{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:09:55Z","timestamp":1760234995038,"version":"build-2065373602"},"reference-count":90,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T00:00:00Z","timestamp":1624924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871223"],"award-info":[{"award-number":["41871223"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-frequency disturbance forest ecosystems undergo complex and frequent changes at various spatiotemporal scales owing to natural and anthropogenic factors. Effectively capturing the characteristics of these spatiotemporal changes from satellite image time series is a powerful and practical means for determining their causes and predicting their trends. Herein, we combined the spatiotemporal cube and vegetation indices to develop the improved spatiotemporal cube (IST-cube) model. We used this to acquire the spatiotemporal dynamics of forest ecosystems from 1987 to 2020 in the study area and then classified it into four spatiotemporal scales. The results showed that the cube-core only exists in the increasing IST-cubes, which are distributed in residential areas and forests. The length of the IST-cube implies the duration of triggers. Human activities result in long-term small-scope IST-cubes, and the impact in the vicinity of residential areas is increasing while there is no change within. Meteorological disasters cause short-term, large scope, and irregular impacts. Land use type change causes short-term small scope IST-cubes and a regular impact. Overall, we report the robustness and strength of the IST-cube model in capturing spatiotemporal changes in forest ecosystems, providing a novel method to examine complex changes in forest ecosystems via remote sensing.<\/jats:p>","DOI":"10.3390\/rs13132537","type":"journal-article","created":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T22:39:43Z","timestamp":1625006383000},"page":"2537","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Scale Spatiotemporal Change Characteristics Analysis of High-Frequency Disturbance Forest Ecosystem Based on Improved Spatiotemporal Cube Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Yangcen","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"given":"Xiangnan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"given":"Meiling","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"given":"Xinyu","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]},{"given":"Tao","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1111\/1365-2664.12733","article-title":"Disentangling relationships between plant diversity and decomposition processes under forest restoration","volume":"54","author":"Fujii","year":"2017","journal-title":"J. Appl. Ecol."},{"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","first-page":"133","DOI":"10.1111\/j.1365-3059.2010.02406.x","article-title":"Climate change and forest diseases","volume":"60","author":"Sturrock","year":"2011","journal-title":"Plant Pathol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/s41559-018-0490-x","article-title":"The exceptional value of intact forest ecosystems","volume":"2","author":"Watson","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/343051a0","article-title":"Climate-induced changes in forest disturbance and vegetation","volume":"343","author":"Overpeck","year":"1990","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1139\/er-2013-0057","article-title":"How do natural disturbances and human activities affect soils and tree nutrition and growth in the Canadian boreal forest?","volume":"22","author":"Maynard","year":"2014","journal-title":"Environ. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.foreco.2005.08.015","article-title":"Forest soils and carbon sequestration","volume":"220","author":"Lal","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wei, C., Karger, D.N., and Wilson, A.M. (2020). Spatial detection of alpine treeline ecotones in the Western United States. Remote Sens. Environ., 240.","DOI":"10.1016\/j.rse.2020.111672"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1002\/fee.2190","article-title":"Disturbance refugia within mosaics of forest fire, drought, and insect outbreaks","volume":"18","author":"Krawchuk","year":"2020","journal-title":"Front. Ecol. Environ."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.tplants.2014.10.008","article-title":"Global satellite monitoring of climate-induced vegetation disturbances","volume":"20","author":"McDowell","year":"2015","journal-title":"Trends Plant Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/S0305-9006(03)00069-2","article-title":"National level forest monitoring and modeling in Canada","volume":"61","author":"Wulder","year":"2004","journal-title":"Prog. Plan."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111403","DOI":"10.1016\/j.rse.2019.111403","article-title":"Prevalence of multiple forest disturbances and impact on vegetation regrowth from interannual Landsat time series (1985\u20132015)","volume":"233","author":"Hermosilla","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Shimizu, K., Ota, T., and Mizoue, N. (2019). Detecting forest changes using dense Landsat 8 and Sentinel-1 time series data in tropical seasonal forests. Remote Sens., 11.","DOI":"10.3390\/rs11161899"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105471","DOI":"10.1016\/j.ecolind.2019.105471","article-title":"Forest health assessment for geo-environmental planning and management in hilltop mining areas using Hyperion and Landsat data","volume":"106","author":"Kayet","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2005.11.016","article-title":"Remote sensing image-based analysis of the relationship between urban heat island and land use\/cover changes","volume":"104","author":"Chen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for object-based image analysis (obia): A review of algorithms and challenges from remote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-018-0360-0","article-title":"Remote sensing classification method of vegetation dynamics based on time series Landsat image: A case of opencast mining area in China","volume":"2018","author":"Xu","year":"2018","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Caballero Espejo, J., Messinger, M., Rom\u00e1n-Da\u00f1obeytia, F., Ascorra, C., Fernandez, L.E., and Silman, M. (2018). Deforestation and forest degradation due to gold mining in the Peruvian Amazon: A 34-year perspective. Remote Sens., 10.","DOI":"10.20944\/preprints201811.0113.v2"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Souza-Filho, P.W.M., Nascimento, W.R., Santos, D.C., Weber, E.J., Silva, R.O., and Siqueira, J.O. (2018). A GEOBIA approach for multitemporal land-cover and land-use change analysis in a tropical watershed in the southeastern Amazon. Remote Sens., 10.","DOI":"10.3390\/rs10111683"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.rse.2015.09.004","article-title":"Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics","volume":"170","author":"Hermosilla","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.scitotenv.2018.06.341","article-title":"Identification of the disturbance and trajectory types in mining areas using multitemporal remote sensing images","volume":"644","author":"Yang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Meng, Y., Liu, X., Ding, C., Xu, B., Zhou, G., and Zhu, L. (2020). Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecol. Inform., 57.","DOI":"10.1016\/j.ecoinf.2020.101064"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Cohen, W.B., Healey, S.P., Yang, Z., Stehman, S.V., Brewer, C.K., Brooks, E.B., Gorelick, N., Huang, C., Hughes, M.J., and Kennedy, R.E. (2017). How similar are forest disturbance maps derived from different Landsat time series algorithms?. Forests, 8.","DOI":"10.3390\/f8040098"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1016\/j.rse.2009.04.014","article-title":"Monitoring forest changes in the southwestern United States using multitemporal Landsat data","volume":"113","author":"Vogelmann","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Kang, Y., Cho, N., and Son, S. (2018). Spatiotemporal characteristics of elderly population\u2019s traffic accidents in Seoul using space-time cube and space-time kernel density estimation. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196845"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1002\/jmv.25834","article-title":"An analysis of spatiotemporal pattern for COIVD-19 in China based on space-time cube","volume":"92","author":"Mo","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111212","DOI":"10.1016\/j.rse.2019.111212","article-title":"A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and change detection","volume":"231","author":"Xi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1126\/science.aac6759","article-title":"Forest health and global change","volume":"349","author":"Trumbore","year":"2015","journal-title":"Science"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.agee.2004.09.007","article-title":"A review of carbon sequestration dynamics in the Himalayan region as a function of land-use change and forest\/soil degradation with special reference to Nepal","volume":"105","author":"Upadhyay","year":"2005","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1016\/j.scitotenv.2018.09.115","article-title":"NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015","volume":"650","author":"Chu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1016\/j.scitotenv.2017.09.145","article-title":"Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data","volume":"618","author":"Lamchin","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0034-4257(84)90035-X","article-title":"Intensive forest clearing in Rondonia, Brazil, as detected by satellite remote sensing","volume":"15","author":"Tucker","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.scitotenv.2018.12.290","article-title":"Assessing climate impact on forest cover in areas undergoing substantial land cover change using Landsat imagery","volume":"659","author":"Yang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1016\/j.rse.2009.05.015","article-title":"Predicting Enhanced Vegetation Index (EVI) curves for ecosystem modeling applications","volume":"113","author":"Gurung","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1111\/1755-6724.13243","article-title":"Trace Element Geochemistry of Devonian Strata in the Shizhuyuan Ore District, Hunan Province","volume":"1","author":"Cheng","year":"2017","journal-title":"Acta Geol. Sin."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Zhu, C., Zhang, X., Zhang, N., Hassan, M.A., and Zhao, L. (2018). Assessing the defoliation of pine forests in a long time-series and spatiotemporal prediction of the defoliation using Landsat data. Remote Sens., 10.","DOI":"10.3390\/rs10030360"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111317","DOI":"10.1016\/j.rse.2019.111317","article-title":"Using Landsat observations (1988\u20132017) and Google Earth Engine to detect vegetation cover changes in rangelands-A first step towards identifying degraded lands for conservation","volume":"232","author":"Xie","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"124026","DOI":"10.1088\/1748-9326\/aaf0ec","article-title":"Drying drives decline in muskrat population in the Peace-Athabasca Delta, Canada","volume":"13","author":"Ward","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e01848","DOI":"10.1002\/eap.1848","article-title":"Recent drought and tree mortality effects on the avian community in southern Sierra Nevada: A glimpse of the future?","volume":"29","author":"Roberts","year":"2019","journal-title":"Ecol. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.geoderma.2018.05.016","article-title":"Mapping the transition from pre-European settlement to contemporary soil conditions in the Lower Hunter Valley, Australia","volume":"329","author":"Huang","year":"2018","journal-title":"Geoderma"},{"key":"ref_48","unstructured":"Sorooshian, S., Hsu, K., Braithwaite, D., and Ashouri, H. (2015). The NOAA CDR Program, 2014: NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), version 1, revision 1. NOAA Natl. Cent. Environ. Inf. Accessed, 18."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-13-00068.1","article-title":"PERSIANNCDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies","volume":"96","author":"Ashouri","year":"2015","journal-title":"Bull. Am. Meteor. Soc."},{"key":"ref_50","unstructured":"Hunan Meteorological Bureau (2021, January 07). Classification standard of flood and waterlogging weather in Hunan Province, Available online: http:\/\/hn.cma.gov.cn\/xxgk\/gkml\/tzgg\/202012\/t20201202_2449482.html."},{"key":"ref_51","unstructured":"Palmer, W.C. (1965). Meteorological Drought, US Department of Commerce, Weather Bureau."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1002\/2015JD024285","article-title":"Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982\u20132011 by using PDSI indices and agriculture drought survey data","volume":"121","author":"Yan","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.191","article-title":"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015","volume":"5","author":"Abatzoglou","year":"2018","journal-title":"Sci. Data"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, X., Tarpley, D., and Sullivan, J.T. (2007). Diverse responses of vegetation phenology to a warming climate. Geophys. Res. Lett., 34.","DOI":"10.1029\/2007GL031447"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2018.08.022","article-title":"A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter","volume":"217","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hinojo-Hinojo, C., and Goulden, M.L. (2020). Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production. Remote Sens., 12.","DOI":"10.3390\/rs12091405"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sims, D.A., Rahman, A.F., Cordova, V.D., El-Masri, B.Z., Baldocchi, D.D., Flanagan, L.B., Goldstein, A.H., Hollinger, D.Y., Misson, L., and Monson, R.K. (2006). On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. J. Geophys. Res. Biogeosci., 111.","DOI":"10.1029\/2006JG000162"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"303","DOI":"10.3390\/rs4010303","article-title":"Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data","volume":"4","author":"Hashimoto","year":"2012","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.agrformet.2014.06.007","article-title":"A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation","volume":"197","author":"Son","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.agrformet.2019.02.002","article-title":"Remote-sensing disturbance detection index to identify spatio-temporal varying flood impact on crop production","volume":"269\u2013270","author":"Chen","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A.H., Cohen, W.B., Qiu, S., and Zhou, C. (2020). Continuous monitoring of land disturbance based on Landsat time series. Remote Sens. Environ., 238.","DOI":"10.1016\/j.rse.2019.03.009"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/34.295913","article-title":"Seeded region growing","volume":"16","author":"Adams","year":"1994","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"681","DOI":"10.14358\/PERS.84.11.681","article-title":"Coupling relationship among scale parameter, segmentation accuracy, and classification accuracy in geobia","volume":"84","author":"Ming","year":"2018","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_65","unstructured":"Baatz, M. (2000). Multi resolution segmentation: An optimum approach for high quality multi scale image segmentation. Beutrage zum AGIT-Symposium, Salzburg."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.21105\/joss.01556","article-title":"pyMannKendall: A python package for non parametric Mann Kendall family of trend tests","volume":"4","author":"Hussain","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TAC.1959.1104847","article-title":"On adaptive control processes","volume":"4","author":"Bellman","year":"1959","journal-title":"IRE Trans. Autom. Control"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite image time series analysis under time warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. (2016). Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sens., 8.","DOI":"10.3390\/rs8010019"},{"key":"ref_71","unstructured":"Wannes Meert, K.H. (2021, January 08). Toon Van Craenendonck. (Version v2.0.0). Available online: https:\/\/github.com\/wannesm\/dtaidistance."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mueen, A., and Keogh, E. (2016, January 13\u201317). Extracting optimal performance from dynamic time warping. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2945383"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhan, Q., Yang, C., and Wang, J. (2018). Characterizing the spatio-temporal pattern of land surface temperature through time series clustering: Based on the latent pattern and morphology. Remote Sens., 10.","DOI":"10.3390\/rs10040654"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1078\/1433-8319-00042","article-title":"Tropical forest recovery: Legacies of human impact and natural disturbances","volume":"6","author":"Chazdon","year":"2003","journal-title":"Perspect. Plant Ecol. Evol. Syst."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.envsci.2007.01.009","article-title":"Factoring out natural and indirect human effects on terrestrial carbon sources and sinks","volume":"10","author":"Canadell","year":"2007","journal-title":"Environ. Sci. Policy"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1111\/j.1365-2486.2005.00932.x","article-title":"Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories","volume":"11","author":"Vetter","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.landusepol.2017.06.013","article-title":"Forest cover change and flood hazards in India","volume":"67","author":"Bhattacharjee","year":"2017","journal-title":"Land Use Policy"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1177\/070674370605100603","article-title":"An epidemiologic study of posttraumatic stress disorder in flood victims in Hunan China","volume":"51","author":"Liu","year":"2006","journal-title":"Can. J. Psychiat."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s11069-007-9197-z","article-title":"Flood hazard in Hunan province of China: An economic loss analysis","volume":"47","author":"Huang","year":"2008","journal-title":"Nat. Hazard."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s00477-012-0589-6","article-title":"Analysis of dry\/wet conditions using the standardized precipitation index and its potential usefulness for drought\/flood monitoring in Hunan Province, China","volume":"27","author":"Du","year":"2013","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wu, D., Yin, H., Xu, S., and Zhao, Y. (2011). Risk factors for posttraumatic stress reactions among Chinese students following exposure to a snowstorm disaster. BMC Public Health, 11.","DOI":"10.1186\/1471-2458-11-96"},{"key":"ref_82","unstructured":"Song, L., and Fan, Y. (2012). Yearbook of Meterorological Disasters in China, China Meteorological Press."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.foreco.2006.12.016","article-title":"Forest fragmentation and its correlation to human land use change in the state of Selangor, peninsular Malaysia","volume":"241","author":"Abdullah","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1017\/S0376892906003122","article-title":"Interactions between land use\/land cover change, forest fires and landscape structure in Sierra de Gredos (Central Spain)","volume":"33","author":"Viedma","year":"2006","journal-title":"Environ. Conserv."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L. (2017). A review of wetland remote sensing. Sensors, 17.","DOI":"10.3390\/s17040777"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Lu, C., Ren, C., Wang, Z., Zhang, B., Man, W., Yu, H., Gao, Y., and Liu, M. (2019). Monitoring and Assessment of Wetland Loss and Fragmentation in the Cross-Boundary Protected Area: A Case Study of Wusuli River Basin. Remote Sens., 11.","DOI":"10.3390\/rs11212581"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1111\/j.1466-8238.2011.00686.x","article-title":"Refugia: Identifying and understanding safe havens for biodiversity under climate change","volume":"21","author":"Keppel","year":"2012","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1111\/2041-210X.13025","article-title":"Understanding and assessing vegetation health by in situ species and remote-sensing approaches","volume":"9","author":"Lausch","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_89","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_90","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2537\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:26:34Z","timestamp":1760163994000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,29]]},"references-count":90,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132537"],"URL":"https:\/\/doi.org\/10.3390\/rs13132537","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,6,29]]}}}