{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:40:59Z","timestamp":1772120459688,"version":"3.50.1"},"reference-count":113,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents two categories of features extraction and mapping suite, a very high-resolution suite and an ultra-resolution suite at 2 m and 0.5 m resolutions, respectively, for the differentiation and mapping of land cover and community-level vegetation types. The features extraction flow of the ultra-resolution suite involves pan-sharpening of the multispectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features. The performance of the ultra-resolution features extraction suite was compared with the very high-resolution features extraction suite that involves the calculation of radiometric indices and color-transformation of the multi-spectral image. This research was implemented in three mountainous ecosystems located in a cool temperate region. Three machine learning classifiers, Random Forests, XGBoost, and SoftVoting, were employed with a 10-fold cross-validation method for quantitatively evaluating the performance of the two suites. The ultra-resolution suite provided 5.3% more accuracy than the very high-resolution suite using single-date autumn images. Addition of summer images gained 12.8% accuracy for the ultra-resolution suite and 13.2% accuracy for the very high-resolution suite across all sites, while the ultra-resolution suite showed 4.9% more accuracy than the very high-resolution suite. The features extraction and mapping suites presented in this research are expected to meet the growing need for differentiating land cover and community-level vegetation types at a large scale.<\/jats:p>","DOI":"10.3390\/rs14133145","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"3145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5706-4417","authenticated-orcid":false,"given":"Ram C.","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Informatics, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","first-page":"831","article-title":"The Landsat Program: Its Origins, Evolution, and Impacts","volume":"63","author":"Lauer","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free Access to Landsat Imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the Archive: How Free Data Has Enabled the Science and Monitoring Promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat Satellite: The Landsat Data Continuity Mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2019.02.016","article-title":"Benefits of the Free and Open Landsat Data Policy","volume":"224","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, J., and Roy, D. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sens., 9.","DOI":"10.3390\/rs9090902"},{"key":"ref_8","unstructured":"Shen, S.S., and Lewis, P.E. (2012, January 23\u201327). WorldView-2 and the Evolution of the DigitalGlobe Remote Sensing Satellite Constellation: Introductory Paper for the Special Session on WorldView-2. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, Baltimore, MD, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13717-020-00255-4","article-title":"Current and Near-Term Advances in Earth Observation for Ecological Applications","volume":"10","author":"Ustin","year":"2021","journal-title":"Ecol. Process"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Qin, R., and Liu, T. (2022). A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images\u2014Analysis Unit, Model Scalability and Transferability. Remote Sens., 14.","DOI":"10.3390\/rs14030646"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1590\/S0001-37652002000400011","article-title":"Spatial Resolution Influence on the Identification of Land Cover Classes in the Amazon Environment","volume":"74","author":"Ponzoni","year":"2002","journal-title":"An. Acad. Bras. Ci\u00eanc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1080\/01431160120548","article-title":"Tropical Forest Mapping from Coarse Spatial Resolution Satellite Data: Production and Accuracy Assessment Issues","volume":"22","author":"Achard","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, K., Zhang, Z., Yu, W., Zhao, P., Yue, J., Deng, Y., and Geng, J. (2021). How Spatial Resolution Affects Forest Phenology and Tree-Species Classification Based on Satellite and Up-Scaled Time-Series Images. Remote Sens., 13.","DOI":"10.3390\/rs13142716"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"666251","DOI":"10.3389\/frsen.2021.666251","article-title":"Effects of Spatial Resolution on Burned Forest Classification with ICESat-2 Photon Counting Data","volume":"2","author":"Liu","year":"2021","journal-title":"Front. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1641\/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2","article-title":"High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization","volume":"54","author":"Wulder","year":"2004","journal-title":"BioScience"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1007\/s10980-019-00820-z","article-title":"Influence of High-Resolution Data on the Assessment of Forest Fragmentation","volume":"34","author":"Wickham","year":"2019","journal-title":"Landsc. Ecol."},{"key":"ref_17","first-page":"101988","article-title":"A Study on Trade-Offs between Spatial Resolution and Temporal Sampling Density for Wheat Yield Estimation Using Both Thermal and Calendar Time","volume":"86","author":"Durgun","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"325","DOI":"10.2307\/3037976","article-title":"Phenological Strategies of Plant Species in the Tropical Savanna and the Semi-Deciduous Forest of the Venezuelan Llanos","volume":"3","author":"Monasterio","year":"1976","journal-title":"J. Biogeogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"164","DOI":"10.2307\/2389072","article-title":"Seasonal Patterns of Leaf Growth and Loss, Flowering and Fruiting on a Subtropical Central Pacific Island","volume":"28","author":"Jones","year":"1996","journal-title":"Biotropica"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","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_21","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1049\/el:19941164","article-title":"Comparison of Colour Transformations for Image Segmentation","volume":"30","author":"Lee","year":"1994","journal-title":"Electron. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1006\/ciun.1993.1024","article-title":"A Review of Recent Texture Segmentation and Feature Extraction Techniques","volume":"57","author":"Reed","year":"1993","journal-title":"CVGIP Image Underst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8975","DOI":"10.1109\/ACCESS.2018.2890743","article-title":"Texture Feature Extraction Methods: A Survey","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Smith, A.R. (1978, January 23\u201325). Color Gamut Transform Pairs. Proceedings of the 5th Annual Conference on Computer Graphics and Interactive techniques\u2014SIGGRAPH \u201978, Atlanta, GA, USA.","DOI":"10.1145\/800248.807361"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.compag.2016.11.021","article-title":"Automatic Crop Detection under Field Conditions Using the HSV Colour Space and Morphological Operations","volume":"133","author":"Hamuda","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chang, C.-L., and Lin, K.-M. (2018). Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme. Robotics, 7.","DOI":"10.3390\/robotics7030038"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, D., Fang, S., Yang, Z., Wang, L., Tang, W., Li, Y., and Tong, C. (2018). A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features. Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7060224"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JSTARS.2008.2007514","article-title":"Detection, Characterization, and Modeling Vegetation in Urban Areas from High-Resolution Aerial Imagery","volume":"1","author":"Iovan","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1007\/s12524-019-01028-z","article-title":"Urban Greening Tree Species Classification Based on HSV Colour Space of WorldView-2","volume":"47","author":"Liu","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s12145-018-0369-z","article-title":"Performance Evaluation of Textural Features in Improving Land Use\/Land Cover Classification Accuracy of Heterogeneous Landscape Using Multi-Sensor Remote Sensing Data","volume":"12","author":"Mishra","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112175","DOI":"10.1016\/j.rse.2020.112175","article-title":"Satellite Image Texture Captures Vegetation Heterogeneity and Explains Patterns of Bird Richness","volume":"253","author":"Farwell","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6384","DOI":"10.1080\/01431161.2020.1734254","article-title":"Delineation of the Forest-Tundra Ecotone Using Texture-Based Classification of Satellite Imagery","volume":"41","author":"Guo","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100145","DOI":"10.1016\/j.tfp.2021.100145","article-title":"Field Experiment Demonstrates the Potential Utility of Satellite-Derived Reflectance Indices for Monitoring Regeneration of Boreal Forest Communities","volume":"6","author":"Ireland","year":"2021","journal-title":"Trees For. People"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, M., Fei, X., Zhang, Y., Chen, Z., Wang, X., Tsou, J.Y., Liu, D., and Lu, X. (2018). Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP). Remote Sens., 10.","DOI":"10.3390\/rs10050778"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, J., Wang, W., and Lin, C. (2017). A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm. Sensors, 17.","DOI":"10.3390\/s17071474"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.01.019","article-title":"Tree Species Classification in Tropical Forests Using Visible to Shortwave Infrared WorldView-3 Images and Texture Analysis","volume":"149","author":"Ferreira","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tamondong, A.M., Blanco, A.C., Fortes, M.D., and Nadaoka, K. (2013, January 21\u201326). Mapping of Seagrass and Other Benthic Habitats in Bolinao, Pangasinan Using Worldview-2 Satellite Image. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium\u2014IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723091"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4698","DOI":"10.1080\/01431161.2014.919685","article-title":"Mapping Freshwater Marsh Species Distributions Using WorldView-2 High-Resolution Multispectral Satellite Imagery","volume":"35","author":"Carle","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5619","DOI":"10.1080\/01431161.2018.1466084","article-title":"Satellite-Based Salt Marsh Elevation, Vegetation Height, and Species Composition Mapping Using the Superspectral WorldView-3 Imagery","volume":"39","author":"Collin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.06.011","article-title":"A Comparative Analysis of High Spatial Resolution IKONOS and WorldView-2 Imagery for Mapping Urban Tree Species","volume":"124","author":"Pu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4515","DOI":"10.3390\/rs6054515","article-title":"Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality","volume":"6","author":"Waser","year":"2014","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s12518-021-00358-3","article-title":"Classification of Tree Species in a Heterogeneous Urban Environment Using Object-Based Ensemble Analysis and World View-2 Satellite Imagery","volume":"13","author":"Jombo","year":"2021","journal-title":"Appl. Geomat."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"210","DOI":"10.4314\/sajg.v3i2.7","article-title":"Assessment of the Contribution of WorldView-2 Strategically Positioned Bands in Bracken Fern (Pteridium aquilinum (L.) Kuhn) Mapping","volume":"3","author":"Ngubane","year":"2014","journal-title":"S. Afr. J. Geomat."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.jaridenv.2017.05.001","article-title":"Mapping Prosopis Glandulosa (Mesquite) in the Semi-Arid Environment of South Africa Using High-Resolution WorldView-2 Imagery and Machine Learning Classifiers","volume":"145","author":"Adam","year":"2017","journal-title":"J. Arid Environ."},{"key":"ref_47","unstructured":"Mureriwa, N.F., Adam, E., and Adelabu, S. (August, January 28). Cost Effective Approach for Mapping Prosopis Invasion in Arid South Africa Using SPOT-6 Imagery and Two Machine Learning Classifiers. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Schulze-Br\u00fcninghoff, D., Wachendorf, M., and Astor, T. (2021). Potentials and Limitations of WorldView-3 Data for the Detection of Invasive Lupinus Polyphyllus Lindl. in Semi-Natural Grasslands. Remote Sens., 13.","DOI":"10.3390\/rs13214333"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.3390\/rs3102263","article-title":"Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification","volume":"3","author":"Novack","year":"2011","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.3390\/rs3112440","article-title":"An Object-Based Classification of Mangroves Using a Hybrid Decision Tree\u2014Support Vector Machine Approach","volume":"3","author":"Heumann","year":"2011","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1080\/2150704X.2015.1084550","article-title":"Object-Based Classification with Rotation Forest Ensemble Learning Algorithm Using Very-High-Resolution WorldView-2 Image","volume":"6","author":"Kavzoglu","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chuang, Y.-C., and Shiu, Y.-S. (2016). A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping. Sensors, 16.","DOI":"10.3390\/s16050594"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jackson, C.M., and Adam, E. (2021). Machine Learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset. Remote Sens., 13.","DOI":"10.3390\/rs13244970"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5539\/jgg.v14n1p43","article-title":"Classification and Mapping of Plant Communities Using Multi-Temporal and Multi-Spectral Satellite Images","volume":"14","author":"Sharma","year":"2022","journal-title":"J. Geogr. Geol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"203","DOI":"10.3390\/ecologies2020012","article-title":"Genus-Physiognomy-Ecosystem (GPE) System for Satellite-Based Classification of Plant Communities","volume":"2","author":"Sharma","year":"2021","journal-title":"Ecologies"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_57","first-page":"427","article-title":"Assessing Geometric Accuracy of the Orthorectification Process from GeoEye-1 and WorldView-2 Panchromatic Images","volume":"21","author":"Aguilar","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Belfiore, O., and Parente, C. (2016). Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery. Algorithms, 9.","DOI":"10.3390\/a9040067"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TGRS.2005.852480","article-title":"SCS+C: A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain","volume":"43","author":"Soenen","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3390\/rs1030184","article-title":"Comparison of Topographic Correction Methods","volume":"1","author":"Richter","year":"2009","journal-title":"Remote Sens."},{"key":"ref_61","unstructured":"Updike, T., and Comp, C. (2010). Radiometric Use of WorldView-2 Imagery, DigitalGlobe. Technical Note."},{"key":"ref_62","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.foreco.2005.06.013","article-title":"Characterizing and Mapping Forest Fire Fuels Using ASTER Imagery and Gradient Modeling","volume":"217","author":"Falkowski","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_68","unstructured":"Shen, S.S., and Lewis, P.E. (2012, January 23\u201327). Using WorldView-2 Vis-NIR Multispectral Imagery to Support Land Mapping and Feature Extraction Using Normalized Difference Index Ratios. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, Baltimore, MD, USA."},{"key":"ref_69","first-page":"221","article-title":"Semi-Empirical Indices to Assess Carotenoids\/Chlorophyll-a Ratio from Leaf Spectral Reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Mhangara, P., Mapurisa, W., and Mudau, N. (2020). Comparison of Image Fusion Techniques Using Satellite Pour l\u2019Observation de La Terre (SPOT) 6 Satellite Imagery. Appl. Sci., 10.","DOI":"10.3390\/app10051881"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.7717\/peerj-cs.127","article-title":"Accelerating the XGBoost Algorithm Using GPU Computing","volume":"3","author":"Mitchell","year":"2017","journal-title":"PeerJ Comput. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Bhagwat, R.U., and Uma Shankar, B. (2019, January 29\u201331). A Novel Multilabel Classification of Remote Sensing Images Using XGBoost. Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India.","DOI":"10.1109\/I2CT45611.2019.9033768"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Zhang, H., Eziz, A., Xiao, J., Tao, S., Wang, S., Tang, Z., Zhu, J., and Fang, J. (2019). High-Resolution Vegetation Mapping Using EXtreme Gradient Boosting Based on Extensive Features. Remote Sens., 11.","DOI":"10.3390\/rs11121505"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Muthoka, J.M., Salakpi, E.E., Ouko, E., Yi, Z.-F., Antonarakis, A.S., and Rowhani, P. (2021). Mapping Opuntia Stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers. Remote Sens., 13.","DOI":"10.3390\/rs13081494"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhang, T., Su, J., Xu, Z., Luo, Y., and Li, J. (2021). Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier. Appl. Sci., 11.","DOI":"10.3390\/app11020543"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Huang, L., Liu, Y., Huang, W., Dong, Y., Ma, H., Wu, K., and Guo, A. (2022). Combining Random Forest and XGBoost Methods in Detecting and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements. Agriculture, 12.","DOI":"10.3390\/agriculture12010074"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"7370","DOI":"10.1080\/01431161.2018.1468117","article-title":"A Multiple Classifier System to Improve Mapping Complex Land Covers: A Case Study of Wetland Classification Using SAR Data in Newfoundland, Canada","volume":"39","author":"Amani","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s11629-021-7130-7","article-title":"Diversity-Accuracy Assessment of Multiple Classifier Systems for the Land Cover Classification of the Khumbu Region in the Himalayas","volume":"19","author":"Hanson","year":"2022","journal-title":"J. Mt. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"(2019). Mugiraneza; Nascetti; Ban WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices. Remote Sens., 11.","DOI":"10.3390\/rs11182128"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Wilson, K.L., Wong, M.C., and Devred, E. (2022). Comparing Sentinel-2 and WorldView-3 Imagery for Coastal Bottom Habitat Mapping in Atlantic Canada. Remote Sens., 14.","DOI":"10.3390\/rs14051254"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"4764","DOI":"10.3390\/s120404764","article-title":"Multiple Classifier System for Remote Sensing Image Classification: A Review","volume":"12","author":"Du","year":"2012","journal-title":"Sensors"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4700","DOI":"10.1080\/01431161.2017.1331059","article-title":"Mapping Vegetation and Land Cover in a Large Urban Area Using a Multiple Classifier System","volume":"38","author":"Shi","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Rommel, E., Giese, L., Fricke, K., Kath\u00f6fer, F., Heuner, M., M\u00f6lter, T., Deffert, P., Asgari, M., N\u00e4the, P., and Dzunic, F. (2022). Very High-Resolution Imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types. Remote Sens., 14.","DOI":"10.3390\/rs14040954"},{"key":"ref_89","first-page":"1329","article-title":"Comparison of High Spatial Resolution Imagery for Efficient Generation of GIS Vegetation Layers","volume":"66","author":"Coulter","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2014.03.009","article-title":"Who Launched What, When and Why; Trends in Global Land-Cover Observation Capacity from Civilian Earth Observation Satellites","volume":"103","author":"Belward","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1002\/rse2.61","article-title":"Impact of Satellite Imagery Spatial Resolution on Land Use Classification Accuracy and Modeled Water Quality","volume":"4","author":"Fisher","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.rse.2006.04.010","article-title":"Vegetation Cover Mapping in India Using Multi-Temporal IRS Wide Field Sensor (WiFS) Data","volume":"103","author":"Joshi","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"073573","DOI":"10.1117\/1.JRS.7.073573","article-title":"Improved Land Cover Mapping Using High Resolution Multiangle 8-Band WorldView-2 Satellite Remote Sensing Data","volume":"7","author":"Jawak","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.jenvman.2014.05.027","article-title":"Identification and Mapping of Natural Vegetation on a Coastal Site Using a Worldview-2 Satellite Image","volume":"144","author":"Rapinel","year":"2014","journal-title":"J. Environ. Manag."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"083527","DOI":"10.1117\/1.JRS.8.083527","article-title":"Comparison between WorldView-2 and SPOT-5 Images in Mapping the Bracken Fern Using the Random Forest Algorithm","volume":"8","author":"Odindi","year":"2014","journal-title":"J. Appl. Remote Sens"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1038\/s41598-022-05332-6","article-title":"Mapping Native and Non-Native Vegetation in the Brazilian Cerrado Using Freely Available Satellite Products","volume":"12","author":"Lewis","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"4016","DOI":"10.1109\/JSTARS.2018.2849073","article-title":"Improving the Efficiency of Land Cover Classification by Combining Segmentation, Hierarchical Clustering, and Active Learning","volume":"11","author":"Wuttke","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Lassiter, A., and Darbari, M. (2020). Assessing Alternative Methods for Unsupervised Segmentation of Urban Vegetation in Very High-Resolution Multispectral Aerial Imagery. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230856"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Ga\u0161parovi\u0107, M., and Dobrini\u0107, D. (2020). Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12121952"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"4825","DOI":"10.1109\/JSTARS.2015.2461136","article-title":"Performance of Support Vector Machines and Artificial Neural Network for Mapping Endangered Tree Species Using WorldView-2 Data in Dukuduku Forest, South Africa","volume":"8","author":"Omer","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Tang, Y., Jing, L., Li, H., Liu, Q., Yan, Q., and Li, X. (2016). Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China. Sensors, 16.","DOI":"10.3390\/s16111957"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Saad, F., Biswas, S., Huang, Q., Corte, A.P.D., Coraiola, M., Macey, S., Carlucci, M.B., and Leimgruber, P. (2021). Detectability of the Critically Endangered Araucaria Angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR. Land, 10.","DOI":"10.3390\/land10121316"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Bransky, N., Sankey, T., Sankey, B.J., Johnson, M., and Jamison, L. (2021). Monitoring Tamarix Changes Using WorldView-2 Satellite Imagery in Grand Canyon National Park, Arizona. Remote Sens., 13.","DOI":"10.3390\/rs13050958"},{"key":"ref_105","first-page":"298","article-title":"A Comparison of Selected Classification Algorithms for Mapping Bamboo Patches in Lower Gangetic Plains Using Very High Resolution WorldView 2 Imagery","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_106","first-page":"333","article-title":"Mapping Eucalypts Trees Using High Resolution Multispectral Images: A Study Comparing WorldView 2 vs. SPOT 7","volume":"24","author":"Abutaleb","year":"2021","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1007\/s10668-021-01596-6","article-title":"Vegetation Type and Land Cover Mapping in a Semi-Arid Heterogeneous Forested Wetland of India: Comparing Image Classification Algorithms","volume":"24","author":"Deval","year":"2022","journal-title":"Environ. Dev. Sustain."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"16917","DOI":"10.3390\/rs71215861","article-title":"Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Wendelberger, K., Gann, D., and Richards, J. (2018). Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park. Sensors, 18.","DOI":"10.3390\/s18030829"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1080\/07038992.2017.1371583","article-title":"Influence of Pansharpening in Obtaining Accurate Vegetation Maps","volume":"43","author":"Marcello","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Castillejo-Gonz\u00e1lez, I. (2018). Mapping of Olive Trees Using Pansharpened QuickBird Images: An Evaluation of Pixel- and Object-Based Analyses. Agronomy, 8.","DOI":"10.3390\/agronomy8120288"},{"key":"ref_112","first-page":"80","article-title":"Assessing the Potential of Multi-Seasonal WorldView-2 Imagery for Mapping West African Agroforestry Tree Species","volume":"50","author":"Karlson","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Adamo, M., Tomaselli, V., Tarantino, C., Vicario, S., Veronico, G., Lucas, R., and Blonda, P. (2020). Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy. Remote Sens., 12.","DOI":"10.3390\/rs12091447"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:41:08Z","timestamp":1760139668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,30]]},"references-count":113,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133145"],"URL":"https:\/\/doi.org\/10.3390\/rs14133145","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,30]]}}}