{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:12:13Z","timestamp":1777353133210,"version":"3.51.4"},"reference-count":100,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Forestry Agency"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurately mapping land use\/land cover changes (LULCC) and forest disturbances provides valuable information for understanding the influence of anthropogenic activities on the environment at regional and global scales. Many approaches using satellite remote sensing data have been proposed for characterizing these long-term changes. However, a spatially and temporally consistent mapping of both LULCC and forest disturbances at medium spatial resolution is still limited despite their critical contributions to the carbon cycle. In this study, we examined the applicability of Landsat time series temporal segmentation and random forest classifiers to mapping LULCC and forest disturbances in Vietnam. We used the LandTrendr temporal segmentation algorithm to derive key features of land use\/land cover transitions and forest disturbances from annual Landsat time series data. We developed separate random forest models for classifying land use\/land cover and detecting forest disturbances at each segment and then derived LULCC and forest disturbances that coincided with each other during the period of 1988\u20132019. The results showed that both LULCC classification and forest disturbance detection achieved low accuracy in several classes (e.g., producer\u2019s and user\u2019s accuracies of 23.7% and 78.8%, respectively, for forest disturbance class); however, the level of accuracy was comparable to that of existing datasets using the same reference samples in the study area. We found relatively high confusion between several land use\/land cover classes (e.g., grass\/shrub, forest, and cropland) that can explain the lower overall accuracies of 67.6% and 68.4% in 1988 and 2019, respectively. The mapping of forest disturbances and LULCC suggested that most forest disturbances were followed by forest recovery, not by transitions to other land use\/land cover classes. The landscape complexity and ephemeral forest disturbances contributed to the lower classification and detection accuracies in this study area. Nevertheless, temporal segmentation and derived features from LandTrendr were useful for the consistent mapping of LULCC and forest disturbances. We recommend that future studies focus on improving the accuracy of forest disturbance detection, especially in areas with subtle landscape changes, as well as land use\/land cover classification in ambiguous and complex landscapes. Using more training samples and effective variables would potentially improve the classification and detection accuracies.<\/jats:p>","DOI":"10.3390\/rs15030851","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T03:36:57Z","timestamp":1675395417000},"page":"851","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Mapping Land Use\/Land Cover Changes and Forest Disturbances in Vietnam Using a Landsat Temporal Segmentation Algorithm"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3826-5659","authenticated-orcid":false,"given":"Katsuto","family":"Shimizu","sequence":"first","affiliation":[{"name":"Department of Forest Management, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Ibaraki, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wataru","family":"Murakami","sequence":"additional","affiliation":[{"name":"Department of Disaster Prevention, Meteorology and Hydrology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Ibaraki, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takahisa","family":"Furuichi","sequence":"additional","affiliation":[{"name":"Department of Disaster Prevention, Meteorology and Hydrology, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Ibaraki, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9681-492X","authenticated-orcid":false,"given":"Ronald C.","family":"Estoque","sequence":"additional","affiliation":[{"name":"Center for Biodiversity and Climate Change, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Ibaraki, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.rse.2017.08.030","article-title":"Land use and land cover change in Inner Mongolia\u2014Understanding the effects of China\u2019s re-vegetation programs","volume":"204","author":"Yin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"142839","DOI":"10.1016\/j.scitotenv.2020.142839","article-title":"Carbon loss and removal due to forest disturbance and regeneration in the Amazon","volume":"764","author":"Bullock","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112336","DOI":"10.1016\/j.rse.2021.112336","article-title":"Spatiotemporal assessment of land use\/land cover change and associated carbon emissions and uptake in the Mekong River Basin","volume":"256","author":"Tang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41586-018-0411-9","article-title":"Global land change from 1982 to 2016","volume":"560","author":"Song","year":"2018","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1038\/nclimate2444","article-title":"Land-use protection for climate change mitigation","volume":"4","author":"Popp","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1038\/s41586-021-04376-4","article-title":"New land-use-change emissions indicate a declining CO2 airborne fraction","volume":"603","author":"Houghton","year":"2022","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1126\/science.aat1203","article-title":"Combating deforestation: From satellite to intervention","volume":"360","author":"Finer","year":"2018","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1080\/15481603.2022.2096184","article-title":"FROM-GLC Plus: Toward near real-time and multi-resolution land cover mapping","volume":"59","author":"Yu","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/s41597-022-01307-4","article-title":"Dynamic World, Near real-time global 10 m land use land cover mapping","volume":"9","author":"Brown","year":"2022","journal-title":"Sci. Data"},{"key":"ref_12","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2021). ESA WorldCover 10 m 2020, v100 2021, European Space Agency."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"113195","DOI":"10.1016\/j.rse.2022.113195","article-title":"Fifty years of Landsat science and impacts","volume":"280","author":"Wulder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.isprsjprs.2019.10.004","article-title":"A comprehensive evaluation of disturbance agent classification approaches: Strengths of ensemble classification, multiple indices, spatio-temporal variables, and direct prediction","volume":"158","author":"Shimizu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111051","DOI":"10.1016\/j.rse.2019.01.013","article-title":"Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting","volume":"238","author":"Olofsson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1016\/j.rse.2011.09.009","article-title":"A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests","volume":"115","author":"Meigs","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"064002","DOI":"10.1088\/1748-9326\/11\/6\/064002","article-title":"Time series analysis of satellite data reveals continuous deforestation of New England since the 1980s","volume":"11","author":"Olofsson","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_24","first-page":"102806","article-title":"Demystifying LandTrendr and CCDC temporal segmentation","volume":"110","author":"Pasquarella","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1080\/07038992.2014.987376","article-title":"Forest Monitoring Using Landsat Time Series Data: A Review","volume":"40","author":"Banskota","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2017.11.015","article-title":"A LandTrendr multispectral ensemble for forest disturbance detection","volume":"205","author":"Cohen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.rse.2018.08.028","article-title":"A spatial and temporal analysis of forest dynamics using Landsat time-series","volume":"217","author":"Nguyen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112521","DOI":"10.1016\/j.rse.2021.112521","article-title":"LandTrendr smoothed spectral profiles enhance woody encroachment monitoring","volume":"262","author":"Gelabert","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102283","DOI":"10.1016\/j.gloenvcha.2021.102283","article-title":"No peace for the forest: Rapid, widespread land changes in the Andes-Amazon region following the Colombian civil war","volume":"69","author":"Gjerdseth","year":"2021","journal-title":"Glob. Environ. Chang."},{"key":"ref_32","first-page":"102688","article-title":"A 30 m-resolution land use-land cover product for the Colombian Andes and Amazon using cloud-computing","volume":"107","author":"Clerici","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_34","first-page":"175","article-title":"Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine","volume":"69","author":"Zurqani","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1080\/15481603.2022.2115619","article-title":"Using high-resolution imagery and deep learning to classify land-use following deforestation: A case study in Ethiopia","volume":"59","author":"Masolele","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.landusepol.2007.06.001","article-title":"The causes of the reforestation in Vietnam","volume":"25","author":"Meyfroidt","year":"2008","journal-title":"Land Use Policy"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bui, D.H., and Mucsi, L. (2021). From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules. Remote Sens., 13.","DOI":"10.3390\/rs13091700"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Di Napoli, M., Marsiglia, P., Di Martire, D., Ramondini, M., Ullo, S.L., and Calcaterra, D. (2020). Landslide Susceptibility Assessment of Wildfire Burnt Areas through Earth-Observation Techniques and a Machine Learning-Based Approach. Remote Sens., 12.","DOI":"10.3390\/rs12152505"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"126500","DOI":"10.1016\/j.jhydrol.2021.126500","article-title":"GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam","volume":"599","author":"Luu","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100341","DOI":"10.1016\/j.ancene.2022.100341","article-title":"Cropland abandonment and flood risks: Spatial analysis of a case in North Central Vietnam","volume":"38","author":"Nguyen","year":"2022","journal-title":"Anthropocene"},{"key":"ref_41","first-page":"12","article-title":"Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg\u2013Marquardt and Bayesian regularized neural networks","volume":"171\u2013172","author":"Pradhan","year":"2012","journal-title":"Geomorphology"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9979","DOI":"10.1038\/s41598-021-89034-5","article-title":"First comprehensive quantification of annual land use\/cover from 1990 to 2020 across mainland Vietnam","volume":"11","author":"Phan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hoang, T.T., Truong, V.T., Hayashi, M., Tadono, T., and Nasahara, K.N. (2020). New JAXA High-Resolution Land Use\/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12172707"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Truong, V.T., Hoang, T.T., Cao, D.P., Hayashi, M., Tadono, T., and Nasahara, K.N. (2019). JAXA Annual Forest Cover Maps for Vietnam during 2015\u20132018 Using ALOS-2\/PALSAR-2 and Auxiliary Data. Remote Sens., 11.","DOI":"10.3390\/rs11202412"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mermoz, S., Bouvet, A., Koleck, T., Ball\u00e8re, M., Le Toan, T., and Wallace, L. (2021). Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data. Remote Sens., 13.","DOI":"10.3390\/rs13234877"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Balling, J., Verbesselt, J., De Sy, V., Herold, M., and Reiche, J. (2021). Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts. Forests, 12.","DOI":"10.3390\/f12040456"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Reiche, J., Verhoeven, R., Verbesselt, J., Hamunyela, E., Wielaard, N., and Herold, M. (2018). Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts. Remote Sens., 10.","DOI":"10.3390\/rs10050777"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1080\/17538947.2022.2061618","article-title":"Combining post-disturbance land cover and tree canopy cover from Landsat time series data for mapping deforestation, forest degradation, and recovery across Cambodia","volume":"15","author":"Shimizu","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_51","unstructured":"(2020, February 25). Global Administrative Areas GADM Database. Available online: https:\/\/gadm.org\/data.html."},{"key":"ref_52","unstructured":"FAO (2020). Global Forest Resources Assessment 2020, Food and Agriculture Organization."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1007\/s13280-012-0348-4","article-title":"Drivers of forest cover dynamics in smallholder farming systems: The case of northwestern vietnam","volume":"42","author":"Jadin","year":"2013","journal-title":"Ambio"},{"key":"ref_54","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_55","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_56","unstructured":"Masek, J.G., Vermote, E.F., Saleous, N., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T.K. (2013). LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2, ORNL Distributed Active Archive Center."},{"key":"ref_57","unstructured":"Gray, J., and Sulla-Menashe, D.F.M.A. (2019). User Guide to Collection 6 MODIS Land Cover Dynamics (MCD12Q2) Product, USGS."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10050691"},{"key":"ref_62","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment (LA): Sampling and Analysis Methods, Rocky Mountain Research Station."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hislop, S., Jones, S., Soto-Berelov, M., Skidmore, A., Haywood, A., and Nguyen, T.H. (2018). Using landsat spectral indices in time-series to assess wildfire disturbance and recovery. Remote Sens., 10.","DOI":"10.3390\/rs10030460"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2018.11.025","article-title":"A fusion approach to forest disturbance mapping using time series ensemble techniques","volume":"221","author":"Hislop","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"120292","DOI":"10.1016\/j.foreco.2022.120292","article-title":"Vegetation recovery rates provide insight into reburn severity in southwestern Oregon, USA","volume":"519","author":"Weber","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1139\/cjfr-2016-0244","article-title":"Using Landsat time series imagery to detect forest disturbance in selectively logged tropical forests in Myanmar","volume":"47","author":"Shimizu","year":"2017","journal-title":"Can. J. For. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Mugiraneza, T., Nascetti, A., and Ban, Y. (2020). Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing. Remote Sens., 12.","DOI":"10.3390\/rs12182883"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s41597-022-01260-2","article-title":"A global map of planting years of plantations","volume":"9","author":"Du","year":"2022","journal-title":"Sci. Data"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/0034-4257(85)90102-6","article-title":"A TM Tasseled Cap equivalent transformation for reflectance factor data","volume":"17","author":"Crist","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","unstructured":"Rousel, J., Haas, R., Schell, J., and Deering, D. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite\u20141 Symposium, Washington, DC, USA."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.05.005","article-title":"Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA","volume":"166","author":"Kennedy","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_76","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111165","DOI":"10.1016\/j.rse.2019.04.018","article-title":"Improved change monitoring using an ensemble of time series algorithms","volume":"238","author":"Bullock","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.rse.2011.09.024","article-title":"Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan","volume":"122","author":"Kennedy","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Cohen, W.B., Healey, S., Yang, Z., Stehman, S., Brewer, C., Brooks, E., Gorelick, N., Huang, C., Hughes, M., and Kennedy, R. (2017). How similar are forest disturbance maps derived from different Landsat time series algorithms?. Forests, 8.","DOI":"10.3390\/f8040098"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"111492","DOI":"10.1016\/j.rse.2019.111492","article-title":"Mitigating the effects of omission errors on area and area change estimates","volume":"236","author":"Olofsson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"825190","DOI":"10.3389\/frsen.2022.825190","article-title":"Global Trends of Forest Loss Due to Fire From 2001 to 2019","volume":"3","author":"Tyukavina","year":"2022","journal-title":"Front. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"110968","DOI":"10.1016\/j.rse.2018.11.011","article-title":"Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis","volume":"238","author":"Bullock","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"e1600821","DOI":"10.1126\/sciadv.1600821","article-title":"The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013","volume":"3","author":"Potapov","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"112935","DOI":"10.1016\/j.rse.2022.112935","article-title":"Mapping causal agents of disturbance in boreal and arctic ecosystems of North America using time series of Landsat data","volume":"272","author":"Zhang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_86","unstructured":"Cochran, W.G. (1977). Sampling Techniques, John Willey & Sons Inc.. [3rd ed.]."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"111199","DOI":"10.1016\/j.rse.2019.05.018","article-title":"Key issues in rigorous accuracy assessment of land cover products","volume":"231","author":"Stehman","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Bey, A., D\u00edaz, A.S.P., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., Bastin, J.F., Moore, R., Federici, S., and Rezende, M. (2016). Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation. Remote Sens., 8.","DOI":"10.3390\/rs8100807"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.rse.2005.07.013","article-title":"Combining spectral and spatial information to map canopy damage from selective logging and forest fires","volume":"98","author":"Souza","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4923","DOI":"10.1080\/01431161.2014.930207","article-title":"Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes","volume":"35","author":"Stehman","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_92","first-page":"102555","article-title":"Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan","volume":"104","author":"Shimizu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.rse.2018.03.032","article-title":"A spatial ensemble approach for broad-area mapping of land surface properties","volume":"210","author":"Hooper","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.foreco.2016.05.010","article-title":"Forest disturbance interactions and successional pathways in the Southern Rocky Mountains","volume":"375","author":"Liang","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_95","first-page":"295","article-title":"Global data and tools for local forest cover loss and REDD+ performance assessment: Accuracy, uncertainty, complementarity and impact","volume":"80","author":"Bos","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_96","first-page":"102316","article-title":"Comparison of two algorithms for estimating stand-level changes and change indicators in a boreal forest in Norway","volume":"98","author":"Gobakken","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Shimizu, K., Ota, T., and Mizoue, N. (2020). Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests. Remote Sens., 12.","DOI":"10.3390\/rs12152438"},{"key":"ref_98","first-page":"102063","article-title":"Space-time detection of deforestation, forest degradation and regeneration in montane forests of Eastern Tanzania","volume":"88","author":"Hamunyela","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Aryal, R.R., Wespestad, C., Kennedy, R., Dilger, J., Dyson, K., Bullock, E., Khanal, N., Kono, M., Poortinga, A., and Saah, D. (2021). Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal. Remote Sens., 13.","DOI":"10.3390\/rs13142666"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Guo, X., Ye, J., and Hu, Y. (2022). Analysis of Land Use Change and Driving Mechanisms in Vietnam during the Period 2000\u20132020. Remote Sens., 14.","DOI":"10.3390\/rs14071600"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/851\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:23:01Z","timestamp":1760120581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":100,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030851"],"URL":"https:\/\/doi.org\/10.3390\/rs15030851","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,3]]}}}