RESUMEN
Studying the spatial-temporal evolution of oasis urban landscape patterns can provide a unique reference for future sustainable development. This study aimed to characterize the spatio-temporal evolution of landscape patterns in the past 20 years. The remote sensing and spatial analysis techniques included land transfer matrix, orientation evolution combined with landscape index, natural driving factor, and mass center migration model. The results showed that (1) two decades of urbanization brought prominent LULC changes. An increase of 464.8 km2 in the building area denoted the dominant change. (2) Changes in building, bare land, and green space occurred mainly in the northwest orientation. The patch Aggregation Index (AI) increased continually during building expansion. Meanwhile, the Landscape Division Index (DIVISION) experienced a progressive and complementary decline. (3) Increase in building land was associated with topography (DEM) and vegetation cover (NDVI). A lower elevation induced a larger building increment. Around NDVI average value for bare land (0.137) and farmland (0.477), building land had the largest expansion, verifying its principal land sources. (4) The center of gravity of building land overall migrated towards the north, and green space and bare land towards the south due to building encroachment. The findings could inform future sustainable urban development.
Asunto(s)
Monitoreo del Ambiente , Urbanización , Monitoreo del Ambiente/métodos , Ciudades , Tecnología de Sensores Remotos , Desarrollo Sostenible , Conservación de los Recursos Naturales , China , EcosistemaRESUMEN
Societal and technological advances have triggered demands to improve urban environmental quality. Urban green space (UGS) can provide effective cooling service and thermal comfort to alleviate warming impacts. We investigated the relative influence of a comprehensive spectrum of UGS landscape and vegetation factors on surface temperature in arid Urumqi city in northwest China. Built-up area range was extracted from Luojia 1-01 (LJ1-01) satellite data, and within this range, the landscape metric information and vegetation index information of UGS were obtained based on PlanetScope data, and a total of 439 sampling grids (1 km × 1 km) were generated. The urban surface temperature of built-up areas was extracted from Landsat8-TIRS images. The 12 landscape metrics and 14 vegetation indexes were assigned as independent variables, and surface temperature the dependent variable. Support Vector Machine (SVM), Gradient Boost Regression Tree (GBRT) and Random Forest (RF) were enlisted to establish numerical models to predict surface temperature. The results showed that: (1) It was feasible to predict local surface temperature using a combination of landscape metrics and vegetation indexes. Among the three models, RF demonstrated the best accuracy. (2) Collectively, all the factors play a role in the surface-temperature prediction. The most influential factor was Difference Vegetation Index (DVI), followed by Green Normalized Difference Vegetation Index (GNDVI), Class Area (CA) and AREA. This study developed remote sensing techniques to extract a basket of UGS factors to predict the surface temperature at local urban sites. The methods could be applied to other cities to evaluate the cooling impacts of green infrastructures. The findings could provide a scientific basis for ecological spatial planning of UGS to optimize cooling benefits in the arid region.