{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:21:49Z","timestamp":1768814509944,"version":"3.49.0"},"reference-count":81,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971577"],"award-info":[{"award-number":["31971577"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["31971577"],"award-info":[{"award-number":["31971577"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest fires are major disturbances in forest ecosystems. The rapid detection of the spatial and temporal characteristics of fires is essential for formulating targeted post-fire vegetation restoration measures and assessing fire-induced carbon emissions. We propose an accurate and efficient framework for extracting the spatiotemporal characteristics of fires using vegetation change tracker (VCT) products and the Google Earth Engine (GEE) platform. The VCT was used to extract areas of persistent forest and forest disturbance patches from Landsat images of Xichang and Muli, Liangshan prefecture, Sichuan province in southwestern China and Huma, Heilongjiang province, in northeastern China. All available Landsat images in the GEE platform in a year were normalized using the VCT-derived persisting forest mask to derive three standardized vegetation indices (normalized burn ratio (NBRr), normalized difference moisture index (NDMIr), and normalized difference vegetation index (NDVIr)). Historical forest disturbance events in Xichang were used to train two decision trees using the C4.5 data mining tool. The differenced NBRr, NDMIr, and NDVIr (dNBRr, dNDMIr, and dNDVIr) were obtained by calculating the difference in the index values between two temporally adjacent images. The occurrence time of disturbance events were extracted using the thresholds identified by decision tree 1. The use of all available images in GEE narrowed the disturbance occurrence time down to 16 days. This period was extended if images were not available or had cloud cover. Fire disturbances were distinguished from other disturbances by comparing the dNBRr, dNDMIr, and dNDVIr values with the thresholds identified by decision tree 2. The results showed that the proposed framework performed well in three study areas. The temporal accuracy for detecting disturbances in the three areas was 94.33%, 90.33%, and 89.67%, the classification accuracy of fire and non-fire disturbances was 85.33%, 89.67%, and 83.67%, and the Kappa coefficients were 0.71, 0.74, and 0.67, respectively. The proposed framework enables the efficient and rapid extraction of the spatiotemporal characteristics of forest fire disturbances using frequent Landsat time-series data, GEE, and VCT products. The results can be used in forest fire disturbance databases and to implement targeted post-disturbance vegetation restoration practices.<\/jats:p>","DOI":"10.3390\/rs15020413","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T03:02:06Z","timestamp":1673319726000},"page":"413","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A New Method for the Rapid Determination of Fire Disturbance Events Using GEE and the VCT Algorithm\u2014A Case Study in Southwestern and Northeastern China"],"prefix":"10.3390","volume":"15","author":[{"given":"Junhong","family":"Ye","sequence":"first","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Min","family":"Sun","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Qinqin","family":"Liu","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Ning","family":"Ding","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5689-5091","authenticated-orcid":false,"given":"Mingshi","family":"Li","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119220","DOI":"10.1016\/j.foreco.2021.119220","article-title":"Exploring the Influence of Large Trees on Temperate Forest Spatial Structure from the Angle of Mingling","volume":"492","author":"Liu","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/j.foreco.2009.12.029","article-title":"Forest Carbon Storage in the Northeastern United States: Net Effects of Harvesting Frequency, Post-Harvest Retention, and Wood Products","volume":"259","author":"Nunery","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.agrformet.2013.04.014","article-title":"Temporal Dynamics of Soil Moisture in a Northern Temperate Mixed Successional Forest after a Prescribed Intermediate Disturbance","volume":"180","author":"He","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.ecolind.2008.07.005","article-title":"Validation of a Remote Sensing Based Index of Forest Disturbance Using Streamwater Nitrogen Data","volume":"9","author":"Eshleman","year":"2009","journal-title":"Ecol. Indic."},{"key":"ref_5","first-page":"213","article-title":"Tropical Forest Reorganization after Cyclone and Fire Disturbance in Samoa: Remnant Trees as Biological Legacies","volume":"5","author":"Elmqvist","year":"2002","journal-title":"Ecol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An Automated Approach for Reconstructing Recent Forest Disturbance History Using Dense Landsat Time Series Stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/0033-5894(73)90003-3","article-title":"Fire in the Virgin Forests of the Boundary Waters Canoe Area, Minnesota","volume":"3","author":"Heinselman","year":"1973","journal-title":"Quat. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.2307\/1941553","article-title":"The Influence of Island and Mainland Lakeshore Landscapes on Boreal Forest Fire Regimes","volume":"72","author":"Bergeron","year":"1991","journal-title":"Ecology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"46","DOI":"10.18282\/rs.v9i2.1373","article-title":"Application of Remote Sensing Technology in Forest Resources Investigation","volume":"9","author":"Cao","year":"2020","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.rse.2004.02.013","article-title":"Spectrometry for Urban Area Remote Sensing\u2013Development and Analysis of a Spectral Library from 350 to 2400 Nm","volume":"91","author":"Herold","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.landurbplan.2006.02.014","article-title":"Land Development, Land Use, and Urban Sprawl in Puerto Rico Integrating Remote Sensing and Population Census Data","volume":"79","author":"Martinuzzi","year":"2007","journal-title":"Landsc. Urban Plan."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/978-3-540-30207-0_84","article-title":"Services for Parallel Remote-Sensing Image Processing Based on Computational Grid","volume":"3252","author":"Yang","year":"2004","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/014311699213659","article-title":"Monitoring Land-Cover Changes: A Comparison of Change Detection Techniques","volume":"20","author":"Mas","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"5449","DOI":"10.1080\/01431160903369642","article-title":"Automated Masking of Cloud and Cloud Shadow for Forest Change Analysis Using Landsat Images","volume":"31","author":"Huang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating Daily Land Surface Temperature at Landsat Resolution by Fusing Landsat and MODIS Data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting Trends in Forest Disturbance and Recovery Using Yearly Landsat Time Series: 1. LandTrendr-Temporal Segmentation Algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2015.02.012","article-title":"Robust Monitoring of Small-Scale Forest Disturbances in a Tropical Montane Forest Using Landsat Time Series","volume":"161","author":"DeVries","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/LGRS.2015.2418159","article-title":"Use of Vegetation Change Tracker and Support Vector Machine to Map Disturbance Types in Greater Yellowstone Ecosystems in a 1984-2010 Landsat Time Series","volume":"12","author":"Zhao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.12.006","article-title":"Quantifying Burn Severity in a Heterogeneous Landscape with a Relative Version of the Delta Normalized Burn Ratio (DNBR)","volume":"109","author":"Miller","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104587","DOI":"10.1016\/j.jaridenv.2021.104587","article-title":"Evaluating Post-Fire Recovery of Latroon Dry Forest Using Landsat ETM+, Unmanned Aerial Vehicle and Field Survey Data","volume":"193","author":"Qarallah","year":"2021","journal-title":"J. Arid Environ."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s40663-021-00352-6","article-title":"Forest Disturbances and the Attribution Derived from Yearly Landsat Time Series over 1990\u20132020 in the Hengduan Mountains Region of Southwest China","volume":"8","author":"Li","year":"2021","journal-title":"For. Ecosyst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change Detection Techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"00017","DOI":"10.1051\/matecconf\/201824700017","article-title":"Classification of a Burnt Area Based on Spectral Images","volume":"247","author":"Szajewska","year":"2018","journal-title":"MATEC Web Conf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1016\/j.rse.2010.03.013","article-title":"Estimating Burn Severity from Landsat DNBR and RdNBR Indices across Western Canada","volume":"114","author":"Soverel","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/j.rse.2008.12.012","article-title":"Comparison of Two Types of Forest Disturbance Using Multitemporal Landsat TM\/ETM+ Imagery and Field Vegetation Data","volume":"113","author":"Hais","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1016\/j.rse.2010.07.010","article-title":"Detecting Trends in Forest Disturbance and Recovery Using Yearly Landsat Time Series: 2. TimeSync\u2013Tools for Calibration and Validation","volume":"114","author":"Cohen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"470","DOI":"10.3390\/rs6010470","article-title":"Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review","volume":"6","author":"Chu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_32","first-page":"11","article-title":"Landscape Assessment (LA) Sampling and Analysis Methods","volume":"1","author":"Key","year":"2006","journal-title":"USDA For. Serv.-Gen. Tech. Rep. RMRS-GTR"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1071\/WF05097","article-title":"Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects","volume":"15","author":"Lentile","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.rse.2004.10.012","article-title":"Comparison of Time Series Tasseled Cap Wetness and the Normalized Difference Moisture Index in Detecting Forest Disturbances","volume":"94","author":"Jin","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1080\/10106049.2019.1661032","article-title":"Monitoring Forest Disturbance Using Time-Series MODIS NDVI in Michoac\u00e1n, Mexico","volume":"36","author":"Gao","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/s12038-012-9241-3","article-title":"Simple Luminosity Normalization of Greenness, Yellowness and Redness\/Greenness for Comparison of Leaf Spectral Profiles in Multi-Temporally Acquired Remote Sensing Images","volume":"37","author":"Doi","year":"2012","journal-title":"J. Biosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1016\/j.jacc.2004.11.070","article-title":"Prevention of Atrial Fibrillation with Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers: A Meta-Analysis","volume":"45","author":"Healey","year":"2005","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1016\/j.rse.2008.06.016","article-title":"Dynamics of National Forests Assessed Using the Landsat Record: Case Studies in Eastern United States","volume":"113","author":"Huang","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.rse.2002.09.003","article-title":"Seasonal Landscape and Spectral Vegetation Index Dynamics in the Brazilian Cerrado: An Analysis within the Large-Scale Biosphere-Atmosphere Experiment in Amaz\u00f4nia (LBA)","volume":"87","author":"Ferreira","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"S83","DOI":"10.1175\/BAMS-D-20-0165.1","article-title":"Attribution of the Extreme Drought-Related Risk of Wildfires in Spring 2019 over Southwest China","volume":"102","author":"Du","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wu, L., Li, Z., Liu, X., Zhu, L., Tang, Y., Zhang, B., Xu, B., Liu, M., Meng, Y., and Liu, B. (2020). Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland. Remote Sens., 12.","DOI":"10.3390\/rs12020341"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary Analysis of the Performance of the Landsat 8\/OLI Land Surface Reflectance Product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Dataset for North America, 1990-2000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"033515","DOI":"10.1117\/1.3104620","article-title":"Automated Registration and Orthorectification Package for Landsat and Landsat-like Data Processing","volume":"3","author":"Masek","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tan, B., Wolfe, R., Masek, J., Gao, F., and Vermote, E.F. (2010). An Illumination Correction Algorithm on Landsat-TM Data. Int. Geosci. Remote Sens. Symp. (IGARSS), 1964\u20131967.","DOI":"10.1109\/IGARSS.2010.5653492"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1007\/s11769-017-0880-z","article-title":"Spatio-Temporal Variations in Plantation Forests\u2019 Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations (1986\u20132015)","volume":"27","author":"Shen","year":"2017","journal-title":"Chin. Geogr. Sci."},{"key":"ref_47","first-page":"4","article-title":"Automated Estimation of Forest Stand Age Using Vegetation Change Tracker and Machine Learning","volume":"8","author":"Kauffman","year":"2016","journal-title":"Math. Comput. For. Nat.-Resour. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2015.10.024","article-title":"Effects of Fire Severity and Post-Fire Climate on Short-Term Vegetation Recovery of Mixed-Conifer and Red Fir Forests in the Sierra Nevada Mountains of California","volume":"171","author":"Meng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Qiu, J., Wang, H., Shen, W., Zhang, Y., Su, H., and Li, M. (2021). Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxing\u2019anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040792"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/10106049109354290","article-title":"Mapping Burns and Natural Reforestation Using Thematic Mapper Data","volume":"6","author":"Caselles","year":"1991","journal-title":"Geocarto Int."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gao, C., An, R., Wang, W., Shi, C., Wang, M., Liu, K., Wu, X., Wu, G., and Shu, L. (2021). Asymmetrical Lightning Fire Season Expansion in the Boreal Forest of Northeast China. Forests, 12.","DOI":"10.3390\/f12081023"},{"key":"ref_54","first-page":"235","article-title":"C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning)","volume":"302","author":"Quinlan","year":"1992","journal-title":"Morgan Kaufmann San Mateo Calif."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/17538940902801614","article-title":"Development of Time Series Stacks of Landsat Images for Reconstructing Forest Disturbance History","volume":"2","author":"Huang","year":"2009","journal-title":"Int. J. Digit. Earth"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.rse.2013.05.033","article-title":"Using Landsat-Derived Disturbance and Recovery History and Lidar to Map Forest Biomass Dynamics","volume":"151","author":"Pflugmacher","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"112648","DOI":"10.1016\/j.rse.2021.112648","article-title":"Monitoring Temperate Forest Degradation on Google Earth Engine Using Landsat Time Series Analysis","volume":"265","author":"Chen","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_58","first-page":"102386","article-title":"Sub-Annual Tropical Forest Disturbance Monitoring Using Harmonized Landsat and Sentinel-2 Data","volume":"102","author":"Chen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","unstructured":"Rose, M.B., and Nagle, N.N. (2021). Characterizing Forest Dynamics with Landsat-Derived Phenology Curves. Remote Sens., 13.","DOI":"10.3390\/rs13020267"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting Trend and Seasonal Changes in Satellite Image Time Series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1071\/WF12055","article-title":"Trend Analysis of Medium- and Coarse-Resolution Time Series Image Data for Burned Area Mapping in a Mediterranean Ecosystem","volume":"23","author":"Katagis","year":"2014","journal-title":"Int. J. Wildland Fire"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111968","DOI":"10.1016\/j.rse.2020.111968","article-title":"Landsat 9: Empowering Open Science and Applications through Continuity","volume":"248","author":"Masek","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/14498596.2015.974227","article-title":"Using MODIS Data to Analyse Post-Fire Vegetation Recovery in Australian Eucalypt Forests","volume":"60","author":"Caccamo","year":"2015","journal-title":"J. Spat. Sci."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1111\/1365-2745.12950","article-title":"Influences of Fire\u2013Vegetation Feedbacks and Post-Fire Recovery Rates on Forest Landscape Vulnerability to Altered Fire Regimes","volume":"106","author":"Tepley","year":"2018","journal-title":"J. Ecol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ecolind.2018.02.008","article-title":"Indicator-Based Assessment of Post-Fire Recovery Dynamics Using Satellite NDVI Time-Series","volume":"89","author":"Bruno","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nclimate3329","article-title":"Lightning as a Major Driver of Recent Large Fire Years in North American Boreal Forests","volume":"7","author":"Veraverbeke","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1046\/j.1526-100X.2002.01052.x","article-title":"Season of Burn Influences Fire Behavior and Fuel Consumption in Restored Shortleaf Pine\u2013Grassland Communities","volume":"10","author":"Sparks","year":"2002","journal-title":"Restor. Ecol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s10584-017-2030-0","article-title":"Attributing Extreme Fire Risk in Western Canada to Human Emissions","volume":"144","author":"Zwiers","year":"2017","journal-title":"Clim. Change"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1134\/S1995425519030065","article-title":"Reconstruction of the Holocene Dynamics of Forest Fires in the Central Part of Meshcherskaya Lowlands According to Antracological Analysis","volume":"12","author":"Kupriyanov","year":"2019","journal-title":"Contemp. Probl. Ecol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1007\/s00267-020-01389-z","article-title":"Factors Affecting the Behavior of Large Forest Fires in Turkey","volume":"67","year":"2021","journal-title":"Environ. Manag."},{"key":"ref_73","first-page":"113","article-title":"Weather Conditions and Forest Fire Propagation-the Case of the Carmel Fire, December 2010","volume":"58","author":"Kutiel","year":"2012","journal-title":"Isr. J. Ecol. Evol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11676-016-0361-8","article-title":"The Progress of Operational Forest Fire Monitoring with Infrared Remote Sensing","volume":"28","author":"Hua","year":"2017","journal-title":"J. For. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"105007","DOI":"10.1088\/1748-9326\/9\/10\/105007","article-title":"Remote Sensing Estimates of Stand-Replacement Fires in Russia, 2002-2011","volume":"9","author":"Krylov","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhao, W., Chen, J., Qu, Y., Wu, D., and Chen, X. (2021). Mapping Large-Scale Forest Disturbance Types with Multi-Temporal Cnn Framework. Remote Sens., 13.","DOI":"10.3390\/rs13245177"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1016\/j.rse.2009.04.008","article-title":"Evaluation of Annual Forest Disturbance Monitoring Using a Static Decision Tree Approach and 250 m MODIS Data","volume":"113","author":"Pouliot","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Shimizu, K., Ahmed, O.S., Ponce-Hernandez, R., Ota, T., Win, Z.C., Mizoue, N., and Yoshida, S. (2017). Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar. Forests, 8.","DOI":"10.3390\/f8060218"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.rse.2013.12.013","article-title":"Influence of Lidar, Landsat Imagery, Disturbance History, Plot Location Accuracy, and Plot Size on Accuracy of Imputation Maps of Forest Composition and Structure","volume":"143","author":"Zald","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"523","DOI":"10.5589\/m10-006","article-title":"Disturbance Capture and Attribution through the Integration of Landsat and IRS-1C Imagery","volume":"35","author":"Stewart","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"521","DOI":"10.5589\/m14-004","article-title":"Interpretation of Forest Disturbance Using a Time Series of Landsat Imagery and Canopy Structure from Airborne Lidar","volume":"39","author":"Ahmed","year":"2014","journal-title":"Can. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/413\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:23Z","timestamp":1760119523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":81,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020413"],"URL":"https:\/\/doi.org\/10.3390\/rs15020413","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}