{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T19:26:19Z","timestamp":1775589979905,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"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":["42101367"],"award-info":[{"award-number":["42101367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021J05041"],"award-info":[{"award-number":["2021J05041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022FKJ03"],"award-info":[{"award-number":["2022FKJ03"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["TZH2022-02"],"award-info":[{"award-number":["TZH2022-02"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["42101367"],"award-info":[{"award-number":["42101367"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2021J05041"],"award-info":[{"award-number":["2021J05041"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2022FKJ03"],"award-info":[{"award-number":["2022FKJ03"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["TZH2022-02"],"award-info":[{"award-number":["TZH2022-02"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian Forestry Science and Technology Key Project","award":["42101367"],"award-info":[{"award-number":["42101367"]}]},{"name":"Fujian Forestry Science and Technology Key Project","award":["2021J05041"],"award-info":[{"award-number":["2021J05041"]}]},{"name":"Fujian Forestry Science and Technology Key Project","award":["2022FKJ03"],"award-info":[{"award-number":["2022FKJ03"]}]},{"name":"Fujian Forestry Science and Technology Key Project","award":["TZH2022-02"],"award-info":[{"award-number":["TZH2022-02"]}]},{"name":"Open Fund Project of the Academy of Carbon Neutrality of Fujian Normal University","award":["42101367"],"award-info":[{"award-number":["42101367"]}]},{"name":"Open Fund Project of the Academy of Carbon Neutrality of Fujian Normal University","award":["2021J05041"],"award-info":[{"award-number":["2021J05041"]}]},{"name":"Open Fund Project of the Academy of Carbon Neutrality of Fujian Normal University","award":["2022FKJ03"],"award-info":[{"award-number":["2022FKJ03"]}]},{"name":"Open Fund Project of the Academy of Carbon Neutrality of Fujian Normal University","award":["TZH2022-02"],"award-info":[{"award-number":["TZH2022-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest disturbance detection is of great significance for understanding forest dynamics. The Landsat-based detection of the Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used for forest disturbance mapping. However, there are still two limitations in LandTrendr: first, it only used for summer-composited observations, which may delay the detection of forest disturbances that occurred in autumn and winter by one year, and second, it detected all disturbance types simultaneously using a single spectral index, which may reduce the mapping accuracy for certain forest disturbance types. Here, we modified LandTrendr (mLandTrendr) for forest disturbance mapping in China by using multi-season observations and multispectral indices. Validations using the randomly selected 1957 reference forest disturbance samples across China showed that the overall accuracy (F1 score) of forest disturbance detection in China was improved by 21% with these two modifications. The mLandTrendr can quickly and accurately detect forest disturbance and can be extended to national and global forest disturbance mapping for various forest types.<\/jats:p>","DOI":"10.3390\/rs15092381","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Improving LandTrendr Forest Disturbance Mapping in China Using Multi-Season Observations and Multispectral Indices"],"prefix":"10.3390","volume":"15","author":[{"given":"Dean","family":"Qiu","sequence":"first","affiliation":[{"name":"Key Laboratory for Humid Subtropical Ecogeographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"}]},{"given":"Yunjian","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Humid Subtropical Ecogeographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6448-4428","authenticated-orcid":false,"given":"Rong","family":"Shang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Humid Subtropical Ecogeographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"},{"name":"Academy of Carbon Neutrality, Fujian Normal University, Fuzhou 350007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8682-1293","authenticated-orcid":false,"given":"Jing M.","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Humid Subtropical Ecogeographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"},{"name":"Department of Geography and Planning, University of Toronto, Ontario, ON M5S 3G3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2018.11.025","article-title":"A Fusion Approach to Forest Disturbance Mapping Using Time Series Ensemble Techniques","volume":"221","author":"Hislop","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/nclimate1354","article-title":"Estimated Carbon Dioxide Emissions from Tropical Deforestation Improved by Carbon-Density Maps","volume":"2","author":"Baccini","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.5194\/bg-9-2683-2012","article-title":"High-resolution mapping of forest carbon stocks in the Colombian Amazon","volume":"9","author":"Asner","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"113372","DOI":"10.1016\/j.rse.2022.113372","article-title":"Toward Consistent Change Detection across Irregular Remote Sensing Time Series Observations","volume":"285","author":"Tollerud","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Qiu, S., Lin, Y.K., Shang, R., Zhang, J.X., Ma, L., and Zhu, Z. (2019). Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. Remote Sens., 11.","DOI":"10.3390\/rs11010051"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100023","DOI":"10.1016\/j.srs.2021.100023","article-title":"Evaluating the Impacts of Models, Data Density and Irregularity on Reconstructing and Forecasting Dense Landsat Time Series","volume":"4","author":"Zhang","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113073","DOI":"10.1016\/j.rse.2022.113073","article-title":"Near-Real-Time Monitoring of Land Disturbance with Harmonized Landsats 7\u20138 and Sentinel-2 Data","volume":"278","author":"Shang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111116","DOI":"10.1016\/j.rse.2019.03.009","article-title":"Continuous Monitoring of Land Disturbance Based on Landsat Time Series","volume":"238","author":"Zhu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting Trends in Forest Disturbance and Recovery Using Yearly Landsat Time Series: 1. LandTrendr-Temporal Segmentation Algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1016\/j.rse.2008.06.016","article-title":"Dynamics of National Forests Assessed Using the Landsat Record: Case Studies in Eastern United States","volume":"113","author":"Huang","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1109\/TGRS.2013.2272545","article-title":"On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data","volume":"52","author":"Brooks","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2018.02.050","article-title":"Mapping Agricultural Land Abandonment from Spatial and Temporal Segmentation of Landsat Time Series","volume":"210","author":"Yin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change Detection Using Landsat Time Series: A Review of Frequencies, Preprocessing, Algorithms, and Applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112752","DOI":"10.1016\/j.rse.2021.112752","article-title":"Remote Sensing Annual Dynamics of Rapid Permafrost Thaw Disturbances with LandTrendr","volume":"268","author":"Runge","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"102293","article-title":"Mapping Mangrove Dynamics and Colonization Patterns at the Suriname Coast Using Historic Satellite Data and the LandTrendr Algorithm","volume":"97","author":"Shen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cohen, W.B., Healey, S.P., Yang, Z.Q., Zhu, Z., and Gorelick, N. (2020). Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance. Remote Sens., 12.","DOI":"10.3390\/rs12101673"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for Geo-Big Data Applications: A Meta-Analysis and Systematic Review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kennedy, R.E., Yang, Z.Q., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10050691"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1111\/gcb.13904","article-title":"A Large-Area, Spatially Continuous Assessment of Land Cover Map Error and Its Impact on Downstream Analyses","volume":"24","author":"Estes","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, L.H., Liu, X.N., Wu, L., Tang, Y.B., and Meng, Y.Y. (2019). Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101234"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s42408-018-0021-9","article-title":"Examining Post-Fire Vegetation Recovery with Landsat Time Series Analysis in Three Western North American Forest Types","volume":"15","author":"Bright","year":"2019","journal-title":"FIRE Ecol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.rse.2011.11.006","article-title":"Using Annual Time-Series of Landsat Images to Assess the Effects of Forest Restitution in Post-Socialist Romania","volume":"118","author":"Griffiths","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13021-016-0066-5","article-title":"Attribution of Net Carbon Change by Disturbance Type across Forest Lands of the Conterminous United States","volume":"11","author":"Harris","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.jclepro.2018.01.050","article-title":"Detecting the Dynamics of Vegetation Disturbance and Recovery in Surface Mining Area via Landsat Imagery and LandTrendr Algorithm","volume":"178","author":"Yang","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.foreco.2014.11.030","article-title":"Spatiotemporal Dynamics of Recent Mountain Pine Beetle and Western Spruce Budworm Outbreaks across the Pacific Northwest Region, USA","volume":"339","author":"Meigs","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2015.09.015","article-title":"Boreal Shield Forest Disturbance and Recovery Trends Using Landsat Time Series","volume":"170","author":"Frazier","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2011.09.025","article-title":"Using Landsat-Derived Disturbance History (1972-2010) to Predict Current Forest Structure","volume":"122","author":"Pflugmacher","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","first-page":"102310","article-title":"Characterizing Forest Disturbances across the Argentine Dry Chaco Based on Landsat Time Series","volume":"98","author":"Pflugmacher","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.isprsjprs.2019.10.004","article-title":"A Comprehensive Evaluation of Disturbance Agent Classification Approaches: Strengths of Ensemble Classification, Multiple Indices, Spatio-Temporal Variables, and Direct Prediction","volume":"158","author":"Shimizu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2015.03.001","article-title":"Cross-Border Forest Disturbance and the Role of Natural Rubber in Mainland Southeast Asia Using Annual Landsat Time Series","volume":"169","author":"Grogan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cohen, W.B., Healey, S.P., Yang, Z., Stehman, S.V., Brewer, C.K., Brooks, E.B., Gorelick, N., Huang, C., Hughes, M.J., and Kennedy, R.E. (2017). How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?. Forests, 8.","DOI":"10.3390\/f8040098"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current Status of Landsat Program, Science, and Applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the Archive: How Free Data Has Enabled the Science and Monitoring Promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2019.02.016","article-title":"Benefits of the Free and Open Landsat Data Policy","volume":"224","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and Product Vision for Terrestrial Global Change Research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111439","DOI":"10.1016\/j.rse.2019.111439","article-title":"Harmonizing Landsat 8 and Sentinel-2: A Time-Series-Based Reflectance Adjustment Approach","volume":"235","author":"Shang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"Key, C., and Benson, N. (2006). Firemon: Fire Effects Monitoring and Inventory System, United States Department of Agriculture."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/0034-4257(85)90102-6","article-title":"A TM Tasseled Cap Equivalent Transformation for Reflectance Factor Data","volume":"17","author":"Crist","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of Live Aboveground Forest Biomass Dynamics with Landsat Time-Series and Field Inventory Data: A Comparison of Empirical Modeling Approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1111\/j.1365-2486.2011.02543.x","article-title":"Effects of Biotic Disturbances on Forest Carbon Cycling in the United States and Canada","volume":"18","author":"Hicke","year":"2012","journal-title":"Glob. Chang. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1111\/gcb.12194","article-title":"Altered Dynamics of Forest Recovery under a Changing Climate","volume":"19","author":"Miller","year":"2013","journal-title":"Glob. Chang. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A Review of Large Area Monitoring of Land Cover Change Using Landsat Data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.rse.2018.08.028","article-title":"A Spatial and Temporal Analysis of Forest Dynamics Using Landsat Time-Series","volume":"217","author":"Nguyen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2017.11.015","article-title":"A LandTrendr Multispectral Ensemble for Forest Disturbance Detection","volume":"205","author":"Cohen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_50","first-page":"102806","article-title":"Demystifying LandTrendr and CCDC Temporal Segmentation","volume":"110","author":"Pasquarella","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.05.005","article-title":"Attribution of Disturbance Change Agent from Landsat Time-Series in Support of Habitat Monitoring in the Puget Sound Region, USA","volume":"166","author":"Kennedy","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.rse.2011.09.024","article-title":"Spatial and Temporal Patterns of Forest Disturbance and Regrowth within the Area of the Northwest Forest Plan","volume":"122","author":"Kennedy","year":"2012","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2381\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:27:48Z","timestamp":1760124468000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2381"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":52,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092381"],"URL":"https:\/\/doi.org\/10.3390\/rs15092381","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]}}}