{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:56:48Z","timestamp":1764251808732,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"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":["32171784","2020NK2051","CX20210854"],"award-info":[{"award-number":["32171784","2020NK2051","CX20210854"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovative and Construction special funds of Hunan Province","award":["32171784","2020NK2051","CX20210854"],"award-info":[{"award-number":["32171784","2020NK2051","CX20210854"]}]},{"name":"postgraduate scientific research Innovative project of Hunan province","award":["32171784","2020NK2051","CX20210854"],"award-info":[{"award-number":["32171784","2020NK2051","CX20210854"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tree crown diameter (CD) values, relating to the rate of material exchange between the forest and the atmosphere, can be used to evaluate forest biomass and carbon stock. To map tree CD values using meter-level optical remote sensing images, we propose a novel method that interprets the relationships between the spectral reflectance of pixels and the CD. The approach employs the spectral reflectance of pixels in the tree crown to express the diversity of inclination angles of leaves based on the radiative transfer model and the spatial heterogeneity of these pixels. Then, simulated and acquired GF-2 images are applied to verify the relationships between spatial heterogeneity and the tree CD. Meanwhile, filter-based and object-based methods are also employed to extract three types of variables (spectral features, texture features, and spatial heterogeneity). Finally, the tree CD values are mapped by four models (random forest (RF), K-nearest neighbor (K-NN), support vector machine (SVM), and multiple linear regression (MLR)), using three single types of variables and combinations of variables with different strategies. The results imply that the spatial heterogeneity of spectral reflectance is significantly positively correlated with tree CD values and is more sensitive to tree CD values than traditional spectral features and textural features. Furthermore, the ability of spatial heterogeneity to map tree CD values is significantly higher than traditional variable sets after obtaining stable features with appropriate filter window sizes. The results also demonstrate that the accuracy of mapped tree CD values is significantly improved using combined variable sets with different feature extraction methods. For example, in our experiments, the R2 and rRMSE values of the optimal results ranged from 0.60 to 0.66, and from 15.76% to 16.68%, respectively. It is confirmed that spatial heterogeneity with high sensitivity can effectively map tree CD values, and the accuracy of mapping tree CD values can be greatly improved using a combination of spectral features extracted by an object-based method and spatial heterogeneity extracted by a filter-based method.<\/jats:p>","DOI":"10.3390\/rs15071806","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T01:33:00Z","timestamp":1680053580000},"page":"1806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Interpretation and Mapping Tree Crown Diameter Using Spatial Heterogeneity in Relation to the Radiative Transfer Model Extracted from GF-2 Images in Planted Boreal Forest Ecosystems"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8688-9458","authenticated-orcid":false,"given":"Zhaohua","family":"Liu","sequence":"first","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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education), Qinghai Normal University, Xining 810008, China"},{"name":"Qinghai Province Key Laboratory of Physical Geography and Environmental Process, College of Geographical Science, Qinghai Normal University, Xining 810008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Liu","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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peisong","family":"Yang","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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-3874","authenticated-orcid":false,"given":"Tingchen","family":"Zhang","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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilin","family":"Ye","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"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1093\/treephys\/21.12-13.777","article-title":"Leaf area distribution and radiative transfer in open-canopy forests: Implications for mass and energy exchange","volume":"21","author":"Law","year":"2001","journal-title":"Tree Physiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1016\/j.rse.2009.12.022","article-title":"Estimating average tree crown size using spatial information from Ikonos and QuickBird images: Across-sensor and across-site comparisons","volume":"114","author":"Song","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3305","DOI":"10.1080\/01431160600993413","article-title":"Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery","volume":"28","author":"Song","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1139\/X10-073","article-title":"Using error-in-variable regression to predict tree diameter and crown width from remotely sensed imagery","volume":"40","author":"Zhang","year":"2010","journal-title":"Can. J. For. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Z.H., Ye, Z.L., Xu, X.D., Lin, H., Zhang, T.C., and Long, J.P. (2022). Mapping Forest Stock Volume Based on Growth Characteristics of Crown Using Multi-Temporal Landsat 8 OLI and ZY-3 Stereo Images in Planted Eucalyptus Forest. Remote Sens., 14.","DOI":"10.3390\/rs14205082"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.11.001","article-title":"Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"147335","DOI":"10.1016\/j.scitotenv.2021.147335","article-title":"Estimating the aboveground biomass of coniferous Forest in Northeast China using spectral variables, land surface temperature and soil moisture","volume":"785","author":"Jiang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Long, J., Lin, H., Wang, G., Sun, H., and Yan, E. (2019). Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images. Remote Sens., 11.","DOI":"10.3390\/rs11161872"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/JSTARS.2021.3131812","article-title":"Analyzing the Saturation of Growing Stem Volume Based on ZY-3 Stereo and Multispectral Images in Planted Coniferous Forest","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1080\/01431161.2010.494184","article-title":"A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing","volume":"32","author":"Ke","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"194008291772178","DOI":"10.1177\/1940082917721787","article-title":"Tree Crown Size Estimated Using Image Processing: A Biodiversity Index for Sloping Subtropical Broad-Leaved Forests","volume":"10","author":"Matsumoto","year":"2017","journal-title":"Trop. Conserv. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/TGRS.1986.289706","article-title":"Geometric-Optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy","volume":"24","author":"Li","year":"1986","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3625","DOI":"10.1080\/01431161003762355","article-title":"A comparison of three methods for automatic tree crown detection and delineation from high spatial resolution imagery","volume":"32","author":"Ke","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.14358\/PERS.72.11.1287","article-title":"The individual tree crown approach applied to Ikonos images of a coniferous plantation area","volume":"72","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","first-page":"133","article-title":"Forest information extraction from high spatial resolution images using an individual tree crown approach","volume":"12","year":"2003","journal-title":"Quintessence"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1111\/j.1744-7429.2002.tb00568.x","article-title":"Estimating Canopy Structure in an Amazon Forest from Laser Range Finder and IKONOS Satellite Observations","volume":"34","author":"Asner","year":"2010","journal-title":"Biotropica"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1111\/j.1744-7429.2007.00353.x","article-title":"Amazon Forest Structure from IKONOS Satellite Data and the Automated Characterization of Forest Canopy Properties","volume":"40","author":"Palace","year":"2008","journal-title":"Biotropica"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/S0034-4257(02)00050-0","article-title":"Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration","volume":"82","author":"Pouliot","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"255","DOI":"10.5589\/m05-011","article-title":"Approaches for optimal automated individual tree crown detection in regenerating coniferous forests","volume":"31","author":"Pouliot","year":"2005","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0034-4257(03)00013-0","article-title":"Stand delineation and composition estimation using semi-automated individual tree crown analysis","volume":"85","author":"Leckie","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2017.10.018","article-title":"Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data","volume":"204","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1109\/36.763304","article-title":"Investigation of directional reflectance in boreal forests with an improved four-scale model and airborne POLDER data","volume":"37","author":"Leblanc","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1016\/j.rse.2009.05.009","article-title":"Estimation of forest structural parameters using 5 and 10 m SPOT-5 satellite data","volume":"113","author":"Wolter","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8522","DOI":"10.1109\/TGRS.2019.2921392","article-title":"Exploration of Machine Learning Techniques in Emulating a Coupled Soil\u2013Canopy\u2013Atmosphere Radiative Transfer Model for Multi-Parameter Estimation from Satellite Observations","volume":"57","author":"Shi","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.isprsjprs.2020.04.009","article-title":"Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa","volume":"166","author":"Masemola","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Li, X., Long, J., Zhang, M., Liu, Z., and Lin, H. (2021). Coniferous Plantations Growing Stock Volume Estimation Using Advanced Remote Sensing Algorithms and Various Fused Data. Remote Sens., 13.","DOI":"10.3390\/rs13173468"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, M., Long, J.P., and Lin, H. (2021). A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13193910"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Xu, X., Lin, H., Liu, Z., Ye, Z., Li, X., and Long, J. (2021). A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest. Remote Sens., 13.","DOI":"10.3390\/rs13224631"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.rse.2018.11.036","article-title":"LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes","volume":"221","author":"Qi","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1109\/36.508411","article-title":"Three-dimensional forest light interaction model using a Monte Carlo method","volume":"34","author":"North","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0034-4257(84)90057-9","article-title":"Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model","volume":"16","author":"Verhoef","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Boschetti, M., Colombo, R., Meroni, M., Busetto, L., Panigada, C., Brivio, P.A., Marino, C.M., and Miller, J.R. (2003, January 3\u20135). Use of semi-empirical and radiative transfer models to estimate biophysical parameters in a sparse canopy forest. Proceedings of the SPIE\u2014The International Society for Optical Engineering, San Diego, CA, USA.","DOI":"10.1117\/12.463081"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kuusk, A. (2012, January 22\u201327). Assessing forest parameters by radiative transfer modelling. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351972"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.agrformet.2018.02.010","article-title":"Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery","volume":"253\u2013254","author":"Wang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TGRS.2010.2071416","article-title":"Inversion of a Radiative Transfer Model for Estimating Forest LAI from Multisource and Multiangular Optical Remote Sensing Data","volume":"49","author":"Yang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"299","DOI":"10.2307\/1941935","article-title":"Remote Estimation of Crown Size, Stand Density, and Biomass on the Oregon Transect","volume":"4","author":"Wu","year":"1994","journal-title":"Ecol. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1080\/01431160120769","article-title":"Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia","volume":"22","author":"Franklin","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/S0034-4257(99)00098-X","article-title":"High Spatial Resolution Remote Sensing Data for Forest Ecosystem Classification: An Examination of Spatial Scale","volume":"72","author":"Treitz","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/S0034-4257(96)00242-8","article-title":"Automated forest structure mapping from high resolution imagery based on directional semivariogram estimates","volume":"61","author":"Cavayas","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/TGRS.2009.2027702","article-title":"Automatic Mapping of Linear Woody Vegetation Features in Agricultural Landscapes Using Very High Resolution Imagery","volume":"48","author":"Aksoy","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","first-page":"153","article-title":"Multiscale quantification of urban composition from EO-1\/Hyperion data using object-based spectral unmixing","volume":"47","author":"Zhang","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, X., Lin, H., Long, J., and Xu, X. (2021). 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. Remote Sens., 13.","DOI":"10.3390\/rs13142740"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Blinn, J.F. (1977). Models of Light Reflection for Computer Synthesized Pictures, ACM.","DOI":"10.1145\/563858.563893"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1049\/iet-ipr.2017.0825","article-title":"Curvelet-based multiscale denoising using non-local means & guided image filter","volume":"12","author":"Panigrahi","year":"2018","journal-title":"IET Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature Selection with the Boruta Package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jiang, F.G., Andrew, R.S., Mykola, K., Wang, G.X., 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_54","doi-asserted-by":"crossref","first-page":"111041","DOI":"10.1016\/j.jenvman.2020.111014","article-title":"A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment","volume":"271","author":"Tang","year":"2020","journal-title":"J. Environ. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1806\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:05:22Z","timestamp":1760123122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":54,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15071806"],"URL":"https:\/\/doi.org\/10.3390\/rs15071806","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,3,28]]}}}