{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T15:22:53Z","timestamp":1772810573647,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T00:00:00Z","timestamp":1705276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Forest Growers Levy Trust"},{"name":"New Zealand Ministry for Business Innovation and Employment"},{"name":"Scion, the New Zealand Forest Research Institute Ltd."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study demonstrates a framework for using high-resolution satellite imagery to automatically map and monitor outbreaks of red needle cast (Phytophthora pluvialis) in planted pine forests. This methodology was tested on five WorldView satellite scenes collected over two sites in the Gisborne Region of New Zealand\u2019s North Island. All scenes were acquired in September: four scenes were acquired yearly (2018\u20132020 and 2022) for Wharerata, while one more was obtained in 2019 for Tauwhareparae. Training areas were selected for each scene using manual delineation combined with pixel-level thresholding rules based on band reflectance values and vegetation indices (selected empirically) to produce \u2018pure\u2019 training pixels for the different classes. A leave-one-scene-out, pixel-based random forest classification approach was then used to classify all images into (i) healthy pine forest, (ii) unhealthy pine forest or (iii) background. The overall accuracy of the models on the internal validation dataset ranged between 92.1% and 93.6%. Overall accuracies calculated for the left-out scenes ranged between 76.3% and 91.1% (mean overall accuracy of 83.8%), while user\u2019s and producer\u2019s accuracies across the three classes were 60.2\u201399.0% (71.4\u201391.8% for unhealthy pine forest) and 54.4\u2013100% (71.9\u201397.2% for unhealthy pine forest), respectively. This work demonstrates the possibility of using a random forest classifier trained on a set of satellite scenes for the classification of healthy and unhealthy pine forest in new and completely independent scenes. This paves the way for a scalable and largely autonomous forest health monitoring system based on annual acquisitions of high-resolution satellite imagery at the time of peak disease expression, while greatly reducing the need for manual interpretation and delineation.<\/jats:p>","DOI":"10.3390\/rs16020338","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T04:54:38Z","timestamp":1705294478000},"page":"338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Detection of Phytophthora pluvialis Outbreaks in Radiata Pine Plantations Using Multi-Scene, Multi-Temporal Satellite Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7520-126X","authenticated-orcid":false,"given":"Nicol\u00f2","family":"Camarretta","sequence":"first","affiliation":[{"name":"Scion, Titokorangi Drive, Private Bag 3020, Rotorua 3046, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5277-2449","authenticated-orcid":false,"given":"Grant D.","family":"Pearse","sequence":"additional","affiliation":[{"name":"Scion, Titokorangi Drive, Private Bag 3020, Rotorua 3046, New Zealand"},{"name":"College of Science and Engineering, Flinders University, Sturt Rd, Bedford Park 5042, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8242-8358","authenticated-orcid":false,"given":"Benjamin S. C.","family":"Steer","sequence":"additional","affiliation":[{"name":"Scion, Titokorangi Drive, Private Bag 3020, Rotorua 3046, New Zealand"}]},{"given":"Emily","family":"McLay","sequence":"additional","affiliation":[{"name":"Scion, Titokorangi Drive, Private Bag 3020, Rotorua 3046, New Zealand"}]},{"given":"Stuart","family":"Fraser","sequence":"additional","affiliation":[{"name":"Scion, Titokorangi Drive, Private Bag 3020, Rotorua 3046, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-9134","authenticated-orcid":false,"given":"Michael S.","family":"Watt","sequence":"additional","affiliation":[{"name":"Scion, 10 Kyle Street, Christchurch 8011, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1146\/annurev-ecolsys-110218-024934","article-title":"Interacting Effects of Global Change on Forest Pest and Pathogen Dynamics","volume":"50","author":"Rizzo","year":"2019","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.3389\/fpls.2020.601009","article-title":"The Threat of the Combined Effect of Biotic and Abiotic Stress Factors in Forestry Under a Changing Climate","volume":"11","author":"Teshome","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.foreco.2015.03.014","article-title":"Projecting Global Forest Area towards 2030","volume":"352","author":"Sandker","year":"2015","journal-title":"For. Ecol. Manage"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.pt.2014.12.003","article-title":"Parasites and Biological Invasions: Parallels, Interactions, and Control","volume":"31","author":"Dunn","year":"2015","journal-title":"Trends Parasitol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.foreco.2016.09.032","article-title":"Drivers of Emerging Fungal Diseases of Forest Trees","volume":"381","author":"Ghelardini","year":"2016","journal-title":"For. Ecol. Manag"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Richardson, D.M., Williams, P.A., and Hobbs, R.J. (1994). Pine Invasions in the Southern Hemisphere: Determinants of Spread and Invadability. J. Biogeogr., 21.","DOI":"10.2307\/2845655"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/00049158.2007.10675023","article-title":"Achievements in Forest Tree Improvement in Australia and New Zealand 8. Successful Introduction and Breeding of Radiata Pine in Australia","volume":"70","author":"Wu","year":"2007","journal-title":"Aust. For."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1007\/s10530-017-1514-1","article-title":"Ecology of Forest Insect Invasions","volume":"19","author":"Brockerhoff","year":"2017","journal-title":"Biol. Invasions"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1111\/j.1469-8137.2012.04364.x","article-title":"Biogeographical Patterns and Determinants of Invasion by Forest Pathogens in Europe","volume":"197","author":"Santini","year":"2013","journal-title":"New Phytol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1038\/s41579-019-0236-z","article-title":"Microbial Invasions in Terrestrial Ecosystems","volume":"17","author":"Thakur","year":"2019","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1111\/geb.12214","article-title":"The Global Spread of Crop Pests and Pathogens","volume":"23","author":"Bebber","year":"2014","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106020","DOI":"10.1016\/j.ecolind.2019.106020","article-title":"Invasive Alien Plant Species: Their Impact on Environment, Ecosystem Services and Human Health","volume":"111","author":"Singh","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/nature10947","article-title":"Emerging Fungal Threats to Animal, Plant and Ecosystem Health","volume":"484","author":"Fisher","year":"2012","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1890\/080083","article-title":"How Well Do We Understand the Impacts of Alien Species on Ecosystem Services? A Pan-European, Cross-Taxa Assessment","volume":"8","author":"Basnou","year":"2010","journal-title":"Front. Ecol. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1890\/15-1176","article-title":"Nonnative Forest Insects and Pathogens in the United States: Impacts and Policy Options","volume":"26","author":"Lovett","year":"2016","journal-title":"Ecol. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e51","DOI":"10.1111\/csp2.51","article-title":"Allocation of Invasive Plant Management Expenditures for Conservation: Lessons from Florida, USA","volume":"1","author":"Hiatt","year":"2019","journal-title":"Conserv. Sci. Pract."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40490-014-0006-7","article-title":"Pathogenicity of Phytophthora Pluvialis to Pinus Radiata and Its Relation with Red Needle Cast Disease in New Zealand","volume":"44","author":"Dick","year":"2014","journal-title":"N. Z. J. For. Sci."},{"key":"ref_18","unstructured":"Ramsfield, T.D., Dick, M.A., Beever, R.E., Horner, I.J., McAlonan, M.J., and Hill, C.F. (2007, January 26\u201321). Phytophthora Kernoviae in New Zealand. Proceedings of the Fourth Meeting of IUFRO Working Party S07.02.09, Monterey, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hood, I.A., Husheer, S., Gardner, J.F., Evanson, T.W., Tieman, G., Banham, C., Wright, L.C., and Fraser, S. (2022). Infection Periods of Phytophthora Pluvialis and Phytophthora Kernoviae in Relation to Weather Variables and Season in Pinus Radiata Forests in New Zealand. N. Z. J. For. Sci., 52.","DOI":"10.33494\/nzjfs522022x224x"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e12588","DOI":"10.1111\/efp.12588","article-title":"Impact of Weather Variables and Season on Sporulation of Phytophthora Pluvialis and Phytophthora Kernoviae","volume":"50","author":"Fraser","year":"2020","journal-title":"For. Pathol."},{"key":"ref_21","unstructured":"New Zealand Forest Owners Association (2017). New Zealand Forestry Bulletin, New Zealand Forest Owners Association."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"18","DOI":"10.33494\/nzjfs522022x211x","article-title":"Efficacy and Optimal Timing of Low-Volume Aerial Applications of Copper Fungicides for the Control of Red Needle Cast of Pine","volume":"52","author":"Fraser","year":"2022","journal-title":"N. Z. J. For. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119984","DOI":"10.1016\/j.foreco.2021.119984","article-title":"Comparison of Field Survey and Remote Sensing Techniques for Detection of Bark Beetle-Infested Trees","volume":"506","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Deng, X., Zhu, Z., Yang, J., Zheng, Z., Huang, Z., Yin, X., Wei, S., and Lan, Y. (2020). Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing. Remote Sens., 12.","DOI":"10.3390\/rs12172678"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111238","DOI":"10.1016\/j.rse.2019.111238","article-title":"Integrating Multi-Sensor Remote Sensing and Species Distribution Modeling to Map the Spread of Emerging Forest Disease and Tree Mortality","volume":"231","author":"He","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2519","DOI":"10.1002\/eap.2519","article-title":"Early Detection of a Tree Pathogen Using Airborne Remote Sensing","volume":"32","author":"Weingarten","year":"2022","journal-title":"Ecol. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Han, Z., Hu, W., Peng, S., Lin, H., Zhang, J., Zhou, J., Wang, P., and Dian, Y. (2022). Detection of Standing Dead Trees after Pine Wilt Disease Outbreak with Airborne Remote Sensing Imagery by Multi-Scale Spatial Attention Deep Learning and Gaussian Kernel Approach. Remote Sens., 14.","DOI":"10.3390\/rs14133075"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, J., Lu, X., Chen, L., Sun, H., Wang, S., and Fang, G. (2022). Accurate Identification of Pine Wood Nematode Disease with a Deep Convolution Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14040913"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"120126","DOI":"10.1016\/j.foreco.2022.120126","article-title":"Identifying Conifer Mortality Induced by Armillaria Root Disease Using Airborne Lidar and Orthoimagery in South Central Oregon","volume":"511","author":"Oblinger","year":"2022","journal-title":"For. Ecol. Manage."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"429","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-429-2020","article-title":"Using Multitemporal Hyper- and Multispectral Uav Imaging for Detecting Bark Beetle Infestation on Norway Spruce","volume":"XLIII-B3-2","author":"Honkavaara","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1111\/afe.12267","article-title":"Performance of the Tree-Killing Bark Beetles Ips Typographus and Pityogenes Chalcographus in Non-Indigenous Lodgepole Pine and Their Historical Host Norway Spruce","volume":"20","author":"Schroeder","year":"2018","journal-title":"Agric. For. Entomol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bozzini, A., Francini, S., Chirici, G., Battisti, A., and Faccoli, M. (2023). Spruce Bark Beetle Outbreak Prediction through Automatic Classification of Sentinel-2 Imagery. Forests, 14.","DOI":"10.3390\/f14061116"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Junttila, S., N\u00e4si, R., Koivum\u00e4ki, N., Imangholiloo, M., Saarinen, N., Raisio, J., Holopainen, M., Hyypp\u00e4, H., Hyypp\u00e4, J., and Lyytik\u00e4inen-Saarenmaa, P. (2022). Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season. Remote Sens., 14.","DOI":"10.3390\/rs14040909"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110349","DOI":"10.1016\/j.ecolind.2023.110349","article-title":"Spectral Separability of Bark Beetle Infestation Stages: A Single-Tree Time-Series Analysis Using Planet Imagery","volume":"153","author":"Dalponte","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2842","DOI":"10.1111\/j.1365-2486.2011.02452.x","article-title":"Unraveling the Drivers of Intensifying Forest Disturbance Regimes in Europe","volume":"17","author":"Seidl","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Klou\u010dek, T., Kom\u00e1rek, J., Surov\u00fd, P., Hrach, K., Janata, P., and Va\u0161\u00ed\u010dek, B. (2019). The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens., 11.","DOI":"10.3390\/rs11131561"},{"key":"ref_38","first-page":"102335","article-title":"Early Detection of Bark Beetle Infestation in Norway Spruce Forests of Central Europe Using Sentinel-2","volume":"100","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2006.06.007","article-title":"Integrating Remotely Sensed and Ancillary Data Sources to Characterize a Mountain Pine Beetle Infestation","volume":"105","author":"Coops","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"61","article-title":"Automated Detection and Mapping of Crown Discolouration Caused by Jack Pine Budworm with 2.5 m Resolution Multispectral Imagery","volume":"7","author":"Leckie","year":"2005","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.rse.2010.09.013","article-title":"Assessment of MODIS NDVI Time Series Data Products for Detecting Forest Defoliation by Gypsy Moth Outbreaks","volume":"115","author":"Spruce","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.isprsjprs.2013.10.010","article-title":"Integrating Environmental Variables and WorldView-2 Image Data to Improve the Prediction and Mapping of Thaumastocoris Peregrinus (Bronze Bug) Damage in Plantation Forests","volume":"87","author":"Oumar","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","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_44","first-page":"163","article-title":"Pathogens in Exotic Plantation Forestry","volume":"1","author":"Wingfield","year":"1999","journal-title":"Int. For. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"29","DOI":"10.2989\/20702620.2012.748255","article-title":"Discriminating the Occurrence of Pitch Canker Fungus in Pinus Radiata Trees Using QuickBird Imagery and Artificial Neural Networks","volume":"75","author":"Poona","year":"2013","journal-title":"South. For."},{"key":"ref_46","unstructured":"Gisborne District Council\u2014Te Kaunihera o Te Tair\u0101whiti (2022, June 12). Our Air, Climate & Waste\u2014T\u014d T\u0101tau Hau, \u0100huarangi, Para Hoki, Available online: https:\/\/www.gdc.govt.nz\/__data\/assets\/pdf_file\/0013\/11317\/soe-report-2020-air-climate-waste.pdf."},{"key":"ref_47","unstructured":"(2022, August 14). Manaaki Whenua\u2014Landcare Research The New Zealand SoilsMapViewer. Available online: https:\/\/soils-maps.landcareresearch.co.nz\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1016\/j.ecolmodel.2009.04.004","article-title":"Soil Erosion Hazard Evaluation\u2014An Integrated Use of Remote Sensing, GIS and Statistical Approaches with Biophysical Parameters towards Management Strategies","volume":"220","author":"Rahman","year":"2009","journal-title":"Ecol. Modell."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"McDougal, R.L., Cunningham, L., Hunter, S., Caird, A., Flint, H., Lewis, A., and Ganley, R.J. (2021). Molecular Detection of Phytophthora Pluvialis, the Causal Agent of Red Needle Cast in Pinus Radiata. J. Microbiol. Methods, 189.","DOI":"10.1016\/j.mimet.2021.106299"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"30","DOI":"10.4314\/sajg.v6i1.3","article-title":"Transferability of Decision Trees for Land Cover Classification in a Heterogeneous Area","volume":"6","author":"Verhulp","year":"2017","journal-title":"S. Afr. J. Geomat."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1890\/11-0252.1","article-title":"Gradient Forests: Calculating Importance Gradients on Physical Predictors","volume":"93","author":"Ellis","year":"2012","journal-title":"Ecology"},{"key":"ref_53","first-page":"18","article-title":"Classification and Regression by RandomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_54","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_55","first-page":"83","article-title":"Comparing Different Machine Learning Options To Map Bark Beetle Infestations in Croatia","volume":"XLVIII-4\/W","author":"Cetl","year":"2023","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirami, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-9-307","article-title":"Conditional Variable Importance for Random Forests","volume":"9","author":"Strobl","year":"2008","journal-title":"BMC Bioinform."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_61","unstructured":"Nakazawa, M. (2023, February 15). Fmsb, R Package Version 0.7.1. 2021. Available online: https:\/\/rdrr.io\/cran\/fmsb\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random Forests for Classification in Ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Dalponte, M., Solano-Correa, Y.T., Frizzera, L., and Gianelle, D. (2022). Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data. Remote Sens., 14.","DOI":"10.3390\/rs14133135"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., and Herrera, F. (2019). Detection of Fir Trees (Abies Sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11060643"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Gomez, D.F., Ritger, H.M.W., Pearce, C., Eickwort, J., and Hulcr, J. (2020). Ability of Remote Sensing Systems to Detect Bark Beetle Spots in the Southeastern US. Forests, 11.","DOI":"10.3390\/f11111167"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"100109","DOI":"10.1016\/j.srs.2023.100109","article-title":"Transferability of a Mask R\u2013CNN Model for the Delineation and Classification of Two Species of Regenerating Tree Crowns to Untrained Sites","volume":"9","author":"Chadwick","year":"2024","journal-title":"Sci. Remote Sens."},{"key":"ref_67","first-page":"102946","article-title":"Automatic Detection of Snow Breakage at Single Tree Level Using YOLOv5 Applied to UAV Imagery","volume":"112","author":"Puliti","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","first-page":"106","article-title":"Spatial Application of Random Forest Models for Fine-Scale Coastal Vegetation Classification Using Object Based Analysis of Aerial Orthophoto and DEM Data","volume":"42","author":"Juel","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_69","unstructured":"Pei-Gee, P.H. (2009). Geoscience and Remote Sensing, InTech."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3588","DOI":"10.3390\/rs70403588","article-title":"Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series","volume":"7","author":"Lambert","year":"2015","journal-title":"Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Migas-Mazur, R., Kycko, M., Zwijacz-Kozica, T., and Zagajewski, B. (2021). Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains. Remote Sens., 13.","DOI":"10.3390\/rs13163314"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kwan, C., Ayhan, B., Budavari, B., Lu, Y., Perez, D., Li, J., Bernabe, S., and Plaza, A. (2020). Deep Learning for Land Cover Classification Using Only a Few Bands. Remote Sens., 12.","DOI":"10.3390\/rs12122000"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2542","DOI":"10.1080\/01431161.2018.1528400","article-title":"Evaluating Multiple Classifier System for the Reduction of Salt-and-Pepper Noise in the Classification of Very-High-Resolution Satellite Images","volume":"40","author":"Hirayama","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2754","DOI":"10.1109\/JSTARS.2021.3058421","article-title":"Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform","volume":"14","author":"Hua","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_75","first-page":"48","article-title":"Management of Red Needle Cast Caused by Phytophthora Pluvialis a New Disease of Radiata Pine in New Zealand","volume":"67","author":"Ganley","year":"2014","journal-title":"N. Z. 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