{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:47:52Z","timestamp":1775666872202,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,31]],"date-time":"2019-01-31T00:00:00Z","timestamp":1548892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["86893"],"award-info":[{"award-number":["86893"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control.<\/jats:p>","DOI":"10.3390\/rs11030278","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T03:08:05Z","timestamp":1548990485000},"page":"278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa"],"prefix":"10.3390","volume":"11","author":[{"given":"Samuel Takudzwa","family":"Kumbula","sequence":"first","affiliation":[{"name":"School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P\/Bag X01, Scottsville 3209, Pietermaritzburg, South Africa"}]},{"given":"Paramu","family":"Mafongoya","sequence":"additional","affiliation":[{"name":"School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P\/Bag X01, Scottsville 3209, Pietermaritzburg, South Africa"}]},{"given":"Kabir Yunus","family":"Peerbhay","sequence":"additional","affiliation":[{"name":"School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P\/Bag X01, Scottsville 3209, Pietermaritzburg, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2381-4915","authenticated-orcid":false,"given":"Romano Trent","family":"Lottering","sequence":"additional","affiliation":[{"name":"School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P\/Bag X01, Scottsville 3209, Pietermaritzburg, South Africa"}]},{"given":"Riyad","family":"Ismail","sequence":"additional","affiliation":[{"name":"School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P\/Bag X01, Scottsville 3209, Pietermaritzburg, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,31]]},"reference":[{"key":"ref_1","first-page":"67","article-title":"Plantation disease and pest management in the next century","volume":"190","author":"Wingfield","year":"2001","journal-title":"S. Afr. For. J."},{"key":"ref_2","unstructured":"DAFF (2017, June 25). A Profile of the South African Forestry Market Value Chain. Available online: http:\/\/www.nda.agric.za\/doaDev\/sideMenu\/Marketing\/Annual Publications\/Commodity Profiles\/field crops\/Forestry Market Value Chain Profile 2016.pdf."},{"key":"ref_3","unstructured":"DAFF (2017, August 12). Forestry Regulation & Oversight, Available online: https:\/\/www.daff.gov.za\/daffweb3\/Branches\/Forestry-Natural-Resources-Management\/Forestry-Regulation-Oversight\/Facts-and-Figures\/plantationsmore."},{"key":"ref_4","first-page":"852540","article-title":"Eucalyptus and water use in South Africa","volume":"2013","author":"Albaugh","year":"2013","journal-title":"Int. J. For. Res."},{"key":"ref_5","unstructured":"Swain, T.-L., and Gardner, R.A.W. (2003). A Summary of Current Knowledge of Cold Tolerant Eucalypt Species (CTE\u2019s) Grown in South Africa, University of Natal, Institute for Commercial Forestry Research."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"139","DOI":"10.2989\/SOUTH.FOR.2008.70.2.9.537","article-title":"Eucalypt pests and diseases: Growing threats to plantation productivity","volume":"70","author":"Wingfield","year":"2008","journal-title":"South. For. J. For. Sci."},{"key":"ref_7","unstructured":"Seta (2017, August 12). A Profile of the Forestry and Wood Products Sub-Sector. Available online: http:\/\/www.fpmseta.org.za\/downloads\/FPM_sub-sector_forestry_wood_final.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1080\/13658816.2013.772183","article-title":"Determining the susceptibility of Eucalyptus nitens forests to Coryphodema tristis (cossid moth) occurrence in Mpumalanga, South Africa","volume":"27","author":"Adam","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_9","first-page":"23","article-title":"A survey of cossid moth attack in Eucalyptus nitens on the Mpumalanga Highveld of South Africa","volume":"206","author":"Boreham","year":"2006","journal-title":"S. Afr For. J."},{"key":"ref_10","first-page":"26","article-title":"A new lepidopteran insect pest discovered on commercially grown Eucalyptus nitens in South Africa: Research in action","volume":"101","author":"Gebeyehu","year":"2005","journal-title":"S. Afr. J. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bouwer, M.C., Slippers, B., Degefu, D., Wingfield, M.J., Lawson, S., and Rohwer, E.R. (2015). Identification of the sex pheromone of the tree infesting Cossid moth Coryphodema tristis (Lepidoptera: Cossidae). PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118575"},{"key":"ref_12","unstructured":"FAO (2007). Overview of Forest Pests South Africa, FAO."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4246","DOI":"10.1038\/s41598-017-04562-3","article-title":"Niche comparison among two invasive leafminer species and their parasitoid Opius biroi: Implications for competitive displacement","volume":"7","author":"Xing","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pause, M., Schweitzer, C., Rosenthal, M., Keuck, V., Bumberger, J., Dietrich, P., Heurich, M., Jung, A., and Lausch, A. (2016). In situ\/remote sensing integration to assess forest health\u2014A review. Remote Sens., 8.","DOI":"10.3390\/rs8060471"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"175","DOI":"10.2989\/SHFJ.2007.69.3.7.357","article-title":"Predicting Mycosphaerella leaf disease severity in a Eucalyptus globulus plantation using digital multi-spectral imagery","volume":"69","author":"Pietrzykowski","year":"2007","journal-title":"South. Hemisph. For. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1071\/WR16170","article-title":"Modelling the susceptibility of pine stands to bark stripping by Chacma baboons (Papio ursinus) in the Mpumalanga Province of South Africa","volume":"44","author":"Germishuizen","year":"2017","journal-title":"Wildl. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.agsy.2017.01.019","article-title":"Modelling the impacts of pests and diseases on agricultural systems","volume":"155","author":"Donatelli","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2015.11.010","article-title":"Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa","volume":"112","author":"Lottering","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2236","DOI":"10.1080\/01431161.2012.743694","article-title":"Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests","volume":"34","author":"Oumar","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/JSTARS.2013.2258329","article-title":"Spectral discrimination of insect defoliation levels in mopane woodland using hyperspectral data","volume":"7","author":"Adelabu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_21","first-page":"W3","article-title":"Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program","volume":"7","author":"Belward","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ng, W.-T., Rima, P., Einzmann, K., Immitzer, M., Atzberger, C., and Eckert, S. (2017). Assessing the Potential of Sentinel-2 and Pl\u00e9iades Data for the Detection of Prosopis and Vachellia spp. in Kenya. Remote Sens., 9.","DOI":"10.3390\/rs9010074"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A calibration and products validation status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Radoux, J., Chom\u00e9, G., Jacques, D., Waldner, F., Bellemans, N., Matton, N., Lamarche, C., D\u2019Andrimont, R., and Defourny, P. (2016). Sentinel-2\u2019s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060488"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves","volume":"19","author":"Blackburn","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1080\/01431169408954109","article-title":"Ratios of leaf reflectances in narrow wavebands as indicators of plant stress","volume":"15","author":"Carter","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/36.934080","article-title":"Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data","volume":"39","author":"Miller","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/S0176-1617(96)80285-9","article-title":"Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1080\/014311697217558","article-title":"Remote estimation of chlorophyll content in higher plant leaves","volume":"18","author":"Gitelson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1046\/j.0028-646X.2001.00289.x","article-title":"An evaluation of noninvasive methods to estimate foliar chlorophyll content","volume":"153","author":"Richardson","year":"2002","journal-title":"New Phytol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"L11402","DOI":"10.1029\/2006GL026457","article-title":"Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves","volume":"33","author":"Gitelson","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1080\/01431160210163074","article-title":"Vegetation indices derived from high-resolution airborne videography for precision crop management","volume":"24","author":"Metternicht","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1111\/ecog.03049","article-title":"Opening the black box: An open-source release of Maxent","volume":"40","author":"Phillips","year":"2017","journal-title":"Ecography"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.apgeog.2018.07.025","article-title":"Modelling potential distribution of bramble (rubus cuneifolius) using topographic, bioclimatic and remotely sensed data in the KwaZulu-Natal Drakensberg, South Africa","volume":"99","author":"Ndlovu","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1111\/j.2006.0906-7590.04596.x","article-title":"Novel Methods Improve Prediction of Species\u2019 Distributions from Occurrence Data","volume":"29","author":"Elith","year":"2006","journal-title":"Ecography"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Biber-Freudenberger, L., Ziemacki, J., Tonnang, H.E.Z., and Borgemeister, C. (2016). Future Risks of Pest Species under Changing Climatic Conditions. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0153237"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.apgeog.2016.09.008","article-title":"Modelling the spatial-temporal distribution of tsetse (Glossina pallidipes) as a function of topography and vegetation greenness in the Zambezi Valley of Zimbabwe","volume":"76","author":"Matawa","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.ecoleng.2016.04.010","article-title":"Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China","volume":"92","author":"Yi","year":"2016","journal-title":"Ecol. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5973","DOI":"10.1002\/ece3.2332","article-title":"A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area","volume":"6","author":"Shabani","year":"2016","journal-title":"Ecol. Evol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.ecolmodel.2005.03.026","article-title":"Maximum entropy modeling of species geographic distributions","volume":"190","author":"Phillips","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1214\/aos\/1176345462","article-title":"The jackknife estimate of variance","volume":"9","author":"Efron","year":"1981","journal-title":"Ann. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1111\/j.0906-7590.2008.5203.x","article-title":"Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation","volume":"31","author":"Phillips","year":"2008","journal-title":"Ecography"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e3446","DOI":"10.7717\/peerj.3446","article-title":"Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?","volume":"5","author":"Hageer","year":"2017","journal-title":"PeerJ"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Molloy, S.W., Davis, R.A., and van Etten, E.J.B. (2016). Incorporating field studies into species distribution and climate change modelling: A case study of the koomal Trichosurus vulpecula hypoleucus (Phalangeridae). PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0154161"},{"key":"ref_47","first-page":"7","article-title":"Evaluation by prediction of the natural range shrinkage of Quercus ilex L. in eastern Algeria","volume":"68","author":"Tabet","year":"2018","journal-title":"\u0130stanbul \u00dcniversitesi Orman Fak\u00fcltesi Dergisi"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, R., Li, Q., He, S., Liu, Y., Wang, M., and Jiang, G. (2018). Modeling and mapping the current and future distribution of Pseudomonas syringae pv. actinidiae under climate change in China. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192153"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1111\/j.1365-2664.2009.01765.x","article-title":"Ground validation of presence-only modelling with rare species: A case study on barbastelles Barbastella barbastellus (Chiroptera: Vespertilionidae)","volume":"47","author":"Rebelo","year":"2010","journal-title":"J. Appl. Ecol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/j.1365-2664.2006.01214.x","article-title":"Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS)","volume":"43","author":"Allouche","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1111\/jbi.12058","article-title":"Selecting thresholds for the prediction of species occurrence with presence-only data","volume":"40","author":"Liu","year":"2013","journal-title":"J. Biogeogr."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.biocon.2017.11.035","article-title":"Assessment and prioritisation of plant species at risk from myrtle rust (Austropuccinia psidii) under current and future climates in Australia","volume":"218","author":"Berthon","year":"2018","journal-title":"Biol. Conserv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1111\/j.1472-4642.2010.00725.x","article-title":"A statistical explanation of MaxEnt for ecologists","volume":"17","author":"Elith","year":"2011","journal-title":"Divers. Distrib."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"711","DOI":"10.5194\/isprs-archives-XLI-B8-711-2016","article-title":"Use of a multispectral UAV photogrammetry for detection and tracking of forest disturbance dynamics","volume":"XLI-B8","author":"Langhammer","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.06.015","article-title":"Detection of spruce beetle-induced tree mortality using high- and medium-resolution remotely sensed imagery","volume":"168","author":"Hart","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s00468-011-0591-5","article-title":"Relationships between endophyte diversity and leaf optical properties","volume":"26","author":"Oki","year":"2012","journal-title":"Trees"},{"key":"ref_57","unstructured":"Matawa, F., Murwira, K.S., and Shereni, W. (2013). Modelling the distribution of suitable Glossina Spp. habitat in the North Western parts of Zimbabwe using remote sensing and climate data. Geoinform. Geostast. Overv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1016\/j.rse.2011.09.002","article-title":"Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland","volume":"115","author":"Eitel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Murfitt, J., He, Y., Yang, J., Mui, A., and De Mille, K. (2016). Ash decline assessment in emerald ash borer infested natural forests using high spatial resolution images. Remote Sens., 8.","DOI":"10.3390\/rs8030256"},{"key":"ref_60","first-page":"49","article-title":"Remote sensing of forest insect disturbances: Current state and future directions","volume":"60","author":"Senf","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"39","DOI":"10.2989\/SHFJ.2007.69.1.5.167","article-title":"Forest health and vitality: The detection and monitoring of Pinus patula trees infected by Sirex noctilio using digital ultispectral imagery (DMSI)","volume":"69","author":"Ismail","year":"2007","journal-title":"South. Hemisph. For. J."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/278\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:29:55Z","timestamp":1760185795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,31]]},"references-count":61,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030278"],"URL":"https:\/\/doi.org\/10.3390\/rs11030278","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,31]]}}}