{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:54:31Z","timestamp":1760147671479,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency (ESA)","doi-asserted-by":"publisher","award":["RS4EST"],"award-info":[{"award-number":["RS4EST"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ash dieback pandemic, caused by the invasive fungus Hymenoscyphus fraxineus, represents one of Europe\u2019s biggest threats to preserving natural biodiversity. To ensure the suppression of forest damage caused by fungi, timely recognition of the symptoms of ash dieback and further continuous monitoring on an adequate spatial scale are essential. Visual crown damage assessment is currently the most common method used for identifying ash dieback, but it lacks the spatial and temporal coverage required for effective disease suppression. Remote sensing technologies, with the capabilities of fast and repetitive retrieval of information over a large spatial scale, could present efficient supplementary methods for ash damage detection and disease monitoring. In this study, we provided a synthesis of the existing remote sensing methods and applications that considers ash dieback disease, and we described the lifecycle of the disease using the major symptoms that remote sensing technologies can identify. Unfortunately, although effective methods of monitoring biotic damage through remote sensing have been developed, ash dieback has only been addressed in two research studies in the United Kingdom and Germany. These studies were based on single-date hyperspectral and very-high-resolution imagery in combination with machine learning, using previously specified ground-truth information regarding crown damage status. However, no study exists using high-resolution imagery such as Sentinel-2 or radar Sentinel-1, although some preliminary project results show that these coarser sources of information could be applicable for ash dieback detection and monitoring in cases of Fraxinus angustifolia, which forms pure, more homogenous stands in Southern Europe.<\/jats:p>","DOI":"10.3390\/rs15051178","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T01:39:47Z","timestamp":1677029987000},"page":"1178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Monitoring Ash Dieback in Europe\u2014An Unrevealed Perspective for Remote Sensing?"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2345-7882","authenticated-orcid":false,"given":"Mateo","family":"Ga\u0161parovi\u0107","sequence":"first","affiliation":[{"name":"Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, Ka\u010di\u0107eva 26, 10000 Zagreb, Croatia"}]},{"given":"Ivan","family":"Pila\u0161","sequence":"additional","affiliation":[{"name":"Division of Ecology, Croatian Forest Research Institute, Cvjetno Naselje 41, 10450 Jastrebarsko, Croatia"}]},{"given":"Damir","family":"Klobu\u010dar","sequence":"additional","affiliation":[{"name":"Production and Development Department, Croatian Forests Ltd., Ivana Me\u0161trovi\u0107a 28, 48000 Koprivnica, Croatia"}]},{"given":"Iva","family":"Ga\u0161parovi\u0107","sequence":"additional","affiliation":[{"name":"Sector for Spatial Data Infrastructure, State Geodetic Administration, Gru\u0161ka 20, 10000 Zagreb, Croatia"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/geb.12558","article-title":"Biotic disturbances in Northern Hemisphere forests\u2014A synthesis of recent data, uncertainties and implications for forest monitoring and modelling","volume":"26","author":"Kautz","year":"2017","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s40725-017-0056-1","article-title":"Application of Remote Sensing Technologies for Assessing Planted Forests Damaged by Insect Pests and Fungal Pathogens: A Review","volume":"3","author":"Stone","year":"2017","journal-title":"Curr. For. Rep."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Torres, P., Rodes-Blanco, M., Viana-Soto, A., Nieto, H., and Garc\u00eda, M. (2021). The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis. Forests, 12.","DOI":"10.3390\/f12081134"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Choi, W.I., and Park, Y.-S. (2022). Management of Forest Pests and Diseases. Forests, 13.","DOI":"10.3390\/f13111765"},{"key":"ref_6","unstructured":"European Commission, Directorate-General for Environment, Atzberger, C., Zeug, G., Defourny, P., Arag\u00e3o, L., Hammarstr\u00f6m, L., and Immitzer, M. (2020). Monitoring of Forests through Remote Sensing, Publications Office. Final report."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4515","DOI":"10.3390\/rs6054515","article-title":"Evaluating the potential of worldview-2 data to classify tree species and different levels of ash mortality","volume":"6","author":"Waser","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1002\/rse2.190","article-title":"Monitoring ash dieback (Hymenoscyphus fraxineus) in British forests using hyperspectral remote sensing","volume":"7","author":"Chan","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_9","unstructured":"San-MiguelAyanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., and Mauri, A. (2016). European Atlas of Forest Tree Species, Publ. Off. EU."},{"key":"ref_10","unstructured":"San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., and Mauri, A. (2016). European Atlas of Forest Tree Species, Publ. Off. EU."},{"key":"ref_11","unstructured":"Vasaitis, R., and Enderle, R. (2017). Dieback of European Ash (Fraxinus spp.)\u2014Consequences and Guidelines for Sustainable Management, Swedish University of Agricultural Sciences."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1079\/PAVSNNR201914025","article-title":"An overview of ash (Fraxinus spp.) and the ash dieback disease in Europe","volume":"14","author":"Enderle","year":"2019","journal-title":"CAB. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4764","DOI":"10.1038\/s41598-022-08825-6","article-title":"European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.)","volume":"12","author":"George","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1111\/ppa.12196","article-title":"The ash dieback crisis: Genetic variation in resistance can prove a long-term solution","volume":"63","author":"McKinney","year":"2014","journal-title":"Plant. Pathol."},{"key":"ref_15","unstructured":"Vasaitis, R., and Enderle, R. (2017). Dieback of European ash (Fraxinus spp.): Consequences and Guidelines for Sustainable Management, Swedish University of Agricultural Sciences."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.3389\/fpls.2018.01087","article-title":"Propagule pressure build-up by the invasive Hymenoscyphus fraxineus following its introduction to an ash forest inhabited by the native Hymenoscyphus albidus","volume":"9","author":"Hietala","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"236","DOI":"10.5897\/JAERD12.058","article-title":"Chalara fraxinea incidence in Hungarian ash (Fraxinus excelsior) forests","volume":"4","author":"Koltay","year":"2012","journal-title":"J. Agric. Ext. Rural Dev."},{"key":"ref_18","unstructured":"(2023, January 10). ICP Forests. Available online: http:\/\/icp-forests.net\/."},{"key":"ref_19","unstructured":"CFRI (2019). Damage to Forest Ecosystems of the Republic of Croatia Report for 2019, Croatian Forest Research Institute."},{"key":"ref_20","unstructured":"(2023, January 10). Forest Research, Available online: https:\/\/www.forestresearch.gov.uk\/."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2016). Remote Sensing for Sustainability, CRC Press.","DOI":"10.1201\/9781315371931"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"062527","DOI":"10.1117\/1.JRS.6.062527","article-title":"Digital high spatial resolution aerial imagery to support forest health monitoring: The mountain pine beetle context","volume":"6","author":"Wulder","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bergm\u00fcller, A., Borralho, N., Cabral, P., and Caetano, M. (2022). Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review. Forests, 13.","DOI":"10.3390\/f13060911"},{"key":"ref_24","first-page":"102363","article-title":"A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level","volume":"101","author":"Yu","year":"2021","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Duarte, A., Acevedo-Mu\u00f1oz, L., Gon\u00e7alves, C.I., Mota, L., Sarmento, A., Silva, M., Fabres, S., Borralho, N., and Valente, C. (2020). Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12193153"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117564","DOI":"10.1016\/j.foreco.2019.117564","article-title":"Integration of WorldView-2 and airborne laser scanning data to classify defoliation levels in Quercus ilex L. Dehesas affected by root rot mortality: Management implications","volume":"451","author":"Acosta","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1080\/01431160701730136","article-title":"Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation","volume":"29","author":"Franklin","year":"2007","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bright, B.C., Hudak, A.T., Meddens, A.J.H., Egan, J.M., and Jorgensen, C.L. (2020). Mapping Multiple Insect Outbreaks across Large Regions Annually Using Landsat Time Series Data. Remote Sens., 12.","DOI":"10.3390\/rs12101655"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108436","DOI":"10.1016\/j.agrformet.2021.108436","article-title":"Detecting the oak lace bug infestation in oak forests using MODIS and meteorological data","volume":"306","author":"Kern","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_30","unstructured":"Thornley, R. (2018). Leaf level detection of European Ash (Fraxinus excelsior) and Its Associated Fungal Pathogen Hymenoscyphus Fraxineus Using Spectral Analysis. [Master\u2019s Thesis, Imperial College London]."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Polk, S.L., Chan, A.H., Cui, K., Plemmons, R.J., Coomes, D.A., and Murphy, J.M. (2022, January 19). Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883429"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S296","DOI":"10.4039\/tce.2016.11","article-title":"Remote sensing of forest pest damage: A review and lessons learned from a Canadian perspective","volume":"148","author":"Hall","year":"2016","journal-title":"Can. Entomol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"919","DOI":"10.3832\/ifor1789-009","article-title":"Relationships between overstory and understory structure and diversity in semi-natural mixed floodplain forests at Bosco Fontana (Italy)","volume":"9","author":"Chianucci","year":"2016","journal-title":"iForest"},{"key":"ref_34","first-page":"193","article-title":"The Sava and Drava floodplains: Threatened ecosystems of international importance","volume":"130","year":"2006","journal-title":"\u0160umar. List"},{"key":"ref_35","unstructured":"(2023, January 10). MySustainableForest. Available online: https:\/\/mysustainableforest.com\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fernandez-Carrillo, A., Pato\u010dka, Z., Dobrovoln\u00fd, L., Franco-Nieto, A., and Revilla-Romero, B. (2020). Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data. Remote Sens., 12.","DOI":"10.3390\/rs12213634"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., and Moreno, J. (2019). Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/s40725-019-00098-z","article-title":"Radar Satellite Imagery for Detecting Bark Beetle Outbreaks in Forests","volume":"5","author":"Hollaus","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"17448","DOI":"10.1038\/s41598-018-35770-0","article-title":"Advanced spectroscopy-based phenotyping offers a potential solution to the ash dieback epidemic","volume":"8","author":"Villari","year":"2018","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:38:22Z","timestamp":1760121502000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051178"],"URL":"https:\/\/doi.org\/10.3390\/rs15051178","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,21]]}}}