{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T12:15:04Z","timestamp":1792412104859,"version":"4.4.0"},"reference-count":79,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T00:00:00Z","timestamp":1679702400000},"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":"<jats:p>This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the \u201cLeonardo Da Vinci\u201d Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform \u201cGoogle Earth Engine\u201d (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the \u201cUrban\u201d and \u201cWoodland\u201d classes over the 35-year time span that was considered, being 67.4 km2 and 70.4 km2, respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development.<\/jats:p>","DOI":"10.3390\/ijgi12040141","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:38:36Z","timestamp":1679881116000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Analysis of the Spatiotemporal Urban Expansion of the Rome Coastline through GEE and RF Algorithm, Using Landsat Imagery"],"prefix":"10.3390","volume":"12","author":[{"given":"Francesco","family":"Lodato","sequence":"first","affiliation":[{"name":"Department of Science and Technology for Sustainable Development and One Health, Campus Bio-Medico University of Rome, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7084-3666","authenticated-orcid":false,"given":"Nicola","family":"Colonna","sequence":"additional","affiliation":[{"name":"ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 00123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4843-9445","authenticated-orcid":false,"given":"Giorgio","family":"Pennazza","sequence":"additional","affiliation":[{"name":"Department of Science and Technology for Sustainable Development and One Health, Campus Bio-Medico University of Rome, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1684-178X","authenticated-orcid":false,"given":"Salvatore","family":"Pratic\u00f2","sequence":"additional","affiliation":[{"name":"Dipartimento di Agraria, Universit\u00e0 Mediterranea di Reggio Calabria, Loc. Feo di Vito s.n.c., 89122 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Santonico","sequence":"additional","affiliation":[{"name":"Department of Science and Technology for Sustainable Development and One Health, Campus Bio-Medico University of Rome, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-0157","authenticated-orcid":false,"given":"Luca","family":"Vollero","sequence":"additional","affiliation":[{"name":"Computational Systems and Bioinformatics Lab, Biomedical Engineering Faculty, Campus Bio-Medico University of Rome, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8244-303X","authenticated-orcid":false,"given":"Maurizio","family":"Pollino","sequence":"additional","affiliation":[{"name":"ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 00123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1080\/10106049.2019.1629647","article-title":"Land use\/land cover in view of earth observation: Data sources, input dimensions, and classifiers\u2014A review of the state of the art","volume":"36","author":"Pandey","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2608","DOI":"10.1002\/ldr.2824","article-title":"Abandonment of traditional terraced landscape: A change detection approach (a case study in Costa Viola, Calabria, Italy)","volume":"28","author":"Modica","year":"2017","journal-title":"Land Degrad. Dev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"263","DOI":"10.5194\/esd-3-263-2012","article-title":"Spatio-temporal analysis of the urban\u2013rural gradient structure: An application in a Mediterranean mountainous landscape (Serra San Bruno, Italy)","volume":"3","author":"Modica","year":"2012","journal-title":"Earth Syst. Dyn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/978-3-030-58814-4_43","article-title":"Spatio-Temporal Dynamics of Urban and Natural Areas in the Northern Littoral Zone of Rome","volume":"Volume 12253","author":"Pollino","year":"2020","journal-title":"Computational Science and Its Applications\u2014ICCSA 2020"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105037","DOI":"10.1016\/j.landusepol.2020.105037","article-title":"Assessing soil ecosystem services in urban and peri-urban areas: From urban soils survey to providing support tool for urban planning","volume":"99","author":"Calzolari","year":"2020","journal-title":"Land Use Policy"},{"key":"ref_6","unstructured":"(2023, March 18). Available online: https:\/\/www.un.org\/development\/desa\/publications\/2018-revision-of-world-urbanization-prospects.html."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111739","DOI":"10.1016\/j.rse.2020.111739","article-title":"A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change","volume":"242","author":"Reba","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5721\/EuJRS20124501","article-title":"Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics","volume":"45","author":"Fichera","year":"2012","journal-title":"Eur. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1080\/22797254.2021.2018667","article-title":"Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region","volume":"55","author":"Silva","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2012.10.025","article-title":"Urban growth of the Washington, D.C.\u2013Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover","volume":"129","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3248","DOI":"10.1109\/JSTARS.2014.2344017","article-title":"Unsupervised Change Detection in Multitemporal SAR Images Over Large Urban Areas","volume":"7","author":"Hu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.3390\/ijgi4031265","article-title":"Application of Geo-Information Techniques in Land Use and Land Cover Change Analysis in a Peri-Urban District of Ghana","volume":"4","author":"Appiah","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.regsciurbeco.2016.05.001","article-title":"Geographic determinants of China\u2019s urbanization","volume":"59","author":"Christensen","year":"2016","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2014.09.023","article-title":"Detecting change in urban areas at continental scales with MODIS data","volume":"158","author":"Mertes","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"054011","DOI":"10.1088\/1748-9326\/10\/5\/054011","article-title":"A global map of urban extent from nightlights","volume":"10","author":"Zhou","year":"2015","journal-title":"Environ. Res Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mao, W., Lu, D., Hou, L., Liu, X., and Yue, W. (2020). Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China. Remote Sens., 12.","DOI":"10.3390\/rs12172817"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gbanie, S., Griffin, A., and Thornton, A. (2018). Impacts on the Urban Environment: Land Cover Change Trajectories and Landscape Fragmentation in Post-War Western Area, Sierra Leone. Remote Sens., 10.","DOI":"10.3390\/rs10010129"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Reynolds, R., Liang, L., Li, X., and Dennis, J. (2017). Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record. Remote Sens., 9.","DOI":"10.3390\/rs9010071"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.5721\/EuJRS20154806","article-title":"Monitoring land use changes associated with urbanization: An object based image analysis approach","volume":"48","author":"Samal","year":"2015","journal-title":"Eur. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/1747423X.2018.1533042","article-title":"Time series analysis of satellite data to characterize multiple land use transitions: A case study of urban growth and agricultural land loss in India","volume":"13","author":"Pandey","year":"2018","journal-title":"J. Land Use Sci."},{"key":"ref_23","first-page":"59","article-title":"Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city","volume":"22","author":"Mahmoud","year":"2019","journal-title":"Egypt J. Remote Sens. Sp. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103595","DOI":"10.1016\/j.landurbplan.2019.103595","article-title":"Conceptualizing and characterizing micro-urbanization: A new perspective applied to Africa","volume":"190","author":"Chai","year":"2019","journal-title":"Landsc. Urban Plan."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.future.2014.10.029","article-title":"Remote sensing big data computing: Challenges and opportunities","volume":"51","author":"Ma","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"145","DOI":"10.5194\/isprs-annals-IV-2-W7-145-2019","article-title":"Aggregating Cloud-Free Sentinel-2 Images with Google Earth Engine","volume":"IV-2\/W7","author":"Schmitt","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"663","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-663-2020","article-title":"Specific Alpine Environment Land Cover Classification Methodology: Google Earth Engine Processing for Sentinel-2 Data","volume":"XLIII-B3-2","author":"Belcore","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., and Lippitt, C.D. (2022). Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. Remote Sens., 14.","DOI":"10.3390\/rs14143253"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fattore, C., Abate, N., Faridani, F., Masini, N., and Lasaponara, R. (2021). Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy. Sensors, 21.","DOI":"10.3390\/s21051791"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3001605","DOI":"10.1109\/LGRS.2021.3054168","article-title":"On the Use of Google Earth Engine and Sentinel Data to Detect \u201cLost\u201d Sections of Ancient Roads. The Case of Via Appia","volume":"19","author":"Lasaponara","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Danese, M., Gioia, D., and Biscione, M. (2021, January 13\u201316). Integrated Methods for Cultural Heritage Risk Assessment: Google Earth Engine, Spatial Analysis, Machine Learning. Proceedings of the Computational Science and Its Applications\u2013ICCSA 2021: 21st International Conference, Cagliari, Italy.","DOI":"10.1007\/978-3-030-86970-0_42"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"291","DOI":"10.5194\/isprs-archives-XLII-3-W12-2020-291-2020","article-title":"Google Earth Engine: Application of Algorithms for Remote Sensing of Crops in Tuscany (Italy)","volume":"XLII-3\/W12","author":"Clemente","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Balestra, M., Chiappini, S., Malinverni, E.S., Galli, A., and Marcheggiani, E. (2021, January 13\u201316). A Machine Learning Approach for Mapping Forest Categories: An Application of Google Earth Engine for the Case Study of Monte Sant\u2019Angelo, Central Italy. Proceedings of the Computational Science and Its Applications\u2013ICCSA 2021: 21st International Conference, Cagliari, Italy.","DOI":"10.1007\/978-3-030-87007-2_12"},{"key":"ref_37","first-page":"100907","article-title":"What is going on within google earth engine? A systematic review and meta-analysis","volume":"29","author":"Zema","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Goldblatt, R., You, W., Hanson, G., and Khandelwal, A. (2016). Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine. Remote Sens., 8.","DOI":"10.3390\/rs8080634"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sengupta, D., Chen, R., Meadows, M.E., Choi, Y.R., Banerjee, A., and Zilong, X. (2019). Mapping Trajectories of Coastal Land Reclamation in Nine Deltaic Megacities using Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11222621"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wei, M., Pu, D., He, G., Wang, G., and Long, T. (2021). Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest. Remote Sens., 13.","DOI":"10.3390\/rs13040748"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Carneiro, E., Lopes, W., and Espindola, G. (2021). Urban Land Mapping Based on Remote Sensing Time Series in the Google Earth Engine Platform: A Case Study of the Teresina-Timon Conurbation Area in Brazil. Remote Sens., 13.","DOI":"10.3390\/rs13071338"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4708","DOI":"10.1002\/ldr.4072","article-title":"Characterising historical transformation trajectories of the forest landscape in Rome\u2019s Metropolitan area (Italy) for effective planning of sustainability goals","volume":"32","author":"Solano","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bozzano, F., Esposito, C., Mazzanti, P., Patti, M., and Scancella, S. (2018). Imaging Multi-Age Construction Settlement Behaviour by Advanced SAR Interferometry. Remote Sens., 10.","DOI":"10.3390\/rs10071137"},{"key":"ref_45","first-page":"23","article-title":"Le miniere dei Monti della Tolfa-Allumiere (Roma)","volume":"Volume 106","author":"Pantaloni","year":"2020","journal-title":"I siti della memoria geologica nel territorio del Lazio. Memorie Descrittive della Carta Geologica d\u2019Italia"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"91","DOI":"10.2478\/v10028-010-0002-2","article-title":"The Forest Vegetation of the Tolfa-Ceriti Mountains (Northern Latium\u2014Central Italy)","volume":"9","author":"Pietro","year":"2010","journal-title":"Hacquetia"},{"key":"ref_47","first-page":"309","article-title":"Monitoring vegetation systems in the great plains with ERTS","volume":"1","author":"Rouse","year":"1973","journal-title":"Third Earth Resour. Technol. Satell. Symp."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Capolupo, A., Monterisi, C., and Tarantino, E. (2020). Landsat Images Classification Algorithm (LICA) to automatically extract land cover information in Google Earth Engine environment. Remote Sens., 12.","DOI":"10.3390\/rs12071201"},{"key":"ref_49","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press. [1st ed.]."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"513","DOI":"10.14358\/PERS.87.7.513","article-title":"Review of Spectral Indices for Urban Remote Sensing","volume":"87","author":"Javed","year":"2021","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tassi, A., Gigante, D., Modica, G., Di Martino, L., and Vizzari, M. (2021). Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park. Remote Sens., 13.","DOI":"10.3390\/rs13122299"},{"key":"ref_52","first-page":"1","article-title":"Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil","volume":"2021","author":"Nguyen","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W., Cheng, X., and Wang, L. (2016). A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8060514"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random Forests for Classification in Ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S2095-3119(15)61303-X","article-title":"Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China","volume":"16","author":"Li","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_58","unstructured":"Han, H., Guo, X., and Yu, H. (2016, January 26\u201328). Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest. Proceedings of the 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.3390\/rs5062838","article-title":"The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification","volume":"5","author":"Mellor","year":"2013","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data [Internet], CRC Press.","DOI":"10.1201\/9780429052729"},{"key":"ref_61","first-page":"397","article-title":"Remote Sensing Brief Accuracy Assessment: A User\u2019s Perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Momeni, R., Aplin, P., and Boyd, D. (2016). Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach. Remote Sens., 8.","DOI":"10.3390\/rs8020088"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/22797254.2021.1951623","article-title":"Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery: A case study in a citrus orchard and an onion crop","volume":"54","author":"Modica","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Matarira, D., Mutanga, O., and Naidu, M. (2022). Google Earth Engine for Informal Settlement Mapping: A Random Forest Classification Using Spectral and Textural Information. Remote Sens., 14.","DOI":"10.3390\/rs14205130"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"290","DOI":"10.2112\/SI61-001.28","article-title":"Human Impact on Shoreline Evolution Along the Follonica Gulf (Southern Tuscany): How Tourism May Kill the Goose that Lays the Golden Eggs","volume":"61","author":"Cipriani","year":"2011","journal-title":"J. Coast. Res."},{"key":"ref_68","unstructured":"Pina, H., Martins, F., and Ferreira, C. (2013). The Overarching Issues of the European Space\/Strategies for Spatial (Re)Planning Based on Innovation, Sustainability and Change, Funda\u00e7\u00e3o Universidade Do Porto\u2014Faculdade de Letras da Universidade do Porto."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Manganelli, B., and Murgante, B. (2017). The Dynamics of Urban Land Rent in Italian Regional Capital Cities. Land, 6.","DOI":"10.3390\/land6030054"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/13504509.2011.560453","article-title":"Exploring long-term land cover changes in an urban region of southern Europe","volume":"18","author":"Salvati","year":"2011","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1080\/13504509.2015.1064488","article-title":"In-between sprawl and fires: Long-term forest expansion and settlement dynamics at the wildland\u2013urban interface in Rome, Italy","volume":"22","author":"Biasi","year":"2015","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_72","unstructured":"De Felice, P., and Grillotti Di Giacomo, M.G. (2018). Land Grabbing e Land Concentration. I Predatori Della Terra Tra Neocolonialismo e Crisi Migratorie, Franco Angeli."},{"key":"ref_73","unstructured":"Lelo, K. (2016). Roma Moderna e Contemporanea, Universit\u00e0 Toma tre\u2014CROMA."},{"key":"ref_74","unstructured":"De Felice, P., and Lodato, F. (2020). Oltre la Convenzione Pensare, Studiare, Costruire il Paesaggio Vent\u2019anni Dopo, Societ\u00e0 di Studi Geografici."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1080\/09654313.2012.722941","article-title":"\u201cA Chronicle of a Death Foretold\u201d: Urban Expansion and Land Consumption in Rome, Italy","volume":"21","author":"Salvati","year":"2013","journal-title":"Eur. Plan. Stud."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2022). A Multitemporal Fragmentation-Based Approach for a Dynamics Analysis of Agricultural Terraced Systems: The Case Study of Costa Viola Landscape (Southern Italy). Land, 11.","DOI":"10.3390\/land11040482"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e037","DOI":"10.5424\/fs\/2015243-07855","article-title":"An index for the assessment of degraded Mediterranean forest ecosystems","volume":"24","author":"Modica","year":"2015","journal-title":"For. Syst."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s12210-018-0730-7","article-title":"Safeguarding natural and cultural heritage on Etruscan tombs (La Banditaccia, Cerveteri, Italy)","volume":"29","author":"Caneva","year":"2018","journal-title":"Rend. Lincei Sci. Fis. Nat."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1007\/978-3-642-39646-5_41","article-title":"Free Web Mapping Tools to Characterise Landscape Dynamics and to Favour e-Participation","volume":"Volume 7973","author":"Murgante","year":"2013","journal-title":"Computational Science and Its Applications\u2014ICCSA 2013. Lecture Notes In Computer Science"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/4\/141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:03:04Z","timestamp":1760122984000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/4\/141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,25]]},"references-count":79,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["ijgi12040141"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12040141","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,25]]}}}