{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:14:04Z","timestamp":1775690044145,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T00:00:00Z","timestamp":1621555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Consejo Nacional de Ciencia y Tecnologia (CONACyT)","award":["\u201cCiencia Basica\u201d SEP-285349"],"award-info":[{"award-number":["\u201cCiencia Basica\u201d SEP-285349"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the magnitude of breakpoints, and accuracy assessment; however, few have looked in detail at how the trend and seasonal model components contribute to disturbance detection in different forest types. Here, we use Landsat time series images spanning 1994\u20132018 to map forest disturbance in a western Pacific area of Mexico, where both temperate and tropical dry forests have been subject to severe deforestation and forest degradation processes. Since these two forest types have distinct seasonal characteristics, we investigate how trend and seasonal model components, such as the goodness-of-fit (R2), magnitude of change, amplitude, and model length in a stable historical period, affect forest disturbance detection. We applied the Breaks For Additive Season and Trend Monitor (BFAST) algorithm and after accuracy assessment by stratified random sample points, and we obtained 68% and 86% of user accuracy and 75.6% and 86% of producer\u2019s accuracy in disturbance detection, in tropical dry forests and temperate forests, respectively. We extracted the noncorrelated trend and seasonal model components R2, magnitude, amplitude, length of the stable historical period, and percentage of pixels with NA and tested their effects on disturbance detection employing forest-type specific logistic regression. Our results showed that, for all forests combined, the amplitude and stable historical period length contributed to disturbance detection. While for tropical dry forest alone, amplitude was the main predictor, and for the temperate forest alone, the stable historical period length contributed most to the prediction, although it was not statistically significant. These findings provide insights for improving the results of forest disturbance detection in different forest types.<\/jats:p>","DOI":"10.3390\/rs13112033","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"2033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1345-1583","authenticated-orcid":false,"given":"Yan","family":"Gao","sequence":"first","affiliation":[{"name":"Centro de Investigaciones en Geograf\u00eda Ambiental, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Morelia 58190, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6422-4802","authenticated-orcid":false,"given":"Jonathan V.","family":"Sol\u00f3rzano","sequence":"additional","affiliation":[{"name":"Posgrado en Geograf\u00eda, Centro de Investigaciones en Geograf\u00eda Ambiental, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Morelia 58190, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9969-1339","authenticated-orcid":false,"given":"Alexander","family":"Quevedo","sequence":"additional","affiliation":[{"name":"Posgrado en Geograf\u00eda, Centro de Investigaciones en Geograf\u00eda Ambiental, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Morelia 58190, Mexico"}]},{"given":"Jaime Octavio","family":"Loya-Carrillo","sequence":"additional","affiliation":[{"name":"Posgrado en Geograf\u00eda, Centro de Investigaciones en Geograf\u00eda Ambiental, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Morelia 58190, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1146\/annurev-ecolsys-110512-135914","article-title":"The Structure, Distribution, and Biomass of the World\u2019s Forests","volume":"44","author":"Pan","year":"2013","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1126\/science.aau3445","article-title":"Classifying drivers of global forest loss","volume":"361","author":"Curtis","year":"2018","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H., and Hurtt, G.C. (2009). Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J. Geophys. Res. Space Phys., 114.","DOI":"10.1029\/2008JG000911"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2014.11.005","article-title":"An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites","volume":"158","author":"Hermosilla","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"034008","DOI":"10.1088\/1748-9326\/11\/3\/034008","article-title":"Humid tropical forest disturbance alerts using Landsat data","volume":"11","author":"Hansen","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2307\/1942049","article-title":"Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types","volume":"5","author":"Gamon","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2004.04.009","article-title":"Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements","volume":"91","author":"Fensholt","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0167-5877(05)80004-2","article-title":"Interpreting vegetation indices","volume":"11","author":"Jackson","year":"1991","journal-title":"Prev. Veter-Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.rse.2005.09.017","article-title":"MODIS time-series imagery for forest disturbance detection and quantification of patch size effects","volume":"99","author":"Jin","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2782","DOI":"10.3390\/rs6042782","article-title":"An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data","volume":"6","author":"Neigh","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jaridenv.2014.09.001","article-title":"Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia","volume":"113","author":"Eckert","year":"2015","journal-title":"J. Arid Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3588","DOI":"10.3390\/rs70403588","article-title":"Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series","volume":"7","author":"Lambert","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1111\/geb.12338","article-title":"Global-scale mapping of changes in ecosystem functioning from earth observation-based trends in total and recurrent vegetation","volume":"24","author":"Fensholt","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2014.09.010","article-title":"Detecting changes in vegetation trends using time series segmentation","volume":"156","author":"Jamali","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.3390\/rs70504973","article-title":"A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection","volume":"7","author":"Reiche","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2016.01.028","article-title":"The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring","volume":"177","author":"Kleinschmit","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ghazaryan, G., Dubovyk, O., Kussul, N., and Menz, G. (2016). Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013. Remote Sens., 8.","DOI":"10.3390\/rs8080617"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Murillo-Sandoval, P.J., Hilker, T., Krawchuk, M.A., and Hoek, J.V.D. (2018). Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series. Forests, 9.","DOI":"10.3390\/f9050269"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Prada, M., Cabo, C., Hern\u00e1ndez-Clemente, R., Hornero, A., Majada, J., and Mart\u00ednez-Alonso, C. (2020). Assessing canopy responses to thinnings for sweet chestnut coppice with time-series vegetation indices derived from landsat-8 and sentinel-2 imagery. Remote Sens., 12.","DOI":"10.3390\/rs12183068"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.rse.2015.11.006","article-title":"Using spatial context to improve early detection of deforestation from Landsat time series","volume":"172","author":"Hamunyela","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.rse.2018.02.046","article-title":"Extracting the full value of the Landsat archive: Inter-sensor harmonization for the mapping of Minnesota forest canopy cover (1973\u20132015)","volume":"209","author":"Vogeler","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.rse.2007.03.010","article-title":"Trajectory-based change detection for automated characterization of forest disturbance dynamics","volume":"110","author":"Kennedy","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s10021-013-9669-9","article-title":"United States Forest Disturbance Trends Observed Using Landsat Time Series","volume":"16","author":"Masek","year":"2013","journal-title":"Ecosystems"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"756","DOI":"10.3390\/rs6010756","article-title":"Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa","volume":"6","author":"Hirschmugl","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2015.03.001","article-title":"Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series","volume":"169","author":"Grogan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Schneibel, A., Frantz, D., R\u00f6der, A., Stellmes, M., Fischer, K., and Hill, J. (2017). Using Annual Landsat Time Series for the Detection of Dry Forest Degradation Processes in South-Central Angola. Remote Sens., 9.","DOI":"10.3390\/rs9090905"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2017.03.035","article-title":"A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series","volume":"194","author":"White","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2017.11.007","article-title":"Analyzing spatial and temporal variability in short-term rates of post-fire vegetation return from Landsat time series","volume":"205","author":"Frazier","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2014.11.015","article-title":"Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia","volume":"158","author":"Schmidt","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_36","first-page":"318","article-title":"Performance of vegetation indices from Landsat time series in deforestation monitoring","volume":"52","author":"Schultz","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.isprsjprs.2015.03.015","article-title":"Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia","volume":"107","author":"Dutrieux","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Grogan, K., Pflugmacher, D., Hostert, P., Verbesselt, J., and Fensholt, R. (2016). Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?. Remote Sens., 8.","DOI":"10.3390\/rs8080657"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.apgeog.2016.02.006","article-title":"Conservation-induced resettlement as a driver of land cover change in India: An object-based trend analysis","volume":"69","author":"Platt","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jakovac, C.C., Dutrieux, L.P., Siti, L., Pe\u00f1a-Claros, M., and Bongers, F. (2017). Spatial and temporal dynamics of shifting cultivation in the middle-Amazonas river: Expansion and intensification. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181092"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Romero-Sanchez, M.E., and Ponce-Hernandez, R. (2017). Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators. Forests, 8.","DOI":"10.3390\/f8090302"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.rse.2018.12.020","article-title":"Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica","volume":"221","author":"Smith","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2019.02.003","article-title":"Near real-time monitoring of tropical forest disturbance: New algorithms and assessment framework","volume":"224","author":"Tang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.3390\/rs5052113","article-title":"Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology","volume":"5","author":"Forkel","year":"2013","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1016\/j.rse.2010.08.003","article-title":"Phenological change detection while accounting for abrupt and gradual trends in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Gao, Y., Quevedo, A., Szantoi, Z., and Skutsch, M. (2019). Monitoring forest disturbance using time-series MODIS NDVI in Michoac\u00e1n, Mexico. Geocarto Int., 1\u201317.","DOI":"10.1080\/10106049.2019.1661032"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.3390\/f5102464","article-title":"Combining Satellite Data and Community-Based Observations for Forest Monitoring","volume":"5","author":"Pratihast","year":"2014","journal-title":"Forests"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Schultz, M., Shapiro, A., Clevers, J.G.P.W., Beech, C., and Herold, M. (2018). Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor. Remote Sens., 10.","DOI":"10.3390\/rs10111850"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.rse.2017.10.034","article-title":"Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2","volume":"204","author":"Reiche","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1029\/1998WR900018","article-title":"Evaluating the use of \u201cgoodness-of-fit\u201d Measures in hydrologic and hydroclimatic model validation","volume":"35","author":"LeGates","year":"1999","journal-title":"Water Resour. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7587","DOI":"10.1080\/01431161.2018.1475774","article-title":"Trend forecast based approach for cropland change detection using Lansat-derived time-series metrics","volume":"39","author":"Chen","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.isprsjprs.2018.07.002","article-title":"Towards a polyalgorithm for land use change detection","volume":"144","author":"Saxena","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"599","DOI":"10.3844\/ajessp.2009.599.604","article-title":"Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis","volume":"5","author":"Naill","year":"2009","journal-title":"Am. J. Environ. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ghaderpour, E., and Vujadinovic, T. (2020). The Potential of the Least-Squares Spectral and Cross-Wavelet Analyses for Near-Real-Time Disturbance Detection within Unequally Spaced Satellite Image Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12152446"},{"key":"ref_58","first-page":"445","article-title":"El Bosque Tropical Caducifolio En La Reserva de La BiosferaSierra Manantlan, Jalisco-Colima, Mexico","volume":"5","author":"Cuevas","year":"1998","journal-title":"Bol. IBUG"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Borrego, A., and Skutsch, M. (2019). How Socio-Economic Differences Between Farmers Affect Forest Degradation in Western Mexico. Forests, 10.","DOI":"10.3390\/f10100893"},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.rse.2015.08.020","article-title":"Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series","volume":"169","author":"Devries","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_64","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hamunyela, E., Ro\u015fca, S., Mirt, A., Engle, E., Herold, M., Gieseke, F., and Verbesselt, J. (2020). Implementation of BFAST monitor algorithm on Google Earth engine to support large-area and sub-annual change monitoring using Earth observation data. Remote Sens., 12.","DOI":"10.3390\/rs12182953"},{"key":"ref_66","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_67","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data\u2014Principles and Practices, CPC Press, Taylor & Francis Group. [3rd ed.].","DOI":"10.1201\/9780429052729"},{"key":"ref_68","first-page":"431","article-title":"Using Known Map Category Marginal Frequencies to Improve Estimates of Thematic Map Accuracy","volume":"48","author":"Card","year":"1982","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Belsley, D.A., Kuh, E., and Welsch, R.E. (1980). Regression Diagnostics\u2014Identifying Influential Data and Sources of Collinearity, John Wiley & Sons, Inc.","DOI":"10.1002\/0471725153"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.1007\/s11135-017-0584-6","article-title":"Confounding and collinearity in regression analysis: A cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour","volume":"52","author":"Johnston","year":"2018","journal-title":"Qual. Quant."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2033\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:05:26Z","timestamp":1760162726000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2033"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,21]]},"references-count":70,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112033"],"URL":"https:\/\/doi.org\/10.3390\/rs13112033","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,21]]}}}