Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022

Jose Antonio Sobrino (), Sergio Gimeno, Virginia Crisafulli and Álvaro Sobrino-Gómez
Additional contact information
Jose Antonio Sobrino: Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain
Sergio Gimeno: Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain
Virginia Crisafulli: Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain
Álvaro Sobrino-Gómez: Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain

Land, 2024, vol. 13, issue 7, 1-25

Abstract: Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: ‘Urban Areas’, ‘Dense Vegetation’, ‘Sparse Vegetation’, ‘Water Bodies’, and Other’. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications.

Keywords: land cover; Landsat; change detection (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/7/1072/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/7/1072/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:7:p:1072-:d:1436651

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().