stem volume of Chinese Pine and Larch Plantations based on fused optical data using an improved variable screening method and stacking algorithm. Remote Sens., 12.","DOI":"10.3390\/rs12050871"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.isprsjprs.2016.06.017","article-title":"The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa","volume":"119","author":"Dube","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"102257","article-title":"Impact of the number of dates and their sampling on a NDVI time series reconstruction methodology to monitor urban trees with Ven\u03bcs satellite","volume":"95","author":"Adeline","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s13762-015-0750-0","article-title":"A review of radar remote sensing for biomass estimation","volume":"12","author":"Sinha","year":"2015","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.rse.2015.07.005","article-title":"Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR","volume":"168","author":"Santoro","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112153","DOI":"10.1016\/j.rse.2020.112153","article-title":"Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data","volume":"253","author":"Soja","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fu, L., Liu, Q., Sun, H., Wang, S., Li, Z., Chen, E., Pang, Y., Song, X., and Wang, G. (2018). Development of a system of compatible individual tree diameter and aboveground biomass prediction models using error-in-variable regression and airborne LiDAR data. Remote Sens., 10.","DOI":"10.3390\/rs10020325"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.01.021","article-title":"Integration of airborne LiDAR and vegetation types derived from aerial photography for mapping aboveground live biomass","volume":"121","author":"Chen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1080\/01431161.2020.1820618","article-title":"Assessing of urban vegetation biomass in combination with LiDAR and high-resolution remote sensing images","volume":"42","author":"Zhang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.rse.2016.03.012","article-title":"Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data","volume":"178","author":"Cao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-basedd aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_24","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_25","first-page":"102239","article-title":"Biomass and vegetation coverage survey in the Mu Us sandy land-based on unmanned aerial vehicle RGB images","volume":"94","author":"Guo","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2017). Remote sensing of above-ground biomass. Remote Sens., 9.","DOI":"10.3390\/rs9090935"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D., and Bui, D.T. (2018). 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). Remote Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., Myint, S.W., Du, Z., and Wu, Z. (2020). Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China\u2019s Largest Artificially Planted Mangroves. Remote Sens., 12.","DOI":"10.3390\/rs12122039"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhan, Z., Yu, L., Li, Z., Ren, L., Gao, B., Wang, L., and Luo, Y. (2020). Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China. Forests, 11.","DOI":"10.3390\/f11020172"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. (2016). Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060469"},{"key":"ref_31","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":"For. Ecol. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., and Sun, Y. (2021). Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests, 12.","DOI":"10.3390\/f12020216"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","article-title":"Sure independence screening for ultrahigh dimensional feature space (with discussion)","volume":"70","author":"Fan","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1080\/01621459.2012.695654","article-title":"Feature Screening via Distance Correlation Learning","volume":"107","author":"Li","year":"2012","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_36","first-page":"73","article-title":"Forest Above-Ground Biomass Estimation Using KNN-FIFS Method Based on Multi-Source Remote Sensing Data","volume":"54","author":"Han","year":"2018","journal-title":"Sci. Silvae Sincae"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D. (2018). Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sens., 10.","DOI":"10.3390\/rs10040627"},{"key":"ref_38","first-page":"1","article-title":"Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery","volume":"10","author":"Rasel","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Yokoya, N., Xia, J., Ha, N.T., Le, N.N., Nguyen, T.T.T., Dao, T.H., Vu, T.T.P., Pham, T.D., and Takeuchi, W. (2020). Comparison of machine learning methods for estimating mangrove above-ground biomass using multiple source remote sensing data in the red river delta biosphere reserve, Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12081334"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1080\/07038992.2016.1217485","article-title":"A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation","volume":"42","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jenvman.2018.12.090","article-title":"Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change","volume":"234","author":"Wu","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ma, J., Liang, S., Li, X., and Li, M. (2020). An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products. Remote Sens., 12.","DOI":"10.3390\/rs12244015"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, G., Xie, Z., Jiang, X., Lu, D., and Chen, E. (2019). Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China. Remote Sens., 11.","DOI":"10.3390\/rs11192328"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xie, Z., Chen, Y., Lu, D., Li, G., and Chen, E. (2019). Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data. Remote Sens., 11.","DOI":"10.3390\/rs11020164"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1109\/JSTARS.2020.2994335","article-title":"Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","first-page":"102164","article-title":"Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data","volume":"92","author":"Cai","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","first-page":"409","article-title":"Combining remotely sensed optical and radar data in knn-estimation of forest variables","volume":"49","author":"Holmstrm","year":"2003","journal-title":"For. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Doyog, N.D., Lin, C., Lee, Y.J., Lumbres, R.I.C., Daipan, B.P.O., Bayer, D.C., and Parian, C.P. (2021). Diagnosing pristine pine forest development through pansharpened-surface-reflectance Landsat image derived aboveground biomass productivity. For. Ecol. Manag., 487.","DOI":"10.1016\/j.foreco.2021.119011"},{"key":"ref_49","first-page":"261","article-title":"Testing First Order Autocorrelation: A Simple Parametric Bootstrap Approach to Improve Over the Standard Tests","volume":"38","author":"Chang","year":"2019","journal-title":"Am. J. Math. Manag. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Long, J., Lin, H., Wang, G., Sun, H., and Yan, E. (2020). Estimating the growing stem volume of the planted forest using the general linear model and time series quad-polarimetric sar images. Sensors, 20.","DOI":"10.3390\/s20143957"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1007\/s42965-021-00140-x","article-title":"Estimation of forest aboveground biomass using combination of Landsat 8 and Sentinel-1A data with random forest regression algorithm in Himalayan Foothills","volume":"62","author":"Purohit","year":"2021","journal-title":"Trop. Ecol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2740\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:29:24Z","timestamp":1760164164000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2740"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,12]]},"references-count":51,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142740"],"URL":"https:\/\/doi.org\/10.3390\/rs13142740","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,7,12]]}}}