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For the extremely difficult problem of automatic forested landslide detection, airborne remote sensing technologies, such as LiDAR and optical cameras, can obtain more accurate landslide monitoring data. In practice, however, airborne LiDAR data and optical images are treated independently. The complementary information of the remote sensing data from multiple sources has not been thoroughly investigated. To address this deficiency, we investigate how to use LiDAR data and optical images together to develop an automatic detection model for forested landslide detection. First, a new dataset for detecting forested landslides in the Jiuzhaigou earthquake region is compiled. LiDAR-derived DEM and hillshade maps are used to mitigate the influence of forest cover on the detection of forested landslides. Second, a new deep learning model called DemDet is proposed for the automatic detection of forested landslides. In the feature extraction component of DemDet, a self-supervised learning module is proposed for extracting geometric features from LiDAR-derived DEM. Additionally, a transformer-based deep neural network is proposed for identifying landslides from hillshade maps and optical images. In the data fusion component of DemDet, an attention-based neural network is proposed to combine DEM, hillshade, and optical images. DemDet is able to extract key features from hillshade images, optical images, and DEM, as demonstrated by experimental results on the proposed dataset. In comparison to ResUNet, LandsNet, HRNet, MLP, and SegFormer, DemDet obtains the highest mean accuracy, mIoU, and F1 values, namely 0.95, 0.67, and 0.777. DemDet is therefore capable of autonomously identifying the forest-covered landslides in the Jiuzhaigou earthquake zone. The results of landslide detection mapping reveal that slopes along roads and seismogenic faults are the most crucial areas requiring geohazard prevention.<\/jats:p>","DOI":"10.3390\/rs15153850","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T10:57:33Z","timestamp":1690973853000},"page":"3850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Automatic Detection of Forested Landslides: A Case Study in Jiuzhaigou County, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Dongfen","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0579-4797","authenticated-orcid":false,"given":"Xiaochuan","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu University of Technology, Chengdu 610059, China"},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Zihan","family":"Tu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Chengyong","family":"Fang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yuanzhen","family":"Ju","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s10346-023-02030-w","article-title":"Elevation dependence of landslide activity induced by climate change in the eastern Pamirs","volume":"20","author":"Pei","year":"2023","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2813","DOI":"10.1007\/s10346-021-01674-w","article-title":"Seismic performance assessment of unsaturated soil slope in different groundwater levels","volume":"18","author":"Huang","year":"2021","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108088","DOI":"10.1016\/j.soildyn.2023.108088","article-title":"Seismic fragility and demand hazard analyses for earth slopes incorporating soil property variability","volume":"173","author":"Wang","year":"2023","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1007\/s11069-022-05792-z","article-title":"Deduction of sudden rainstorm scenarios: Integrating decision makers\u2019 emotions, dynamic Bayesian network and DS evidence theory","volume":"116","author":"Xie","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s10346-021-01843-x","article-title":"Landslide detection using deep learning and object-based image analysis","volume":"19","author":"Ghorbanzadeh","year":"2022","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Lu, Z. (2018). Remote sensing of landslides\u2014A review. Remote Sens., 10.","DOI":"10.3390\/rs10020279"},{"key":"ref_7","first-page":"92","article-title":"Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER","volume":"4","author":"Casagli","year":"2016","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0169-555X(95)00071-C","article-title":"Remote sensing techniques for landslide studies and hazard zonation in Europe","volume":"15","author":"Mantovani","year":"1996","journal-title":"Geomorphology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"283","DOI":"10.2113\/gseegeosci.IV.3.283","article-title":"Surface deformation as a guide to kinematics and three-dimensional shape of slow-moving, clay-rich landslides, Honolulu, Hawaii","volume":"4","author":"Baum","year":"1998","journal-title":"Environ. Eng. Geosci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ge, X., Zhao, Q., Wang, B., and Chen, M. (2023). Lightweight landslide detection network for emergency scenarios. Remote Sens., 15.","DOI":"10.3390\/rs15041085"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, B., Wang, N., Xu, C., Chen, F., and Wang, L. (2022). A network for landslide detection using large-area remote sensing images with multiple spatial resolutions. Remote Sens., 14.","DOI":"10.3390\/rs14225759"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, Z., Chang, R., and Chen, Z. (2022). Automatic Detection Method for Loess Landslides Based on GEE and an Improved YOLOX Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14184599"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hou, H., Chen, M., Tie, Y., and Li, W. (2022). A universal landslide detection method in optical remote sensing images based on improved YOLOX. Remote Sens., 14.","DOI":"10.3390\/rs14194939"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1007\/s11069-022-05642-y","article-title":"Comparison of pixel, sub-pixel and object-based image analysis techniques for co-seismic landslides detection in seismically active area in Lesser Himalaya, Pakistan","volume":"115","author":"Saba","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Morales, B., Garcia-Pedrero, A., Lizama, E., Lillo-Saavedra, M., Gonzalo-Mart\u00edn, C., Chen, N., and Somos-Valenzuela, M. (2022). Patagonian andes landslides inventory: The deep learning\u2019s way to their automatic detection. Remote Sens., 14.","DOI":"10.3390\/rs14184622"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1007\/s10346-022-01912-9","article-title":"Evaluation of machine learning-based algorithms for landslide detection across satellite sensors for the 2019 Cyclone Idai event, Chimanimani District, Zimbabwe","volume":"19","author":"Das","year":"2022","journal-title":"Landslides"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fu, R., He, J., Liu, G., Li, W., Mao, J., He, M., and Lin, Y. (2022). Fast seismic landslide detection based on improved mask R-CNN. Remote Sens., 14.","DOI":"10.3390\/rs14163928"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1007\/s10346-022-01915-6","article-title":"Landslide detection from bitemporal satellite imagery using attention-based deep neural networks","volume":"19","author":"Amankwah","year":"2022","journal-title":"Landslides"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, Z., and Xu, C. (2022). Efficient detection of earthquake-triggered landslides based on U-Net++: An example of the 2018 Hokkaido Eastern Iburi (Japan) Mw = 6.6 earthquake. Remote Sens., 14.","DOI":"10.3390\/rs14122826"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B., and Pour, A.B. (2018). A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sens., 10.","DOI":"10.3390\/rs10060975"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nikolakopoulos, K.G., Kyriou, A., and Koukouvelas, I.K. (2022). Developing a guideline of unmanned aerial vehicle\u2019s acquisition geometry for landslide mapping and monitoring. Appl. Sci., 12.","DOI":"10.3390\/app12094598"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111816","DOI":"10.1016\/j.rse.2020.111816","article-title":"Persistent homology on LiDAR data to detect landslides","volume":"246","author":"Syzdykbayev","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fang, C., Fan, X., Zhong, H., Lombardo, L., Tanyas, H., and Wang, X. (2022). A Novel historical landslide detection approach based on LiDAR and lightweight attention U-Net. Remote Sens., 14.","DOI":"10.3390\/rs14174357"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Azmoon, B., Biniyaz, A., and Liu, Z. (2022). Use of high-resolution multi-temporal DEM data for landslide detection. Geosciences, 12.","DOI":"10.3390\/geosciences12100378"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106730","DOI":"10.1016\/j.enggeo.2022.106730","article-title":"Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM","volume":"305","author":"Cai","year":"2022","journal-title":"Eng. Geol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xu, Q., Guo, C., Dong, X., Li, W., Lu, H., Fu, H., and Liu, X. (2021). Mapping and characterizing displacements of landslides with InSAR and airborne LiDAR technologies: A case study of danba county, southwest China. Remote Sens., 13.","DOI":"10.3390\/rs13214234"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, W., Yamazaki, F., and Maruyama, Y. (2019). Detection of earthquake-induced landslides during the 2018 Kumamoto earthquake using multitemporal airborne LiDAR data. Remote Sens., 11.","DOI":"10.3390\/rs11192292"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mezaal, M.R., Pradhan, B., and Rizeei, H.M. (2018). Improving landslide detection from airborne laser scanning data using optimized Dempster\u2013Shafer. Remote Sens., 10.","DOI":"10.3390\/rs10071029"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1007\/s10346-020-01473-9","article-title":"Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","article-title":"A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1080\/01431161.2011.630331","article-title":"Assessing the activity of a large landslide in southern Italy by ground-monitoring and SAR interferometric techniques","volume":"33","author":"Calcaterra","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"923","DOI":"10.5194\/nhess-13-923-2013","article-title":"Interferometric SAR monitoring of the Vallcebre landslide (Spain) using corner reflectors","volume":"13","author":"Crosetto","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.enggeo.2014.03.003","article-title":"Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives","volume":"174","author":"Wasowski","year":"2014","journal-title":"Eng. Geol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, X., Yao, X., Zhou, Z., Liu, Y., Yao, C., and Ren, K. (2022). DRs-UNet: A deep semantic segmentation network for the recognition of active landslides from InSAR imagery in the three rivers region of the Qinghai\u2013Tibet Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14081848"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"963322","DOI":"10.3389\/fenvs.2022.963322","article-title":"Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network","volume":"10","author":"Fu","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"ref_36","first-page":"1","article-title":"Improving landslide detection on SAR data through deep learning","volume":"19","author":"Nava","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nava, L., Bhuyan, K., Meena, S.R., Monserrat, O., and Catani, F. (2022). Rapid mapping of landslides on SAR data by attention U-Net. Remote Sens., 14.","DOI":"10.3390\/rs14061449"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7485","DOI":"10.1038\/s41598-023-34030-0","article-title":"Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan","volume":"13","author":"Hussain","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1007\/s12583-020-1380-0","article-title":"Active landslide detection based on Sentinel-1 data and InSAR technology in Zhouqu county, Gansu province, Northwest China","volume":"32","author":"Dai","year":"2021","journal-title":"J. Earth Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/22797254.2019.1681905","article-title":"Landslide mapping using optical and radar data: A case study from Aminteo, Western Macedonia Greece","volume":"53","author":"Kyriou","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"895","DOI":"10.3390\/rs15040895","article-title":"Globally vs. Locally Trained Machine Learning Models for Landslide Detection: A Case Study of a Glacial Landscape","volume":"15","author":"Lindsay","year":"2023","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1038\/s41598-022-27352-y","article-title":"Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data","volume":"13","author":"Bhuyan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1080\/19475705.2022.2116357","article-title":"Accurate landslide identification by multisource data fusion analysis with improved feature extraction backbone network","volume":"13","author":"Jin","year":"2022","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Xu, Y., Ghamis, P., Kopp, M., and Kreil, D. (2022). Landslide4Sense: Reference benchmark data and deep learning models for landslide detection. arXiv.","DOI":"10.1109\/TGRS.2022.3215209"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 22\u201324). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_46","unstructured":"Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021, January 17\u201323). Zero-shot text-to-image generation. Proceedings of the International Conference on Machine Learning, PMLR, Baltimore, MD, USA."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040277","article-title":"Convolutional neural networks for multimodal remote sensing data classification","volume":"60","author":"Wu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, P., Zhu, X., and Clifton, D.A. (2023). Multimodal learning with transformers: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201320.","DOI":"10.1109\/TPAMI.2023.3275156"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3545572","article-title":"A review on methods and applications in multimodal deep learning","volume":"19","author":"Jabeen","year":"2023","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_50","first-page":"102926","article-title":"Deep learning in multimodal remote sensing data fusion: A comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","first-page":"30","article-title":"Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data","volume":"173","author":"Kerle","year":"2012","journal-title":"Geomorphology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9705","DOI":"10.3390\/rs70809705","article-title":"Identification of forested landslides using LiDar data, object-based image analysis, and machine learning algorithms","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Paw\u0142uszek, K., Marczak, S., Borkowski, A., and Tarolli, P. (2019). Multi-aspect analysis of object-oriented landslide detection based on an extended set of LiDAR-derived terrain features. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8080321"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yavuz, M., Koutalakis, P., Diaconu, D.C., Gkiatas, G., Zaimes, G.N., Tufekcioglu, M., and Marinescu, M. (2023). Identification of Streamside Landslides with the Use of Unmanned Aerial Vehicles (UAVs) in Greece, Romania, and Turkey. Remote Sens., 15.","DOI":"10.3390\/rs15041006"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.rse.2014.07.004","article-title":"Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China","volume":"152","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s10346-015-0587-0","article-title":"Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: A case study in the Cuyahoga Valley National Park, Ohio","volume":"13","author":"Gorsevski","year":"2016","journal-title":"Landslides"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s10346-018-0960-x","article-title":"Coseismic landslides triggered by the 8th August 2017 M s 7.0 Jiuzhaigou earthquake (Sichuan, China): Factors controlling their spatial distribution and implications for the seismogenic blind fault identification","volume":"15","author":"Fan","year":"2018","journal-title":"Landslides"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Tang, X., Tu, Z., Wang, Y., Liu, M., Li, D., and Fan, X. (2022). Automatic detection of coseismic landslides using a new transformer method. Remote Sens., 14.","DOI":"10.3390\/rs14122884"},{"key":"ref_59","unstructured":"Burrough, P.A., McDonnell, R.A., and Lloyd, C.D. (2015). Principles of Geographical Information Systems, Oxford University Press."},{"key":"ref_60","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 16\u201319). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.3390\/rs12152487","article-title":"Automatic mapping of landslides by the ResU-net","volume":"12","author":"Qi","year":"2020","journal-title":"Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6166","DOI":"10.1109\/JSTARS.2020.3028855","article-title":"A new deep-learning-based approach for earthquake-triggered landslide detection from single-temporal RapidEye satellite imagery","volume":"13","author":"Yi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1080\/01431161.2019.1672904","article-title":"Landslide mapping with remote sensing: Challenges and opportunities","volume":"41","author":"Zhong","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3850\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:24:37Z","timestamp":1760127877000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3850"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,2]]},"references-count":66,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153850"],"URL":"https:\/\/doi.org\/10.3390\/rs15153850","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,2]]}}}