{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T21:16:22Z","timestamp":1773436582084,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Guangxi Science and Technology Program","award":["GuikeAD20159037"],"award-info":[{"award-number":["GuikeAD20159037"]}]},{"name":"the Guangxi Science and Technology Program","award":["YCSW2023353"],"award-info":[{"award-number":["YCSW2023353"]}]},{"name":"the Guangxi Science and Technology Program","award":["42122009"],"award-info":[{"award-number":["42122009"]}]},{"name":"the Guangxi Science and Technology Program","award":["GUTQDJJ2017096"],"award-info":[{"award-number":["GUTQDJJ2017096"]}]},{"name":"the Innovation Project of Guangxi Graduate Education","award":["GuikeAD20159037"],"award-info":[{"award-number":["GuikeAD20159037"]}]},{"name":"the Innovation Project of Guangxi Graduate Education","award":["YCSW2023353"],"award-info":[{"award-number":["YCSW2023353"]}]},{"name":"the Innovation Project of Guangxi Graduate Education","award":["42122009"],"award-info":[{"award-number":["42122009"]}]},{"name":"the Innovation Project of Guangxi Graduate Education","award":["GUTQDJJ2017096"],"award-info":[{"award-number":["GUTQDJJ2017096"]}]},{"name":"the National Natural Science Foundation of China","award":["GuikeAD20159037"],"award-info":[{"award-number":["GuikeAD20159037"]}]},{"name":"the National Natural Science Foundation of China","award":["YCSW2023353"],"award-info":[{"award-number":["YCSW2023353"]}]},{"name":"the National Natural Science Foundation of China","award":["42122009"],"award-info":[{"award-number":["42122009"]}]},{"name":"the National Natural Science Foundation of China","award":["GUTQDJJ2017096"],"award-info":[{"award-number":["GUTQDJJ2017096"]}]},{"name":"the Guilin University of Technology Foundation","award":["GuikeAD20159037"],"award-info":[{"award-number":["GuikeAD20159037"]}]},{"name":"the Guilin University of Technology Foundation","award":["YCSW2023353"],"award-info":[{"award-number":["YCSW2023353"]}]},{"name":"the Guilin University of Technology Foundation","award":["42122009"],"award-info":[{"award-number":["42122009"]}]},{"name":"the Guilin University of Technology Foundation","award":["GUTQDJJ2017096"],"award-info":[{"award-number":["GUTQDJJ2017096"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities\u2019 classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking &gt; CatBoost &gt; RF &gt; XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands.<\/jats:p>","DOI":"10.3390\/rs15164003","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T10:40:31Z","timestamp":1692009631000},"page":"4003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Effects of Multi-Growth Periods UAV Images on Classifying Karst Wetland Vegetation Communities Using Object-Based Optimization Stacking Algorithm"],"prefix":"10.3390","volume":"15","author":[{"given":"Ya","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3469-1861","authenticated-orcid":false,"given":"Bolin","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]},{"given":"Xidong","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]},{"given":"Hang","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]},{"given":"Shurong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]},{"given":"Yan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]},{"given":"Hongyuan","family":"Kuang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]},{"given":"Tengfang","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dai, X., Yang, G., Liu, D., and Wan, R. (2020). Vegetation carbon sequestration mapping in herbaceous wetlands by using a MODIS EVI time-series data set: A case in Poyang lake wetland, China. Remote Sens., 12.","DOI":"10.3390\/rs12183000"},{"key":"ref_2","first-page":"102179","article-title":"Global analysis of time-lag and-accumulation effects of climate on vegetation growth","volume":"92","author":"Ding","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"122988","DOI":"10.1016\/j.jclepro.2020.122988","article-title":"Valuing wetland ecosystem services based on benefit transfer: A meta-analysis of China wetland studies","volume":"276","author":"Zhou","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/15481603.2017.1331510","article-title":"Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration","volume":"54","author":"Amani","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13270","DOI":"10.1038\/s41598-022-17620-2","article-title":"Comparison of multi-class and fusion of multiple single-class SegNet model for mapping karst wetland vegetation using UAV images","volume":"12","author":"Deng","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131534","DOI":"10.1016\/j.chemosphere.2021.131534","article-title":"Review on strategies of close-to-natural wetland restoration and a brief case plan for a typical wetland in northern China","volume":"285","author":"Cai","year":"2021","journal-title":"Chemosphere"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105517","DOI":"10.1016\/j.ecolind.2019.105517","article-title":"Mapping coastal wetland soil salinity in different seasons using an improved comprehensive land surface factor system","volume":"107","author":"Chi","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mirmazloumi, S.M., Moghimi, A., Ranjgar, B., Mohseni, F., Ghorbanian, A., Ahmadi, S.A., Amani, M., and Brisco, B. (2021). Status and trends of wetland studies in Canada using remote sensing technology with a focus on wetland classification: A bibliographic analysis. Remote Sens., 13.","DOI":"10.3390\/rs13204025"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.rse.2018.07.021","article-title":"Evaluation of phenospectral dynamics with Sentinel-2A using a bottom-up approach in a northern ombrotrophic peatland","volume":"216","author":"Kalacska","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","first-page":"100569","article-title":"A simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms","volume":"23","author":"Ahmed","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, Z., Huang, R., You, H., Han, X., Yue, T., and Zhou, G. (2023). The Effects of Spatial Resolution and Resampling on the Classification Accuracy of Wetland Vegetation Species and Ground Objects: A Study Based on High Spatial Resolution UAV Images. Drones, 7.","DOI":"10.3390\/drones7010061"},{"key":"ref_12","first-page":"102553","article-title":"Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data","volume":"104","author":"Fu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Diez, Y., Kentsch, S., Fukuda, M., Caceres, M.L.L., Moritake, K., and Cabezas, M. (2021). Deep learning in forestry using uav-acquired rgb data: A practical review. Remote Sens., 13.","DOI":"10.3390\/rs13142837"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.isprsjprs.2019.09.017","article-title":"Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing","volume":"158","author":"Yan","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bhatnagar, S., Gill, L., and Ghosh, B. (2020). Drone image segmentation using machine and deep learning for mapping raised bog vegetation communities. Remote Sens., 12.","DOI":"10.3390\/rs12162602"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yi, Z., Jia, L., and Chen, Q. (2020). Crop classification using multi-temporal Sentinel-2 data in the Shiyang River Basin of China. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-20926"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Feng, D., Yu, L., Cheng, Y., Zhang, M., Liu, X., Xu, Y., Fang, L., Zhu, Z., and Gong, P. (2019). Long-term land cover dynamics (1986\u20132016) of Northeast China derived from a multi-temporal Landsat archive. Remote Sens., 11.","DOI":"10.3390\/rs11050599"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108173","DOI":"10.1016\/j.ecolind.2021.108173","article-title":"Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm","volume":"131","author":"Fu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2019.09.007","article-title":"Multi-season RapidEye imagery improves the classification of wetland and dryland communities in a subtropical coastal region","volume":"157","author":"Cho","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","first-page":"102208","article-title":"Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region","volume":"94","author":"Kollert","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/S2095-3119(15)61321-1","article-title":"How do temporal and spectral features matter in crop classification in Heilongjiang Province, China?","volume":"16","author":"Hu","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_23","first-page":"103202","article-title":"Evaluating capabilities of machine learning algorithms for aquatic vegetation classification in temperate wetlands using multi-temporal Sentinel-2 data","volume":"117","author":"Piaser","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","first-page":"101980","article-title":"Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification","volume":"85","author":"Macintyre","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.isprsjprs.2020.09.023","article-title":"Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches","volume":"169","author":"Tang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2020.03.020","article-title":"National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images","volume":"164","author":"Mao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fallatah, A., Jones, S., Wallace, L., and Mitchell, D. (2022). Combining object-based machine learning with long-term time-series analysis for informal settlement identification. Remote Sens., 14.","DOI":"10.3390\/rs14051226"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, H., and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci., 9.","DOI":"10.3390\/app9204396"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101426","DOI":"10.1016\/j.ecoinf.2021.101426","article-title":"A novel classifier for improving wetland mapping by integrating image fusion techniques and ensemble machine learning classifiers","volume":"65","author":"Mallick","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_31","first-page":"450","article-title":"An efficient feature optimization for wetland mapping by synergistic use of SAR intensity, interferometry, and polarimetry data","volume":"73","author":"Mohammadimanesh","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yao, H., Fu, B., Zhang, Y., Li, S., Xie, S., Qin, J., Fan, D., and Gao, E. (2022). Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14215478"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, R., Yang, C., Li, E., Cai, X., Yang, J., and Xia, Y. (2021). Object-based wetland vegetation classification using multi-feature selection of unoccupied aerial vehicle RGB imagery. Remote Sens., 13.","DOI":"10.3390\/rs13234910"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TSG.2021.3134018","article-title":"A stacked machine and deep learning-based approach for analysing electricity theft in smart grids","volume":"13","author":"Khan","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wen, L., and Hughes, M. (2020). Coastal wetland mapping using ensemble learning algorithms: A comparative study of bagging, boosting and stacking techniques. Remote Sens., 12.","DOI":"10.3390\/rs12101683"},{"key":"ref_36","first-page":"102164","article-title":"Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data","volume":"92","author":"Cai","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104934","DOI":"10.1016\/j.envint.2019.104934","article-title":"A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide","volume":"130","author":"Chen","year":"2019","journal-title":"Environ. Int."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105936","DOI":"10.1016\/j.asoc.2019.105936","article-title":"A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment","volume":"86","author":"Arora","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_40","first-page":"8761","article-title":"Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values","volume":"63","author":"Bajorath","year":"2019","journal-title":"J. Med. Chem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ecolind.2016.09.029","article-title":"Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data","volume":"73","author":"Fu","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.isprsjprs.2014.07.002","article-title":"Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery","volume":"96","author":"Belgiu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ramezan, C.A. (2022). Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification. Remote Sens., 14.","DOI":"10.3390\/rs14246218"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"108533","DOI":"10.1016\/j.ecolind.2022.108533","article-title":"Estimating the grade of storm surge disaster loss in coastal areas of China via machine learning algorithms","volume":"136","author":"Zhang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1109\/TMECH.2020.3009449","article-title":"Ensemble generalized multiclass support-vector-machine-based health evaluation of complex degradation systems","volume":"25","author":"Wu","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"107961","DOI":"10.1016\/j.asoc.2021.107961","article-title":"The network loan risk prediction model based on Convolutional neural network and Stacking fusion model","volume":"113","author":"Li","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1177\/14759217211036880","article-title":"Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights","volume":"21","author":"Malekloo","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"466","DOI":"10.3390\/jtaer16030029","article-title":"What makes an online review more helpful: An interpretation framework using XGBoost and SHAP values","volume":"16","author":"Meng","year":"2020","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_49","first-page":"4514","article-title":"An optimized XGBoost based diagnostic system for effective prediction of heart disease","volume":"34","author":"Budholiya","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"35680","DOI":"10.1109\/ACCESS.2023.3265019","article-title":"Boosting Algorithm to handle Unbalanced Classification of PM2. 5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia using AdaBoost, XGBoost, CatBoost, and LightGBM","volume":"11","author":"Toharudin","year":"2023","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Raju, V.G., Lakshmi, K.P., Jain, V.M., Kalidindi, A., and Padma, V. (2020, January 20\u201322). Study the influence of normalization\/transformation process on the accuracy of supervised classification. Proceedings of the 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT48917.2020.9214160"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2019.01.017","article-title":"A highly automated algorithm for wetland detection using multi-temporal optical satellite data","volume":"224","author":"Ludwig","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"106261","DOI":"10.1016\/j.aap.2021.106261","article-title":"Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP","volume":"159","author":"Wen","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Judah, A., and Hu, B. (2019). The integration of multi-source remotely-sensed data in support of the classification of wetlands. Remote Sens., 11.","DOI":"10.3390\/rs11131537"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"113664","DOI":"10.1016\/j.rse.2023.113664","article-title":"Toward a better understanding of coastal salt marsh mapping: A case from China using dual-temporal images","volume":"295","author":"Zhao","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"114561","DOI":"10.1016\/j.apenergy.2020.114561","article-title":"A novel improved model for building energy consumption prediction based on model integration","volume":"262","author":"Wang","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, Y., Fu, B., Sun, X., Fan, D., Wang, Y., He, H., Gao, E., He, W., and Yao, Y. (2022). Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images. Remote Sens., 14.","DOI":"10.3390\/rs14215533"},{"key":"ref_60","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s10462-020-09876-9","article-title":"A review on machine learning in 3D printing: Applications, potential, and challenges","volume":"54","author":"Goh","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Hartling, S., Sagan, V., Sidike, P., Maimaitijiang, M., and Carron, J. (2019). Urban tree species classification using a WorldView-2\/3 and LiDAR data fusion approach and deep learning. Sensors, 19.","DOI":"10.3390\/s19061284"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"110927","DOI":"10.1016\/j.engstruct.2020.110927","article-title":"Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach","volume":"219","author":"Mangalathu","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s10822-020-00314-0","article-title":"Interpretation of machine learning models using shapley values: Application to compound potency and multi-target activity predictions","volume":"34","author":"Bajorath","year":"2020","journal-title":"J. Comput. Aided Mol. Des."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4003\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:32:09Z","timestamp":1760128329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,12]]},"references-count":64,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15164003"],"URL":"https:\/\/doi.org\/10.3390\/rs15164003","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,12]]}}}