Abstract
The China–Pakistan Economic Corridor (CPEC) is the flagship project of the Belt and Road Initiative. At the end of the CPEC, the Gwadar port on the Arabian Sea is being built quickly, providing an important economical route for the flow of Central Asia’s natural resources to the world. Gwadar city is in a rapid urbanization process and will be developed as a modern, world-class port city in the near future. Therefore, monitoring the urbanization process of Gwadar at both high spatial and temporal resolution is vital for its urban planning, city ecosystem management, and the sustainable development of CPEC. The impervious surface percentage (ISP) is an essential quantitative indicator for the assessment of urban development. Through the integration of remote sensing images and ISP estimation models, ISP can be routinely and periodically estimated. However, due to clouds’ influence and spatial–temporal resolution trade-offs in sensor design, it is difficult to estimate the ISP with both high spatial resolution and dense temporal frequency from only one satellite sensor. In recent years, China has launched a series of Earth resource satellites, such as the HJ (Huangjing, which means environment in Chinese)-1A/B constellation, showing great application potential for rapid Earth surface mapping. This study employs the Random Forest (RF) method for a long-term and fine-scale ISP estimation and analysis of the city of Gwadar, based on the density in temporal and multi-source Chinese satellite images. In the method, high spatial resolution ISP reference data partially covering Gwadar city was first extracted from the 1–2 meter (m) GF (GaoFen, which means high spatial resolution in Chinese)-1/2 fused images. An RF retrieval model was then built based on the training samples extracted from ISP reference data and multi-temporal 30-m HJ-1A/B satellite images. Lastly, the model was used to generate the 30-m time series ISP from 2009 to 2017 for the whole city area based on the HJ-1A/B images. Results showed that the mean absolute error of the estimated ISP was 6.1–8.1% and that the root mean square error (RMSE) of the estimation results was 12.82–15.03%, indicating the consistently high performance of the model. This study highlights the feasibility and potential of using multi-source Chinese satellite images and an RF model to generate long-term ISP estimations for monitoring the urbanization process of the key node city in the CPEC.
Highlights
Rapid urbanization has been the key feature in first two decades of the twenty-first century
The four main cover types were divided into two categories: impervious and non-impervious surfaces
The validation result showed that the average overall classification accuracy was 92.79%, and the 6K.1a–p8p.1a%c.oeInffigceiennetrawl,atsh0e.8er5r6o.rTshfoerheigachhcylaesasrifwiceartieornelaactcivuerlaycyclgoesen,ewrailtlhymcoinnfoirrmflusctthueartieolinasbcilaituyseodf tbhye tihmepteemrvpioourasl dsuiffrfearceenccelsaossfitfhiceatsieolnectreedsuHltJs-1Ato/Bbiemuagseeds inindipffreordenutciynegarsth. eAsrecfaenrebnecseeeInSPinfFoirgumreod4be,l tdheevfietlotipnmg elinnte. sTahreeaaglgsorecglaotseedt3o0thme r1e:f1erliennec,ewdiathtaaanndavtehreaGgeF-R22fuosfe0d.7i6m. aCgoems ipna2r0e1d6waritehilpluresvtriaotuesd sitnudFiiegsu[r3e,137.],Athcceoarcdciunrgactyo othf ethveirseutarileivnatlemrporedtealtiwonas, citomispcaleraabr lethaantdthine tshpeaaticaclepdtisatbrlieburatinogne,oefvtehne wobitthaionneldy rfeofuerresnpceectIrSaPl bwaansdcsoonfstihsteenHtJw-1iAth/BthCaCt Dinctahme herigash.-resolution GF-2 images
Summary
Rapid urbanization has been the key feature in first two decades of the twenty-first century. Accounting for less than 1% of the earth’s surface area, the urban area carries about 90% of the global economic activities [1]. The most apparent characteristic of the urbanization process is that a large amount of croplands, grasslands, forests, and open water spaces are being replaced by impervious surfaces [2]. The term “impervious surface” usually refers to a surface that can prevent water from seeping into the soil, such as roads, roofs, and parking lots [3]. Impervious surface is an important quantitative measure of urban development and ecological environment. Estimating the spatio–temporal dynamics of impervious surface is important for monitoring the urban development context, guiding urban planning policies and protecting the eco-environment [4]
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