{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:21:28Z","timestamp":1775229688938,"version":"3.50.1"},"reference-count":88,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"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":["31770764"],"award-info":[{"award-number":["31770764"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Key R&amp;D Program","award":["2017YFC0504502"],"award-info":[{"award-number":["2017YFC0504502"]}]},{"name":"Fundamental Research Funds for Central Public Welfare Research Institutes","award":["Y2018ZK09"],"award-info":[{"award-number":["Y2018ZK09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.<\/jats:p>","DOI":"10.3390\/rs13244995","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T23:30:00Z","timestamp":1639006200000},"page":"4995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Classifying Individual Shrub Species in UAV Images\u2014A Case Study of the Gobi Region of Northwest China"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5424-396X","authenticated-orcid":false,"given":"Zhipeng","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Desertification Study, Chinese Academy of Forestry, Beijing 100091, China"}]},{"given":"Jie","family":"Ding","sequence":"additional","affiliation":[{"name":"Institute of Desertification Study, Chinese Academy of Forestry, Beijing 100091, China"}]},{"given":"Heyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Desertification Study, Chinese Academy of Forestry, Beijing 100091, China"}]},{"given":"Yiming","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Desertification Study, Chinese Academy of Forestry, Beijing 100091, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1006\/jare.2000.0707","article-title":"Study on shrub community diversity of Ordos Plateau, Inner Mongolia, Northern China","volume":"47","author":"Li","year":"2001","journal-title":"J. Arid Environ."},{"key":"ref_2","first-page":"158","article-title":"Patches structure succession based on spatial point pattern features in semi-arid ecosystems of the water-wind erosion crisscross region","volume":"12","author":"Hao","year":"2017","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1127\/0340-269X\/2009\/0039-0331","article-title":"Plant communities of the southern Mongolian Gobi","volume":"39","author":"Halle","year":"2009","journal-title":"Phytocoenologia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s10841-012-9512-1","article-title":"Shrubs and species identity effects on the distribution and diversity of ground-dwelling arthropods in a Gobi desert","volume":"17","author":"Li","year":"2013","journal-title":"J. Insect Conserv."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1111\/j.2517-6161.1977.tb01615.x","article-title":"Modeling spatial patterns","volume":"39","author":"Ripley","year":"1977","journal-title":"J. R. Stat. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1109\/JSTARS.2018.2830410","article-title":"Individual tree crown detection and delineation from very-high-Resolution UAV images based on Bias field and marker-controlled watershed segmentation algorithms","volume":"11","author":"Huang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","first-page":"418","article-title":"Spatial statistical analysis of tree deaths using airborne digital imagery","volume":"21","author":"Chang","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2005.04.013","article-title":"Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery","volume":"97","author":"Salovaara","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2013.07.016","article-title":"Evaluation of simulated bands in airborne optical sensors for tree species identification","volume":"138","author":"Pant","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.1080\/01431161.2015.1079663","article-title":"Automatic detection and delineation of citrus trees from VHR satellite imagery","volume":"36","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","first-page":"215","article-title":"New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)","volume":"78","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Picos, J., Bastos, G., M\u00edguez, D., Alonso, L., and Armesto, J. (2020). Individual tree detection in a eucalyptus plantation using unmanned aerial vehicle (UAV)-LiDAR. Remote Sens., 12.","DOI":"10.3390\/rs12050885"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1038\/s41598-020-79653-9","article-title":"Explainable identification and mapping of trees using UAV RGB image and deep learning","volume":"11","author":"Onishi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/01431161.2018.1528017","article-title":"Estimation of vegetation fraction using RGB and multispectral images from UAV","volume":"40","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2003.12.014","article-title":"Generation of crown bulk density for Pinus sylvestris L. From lidar","volume":"92","author":"Chuvieco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.isprsjprs.2009.07.001","article-title":"Tree species identification in mixed coniferous forest using airborne laser scanning","volume":"64","author":"Suratno","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.isprsjprs.2010.04.003","article-title":"Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies","volume":"65","author":"Korpela","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.3390\/rs4061741","article-title":"Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles","volume":"4","author":"Zhang","year":"2012","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1139\/juvs-2017-0022","article-title":"Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles","volume":"6","author":"Franklin","year":"2018","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.compag.2018.05.001","article-title":"Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform","volume":"150","author":"Selim","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Iizuka, K., Yonehara, T., Itoh, M., and Kosugi, Y. (2018). Estimating tree height and diameter at breast height (DBH) from digital surface models and orthophotos obtained with an unmanned aerial system for a Japanese Cypress (Chamaecyparis obtusa) Forest. Remote Sens., 10.","DOI":"10.3390\/rs10010013"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13007-019-0418-8","article-title":"Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras","volume":"15","author":"Cen","year":"2019","journal-title":"Plant Methods"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compag.2005.12.001","article-title":"Application of support vector machine technology for weed and nitrogen stress detection in corn","volume":"51","author":"Karimi","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., and Herrera, F. (2019). Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11060643"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101061","DOI":"10.1016\/j.ecoinf.2020.101061","article-title":"Cross-site learning in deep learning RGB tree crown detection","volume":"56","author":"Weinstein","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyypp\u00e4, J., Saari, H., P\u00f6l\u00f6nen, I., and Imai, N.N. (2017). Individual tree detection and classification with UAV-Based photogrammetric point clouds and hyperspectral imaging. Remote Sens., 9.","DOI":"10.3390\/rs9030185"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1111\/jvs.12769","article-title":"Comparing ultra-high spatial resolution remote-sensing methods in mapping peatland vegetation","volume":"30","author":"Juutinen","year":"2019","journal-title":"J. Veg. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Syst-ematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","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_33","first-page":"146","article-title":"Dense image matching of terrestrial imagery for deriving high-resolution topographic properties of vegetation locations in alpine terrain","volume":"66","author":"Niederheiser","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104757","DOI":"10.1016\/j.cageo.2021.104757","article-title":"Classifying tree species in the plantations of southern China based on wavelet analysis and mathematical morphology","volume":"151","author":"Tian","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.infrared.2018.10.012","article-title":"Classification of Hyperspectral Imagery based on spectral gradient, SVM and spatial random forest","volume":"95","author":"Zhao","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_36","first-page":"101891","article-title":"Spatial pattern analysis of Haloxylon ammodendron using UAV imagery\u2014A case study in the Gurbantunggut Desert","volume":"83","author":"Xu","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.3390\/rs5062838","article-title":"The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification","volume":"5","author":"Mellor","year":"2013","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Alvarez-Taboada, F., Paredes, C., and Juli\u00e1n-Pelaz, J. (2017). Mapping of the invasive species Hakea sericea using Unmanned Aerial Vehicle (UAV) and WorldView-2 imagery and an object-oriented approach. Remote Sens., 9.","DOI":"10.3390\/rs9090913"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.isprsjprs.2007.08.007","article-title":"Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site","volume":"63","author":"Mallinis","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11051","DOI":"10.3390\/rs61111051","article-title":"UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas","volume":"6","author":"Barrado","year":"2014","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7677","DOI":"10.1080\/01431161.2010.527392","article-title":"Extracting structural attributes from IKONOS imagery for Eucalyptus plantation forests in KwaZulu-Natal, South Africa, using image texture analysis and artificial neural networks","volume":"32","author":"Gebreslasie","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"096072","DOI":"10.1117\/1.JRS.9.096072","article-title":"Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand","volume":"9","author":"Charoenjit","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"991","DOI":"10.14358\/PERS.69.9.991","article-title":"Spatial Metrics and Image Texture for Mapping Urban Land Use","volume":"11","author":"Herold","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.01.003","article-title":"Image texture as a remotely sensed measure of vegetation structure","volume":"121","author":"Wood","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105802","DOI":"10.1016\/j.ecolind.2019.105802","article-title":"Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?","volume":"109","author":"Park","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.rse.2020.112105","article-title":"Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection","volume":"251","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"Simonetti, E., Simonetti, D., and Preatoni, D. (2014). Phenology-Based Land Cover Classification Using Landsat 8 Time Series, European Commission Joint Research Center."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"433","DOI":"10.5721\/EuJRS20164924","article-title":"Improving the accuracy of Multispectral-based benthic habitats mapping using image rotations: The application of Principle Component Analysis and Independent Component Analysis","volume":"49","author":"Wicaksono","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Herk\u00fcl, K., Kotta, J., Kutser, T., and Vahtm\u00e4e, E. (2013). Relating Remotely Sensed Optical Variability to Marine Benthic Biodiversity. PLoS ONE., 8.","DOI":"10.1371\/journal.pone.0055624"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1080\/01431161003692040","article-title":"Segmented canonical discriminant analysis of in situ hyperspectral data for identifying 13 urban tree species","volume":"32","author":"Pu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1016\/j.rse.2010.01.002","article-title":"Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification","volume":"114","author":"Ke","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/s41586-020-2824-5","article-title":"An unexpectedly large count of trees in the West African Sahara and Sahel","volume":"587","author":"Brandt","year":"2020","journal-title":"Nature."},{"key":"ref_54","first-page":"180","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Cao, J.J., Leng, W.C., Liu, K., Liu, L., He, Z., and Zhu, Y.H. (2018). Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"112033","DOI":"10.1016\/j.rse.2020.112033","article-title":"Automated detection of rock glaciers using deep learning and object-based image analysis","volume":"250","author":"Robson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"263","article-title":"A multiple-point spatially weighted k-NN method for object-based classification","volume":"52","author":"Tang","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"117","DOI":"10.5721\/EuJRS20144708","article-title":"Forest mapping through object-based image analysis of multispectral and LiDAR aerial data","volume":"47","author":"Machala","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Korznikov, K.A., Kislov, D.E., Altman, J., Dole\u017eal, J., Vozmishcheva, A.S., and Krestov, P.V. (2021). Using U-Net-Like Deep Convolutional Neural Networks for Precise Tree Recognition in Very High Resolution RGB (Red, Green, Blue) Satellite Images. Forests., 12.","DOI":"10.3390\/f12010066"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges fromremote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Johnson, B.A., and Ma, L. (2020). Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers\u2019 Views on the Future Priorities. Remote Sens., 12.","DOI":"10.3390\/rs12111772"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Feng, H.K., Jin, X.L., Yuan, H.H., Li, Z.H., Zhou, C.Q., Yang, G.J., and Tian, Q.J. (2018). A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2019.03.003","article-title":"Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery","volume":"151","author":"Maimaitijiang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_66","first-page":"79","article-title":"Combining uav-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color Indices for Weed Identification under Various Soil, Residue, and Lighting Conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1006\/anbo.1997.0544","article-title":"An algorithm for estimating chlorophyll content in leaves using a video camera","volume":"81","author":"Kawashima","year":"1998","journal-title":"Ann. Bot."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3259","DOI":"10.1109\/JSTARS.2016.2554619","article-title":"Estimating leaf area index of maize using airborne discrete-return lidar data","volume":"9","author":"Nie","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11119-005-2324-5","article-title":"Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status","volume":"6","author":"Hunt","year":"2005","journal-title":"Precis. Agric."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4566","DOI":"10.1109\/JSTARS.2014.2317876","article-title":"Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications","volume":"7","author":"Hunt","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11119-005-6787-1","article-title":"Automated crop and weed monitoring in widely spaced cereals","volume":"7","author":"Hague","year":"2006","journal-title":"Precis. Agric."},{"key":"ref_73","first-page":"34","article-title":"Histogram-based spatio-temporal feature classification of vegetation indices time-series for crop mapping","volume":"72","author":"Niazmardi","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3389\/fpls.2018.00237","article-title":"High throughput determination of plant height, ground cover, and above-ground biomass in Wheat with LiDAR","volume":"9","author":"Deery","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2012.04.003","article-title":"An individual tree crown delineation method based on multi-scale segmentation of imagery","volume":"70","author":"Jing","year":"2012","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Wang, H.Y., Shen, Z.F., Zhang, Z.H., Xu, Z.Y., Li, S., Jiao, S.H., and Lei, Y.T. (2021). Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria. Remote Sens., 13.","DOI":"10.3390\/rs13142782"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-019-0394-z","article-title":"Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data","volume":"15","author":"Han","year":"2019","journal-title":"Plant Methods."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"105026","DOI":"10.1016\/j.compag.2019.105026","article-title":"Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images","volume":"166","author":"Liu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_80","first-page":"102485","article-title":"Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids","volume":"103","author":"Yan","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Casapia, X.T., Falen, L., Bartholomeus, H., C\u00e1rdenas, R., Flores, G., Herold, M., Coronado, E.N.H., and Baker, T.R. (2019). Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12010009"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/JSTARS.2013.2262634","article-title":"Investigating the capability of few strategically placed worldview-2 multispectral bands to discriminate Forest species in KwaZulu-Natal, South Africa","volume":"7","author":"Peerbhay","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.patrec.2018.08.032","article-title":"A spatial-spectral SIFT for hyperspectral image matching and classification","volume":"127","author":"Li","year":"2019","journal-title":"Pattern Recogn. Lett."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"166","DOI":"10.4236\/oje.2018.83011","article-title":"Identifying 3 moss species by deep learning, using the \u201cchopped picture\u201d method","volume":"8","author":"Ise","year":"2018","journal-title":"Open J. Ecol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y.Y., Porikli, F., Plaza, A.J., Kehtarnavaz, N., and Terzopoulos, D. (2021). Image Segmentation Using Deep Learning: A Survey. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"83002","DOI":"10.1109\/ACCESS.2021.3086530","article-title":"Deep Neural Architectures for Medical Image Semantic Segmentation: Review","volume":"9","author":"Muhammad","year":"2021","journal-title":"Access IEEE"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4995\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:43:44Z","timestamp":1760168624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,8]]},"references-count":88,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13244995"],"URL":"https:\/\/doi.org\/10.3390\/rs13244995","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,8]]}}}