Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells

Abstract

Global urban expansion has created incentives to convert green spaces to urban/built-up area. Therefore, understanding the distribution and dynamics of the land-cover changes in cities is essential for better understanding of the cities' fundamental characteristics and processes, and of the impact of changing land-cover on potential carbon storage. We present a grid square approach using multi-temporal Landsat data from around 1985-2010 to monitor the spatiotemporal land-cover dynamics of 50 global cities. The maximum-likelihood classification method is applied to Landsat data to define the cities' urbanized areas at different points in time. Subsequently, 1 km 2 grid squares with unique cell IDs are designed to link among land-cover maps for spatio-temporal land-cover change analysis. Then, we calculate land-cover category proportions for each map in 1 km 2 grid cells. Statistical comparison of the land-cover changes in grid square cells shows that urban area expansion in 50 global cities was strongly negatively correlated with forest, cropland and grassland changes. The generated land-cover proportions in 1 km 2 grid cells and the spatial relationships between the changes of land-cover classes are critical for understanding past patterns and the consequences of urban development so as to inform future urban planning, risk management and conservation strategies.

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

“…The classification accuracy assessment indicates an average overall accuracy of 84.1% and Kappa Coefficient of 0.73 being comparable to other studies (e.g., [52,[55][56][57]) (see Tables S1 and S2). Urban expansion mirrors population increase 2 Using the open source Environmental Mapping and Analysis Program version 2.1.1 (EnMAP) 3 Achieved in the ENvironment for Visualizing Images software version 5.2 (ENVI) and as population growth has slowed, urban development has concurrently exhibited a levelling trend compared to expansion previously observed (Figure 3).…”

“…However, due to Landsat 8 OLI sampling different spectral regions, a new classification model was developed using the same training areas, as these were deemed to remain representative of the land cover, but with Landsat 8 OLI spectral wavebands [47,48]. Validation was performed through an accuracy assessment based on an independent dataset (Google Earth high resolution imagery) consistent with Landsat acquisition months following previously published methods (e.g., [22,[49][50][51][52][53] …”

Subpixel land-cover classification for improved urban area estimates using Landsat

“…Dorais and Cardille 2011;Cunningham et al 2015;Song et al 2016;Sun et al 2015;Bagan and Yamagata 2014;Z. Zhu and Woodcock 2014).…”

“…Population growth has resulted in increased urbanization with 54% of the planet's seven billion people in 2014 residing in urban areas required for monitoring change in land-use patterns whereby calculations of urban extent can influence decision-making (e.g. policy for sustainable urban development) (Schneider, Seto, and Webster 2005;Hepinstall-Cymerman, Coe, and Hutyra 2013;Miller and Small 2003;Bagan and Yamagata 2014).…”

Mathematics in Utilizing Remote Sensing Data for Investigating and Modelling Environmental Problems

“…Remotely sensed data collected over a span of years can be used to identify and characterize both natural and anthropogenic changes over large areas of land at a variety of spatial and temporal scales [1][2][3]. As climate change and population growth place increasing pressures on many parts of the world, improved methods for monitoring urban growth across a range of spatial and temporal scales will be vital for understanding and addressing the impacts of urbanization on our natural resources [4,5]. With the advance of machine learning algorithms and computing facilities, many investigations on their real applications are taking place.…”