{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:08:26Z","timestamp":1774627706354,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T00:00:00Z","timestamp":1591920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests.<\/jats:p>","DOI":"10.3390\/rs12121912","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T05:56:27Z","timestamp":1592200587000},"page":"1912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Saeedeh","family":"Eskandari","sequence":"first","affiliation":[{"name":"Forest Research Division, Research Institute of Forests and Rangelands (RIFR), Agricultural Research, Education and Extension Organization (AREEO), Tehran 13185-1166, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Reza Jaafari","sequence":"additional","affiliation":[{"name":"Natural Resources Research Division, Ilam Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ilam 14965\/149, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5051-3662","authenticated-orcid":false,"given":"Patricia","family":"Oliva","sequence":"additional","affiliation":[{"name":"H\u00e9mera Centro de Observaci\u00f3n de la Tierra, Escuela de Ingenier\u00eda Forestal, Facultad de Ciencias, Universidad Mayor, 8340589 Santiago, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9664-8770","authenticated-orcid":false,"given":"Omid","family":"Ghorbanzadeh","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2013Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2013Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1080\/00207230802245898","article-title":"Management of protected areas and conservation of biodiversity in Iran","volume":"65","author":"Makhdoum","year":"2008","journal-title":"Int. J. Environ. Stud."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.mambio.2017.06.002","article-title":"Identifying biodiversity hotspots for threatened mammal species in Iran","volume":"87","author":"Farashi","year":"2017","journal-title":"Mamm. Biol."},{"key":"ref_3","first-page":"13","article-title":"Endemism in the reptile fauna of Iran","volume":"7","author":"Gholamifard","year":"2011","journal-title":"Iran. J. Anim. Biosyst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1163\/15685381-00002946","article-title":"The roles of environmental factors on reptile richness in Iran","volume":"35","author":"Hosseinzadeh","year":"2014","journal-title":"Amphib. Reptil."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/BF00031580","article-title":"Ecology and Late-Quaternary History of the Kurdo-Zagrosian Oak Forest near Lake Zeribar, Western Iran","volume":"68","year":"1986","journal-title":"Vegetatio"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1126\/science.140.3562.65","article-title":"Preliminary Pollen Studies at Lake Zeribar, Zagros Mountains, Southwestern Iran","volume":"140","author":"Wright","year":"1963","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Potts, D.T. (2016). The Archaeology of Elam: Formation and Transformation of an Ancient Iranian State, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9781316148501"},{"key":"ref_8","first-page":"35","article-title":"Slow death of oak trees in Zagros: Reasons, damage, and solutions","volume":"2","author":"Soheili","year":"2017","journal-title":"Strategy For."},{"key":"ref_9","unstructured":"Fattahi, M., Ansari, N., Abbasi, H.R., and Khanhasani, M. (2001). Zagros Forest Management, Research Institute of Forests and Rangelands."},{"key":"ref_10","first-page":"56","article-title":"Habitat suitability modelling of Persian squirrel (Sciurus anomalus) in Zagros forests, western Iran","volume":"2","author":"Khalili","year":"2018","journal-title":"J. Wildl. Biodivers."},{"key":"ref_11","first-page":"51","article-title":"Forest losses and gains in Kurdistan Province, western Iran: Where do we stand?","volume":"20","author":"Sadeghi","year":"2017","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_12","unstructured":"Fattahi, M. (1994). Study of Zagros Forests and the Most Important Degradation Factors, Research Institute of Forests and Rangelands."},{"key":"ref_13","unstructured":"Derikvandi, A., Heidarpour, A., Ahmadi, A., Soosani, J., Mahmoodi, M., and Jaafari, H. (2011, January 15). Investigation of changes in area and density of Middle Zagros forests in different slopes using aerial photos interpretation and applying GIS (Case study: Kaka Reza region in Lorestan Province). Proceedings of the National Conference on Middle Zagros Forests, Capabilities and Impasses, Lorestan, Iran."},{"key":"ref_14","first-page":"77","article-title":"Mapping forest cover change, using aerial photography and IRS\u2013LISSIII imagery (Case study: Ilam Township)","volume":"19","author":"Mahdavi","year":"2012","journal-title":"J. Wood Forest Sci. Technol."},{"key":"ref_15","first-page":"1","article-title":"Land use mapping in Zagros Mountainous region using ETM+ imagery; case study, Sorkhab Watershed, Khoramabad, Lorestan","volume":"14","author":"Shatayi","year":"2007","journal-title":"J. Agric. Sci. Nat. Resour."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/BF02990012","article-title":"Land use\/Land cover changes near Hazira region, Gujarat using remote sensing satellite data","volume":"33","author":"Chauhan","year":"2005","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_17","first-page":"72","article-title":"Comparison of different algorithms for land cover mapping in sensitive habitats of Zagros using Sentinel\u20132 satellite image: (Case study: A part of Ilam province)","volume":"10","author":"Eskandari","year":"2019","journal-title":"J. RS GIS Nat. Resour."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"016004","DOI":"10.1117\/1.JRS.10.016004","article-title":"Markov chains-cellular automata modeling and multicriteria analysis of land cover change in the Lower Nhecol\u00e2ndia subregion of the Brazilian Pantanal wetland","volume":"10","author":"Bacani","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.ecolind.2014.05.003","article-title":"Forest cover dynamics analysis and prediction modeling using logistic regression model","volume":"45","author":"Nandy","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10661-007-9728-9","article-title":"Forest cover change and fragmentation using Landsat data in Macka state forest enterprise in Turkey","volume":"137","author":"Cakir","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_21","first-page":"106","article-title":"Estimating tree species diversity in the savannah using NDVI and woody canopy cover","volume":"66","author":"Madonsela","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.3390\/rs70810017","article-title":"Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest","volume":"7","author":"Karlson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_23","unstructured":"Mohamed, A.E.A. (2016). Mapping Tree Canopy Cover in the Semi-Arid Sahel Using Satellite Remote Sensing and Google Earth Imagery. [Master\u2019s Thesis, Department of Physical Geography and Ecosystem Science, Lund University]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"073524","DOI":"10.1117\/1.JRS.7.073524","article-title":"Comparison of MODIS derived land use and land cover with Ministry of Agriculture reported statistics for India","volume":"7","author":"Acharya","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1109\/JSTARS.2014.2348411","article-title":"Mapping Annual Land Use and Land Cover Changes Using MODIS Time Series","volume":"7","author":"Yin","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1007\/s11442-015-1247-y","article-title":"Land use\/land cover classification and its change detection using multi-temporal MODIS NDVI data","volume":"25","author":"Usman","year":"2015","journal-title":"J. Geogr. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gautam, N.C. (1997, January 3\u20138). IRS-1C applications for land use\/land cover mapping, change detection and planning. Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, Remote Sensing\u2014A Scientific Vision for Sustainable Development, Singapore.","DOI":"10.1109\/IGARSS.1997.609066"},{"key":"ref_28","first-page":"33","article-title":"Land cover classification using IRS LISS III image and DEM in a Rugged Terrain: A case study in Himalayas","volume":"20","author":"Saha","year":"2005","journal-title":"Geocatro Int."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.1080\/01431160110104665","article-title":"Textural analysis of IRS-1D panchromatic data for land cover classification","volume":"23","author":"Rao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Topaloglu, R.H., Sertel, E., and Musaoglu, N. (2016, January 12\u201319). Assessment of classification accuracies of Sentinel\u20132 and Landsat8 data for land cover\/use mapping. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprs-archives-XLI-B8-1055-2016"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"026024","DOI":"10.1117\/1.JRS.11.026024","article-title":"Land subsidence in Tianjin for 2015 to 2016 revealed by the analysis of Sentinel-1A with SBAS-InSAR","volume":"11","author":"Guo","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_32","unstructured":"Phan, T.H.N., Kappas, M., and Degener, J. (2017, January 18\u201319). Land cover classification using Sentinel-2 image data and random forest algorithm. Proceedings of the 19th International Conference on Geoscience and Remote Sensing, Rome, Italy."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1080\/22797254.2018.1442179","article-title":"Comparing support vector machines with logistic regression for calibrating cellular automata land use change models","volume":"51","author":"Mustafa","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_34","first-page":"27","article-title":"Mapping tree cover with Sentinel-2 data using the Support Vector Machine (SVM)","volume":"9","year":"2018","journal-title":"Geoinf. Issues"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_36","first-page":"107","article-title":"Application of Sentinel-2 and EnMAP new satellite data to the mapping of Alpine vegetation of the Karkonosze Mountains","volume":"49","author":"Jedrych","year":"2017","journal-title":"Pol. Cartogr. Rev."},{"key":"ref_37","first-page":"1","article-title":"Use of Sentinel-2 for forest classification in Mediterranean environments","volume":"42","author":"Puletti","year":"2018","journal-title":"Ann. Silvic. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1080\/22797254.2017.1412272","article-title":"Using of Sentinel-2 images for automation of the forest succession detection","volume":"51","author":"Szostak","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1080\/22797254.2017.1372697","article-title":"Composite indicator for monitoring of Norway spruce stand decline","volume":"50","author":"Brovkina","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1080\/22797254.2017.1417745","article-title":"Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2","volume":"51","author":"Hawrylo","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_42","first-page":"14","article-title":"Satellite-based estimation of Mediterranean shrubland structural parameters","volume":"4","author":"Pereira","year":"1995","journal-title":"EARSeL Adv. Remote Sens."},{"key":"ref_43","unstructured":"Oliveira, T.M. (1998). Cartografia Quantitativa de Formacoes Arbustivas Empregando Dados de Deteccao Remota. [Master\u2019s Thesis, Universidade Tecnica de Lisboa, Instituto Superior de Agronomia]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1080\/01431160310001654978","article-title":"Mapping Mediterranean scrub with satellite imagery: Biomass estimation and spectral behaviour","volume":"25","author":"Calvao","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4714","DOI":"10.1080\/01431161.2018.1475777","article-title":"Tracking tree canopy cover changes in space and time in High Nature Value Farmland to prioritize reforestation efforts","volume":"39","author":"Soares","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","first-page":"203","article-title":"Monitoring of forest cover dynamics in eastern area of B\u00e9ni-Mellal Province using ASTER and Sentinel-2A multispectral data","volume":"2","author":"Barakat","year":"2018","journal-title":"Geol. Ecol. Landsc."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Slimani, M.A., El Aboudi, A., Rahimi, A., and Khalil, Z. (2017, January 20\u201325). Use of GIS and Satellite Imagery in the Study of the Spatial Distribution of Vegetation in the Entifa Forest (High Atlas Central, Morocco). Proceedings of the Euro-Mediterranean Conference for Environmental Integration, Sousse, Tunisia.","DOI":"10.1007\/978-3-319-70548-4_508"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.scitotenv.2018.06.039","article-title":"Monitoring ecosystem reclamation recovery using optical remote sensing: Comparison with field measurements and eddy covariance","volume":"642","author":"Chasmer","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Wang, F., Zhao, J., Zhou, J., Yu, S., and Zhao, Z. (2018). Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest. Forests, 9.","DOI":"10.3390\/f9100623"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.foreco.2005.10.056","article-title":"Estimation of tree canopy cover in evergreen oak woodlands using remote sensing","volume":"223","author":"Carreiras","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_51","first-page":"10","article-title":"Analysis of Correlation between Canopy Cover and Vegetation Indices","volume":"7","author":"La","year":"2013","journal-title":"Int. J. Digit. Content Technol. Appl."},{"key":"ref_52","first-page":"35","article-title":"Investigation of land use and the analysis of landscape elements in Sivar village from environmental viewpoint","volume":"38","author":"Eskandari","year":"2012","journal-title":"J. Environ. Stud."},{"key":"ref_53","first-page":"47","article-title":"Comparison of efficiency of artificial neural network and decision tree algorithms in provision of land use map using ETM+ data, case study: Darreshahr Watershed Basin in Ilam Province","volume":"13","author":"Arkhi","year":"2014","journal-title":"Geogr. Space"},{"key":"ref_54","first-page":"59","article-title":"Comparison of two classification methods of maximum probability and artificial neural network of fuzzy Artmap to produce rangeland cover maps (Case study: Rangeland of Doviraj, Dehloran)","volume":"22","author":"Fathizad","year":"2015","journal-title":"Iran. J. Range Desert Res."},{"key":"ref_55","first-page":"1","article-title":"Assessment of forest cover change trends and determination of the main physiographic factors on forest degradation in Ilam Province (Case study: Sirvan county)","volume":"15","author":"Mahdavi","year":"2017","journal-title":"Iran. J. For. Range Prot. Res."},{"key":"ref_56","first-page":"10","article-title":"An Investigation on Zagros forest area changes using aerial photos and satellite imagery; case study, Armardeh forest, Baneh","volume":"15","author":"Amini","year":"2008","journal-title":"J. Agric. Sci. Nat. Resour."},{"key":"ref_57","first-page":"97","article-title":"Monitoring of land use change of Marivan by using TM and ETM+ sensor of Landsat satellite","volume":"3","author":"Yousefi","year":"2012","journal-title":"J. RS GIS Nat. Resour."},{"key":"ref_58","first-page":"119","article-title":"Comparison of maximum likelihood and artificial neural network in extracting land use map, case study: Watershed Basin of Ilam Dam","volume":"20","author":"Niazi","year":"2011","journal-title":"Geogr. Dev."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1007\/s11430-015-8291-2","article-title":"Global cultivated land mapping at 30 m spatial resolution","volume":"59","author":"Cao","year":"2016","journal-title":"Sci. China Earth Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4359","DOI":"10.1080\/01431160500113435","article-title":"Estimation of tree cover using MODIS data at global, continental and regional\/local scales","volume":"26","author":"Hansen","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3727","DOI":"10.1080\/01431160701871104","article-title":"Evaluation of global land cover data sets over the tundra-taiga transition zone in northern most Finland","volume":"29","author":"Heiskanen","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2012.10.026","article-title":"Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles","volume":"129","author":"Gessner","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4900","DOI":"10.3390\/rs5104900","article-title":"Estimation of tree cover in an agricultural parkland of Senegal using rule-based regression tree modeling","volume":"5","author":"Herrmann","year":"2013","journal-title":"Remote Sens."},{"key":"ref_65","unstructured":"Ilam Natural Resources Administration (2016, November 02). Natural Resources Landscape of Ilam Province. Available online: http:\/\/www.ilam.frw.org.ir\/00\/Fa\/StaticPages\/Page.aspx?tid=1689."},{"key":"ref_66","unstructured":"Abdolvand, A. (2018, December 18). Oak in the Zagros. Available online: https:\/\/thymeflower.ir\/oak\u2013in\u2013the\u2013zagros\/."},{"key":"ref_67","unstructured":"ESA (European Space Agency) (2018, June 17). User Guide of Sentinel-2 Level-1C. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user\u2013guides\/sentinel\u20132\u2013msi\/processing\u2013levels\/level\u20131."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1080\/17538947.2015.1031716","article-title":"Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy","volume":"9","author":"Pulighe","year":"2015","journal-title":"Int. J. Digit. Earth"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3846\/20296991.2017.1330767","article-title":"Assessing horizontal positional accuracy of GoogleEarth imagery in the city of Monteral, Canada","volume":"43","author":"Goudarzi","year":"2017","journal-title":"Geod. Cartogr."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"7973","DOI":"10.3390\/s8127973","article-title":"Horizontal Positional Accuracy of Google Earth\u2019s High-Resolution Imagery Archive","volume":"8","author":"Potere","year":"2008","journal-title":"Sensors"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/10106049.2011.568125","article-title":"Positional accuracy of the Google Earth terrain model derived from stratigraphic unconformities in the Big Bend region, Texas, USA","volume":"26","author":"Benker","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"101","DOI":"10.2478\/arsa-2014-0008","article-title":"Positional accuracy assessment of GoogleEarth in Riyadh","volume":"49","author":"Farah","year":"2014","journal-title":"Artif. Satell."},{"key":"ref_73","unstructured":"FRA (Forest Resource Assessment) (2015). Terms and Definitions, FAO Press. Working paper."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","first-page":"1","article-title":"Detecting of land use change with remote sensing technique (Case study: Shahriar Province)","volume":"5","author":"Hajinejad","year":"2014","journal-title":"J. RS GIS Nat. Resour."},{"key":"ref_76","unstructured":"SEOS (2018, July 25). Introduction to Remote Sensing. Available online: http:\/\/seos\u2013project.eu\/modules\/remotesensing\/remotesensing\u2013c06\u2013p03.html."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Chuvieco, E. (2016). Fundamentals of Satellite Remote Sensing: An Environmental Approach, CRC Press, Taylor & Francis Group. [2nd ed.].","DOI":"10.1201\/b19478"},{"key":"ref_78","unstructured":"Richards, J. (2012). Remote Sensing Digital Image Analyst, Springer. [5th ed.]."},{"key":"ref_79","unstructured":"Park, B. (2008). Computer Vision Technology for Food Quality Evaluation, Academic Press, Elsevier Inc."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.3923\/jas.2010.2847.2854","article-title":"Comparison of neural network and maximum likelihood approaches in image classification","volume":"10","author":"Mustapha","year":"2010","journal-title":"J. Appl. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2003.12.002","article-title":"Artificial neural network based techniques for the retrieval of SWE and snow depth from SSM\/I data","volume":"90","author":"Tedesco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_85","unstructured":"Vapnik, V.N., and Vapnik, V. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10707-009-0077-4","article-title":"Support vector machines for urban growth modeling","volume":"14","author":"Huang","year":"2010","journal-title":"GeoInformatica"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_89","unstructured":"Zobeiri, M. (2008). Forest Biometry, Tehran University Press."},{"key":"ref_90","first-page":"194","article-title":"Investigation on Four Sampling Methods for Canopy Cover Estimation in Zagros Oak Forests (Case study: Mehrian Forests of Yasuj City)","volume":"20","author":"Fallah","year":"2012","journal-title":"Iran. J. For. Poplar Res."},{"key":"ref_91","unstructured":"Jenness, J., and Wynne, J.J. (2018, December 22). Cohen\u2019s Kappa and Classification Table Metrics 2.1a. Available online: http:\/\/www.jennessent.com\/arcview\/kappa_stats.htm."},{"key":"ref_92","unstructured":"Forests, Rangelands and Watershed Organization of Iran (FRWOI) (2018, August 14). Forests of Iran. Available online: http:\/\/www.frw.ir\/02\/En\/default.aspx."},{"key":"ref_93","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, W.D. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA. NASA SP\u2013351."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2012.04.001","article-title":"Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points","volume":"70","author":"Shao","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"659","DOI":"10.3832\/ifor1727-010","article-title":"Sensitivity analysis of RapidEye spectral bands and derived vegetation indices for insect defoliation detection in pure Scots pine stands","volume":"10","author":"Marx","year":"2017","journal-title":"iFor. Biogeosci. For."},{"key":"ref_96","unstructured":"Eskandari, S., and Moradi, A. (2020). Mapping the land uses and analysing the landscape elements in south-western Iran: Application of Landsat-7, field data, and landscape metrics. Int. J. Conserv. Sci., 11, in press."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1912\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:38:28Z","timestamp":1760175508000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,12]]},"references-count":96,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12121912"],"URL":"https:\/\/doi.org\/10.3390\/rs12121912","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,12]]}}}