{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T19:04:46Z","timestamp":1774638286226,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"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>One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.<\/jats:p>","DOI":"10.3390\/rs14133227","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T21:15:52Z","timestamp":1657142152000},"page":"3227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2"],"prefix":"10.3390","volume":"14","author":[{"given":"Fahime Arabi","family":"Aliabad","sequence":"first","affiliation":[{"name":"Department of Arid Land Management, Faculty of Natural Resources and Desert Studies, Yazd University, Yazd 8915818411, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6083-1517","authenticated-orcid":false,"given":"Hamid Reza Ghafarian","family":"Malamiri","sequence":"additional","affiliation":[{"name":"Department of Geography, Yazd University, Yazd 8915818411, Iran"},{"name":"Department of Geoscience and Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5260-1161","authenticated-orcid":false,"given":"Saeed","family":"Shojaei","sequence":"additional","affiliation":[{"name":"Department of Arid and Mountainous Region Reclamation, Faculty of Natural Resources, University of Tehran, Tehran 1417935840, Iran"}]},{"given":"Alireza","family":"Sarsangi","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417935840, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3709-4103","authenticated-orcid":false,"given":"Carla Sofia Santos","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Bolin Center for Climate Research, Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden"},{"name":"Research Centre for Natural Resources, Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Agrarian School of Coimbra, 3045-601 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7978-0040","authenticated-orcid":false,"given":"Zahra","family":"Kalantari","sequence":"additional","affiliation":[{"name":"Bolin Center for Climate Research, Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden"},{"name":"Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, 11428 Stockholm, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ecolind.2013.11.022","article-title":"Improving the Measurement of Urban Sprawl: Weighted Urban Proliferation (WUP) and Its Application to Switzerland","volume":"38","author":"Jaeger","year":"2014","journal-title":"Ecol. 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