{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T11:31:12Z","timestamp":1770895872809,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Forestry Science and Technology Innovation and Promotion Project Sponsored by Jiangsu Province","award":["LYKJ(2022)02"],"award-info":[{"award-number":["LYKJ(2022)02"]}]},{"name":"Forestry Science and Technology Innovation and Promotion Project Sponsored by Jiangsu Province","award":["31971577"],"award-info":[{"award-number":["31971577"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LYKJ(2022)02"],"award-info":[{"award-number":["LYKJ(2022)02"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National 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 (PAPD) of Jiangsu Higher Education Institutions","award":["LYKJ(2022)02"],"award-info":[{"award-number":["LYKJ(2022)02"]}]},{"name":"Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions","award":["31971577"],"award-info":[{"award-number":["31971577"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest change monitoring is a fundamental and routine task for forest survey and planning departments, and the resulting forest change information acts as an underlying asset for sustainable forest management strategy development, ecological quality assessment, and carbon cycle research. The traditional ground-based manual monitoring of forest change has the disadvantages of high time and labor costs, low accessibility, and poor timeliness over wide regions. Remote sensing technology has become a popular approach for multi-scale forest change monitoring due to its multiple available spatial, spectral, temporal, and radiometric resolutions and wide coverage. Particularly, the free access policy of long time series archive data of Landsat (around 50 years) has triggered many automated analysis algorithms for landscape-scale forest change analysis, such as VCT, LandTrendr, BFAST, and CCDC. These automated algorithms differ in their principles, parameter settings, execution complexity, and disturbance types to be detected. Thus, selecting a suitable algorithm to satisfy the particular forest management demands is an urgent and challenging task for forest managers in a given forested area. In this study, Lishui City, Zhejiang Province, a typical plantation forest region in Southern China where forest disturbance widely and frequently exists, was selected as the study area. Based on the GEE platform, the algorithmic adaptability of VCT, LandTrendr, and CCDC in monitoring abrupt forest disturbance events was compared and assessed. The results showed that the kappa coefficients of the abrupt disturbance events detected by the three algorithms were at 0.704 (LandTrendr), 0.660 (VCT), and 0.727 (CCDC), and the corresponding overall accuracies were at 0.852, 0.830, and 0.862, respectively. The validated disturbance occurrence time consistency reached nearly 80% for the three algorithms. In light of the characteristics of forest disturbance occurrence in southeastern China as well as the algorithmic complexity, efficiency, and adaptability, LandTrendr was recommended as the most suitable one in this region or other similar regions. Overall, the forest change monitoring process based on GEE is becoming more simplified and easily implemented, and the comparisons and tradeoffs in this study provide a reference for the choice of long time series forest monitoring algorithms.<\/jats:p>","DOI":"10.3390\/rs15225408","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T07:33:18Z","timestamp":1700292798000},"page":"5408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Mapping Forest Abrupt Disturbance Events in Southeastern China\u2014Comparisons and Tradeoffs of Landsat Time Series Analysis Algorithms"],"prefix":"10.3390","volume":"15","author":[{"given":"Ning","family":"Ding","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"}]},{"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":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11676-021-01299-8","article-title":"Analysis of spatio-temporal changes in forest biomass in China","volume":"33","author":"Xu","year":"2021","journal-title":"J. For. Res."},{"key":"ref_2","first-page":"8","article-title":"The 11th session of the United Nations Forum on Forests","volume":"13","author":"Xu","year":"2015","journal-title":"Green China"},{"key":"ref_3","first-page":"1703","article-title":"A review on disturbance ecology of forest","volume":"10","author":"Zhu","year":"2004","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2011.06.027","article-title":"Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems","volume":"122","author":"Vogelmann","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"293","article-title":"An Efficient and Accurate Method for Mapping Forest Clearcuts in the Pacific Northwest Using Landsat Imagery","volume":"64","author":"Cohen","year":"1998","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1080\/01431160152609236","article-title":"Land cover changes around a major east African wildlife reserve: The Mara Ecosystem (Kenya)","volume":"22","author":"Serneels","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/0143116031000101675","article-title":"Review ArticleDigital change detection methods in ecosystem monitoring: A review","volume":"25","author":"Coppin","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cohen, W., 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_9","first-page":"1005","article-title":"Review of remote sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion","volume":"22","author":"Shen","year":"2018","journal-title":"J. Remote Sens."},{"key":"ref_10","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_11","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_12","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_13","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\u2014Tools for calibration and validation","volume":"114","author":"Cohen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.02.009","article-title":"Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time","volume":"162","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"13","article-title":"Research Progress and Application of Remote Sensing Big Data Analysis Based on Google Earth Engine","volume":"19","author":"Cao","year":"2021","journal-title":"Geospat. Inf."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Obata, S., Bettinger, P., Cieszewski, J.C., and Lowe, C.R. (2020). Mapping Forest Disturbances between 1987\u20132016 Using All Available Time Series Landsat TM\/ETM+ Imagery: Developing a Reliable Methodology for Georgia, United States. Forests, 11.","DOI":"10.3390\/f11030335"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"576740","DOI":"10.3389\/fclim.2020.576740","article-title":"A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine","volume":"2","author":"Bullock","year":"2020","journal-title":"Front. Clim."},{"key":"ref_19","first-page":"118","article-title":"Dynamic Monitoring of Construction Land Expansion in Shanxi Province based on Landsat Time Series","volume":"33","author":"Chai","year":"2019","journal-title":"J. Shanxi Norm. Univ. Nat. Sci. Ed."},{"key":"ref_20","unstructured":"Su, W. (2022). Monitoring and Driving Factors of Forest Disturbance and Restoration of \u201cThree Mountains\u201d Nature Reserve in Ningxia. [Master\u2019s Thesis, Ningxia University]."},{"key":"ref_21","first-page":"55","article-title":"Applicability Analysis of LandTrendr and CCDC Algorithms for Vegetation Damage Identification in Shendong Coal Base","volume":"1","author":"Li","year":"2023","journal-title":"Met. Mine"},{"key":"ref_22","first-page":"2093","article-title":"Rapid Monitoring of Tropical Forest Disturbance in Hainan Island Based on GEE Platform and LandTrendr Algorithm","volume":"25","author":"Yin","year":"2023","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_23","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_24","first-page":"133","article-title":"An analysis of the economic effects of forestry in the Lishui area","volume":"3","author":"Xu","year":"1981","journal-title":"J. Zhejiang For. Sci. Technol."},{"key":"ref_25","first-page":"41","article-title":"Natural resources in the Lishui area and proposals for the protection and development of forests","volume":"2","author":"Yang","year":"1983","journal-title":"Environ. Pollut. Control"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, S., Wei, X., Li, D., and Lu, D. (2017). Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data. Remote Sens., 9.","DOI":"10.3390\/rs9050479"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s13595-020-0924-x","article-title":"Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China","volume":"77","author":"Diao","year":"2020","journal-title":"Ann. For. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2019.02.016","article-title":"Benefits of the free and open Landsat data policy","volume":"224","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/TGRS.2003.813213","article-title":"Data continuity of earth observing 1 (eo-1) advanced land imager (ali) and landsat tm and etm+","volume":"41","author":"Bryant","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2011.06.026","article-title":"Forty-year calibrated record of earth-reflected radiance from Landsat: A review","volume":"122","author":"Markham","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"Ieee Geosci. Remote Sens. Lett."},{"key":"ref_33","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_34","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":"Zhe","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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":"Zhe","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","first-page":"15","article-title":"Analysis of Forest Disturbance and Recovery Dynamic Characteristics Based on LandTrendr Time Segmental Algorithm","volume":"15","author":"Liu","year":"2020","journal-title":"J. Subtrop. Resour. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Shen, W., Li, M., and Lv, Y. (2020). Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019. Remote Sens., 12.","DOI":"10.3390\/rs12193191"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6559","DOI":"10.1080\/01431160903241999","article-title":"Use of remote sensing coupled with a vegetation change tracker model to assess rates of forest change and fragmentation in Mississippi, USA","volume":"30","author":"Li","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2016.03.036","article-title":"Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000\u20132014)","volume":"185","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111116","DOI":"10.1016\/j.rse.2019.03.009","article-title":"Continuous monitoring of land disturbance based on Landsat time series","volume":"238","author":"Zhu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"102556","article-title":"A new method for monitoring start of season (SOS) of forest based on multisource remote sensing","volume":"104","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112167","DOI":"10.1016\/j.rse.2020.112167","article-title":"A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection","volume":"252","author":"Ye","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2018.02.064","article-title":"Improved mapping of forest type using spectral-temporal Landsat features","volume":"210","author":"Pasquarella","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.rse.2017.09.029","article-title":"Mapping forest change using stacked generalization: An ensemble approach","volume":"204","author":"Healey","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cohen, W.B., Healey, S.P., Yang, Z., Zhu, Z., and Gorelick, N. (2020). Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance. Remote Sens., 12.","DOI":"10.3390\/rs12101673"},{"key":"ref_46","unstructured":"Hua, J. (2021). Spatiotemporal Patterns of Forest Disturbance and Recovery Using Integrated LandTrendr Algorithm and Machine Learning Method. [Master\u2019s Thesis, Zhejiang A&F University]."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xu, H., Wei, Y., Liu, C., Li, X., and Fang, H. (2019). A Scheme for the Long-Term Monitoring of Impervious\u2212Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11161891"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Guo, J., Li, Q., Xie, H., Li, J., Qiao, L., Zhang, C., Yang, G., and Wang, F. (2022). Monitoring of Vegetation Disturbance and Restoration at the Dumping Sites of the Baorixile Open-Pit Mine Based on the LandTrendr Algorithm. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19159066"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1186\/1179-5395-44-S1-S3","article-title":"Managing planted forests for multiple uses under a changing environment in China","volume":"44","author":"Liu","year":"2014","journal-title":"N. Z. J. For. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5408\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:08Z","timestamp":1760131508000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":49,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15225408"],"URL":"https:\/\/doi.org\/10.3390\/rs15225408","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]}}}