{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T04:52:58Z","timestamp":1771822378373,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we developed a fast and cost-effective remote sensing methodology for characterizing burned areas for the entire country of Iran. We mapped the fire-affected areas using a post-classification supervised method and Landsat 8 time-series images. To this end, the Google Earth Engine (GEE) and Google Colab computing services were used to facilitate the downloading and processing of images as well as allowing for effective implementation of the algorithms. In total, 13 spectral indices were calculated using Landsat 8 images and were added to the nine original bands of Landsat 8. The training polygons of the burned and unburned areas were accurately distinguished based on the information acquired from the Iranian Space Agency (ISA), Sentinel-2 images, and Fire Information for Resource Management System (FIRMS) products. A combination of Genetic Algorithm (GA) and Neural Network (NN) approaches was then implemented to specify 19 optimal features out of the 22 bands. The 19 optimal bands were subsequently applied to two classifiers of NN and Random Forest (RF) in the timespans of 1 January 2019 to 30 December 2020 and of 1 January 2021 to 30 September 2021. The overall classification accuracies of 94% and 96% were obtained for these two classifiers, respectively. The omission and commission errors of both classifiers were also less than 10%, indicating the promising capability of the proposed methodology in detecting the burned areas. To detect the burned areas caused by the wildfire in 2021, the image differencing method was used as well. The resultant models were finally compared to the MODIS fire products over 10 sampled polygons of the burned areas. Overall, the models had a high accuracy in detecting the burned areas in terms of shape and perimeter, which can be further implicated for potential prevention strategies of endangered biodiversity.<\/jats:p>","DOI":"10.3390\/rs14246376","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T08:41:41Z","timestamp":1671439301000},"page":"6376","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Automatic Mapping of Burned Areas Using Landsat 8 Time-Series Images in Google Earth Engine: A Case Study from Iran"],"prefix":"10.3390","volume":"14","author":[{"given":"Houri","family":"Gholamrezaie","sequence":"first","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7254-4475","authenticated-orcid":false,"given":"Mahdi","family":"Hasanlou","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"additional","affiliation":[{"name":"WSP Environment and Infrastructure Canada Limited, Ottawa, ON K2E 7L5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5310-5859","authenticated-orcid":false,"given":"S. Mohammad","family":"Mirmazloumi","sequence":"additional","affiliation":[{"name":"Geomatics Research Unit, Centre Tecnol\u00f2gic de Telecomunicacions de Catalunya (CTTC\/CERCA), Av. Gauss 7, 08860 Castelldefels, Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","first-page":"210","article-title":"Burned Area Detection Based on Landsat Time Series in Savannas of Southern Burkina Faso","volume":"64","author":"Liu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","first-page":"100324","article-title":"Landsat-8 and Sentinel-2 Based Forest Fire Burn Area Mapping Using Machine Learning Algorithms on GEE Cloud Platform over Uttarakhand, Western Himalaya","volume":"18","author":"Bar","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e0232962","DOI":"10.1371\/journal.pone.0232962","article-title":"Exploring the Utility of Sentinel-2 MSI and Landsat 8 OLI in Burned Area Mapping for a Heterogenous Savannah Landscape","volume":"15","author":"Ngadze","year":"2020","journal-title":"PLoS ONE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.12.011","article-title":"Development of a Sentinel-2 Burned Area Algorithm: Generation of a Small Fire Database for Sub-Saharan Africa","volume":"222","author":"Roteta","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.isprsjprs.2019.11.026","article-title":"Automated Training Sample Definition for Seasonal Burned Area Mapping","volume":"160","author":"Malambo","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2017.1354803","article-title":"Evaluating and Comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) Spectral Indices for Estimating Fire Severity in a Mediterranean Pine Ecosystem of Greece","volume":"55","author":"Mallinis","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2018). BAIS2: Burned Area Index for Sentinel-2. Proceedings, 2.","DOI":"10.3390\/ecrs-2-05177"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.rse.2017.06.027","article-title":"Mapping Burned Areas Using Dense Time-Series of Landsat Data","volume":"198","author":"Hawbaker","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2018.05.007","article-title":"Burned Area Estimations Derived from Landsat ETM+ and OLI Data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees","volume":"142","author":"Cabral","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","first-page":"137","article-title":"Evaluation and Comparison of Landsat 8, Sentinel-2 and Deimos-1 Remote Sensing Indices for Assessing Burn Severity in Mediterranean Fire-Prone Ecosystems","volume":"80","author":"Quintano","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","first-page":"741","article-title":"Prototyping an Artificial Neural Network for Burned Area Mapping on a Regional Scale in Mediterranean Areas Using MODIS Images","volume":"13","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","first-page":"97","article-title":"Evaluation of Forest Fire on Madeira Island Using Sentinel-2A MSI Imagery","volume":"58","author":"Navarro","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Teodoro, A., and Amaral, A. (2019). A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data. Environment, 6.","DOI":"10.3390\/environments6030036"},{"key":"ref_14","first-page":"100472","article-title":"An Alternative Approach for Mapping Burn Scars Using Landsat Imagery, Google Earth Engine, and Deep Learning in the Brazilian Savanna","volume":"22","author":"Arruda","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2015.01.022","article-title":"MODIS-Landsat Fusion for Large Area 30m Burned Area Mapping","volume":"161","author":"Boschetti","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Long, T., Zhang, Z., He, G., Jiao, W., Tang, C., Wu, B., Zhang, X., Wang, G., and Yin, R. (2019). 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11050489"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Parks, S.A., Holsinger, L.M., Voss, M.A., Loehman, R.A., and Robinson, N.P. (2018). Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential. Remote Sens., 10.","DOI":"10.3390\/rs10060879"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Akhoondzadeh, M., Amani, M., and Mahdavi, S. (2021). Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and Modis Land Cover Product within the Google Earth Engine Cloud Platform. Remote Sens., 13.","DOI":"10.3390\/rs13020220"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111254","DOI":"10.1016\/j.rse.2019.111254","article-title":"Landsat-8 and Sentinel-2 Burned Area Mapping\u2014A Combined Sensor Multi-Temporal Change Detection Approach","volume":"231","author":"Roy","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alencar, A., Shimbo, J.Z., Lenti, F., Marques, C.B., Zimbres, B., Rosa, M., Arruda, V., Castro, I., Ribeiro, J.P.F.M., and Varela, V. (2020). Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. Remote Sens., 12.","DOI":"10.3390\/rs12060924"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"456","DOI":"10.3390\/rs4020456","article-title":"How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods","volume":"4","author":"Cansler","year":"2012","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.foreco.2015.01.011","article-title":"Quantifying Influences and Relative Importance of Fire Weather, Topography, and Vegetation on Fire Size and Fire Severity in a Chinese Boreal Forest Landscape","volume":"356","author":"Fang","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1080\/22797254.2020.1738900","article-title":"A Novel Fire Index-Based Burned Area Change Detection Approach Using Landsat-8 OLI Data","volume":"53","author":"Liu","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Quintero, N., Viedma, O., Urbieta, I.R., and Moreno, J.M. (2019). Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain. Forests, 10.","DOI":"10.3390\/f10060518"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., and Gill, E. (2019). The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sens., 11.","DOI":"10.3390\/rs11010043"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-Oriented Lulc Classification in Google Earth Engine Combining Snic, Glcm, and Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., and Xue, P. (2020). Land Use\/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture. Remote Sens., 12.","DOI":"10.3390\/rs12193139"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2020). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification Based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Farhadabad, A.R., and Kerle, N. (2020). Post-Disaster Recovery Monitoring with Google Earth Engine. Appl. Sci., 10.","DOI":"10.3390\/app10134574"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Noi Phan, T., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification Using Google Earth Engine and Random Forest Classifier-the Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qu, L., Chen, Z., Li, M., Zhi, J., and Wang, H. (2021). Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13030453"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, Y., and Hu, Y. (2019). Land Cover Changes and Their Driving Mechanisms in Central Asia from 2001 to 2017 Supported by Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11050554"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111340","DOI":"10.1016\/j.rse.2019.111340","article-title":"Spectral Mixture Analysis in Google Earth Engine to Model and Delineate Fire Scars over a Large Extent and a Long Time-Series in a Rainforest-Savanna Transition Zone","volume":"232","author":"Daldegan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.scitotenv.2018.02.278","article-title":"Applying Genetic Algorithms to Set the Optimal Combination of Forest Fire Related Variables and Model Forest Fire Susceptibility Based on Data Mining Models. The Case of Dayu County, China","volume":"630","author":"Hong","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111801","DOI":"10.1016\/j.rse.2020.111801","article-title":"The Landsat Burned Area Algorithm and Products for the Conterminous United States","volume":"244","author":"Hawbaker","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved Land Cover Map of Iran Using Sentinel Imagery within Google Earth Engine and a Novel Automatic Workflow for Land Cover Classification Using Migrated Training Samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","first-page":"373","article-title":"Improving Land Use Land Cover Mapping of a Neural Network with Three Optimizers of Multi-Verse Optimizer, Genetic Algorithm, and Derivative-Free Function","volume":"24","author":"Jamali","year":"2021","journal-title":"Egypt. J. Remote Sens. Sp. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fornacca, D., Ren, G., and Xiao, W. (2018). Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China. Remote Sens., 10.","DOI":"10.3390\/rs10081196"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1016\/j.rse.2011.06.010","article-title":"Evaluating Spectral Indices for Burned Area Discrimination Using MODIS\/ASTER (MASTER) Airborne Simulator Data","volume":"115","author":"Veraverbeke","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1109\/TGRS.2003.819190","article-title":"Forest Fire Scar Detection in the Boreal Forest with Multitemporal SPOT-VEGETATION Data","volume":"41","author":"Gerard","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, D.D., and Zhang, L. (2020). Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China. Sensors, 20.","DOI":"10.3390\/s20072091"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Amani, M., Kakooei, M., Moghimi, A., Ghorbanian, A., Ranjgar, B., Mahdavi, S., Davidson, A., Fisette, T., Rollin, P., and Brisco, B. (2020). Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada. Remote Sens., 12.","DOI":"10.3390\/rs12213561"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1109\/JSTARS.2021.3051422","article-title":"Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis over Iran","volume":"14","author":"Ebrahimy","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mirzadeh, S.M.J., White, L., Banks, S., Montgomery, J., and Hopkinson, C. (2019). Canadian Wetland Inventory Using Google Earth Engine: The First Map and Preliminary Results. Remote Sens., 11.","DOI":"10.3390\/rs11070842"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2011.10.010","article-title":"Optimizing Land Cover Classification Accuracy for Change Detection, a Combined Pixel-Based and Object-Based Approach in a Mountainous Area in Mexico","volume":"34","author":"Seijmonsbergen","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_52","first-page":"100482","article-title":"A Review on Change Detection Method and Accuracy Assessment for Land Use Land Cover","volume":"22","author":"Chughtai","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Panuju, D.R., Paull, D.J., and Griffin, A.L. (2020). Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics. Remote Sens., 12.","DOI":"10.3390\/rs12111781"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sefrin, O., Riese, F.M., and Keller, S. (2021). Deep Learning for Land Cover Change Detection. Remote Sens., 13.","DOI":"10.3390\/rs13010078"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MGRS.2021.3088865","article-title":"Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review","volume":"10","author":"ZhiYong","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"10314","DOI":"10.1109\/JSTARS.2021.3110460","article-title":"Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery","volume":"14","author":"Amani","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., and Hasanlou, M. (2020). A New End-to-End Multi-Dimensional CNN Framework for Land Cover\/Land Use Change Detection in Multi-Source Remote Sensing Datasets. Remote Sens., 12.","DOI":"10.3390\/rs12122010"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Amani, M., Kakooei, M., Ghorbanian, A., Warren, R., Mahdavi, S., Brisco, B., Moghimi, A., Bourgeau-chavez, L., Toure, S., and Paudel, A. (2022). Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14153778"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"7029","DOI":"10.1080\/01431161.2018.1466079","article-title":"Hyperspectral Change Detection: An Experimental Comparative Study","volume":"39","author":"Hasanlou","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sulova, A., and Arsanjani, J.J. (2021). Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13010010"},{"key":"ref_61","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_62","first-page":"39","article-title":"A Data Mining Approach for Global Burned Area Mapping","volume":"73","author":"Ramo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Syifa, M., Panahi, M., and Lee, C.W. (2020). Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA. Remote Sens., 12.","DOI":"10.3390\/rs12040623"},{"key":"ref_64","first-page":"101914","article-title":"A Dynamic Classi Fication Scheme for Mapping Spectrally Similar Classes: Application to Wetland Classification","volume":"83","author":"Mahdavi","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"7370","DOI":"10.1080\/01431161.2018.1468117","article-title":"A Multiple Classifier System to Improve Mapping Complex Land Covers: A Case Study of Wetland Classification Using SAR Data in Newfoundland, Canada","volume":"39","author":"Amani","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Roteta, E., Bastarrika, A., Franquesa, M., and Chuvieco, E. (2021). Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13040816"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3514","DOI":"10.1109\/TGRS.2012.2224874","article-title":"A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms","volume":"51","author":"Pedergnana","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature Selection in Machine Learning: A New Perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","article-title":"Feature Selection for Classification of Hyperspectral Data by SVM","volume":"48","author":"Pal","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/19475683.2018.1552621","article-title":"Annals of GIS Genetic Algorithm-Based Method for Forest Type Classification Using Multi-Temporal NDVI from Landsat TM Imagery","volume":"25","author":"Tao","year":"2019","journal-title":"Ann. GIS"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/LGRS.2009.2037024","article-title":"Change Detection in Satellite Images Using a Genetic Algorithm Approach","volume":"7","author":"Celik","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Santos, F., Dubovyk, O., and Menz, G. (2017). Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms. Remote Sens., 9.","DOI":"10.3390\/rs9010068"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jvcir.2016.11.017","article-title":"Satellite Image Classification Using Genetic Algorithm Trained Radial Basis Function Neural Network, Application to the Detection of Flooded Areas","volume":"42","author":"Singh","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"018503","DOI":"10.1117\/1.JRS.15.018503","article-title":"Machine Learning Inversion Approach for Soil Parameters Estimation over Vegetated Agricultural Areas Using a Combination of Water Cloud Model and Calibrated Integral Equation Model","volume":"15","author":"Ranjbar","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_75","first-page":"252","article-title":"Convolutional Neural Network Hyper-Parameters Optimization Based on Genetic Algorithms","volume":"9","author":"Loussaief","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"De Alban, J.D.T., Connette, G.M., Oswald, P., and Webb, E.L. (2018). Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes. Remote Sens., 10.","DOI":"10.3390\/rs10020306"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ghorbanian, A., Zaghian, S., Asiyabi, R.M., Amani, M., Mohammadzadeh, A., and Jamali, S. (2021). Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13132565"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A Systematic Analysis of Performance Measures for Classification Tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.rse.2014.01.008","article-title":"Validation of the 2008 MODIS-MCD45 Global Burned Area Product Using Stratified Random Sampling","volume":"144","author":"Padilla","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Lessons in Biostatistics Interrater Reliability: The Kappa Statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem. Medica"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1080\/10106049.2019.1608592","article-title":"Incorporating Sentinel-1 SAR Imagery with the MODIS MCD64A1 Burned Area Product to Improve Burn Date Estimates and Reduce Burn Date Uncertainty in Wildland Fire Mapping","volume":"36","author":"Lasko","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_82","first-page":"815","article-title":"Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001-2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products","volume":"6","author":"Leal","year":"2013","journal-title":"Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Fornacca, D., Ren, G., and Xiao, W. (2017). Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires. 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