Remote Sensing and Spatial Analysis for Land-Take Assessment in Basilicata Region (Southern Italy)

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-04, Vol.14 (7), p.1692
• Main Authors: Santarsiero, Valentina, Nolè, Gabriele, Lanorte, Antonio, Tucci, Biagio, Cillis, Giuseppe, Murgante, Beniamino
• Format: Article
• Language: English
• Subjects: Algorithms; Change detection; change detection analysis; Classification; Freeware; geographic information system; Geographic information systems; Land cover; land take; Land use; Landsat; Machine learning; Morphology; Population decline; Remote sensing; Rural areas; Spatial analysis; Support vector machines; SVM algorithm; Topographic databases; Urban areas
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14071692

Land use is one of the drivers of land-cover change (LCC) and represents the conversion of natural to artificial land cover. This work aims to describe the land-take-monitoring activities and analyze the development trend in test areas of the Basilicata region. Remote sensing is the primary technique for extracting land-use/land-cover (LULC) data. In this study, a new methodology of classification of Landsat data (TM–OLI) is proposed to detect land-cover information automatically and identify land take to perform a multi-temporal analysis. Moreover, within the defined model, it is crucial to use the territorial information layers of geotopographic database (GTDB) for the detailed definition of the land take. All stages of the classification process were developed using the supervised classification algorithm support vector machine (SVM) change-detection analysis, thus integrating the geographic information system (GIS) remote sensing data and adopting free and open-source software and data. The application of the proposed method allowed us to quickly extract detailed land-take maps with an overall accuracy greater than 90%, reducing the cost and processing time.