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Land Cover Change Analysis of Hangzhou, China

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Land Cover Change Analysis of Hangzhou, China Land Cover Change Analysis of Hangzhou, China Lu Hou Department of Resource Analysis, Saint Mary’s University of Minnesota, Winona, MN 55987 Keywords: Land Cover Change, Hangzhou, Landsat Images, Supervised Classification, GIS Abstract Hangzhou is one of China's fastest-growing places. Due to the emergence of various e- commerce industries, the number of migrants has increased sharply and the local economy has grown rapidly. These changes have had a large impact on the demand for land utilization in Hangzhou. This study analyzes land cover change in Hangzhou from 2000 to 2018, which provides a reference for the rational planning of land use. Landsat 5 and Landsat 8 images were used for land cover classification, and a land cover change matrix was developed to analyze conversion trends for water bodies, forest, built-up areas, wetlands, cultivated land, and unutilized land. The area percent change and conversion percentage of each class were also determined. A sharp rise in the built-up area, an increase of 199.13%, was discovered after the change detection. Introduction Zhejiang Province, which is located on the southeast coast of China. Hangzhou is the Land use and land cover changes have political, economic, cultural, educational, significant spatial-temporal dynamics and transportation, and financial center of can intuitively reflect the impact of human Zhejiang Province, and it is also the node activities on regional land use (Xu, Wang, city of the Yangtze River Delta Economic Zhang, and Yue, 2014). With the Belt. The location of Hangzhou is where acceleration of urbanization, the rivers, lakes, and mountains blend phenomenon of urban sprawl, which is the together. The world’s longest artificial inefficient and excessive expansion of canal, the Beijing-Hangzhou Grand, urban land use, increased in some large passes through the city, which makes it cities in China and has begun to become even more famous. There is an old saying prominent. At the same time, urban sprawl in China: “Up above there is heaven, down has brought a series of negative effects, below there are Suzhou and Hangzhou.” such as environmental pollution, traffic Hangzhou attracts many visitors because congestion, increases in the input costs of of its reputation for unique and municipal infrastructure, and issues of magnificent scenery. social inequality (Zhang, Yue, and Fan, In present, whenever Hangzhou is 2014). Studying land cover change mentioned, people think not only of provides significant guidance for abundant attractions but also of a company promoting sustainable urban development called Alibaba. Alibaba Group is an e- in order to rationally develop and manage commerce company founded by Ma Yun land and maintain a virtuous balance of in 1999. Accompanied by the economic land ecosystems. situation and the development of the Hangzhou is the capital of Internet, e-commerce has brought ___________________________________________________________________________ Hou, Lu. 2018. Land Cover Change Analysis of Hangzhou, China. Volume 21, Papers in Resource Analysis. 10 pp. Saint Mary’s University of Minnesota University Central Services Press. Winona, MN. Retrieved (date) http://www.gis.smumn.edu. unprecedented opportunities for economic district, Binjiang district, Jianggan district, and social development. As the leading Xiaoshan district, Gongye district, new technology industry, Alibaba is Shangcheng district, and Xiacheng district. slowly transforming the temperament of The study area is approximately 3068 km2 the city from the bones, so that Hangzhou and has a population around 6,896,000. is also known as the "city of e-commerce." Hangzhou has a subtropical monsoon Numerous skilled people from home and climate with an average annual abroad have been attracted by Hangzhou temperature of 17.8 °C (Zhejiang Online, (Chen and Zhao, 2017). According to 2010). LinkedIn's "China's Workplace Globalization List," published on November 2, 2016, Hangzhou ranked after Shanghai, Beijing, Shenzhen, and Guangzhou. It ranks among the top cities in terms of talent inflows in the new first- tier cities. After the G20 summit was held in Hangzhou in September 2016, the net inflow of population exceeded that of Beijing and Shanghai, and Hangzhou now ranks first in the country (Chen and Zhao, 2017). Thus, the local government began large-scale construction of science and technology parks. However, the development of Hangzhou comes at a cost of reducing arable land and green land. This study explores land cover change in Hangzhou from 2000 to 2018 using Landsat satellite images and geographic information system methods. The objective of this study is to develop a land cover matrix to conduct a quantitative assessment on land cover change and Figure 1. Study area. provide a valuable reference for urban Data Processing planning. The data used in this research are Study Area summarized in Table 1. The Landsat 5, Landsat 8 Operational Land Imager (OLI) The study area of Hangzhou is shown in (path 119 and row 39), and Sentinel-2 Figure 1. The hills and mountains of images were downloaded from the United Hangzhou account for 65.6% of the total States Geological Survey (USGS) area, plains account for 26.4%, and rivers, EarthExplorer. In order to avoid the lakes, and reservoirs account for 8%. It is impact of the rainy season, the images located at a longitude of 120°09′41″ E and were selected from May to June, which are latitude of 30°17′37″ N (Zhejiang Online, dry, and all images were within the same 2010). There are eight main districts in vegetation season. Applying these criteria, Hangzhou city: Yuhang district, Xihu the images with better quality were chosen 2 for the study. Methods Table 1. Remotely sensed data sources. Supervised Classification Acquired Image Resolution Date Based on the primary classification 2000/06/13 Landsat 5 30 m standard from the Chinese Academy of 2018/04/28 Landsat 8 OLI 30 m Sciences, there were six land cover types 2018/04/19 Sentinel-2 10 m used in this study (Table 2): water bodies, forest, built-up areas, wetlands, cultivated land, and unutilized land. Supervised It is a complicated process for classification combined with maximum remote sensing images to be used as likelihood classification was selected as analysis data. Every processing step the approach for image classification (Xu affects the accuracy of the data, and et al., 2014). The training samples were therefore the results of the analysis. There distributed across the entire study area. All are two main types of data pre-processing: seven spectral bands were included in the radiometric correction and geometric process and false color was chosen to correction. Radiometric correction usually obtain a better training sample so that the includes orthorectification and classification results were more accurate. atmospheric correction. The USGS offers The classification process was conducted Level 1 and Level 2 remote sensing in Esri’s ArcMap 10.4 software. images. The orthorectification and atmospheric correction has been done by Table 2. Land cover classification. the USGS for Level 1 and Level 2 data. Land Cover Specific Coverage Level 1 data was chosen in this research as Type Level 2 data started in 2013. According to Water bodies Lakes, rivers, reservoirs, Song, Woodcock, Seto, Lenny, and aquaculture ponds Macomber (2001), classification of single- Forest Arbor, shrubs, bamboos, scene remote sensing data using the coastal mangrove lands maximum likelihood method usually does Built-up areas Urban and rural residential areas, other industrial, not require atmospheric correction. Thus, mining, transportation the preprocessing of this research focused sites on geometric correction. Wetland Natural wetlands, paddy First, referencing the Sentinel-2 fields image, ENVI software was used to Cultivated land Dry land, unirrigated geometrically correct the 2018 TM image paddy fields, vegetable plots, other crop fields and choose 25 ground control points to Unutilized land Barren, grassland, sandy make sure the RMS error was within one land pixel. Then, the corrected 2018 image and the 2000 TM image were used to conduct Accuracy Assessment image to image registration. The RMS error of the re-selected point test was less The classification process was than 0.5 pixel. In the end, the study area straightforward; however, the result can be was extracted from the corrected images incorrect if training samples are not well (He, Shi, Chen and Zhou, 2001). chosen. Accuracy assessment was critical to validate the results of classification. A common way to represent accuracy is in 3 the form of an error matrix. ArcMap's spatial overlay Intersect tool to In this research, random points identify the unchanged and changed areas. were generated in ArcMap for both the Using the Microsoft Excel Pivot Table 2000 and 2018 resulting classification function, a land cover transition matrix rasters. Then, the point file was converted was created, which reflected the amount of into a KML file and the KML file was conversion between various types of land imported into Google Earth. Imagery and the intensity of land changes (Guo and corresponding to the time periods of this Zhang, 2017). research were selected to get the observed land cover values for the random points Land Cover Change Analysis (Tuong, Pham, and Lam, 2018). A confusion matrix indicating the Area change refers to the change in the concordance of the results of the extent of a certain type of land cover from supervised classification and the observed the beginning to the end of the study values was constructed for comparison. period. Land conversion refers to the There are plenty of measurements to conversion of a type of land into other assess the interpretation of the error types or other types into the type at the matrix. Kappa coefficient is one of the beginning and end of the study period. most popular measures in addressing the There are many ways to study land cover difference between the actual agreement change.
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