乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T23:32:55Z","timestamp":1725319975949},"reference-count":63,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T00:00:00Z","timestamp":1540857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"The time-series analysis of multi-temporal satellite data is widely used for vegetation regrowth after a wildfire event. Comparisons between pre- and post-fire conditions are the main method used to monitor ecosystem recovery. In the present study, we estimated wildfire disturbance by comparing actual post-fire time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and simulated MODIS EVI based on an artificial neural network assuming no wildfire occurrence. Then, we calculated the similarity of these responses for all sampling sites by applying a dynamic time warping technique. Finally, we applied multidimensional scaling to the warping distances and an optimal fuzzy clustering to identify unique patterns in vegetation recovery. According to the results, artificial neural networks performed adequately, while dynamic time warping and the proposed multidimensional scaling along with the optimal fuzzy clustering provided consistent results regarding vegetation response. For the first two years after the wildfire, medium-high- to high-severity burnt sites were dominated by oaks at elevations greater than 200 m, and presented a clustered (predominant) response of revegetation compared to other sites.<\/jats:p>","DOI":"10.3390\/ijgi7110420","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T07:50:56Z","timestamp":1540972256000},"page":"420","source":"Crossref","is-referenced-by-count":11,"title":["Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6932-2986","authenticated-orcid":false,"given":"Christos","family":"Vasilakos","sequence":"first","affiliation":[{"name":"Department of Geography, University of the Aegean, 81100 Mytilene, Greece"}]},{"given":"George E.","family":"Tsekouras","sequence":"additional","affiliation":[{"name":"Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8507-5201","authenticated-orcid":false,"given":"Palaiologos","family":"Palaiologou","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4942-802X","authenticated-orcid":false,"given":"Kostas","family":"Kalabokidis","sequence":"additional","affiliation":[{"name":"Department of Geography, University of the Aegean, 81100 Mytilene, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moreira, F., Arianoutsou, M., Corona, P., and Las Heras, J. (2012). Fire Ecology and Post-Fire Restoration Approaches in Southern European Forest Types. Post-Fire Management and Restoration of Southern European Forests, Springer.","DOI":"10.1007\/978-94-007-2208-8"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"De Luis, M., Ravent\u00f3s, J., and Gonz\u00e1lez-Hidalgo, J.C. (2006). Post-fire vegetation succession in Mediterranean gorse shrublands. Acta Oecol.","DOI":"10.1016\/j.actao.2006.01.005"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bastos, A., Gouveia, C.M., Dacamara, C.C., and Trigo, R.M. (2011). Modelling post-fire vegetation recovery in Portugal. Biogeosciences.","DOI":"10.5194\/bgd-8-4559-2011"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fernandez-Manso, A., Quintano, C., and Roberts, D.A. (2016). Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2016.06.015"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Meng, R., Wu, J., Zhao, F., Cook, B.D., Hanavan, R.P., and Serbin, S.P. (2018). Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2018.03.019"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.rse.2016.05.018","article-title":"Assessing postfire recovery of chamise chaparral using multi-temporal spectral vegetation index trajectories derived from Landsat imagery","volume":"183","author":"Storey","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1080\/01431161.2016.1271477","article-title":"Combining ground-based measurements and MODIS-based spectral vegetation indices to track biomass accumulation in post-fire chaparral","volume":"38","author":"Uyeda","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3849","DOI":"10.1007\/s10661-011-2228-y","article-title":"Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest","volume":"184","author":"Buma","year":"2012","journal-title":"Environ. Monit. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12314","DOI":"10.3390\/rs70912314","article-title":"A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series","volume":"7","author":"Helman","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ecolind.2018.02.008","article-title":"Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series","volume":"89","author":"Joao","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s11056-017-9608-2","article-title":"Monitoring of post-fire forest regeneration under different restoration treatments based on ALOS\/PALSAR data","volume":"49","author":"Chen","year":"2018","journal-title":"New For."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4025","DOI":"10.1080\/01431161.2013.772313","article-title":"Using AHS hyper-spectral images to study forest vegetation recovery after a fire","volume":"34","author":"Huesca","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape assessment (LA) sampling and analysis methods, FIREMON: Fire Effects Monitoring and Inventory System, General Technical Report RMRS-GTR-164."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.3390\/s8032017","article-title":"Monitoring the effects of forest restoration treatments on post-fire vegetation recovery with MODIS multitemporal data","volume":"8","year":"2008","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, H., Yang, G., Ding, Y., and Zhao, J. (2018). Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing. Remote Sens., 10.","DOI":"10.3390\/rs10071000"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/14498596.2015.974227","article-title":"Using MODIS data to analyse post-fire vegetation recovery in Australian eucalypt forests","volume":"60","author":"Caccamo","year":"2015","journal-title":"J. Spat. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, P., Li, Z., Cribb, M., and Sparrow, M. (2007). A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. J. Geophys. Res. Atmos.","DOI":"10.1029\/2006JD008351"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ecolind.2015.11.026","article-title":"Remote sensing approach to detect post-fire vegetation regrowth in Siberian boreal larch forest","volume":"62","author":"Chu","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/2150704X.2015.1126375","article-title":"Forest recovery trends derived from Landsat time series for North American boreal forests","volume":"37","author":"Pickell","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1071\/WF07129","article-title":"Use of linguistic estimates and vegetation indices to assess post-fire vegetation regrowth in woodland areas","volume":"19","author":"Jacobson","year":"2010","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/14498596.2012.733618","article-title":"Remote sensing of post-fire vegetation recovery; a study using Landsat 5 TM imagery and NDVI in North-East Victoria","volume":"57","author":"Sever","year":"2012","journal-title":"J. Spat. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Walz, Y., Maier, S.W., Dech, S.W., Conrad, C., and Colditz, R.R. (2007). Classification of burn severity using Moderate Resolution Imaging Spectroradiometer (MODIS): A case study in the jarrah-marri forest of southwest Western Australia. J. Geophys. Res., 112.","DOI":"10.1029\/2005JG000118"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ryu, J.-H., Han, K.-S., Hong, S., Park, N.-W., Lee, Y.-W., and Cho, J. (2018). Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea. Remote Sens., 10.","DOI":"10.3390\/rs10060918"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Van Leeuwen, W.J.D., Casady, G.M., Neary, D.G., Bautista, S., Alloza, J.A., Carmel, Y., Wittenberg, L., Malkinson, D., and Orr, B.J. (2010). Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel. Int. J. Wildl. Fire.","DOI":"10.1071\/WF08078"},{"key":"ref_28","first-page":"4","article-title":"Study and assessment of the 2011 evros wildfire impact and behavior [in Greek]","volume":"155","author":"Kalabokidis","year":"2011","journal-title":"Fire Brig. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s11056-005-3626-1","article-title":"Forest re-growth since 1945 in the Dadia forest nature reserve in northern Greece","volume":"32","author":"Triantakonstantis","year":"2006","journal-title":"NEW For."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/S0006-3207(03)00070-3","article-title":"Diversity patterns in butterfly communities of the Greek nature reserve Dadia","volume":"114","author":"Grill","year":"2003","journal-title":"Biol. Conserv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1163\/156853807782152471","article-title":"Diversity, ecological structure and conservation of herpetofauna in a Mediterranean area (Dadia National Park, Greece)","volume":"28","author":"Kati","year":"2007","journal-title":"Amphibia-Reptilia"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1111\/j.1366-9516.2006.00288.x","article-title":"Diversity, ecological structure, and conservation of the landbird community of Dadia reserve, Greece","volume":"12","author":"Kati","year":"2006","journal-title":"Divers. Distrib."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1080\/00222930701835290","article-title":"Population size, breeding rates and conservation status of Eurasian black vulture in the Dadia National Park, Thrace, NE Greece","volume":"42","author":"Skartsi","year":"2008","journal-title":"J. Nat. Hist."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/00222930701835308","article-title":"Range use of a Eurasian black vulture (Aegypius monachus) population in the Dadia-Lefkimi-Soufli National Park and the adjacent areas, Thrace, NE Greece","volume":"42","author":"Vasilakis","year":"2008","journal-title":"J. Nat. Hist."},{"key":"ref_35","first-page":"230","article-title":"Evaluating prescribed fires, Workshops on Wildness fires","volume":"182","author":"Ryan","year":"1985","journal-title":"USDA Serv. Gen. Tech. Rep. INT"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.rse.2006.05.003","article-title":"Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS","volume":"104","author":"Ahl","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1080\/01431161.2011.592865","article-title":"A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert, USA","volume":"33","author":"Sesnie","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","first-page":"132","article-title":"Characterizing the dynamics change of vegetation cover on tropical forestlands using 250 m multi-temporal MODIS EVI","volume":"26","author":"Setiawan","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(02)00129-3","article-title":"Sensitivity of vegetation indices to atmospheric aerosols: Continental-scale observations in Northern Asia","volume":"84","author":"Xiao","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_41","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_42","unstructured":"Didan, K. (2015). MYD13Q1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006 [Data Set], NASA EOSDIS LP DAAC, USGS\/Earth Resources Observation and Science (EROS) Center."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tian, M., Wang, P., and Khan, J. (2016). Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain. Remote Sens., 8.","DOI":"10.3390\/rs8090690"},{"key":"ref_44","first-page":"685","article-title":"Artificial neural networks in time series forecasting: A comparative analysis","volume":"38","author":"Allende","year":"2002","journal-title":"Kybernetika"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1021\/ac60319a045","article-title":"Smoothing and differentiation of data by simplified least square procedure","volume":"44","author":"Steinier","year":"1972","journal-title":"Anal. Chem."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, J., J\u00f6nsson, P., Tamura, M., Gu, Z., Matsushita, B., and Eklundh, L. (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens. Environ.","DOI":"10.1016\/S0034-4257(04)00080-X"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust."},{"key":"ref_50","first-page":"72","article-title":"Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves","volume":"54","author":"Baumann","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","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.","DOI":"10.3390\/rs8010019"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Guan, X., Liu, G., Huang, C., Meng, X., Liu, Q., Wu, C., Ablat, X., Chen, Z., and Wang, Q. (2018). An Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7020075"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Cox, M.A.A., and Cox, T.F. (2008). Multidimensional Scaling. Handbook of Data Visualization, Springer. Springer Handbooks Computational Statistics.","DOI":"10.1007\/978-3-540-33037-0_14"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.fss.2015.10.009","article-title":"Fuzzy rule base simplification using multidimensional scaling and constrained optimization","volume":"297","author":"Tsekouras","year":"2016","journal-title":"Fuzzy Sets Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s12530-016-9160-x","article-title":"A methodology to carry out voting classification tasks using a particle swarm optimization-based neuro-fuzzy competitive learning network","volume":"8","author":"Tamvakis","year":"2017","journal-title":"Evol. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/T-C.1969.222678","article-title":"A Nonlinear Mapping for Data Structure Analysis","volume":"C-18","author":"Sammon","year":"1969","journal-title":"IEEE Trans. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1109\/91.649915","article-title":"An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering","volume":"5","author":"Karayiannis","year":"1997","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/34.85677","article-title":"A validity measure for fuzzy clustering","volume":"13","author":"Xie","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"470","DOI":"10.3390\/rs6010470","article-title":"Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review","volume":"6","author":"Chu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.foreco.2018.02.011","article-title":"Pine vs. oaks revisited: Conversion of Madrean pine-oak forest to oak shrubland after high-severity wildfire in the Sky Islands of Arizona","volume":"414","author":"Barton","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jin, Y., Randerson, J.T., Goetz, S.J., Beck, P.S.A., Loranty, M.M., and Goulden, M.L. (2012). The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests. J. Geophys. Res., 117.","DOI":"10.1029\/2011JG001886"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Thompson, J.R., Spies, T.A., and Ganio, L.M. (2007). Reburn severity in managed and unmanaged vegetation in a large wildfire. Proc. Natl. Acad. Sci. USA.","DOI":"10.1073\/pnas.0700229104"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kong, Y.-L., Huang, Q., Wang, C., Chen, J., Chen, J., and He, D. (2018). Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10030452"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/420\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T08:49:49Z","timestamp":1718268589000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/420"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,30]]},"references-count":63,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["ijgi7110420"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7110420","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,30]]}}}