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Article

Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020)

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International Research Laboratory on Interdisciplinary Global Environmental Studies (IRL iGLOBES), National Scientific Research Center (CNRS), University of Arizona, 845 N. Park Avenue, Marshall Building 5th Floor, Tucson, AZ 85719, USA
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National Scientific Research Center (CNRS)—Pôle de Recherche pour l’Organisation et la Diffusion de l’Information Géographique (PRODIG), Campus Condorcet, Bâtiment Recherche Sud, 5 Cours des Humanités, 93300 Aubervilliers, France
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U.S. Geological Survey, Western Geographic Science Center, Moffett Field, CA 94035, USA
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U.S. Geological Survey, Western Geographic Science Center, Tucson, AZ 85719, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Parth Sarathi Roy
Remote Sens. 2022, 14(9), 2127; https://doi.org/10.3390/rs14092127
Received: 27 January 2022 / Revised: 19 April 2022 / Accepted: 26 April 2022 / Published: 28 April 2022
The Tucson metropolitan area, located in the Sonoran Desert of southeastern Arizona (USA), is affected by both massive population growth and rapid climate change, resulting in important land use and land cover (LULC) changes. As its fragile arid ecosystem and scarce resources are increasingly under pressure, there is a crucial need to monitor such landscape transformations. For such ends, we propose a method to compute yearly 30 m resolution LULC maps of the region from 1986 to 2020, using a combination of Landsat imagery, derived transformation and indices, texture analysis and other ancillary data fed to a Random Forest classifier. The entire process was hosted in the Google Earth Engine with tremendous computing capacities that allowed us to process a large amount of data and to achieve high overall classification accuracy for each year, ranging from 86.7 to 96.3%. Conservative post-processing techniques were also used to mitigate the persistent confusions between the numerous isolated houses in the region and their desert surroundings and to smooth year-specific LULC changes in order to identify general trends. We then show that policies to lessen urban sprawl in the area had little effects and we provide an automated tool to continue monitoring such dynamics in the future. View Full-Text
Keywords: land use classification; Landsat; Random Forest (RF); Google Earth Engine (GEE); cloud computing; urban sprawl; Arizona land use classification; Landsat; Random Forest (RF); Google Earth Engine (GEE); cloud computing; urban sprawl; Arizona
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MDPI and ACS Style

Dubertret, F.; Le Tourneau, F.-M.; Villarreal, M.L.; Norman, L.M. Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020). Remote Sens. 2022, 14, 2127. https://doi.org/10.3390/rs14092127

AMA Style

Dubertret F, Le Tourneau F-M, Villarreal ML, Norman LM. Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020). Remote Sensing. 2022; 14(9):2127. https://doi.org/10.3390/rs14092127

Chicago/Turabian Style

Dubertret, Fabrice, François-Michel Le Tourneau, Miguel L. Villarreal, and Laura M. Norman. 2022. "Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020)" Remote Sensing 14, no. 9: 2127. https://doi.org/10.3390/rs14092127

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