{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:05:35Z","timestamp":1764842735842,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Research Program of Qinghai Province","award":["2020-ZJ-709"],"award-info":[{"award-number":["2020-ZJ-709"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.<\/jats:p>","DOI":"10.3390\/rs15092412","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T02:08:42Z","timestamp":1683252522000},"page":"2412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique"],"prefix":"10.3390","volume":"15","author":[{"given":"Yan","family":"He","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100021, China"},{"name":"Beijing Laboratory of Advanced Information Network, Beijing 100021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7620-2221","authenticated-orcid":false,"given":"Kebin","family":"Jia","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100021, China"},{"name":"Beijing Laboratory of Advanced Information Network, Beijing 100021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1607-2725","authenticated-orcid":false,"given":"Zhihao","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and climate change: Forcings, feedbacks, and the climate benefits of forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xiao, J.-L., Zeng, F., He, Q.-L., Yao, Y.-X., Han, X., and Shi, W.-Y. (2021). Responses of Forest Carbon Cycle to Drought and Elevated CO2. Atmosphere, 12.","DOI":"10.3390\/atmos12020212"},{"key":"ref_3","first-page":"2351","article-title":"Carbon stocks assessment in subtropical forest types of Kashmir Himalayas","volume":"48","author":"Shaheen","year":"2016","journal-title":"Pak. J. Bot"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.ecolecon.2008.12.006","article-title":"Mapping community values for natural capital and ecosystem services","volume":"68","author":"Raymond","year":"2009","journal-title":"Ecol. Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113217","DOI":"10.1016\/j.rse.2022.113217","article-title":"Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning","volume":"281","author":"Nguyen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"AHoscilo, A., and Lewandowska, A. (2019). Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11080929"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s11056-019-09754-5","article-title":"Monitoring Forest Structure to Guide Adaptive Management of Forest Restoration: A Review of Remote Sensing Approaches","volume":"51","author":"Camarretta","year":"2020","journal-title":"New For."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1111\/j.1749-8198.2012.00507.x","article-title":"Vegetation Indices, Remote Sensing and Forest Monitoring","volume":"6","author":"Huete","year":"2012","journal-title":"Geogr. Compass"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shimu, S., Aktar, M., Afjal, M., Nitu, A., Uddin, M., and Al Mamun, M. (2019, January 20\u201322). NDVI based change detection in Sundarban Mangrove Forest using remote sensing data. Proceedings of the 2019 4th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh.","DOI":"10.1109\/EICT48899.2019.9068819"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pesaresi, S., Mancini, A., and Casavecchia, S. (2020). Recognition and Characterization of Forest Plant Communities through Remote-Sensing NDVI Time Series. Diversity, 12.","DOI":"10.3390\/d12080313"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Spruce, J.P., Hicke, J.A., Hargrove, W.W., Grulke, N.E., and Meddens, A.J.H. (2019). Use of MODIS NDVI Products to Map Tree Mortality Levels in Forests Affected by Mountain Pine Beetle Outbreaks. Forests, 10.","DOI":"10.3390\/f10090811"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pesaresi, S., Mancini, A., Quattrini, G., and Casavecchia, S. (2020). Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12071132"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Piragnolo, M., Pirotti, F., Zanrosso, C., Lingua, E., and Grigolato, S. (2021). Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS. Remote Sens., 13.","DOI":"10.3390\/rs13081541"},{"key":"ref_15","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 Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1080\/10106049.2019.1568586","article-title":"Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets","volume":"35","author":"Mansaray","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., and Kycko, M. (2021). Comparison of random forest, support vector machines, and neural networks for post-disaster forest species mapping of the Krkono\u0161e\/Karkonosze Transboundary Biosphere Reserve. Remote Sens., 13.","DOI":"10.3390\/rs13132581"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_19","first-page":"243","article-title":"Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images","volume":"10427","author":"Zafari","year":"2017","journal-title":"Image Signal Process. Remote Sens. XXIII"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1080\/01431161.2020.1856964","article-title":"DAU-Net: A novel water areas segmentation structure for remote sensing image. Int. J","volume":"42","author":"Xia","year":"2021","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, B., An, X., Xu, S., and Chen, Z. (2022). Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image Analysis. J. Indian Soc. Remote Sens., 1\u201314.","DOI":"10.1007\/s12524-022-01496-w"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wei, Z., Jia, K., Jia, X., Liu, P., Ma, Y., Chen, T., and Feng, G. (2022). Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning. Remote Sens., 14.","DOI":"10.3390\/rs14020388"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Boston, T., Van Dijk, A., Larraondo, P.R., and Thackway, R. (2022). Comparing CNNs and Random Forests for Landsat Image Segmentation Trained on a Large Proxy Land Cover Dataset. Remote Sens., 14.","DOI":"10.3390\/rs14143396"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Freudenberg, M., N\u00f6lke, N., Agostini, A., Urban, K., W\u00f6rg\u00f6tter, F., and Kleinn, C. (2019). Large scale palm tree detection in high resolution satellite images using U-Net. Remote Sens., 11.","DOI":"10.3390\/rs11030312"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1002\/rse2.111","article-title":"Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images","volume":"5","author":"Wagner","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wagner, F.H., Dalagnol, R., Tagle Casapia, X., Streher, A.S., Phillips, O.L., Gloor, E., and Arag\u00e3o, L.E. (2020). Regional mapping and spatial distribution analysis of canopy palms in an amazon forest using deep learning and VHR images. Remote Sens., 12.","DOI":"10.3390\/rs12142225"},{"key":"ref_34","first-page":"101897","article-title":"Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia","volume":"82","author":"Flood","year":"2019","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cao, K., and Zhang, X. (2020). An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images. Remote Sens., 12.","DOI":"10.3390\/rs12071128"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wei, Z., Jia, K., Jia, X., Xie, Y., and Jiang, Z. (2021). Large-Scale River Mapping Using Contrastive Learning and Multi-Source Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13152893"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112010","DOI":"10.1016\/j.rse.2020.112010","article-title":"Half a century of forest cover change along the Latvian-Russian border captured by object-based image analysis of Corona and Landsat TM\/OLI data","volume":"249","author":"Rendenieks","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wei, Y., Wang, W., Tang, X., Li, H., Hu, H., and Wang, X. (2022). Classification of Alpine Grasslands in Cold and High Altitudes Based on Multispectral Landsat-8 Images: A Case Study in Sanjiangyuan National Park, China. Remote Sens., 14.","DOI":"10.3390\/rs14153714"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3531","DOI":"10.15244\/pjoes\/147289","article-title":"Density and Stoichiometric Characteristics of Carbon, Nitrogen, and Phosphorus in Surface Soil of Alpine Grassland in Sanjiangyuan","volume":"31","author":"Chen","year":"2022","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s11442-022-1946-0","article-title":"Glacier changes in the Sanjiangyuan Nature Reserve of China during 2000\u20132018","volume":"32","author":"Zhang","year":"2022","journal-title":"J. Geogr. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Han, Z., Dian, Y., Xia, H., Zhou, J., Jian, Y., Yao, C., Wang, X., and Li, Y. (2020). Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9080478"},{"key":"ref_42","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve Restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_43","unstructured":"Sergey, I., and Christian, S. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_44","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Oubara, A., Wu, F., Amamra, A., and Yang, G. (2022). Survey on Remote Sensing Data Augmentation: Advances, Challenges, and Future Perspectives. International Conference on Computing Systems and Applications (CSA), Algiers, Algeria, 17\u201318 May 2022, Springer.","DOI":"10.1007\/978-3-031-12097-8_9"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"He, C., Li, S., Xiong, D., Fang, P., and Liao, M. (2020). Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance. Remote Sens., 12.","DOI":"10.3390\/rs12091501"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Moolayil, J. (2018). An Introduction to Deep Learning and Keras: A Fast-Track Approach to Modern Deep Learning with Python, Apress.","DOI":"10.1007\/978-1-4842-4240-7_1"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Drakopoulos, G., Liapakis, X., Spyrou, E., Tzimas, G., and Sioutas, S. (2019, January 30). Computing long sequences of consecutive fibonacci integers with tensorflow. Proceedings of the International Conference on Artificial Intelligence Applications and Innovations, Dubai, United Arab Emirates.","DOI":"10.1007\/978-3-030-19909-8_13"},{"key":"ref_50","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.30534\/ijatcse\/2020\/175942020","article-title":"Binary cross entropy with deep learning technique for Image classification","volume":"9","year":"2020","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cui, H., Chen, S., Hu, L., Wang, J., Cai, H., Ma, C., Liu, J., and Zou, B. (2023). HY1C\/D-CZI Noctiluca scintillans Bloom Recognition Network Based on Hybrid Convolution and Self-Attention. Remote Sens., 15.","DOI":"10.3390\/rs15071757"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kim, J., and Chi, M. (2021). SAFFNet: Self-Attention-Based Feature Fusion Network for Remote Sensing Few-Shot Scene Classification. Remote Sens., 13.","DOI":"10.3390\/rs13132532"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, Y., Wang, F., Wang, S., and Xu, Z. (2021). SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression. Remote Sens., 13.","DOI":"10.3390\/rs13030499"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wei, Z., and Zhang, Z. (2023). Remote Sensing Image Road Extraction Network Based on MSPFE-Net. Electronics, 12.","DOI":"10.3390\/electronics12071713"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, Z., Gao, X., Zhang, Y., and Zhao, G. (2020). MSLWENet: A Novel Deep Learning Network for Lake Water Body Extraction of Google Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12244140"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Xiang, K., Yuan, W., Wang, L., and Deng, Y. (2020). An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12244181"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jen\u010do, M., Fulajt\u00e1r, E., Bob\u00e1\u013eov\u00e1, H., Mate\u010dn\u00fd, I., Saksa, M., Ko\u017euch, M., Gallay, M., Ka\u0148uk, J., P\u00ed\u0161, V., and Or\u0161ulov\u00e1, V. (2020). Mapping Soil Degradation on Arable Land with Aerial Photography and Erosion Models, Case Study from Danube Lowland, Slovakia. Remote Sens., 12.","DOI":"10.3390\/rs12244047"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, M., and Stein, A. (2020). Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects. Remote Sens., 12.","DOI":"10.3390\/rs12244158"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chehreh, B., Moutinho, A., and Viegas, C. (2023). Latest Trends on Tree Classification and Segmentation Using UAV Data\u2014A Review of Agroforestry Applications. Remote Sens., 15.","DOI":"10.3390\/rs15092263"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2412\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:29:29Z","timestamp":1760124569000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":61,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092412"],"URL":"https:\/\/doi.org\/10.3390\/rs15092412","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}