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

被引:19
|
作者
Dubertret, Fabrice [1 ]
Le Tourneau, Francois-Michel [2 ]
Villarreal, Miguel L. [3 ]
Norman, Laura M. [4 ]
机构
[1] Univ Arizona, Natl Sci Res Ctr CNRS, Int Res Lab Interdisciplinary Global Environm Stu, 845 N Pk Ave,Marshall Bldg 5th Floor, Tucson, AZ 85719 USA
[2] Natl Sci Res Ctr CNRS, Pole Rech Org & Diffus Informat Geog PRODIG, Campus Condorcet,Batiment Rech Sud, F-93300 Aubervilliers, France
[3] US Geol Survey, Western Geog Sci Ctr, Moffett Field, CA 94035 USA
[4] US Geol Survey, Western Geog Sci Ctr, Tucson, AZ 85719 USA
关键词
land use classification; Landsat; Random Forest (RF); Google Earth Engine (GEE); cloud computing; urban sprawl; Arizona; CONTERMINOUS UNITED-STATES; RANDOM FOREST CLASSIFIER; CLIMATE; VEGETATION; SCENARIOS; DATABASE; ARIZONA; DESIGN; INDEX;
D O I
10.3390/rs14092127
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:22
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