Detecting Land Use Changes in a Rapidly Developing City during 1990–2017 Using Satellite Imagery: A Case Study in Hangzhou Urban Area, China

Abstract

As one of the rapidly-developing mega cities in China, Hangzhou has experienced great land use change during the past three decades. By analyzing land use change in designated period, it is beneficial to understand urbanization process in Hangzhou, and undertake further urban management and urban planning. In this study, the land use change from 1990 to 2017 in Hangzhou urban area was detected by a method of supervised classification with Landsat TM images from 1990, 1997, 2004, 2010 and 2017, and analyzed by a Markov matrix. The results show that from 1990 to 2017, a great deal of rural areas transformed into built up areas in the Hangzhou urban area. Consequently, the urban area of Hangzhou increased eight times over the period from 1990 to 2017. This may imply that such a change should be directly related to the Chinese government policy, of which the main factor is rapidly-developing urbanization in China, such as in Hangzhou. Thus, it is believed that China’s land use change is going to be small in the following decades. This may indicate that China’s urban construction is slowing down, while its urban planning is being shifted from construction to management.

Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

“…Land use/land cover (LULC) changes are presently becoming issues of great concern regionally and globally due to numerous environmental challenges. The rapid increase in population and the expansion of urban areas due to urbanization has contributed immensely to the change in land use/land cover patterns and the conversion of fertile land and vegetation to land use of different functions [1][2][3]. The continuous transformation of land uses and widespread deforestation has continuously led to multiple scales of emerging environmental issues with severe effects on the ecosystem.…”

Land Use Change, Spatial Interaction, and Sustainable Development in the Metropolitan Urban Areas, South Sulawesi Province, Indonesia

“…In the case of the Mamminasata Metropolitan City, it has a tendency to grow dynamically in line with the dynamics of demographic, economic and physical-spatial development. The authors of [26] confirmed that exurban development is defined a discontinuous, low-density expansion into fringe land, emphasizing dependency on private mobility, soil consumption, and depolarized economic structures. Physically, Metropolitan Mamminasata urban areas are growing expansively towards the outskirts, even beyond the administrative boundaries of their territories.…”

Examining the informal urban growth trends in a Port city

“…Therefore, to have the highest accuracy in land use land cover (LULC) spatial data, the geo-referenced based high Resolution of previous governmental detailed maps and authorized satellite images [ 71 ] (referred to in table 3.1) of different interval years of Karachi by responsible authority ( http://www.kmc.gos.pk/ ) were used in this research. Simultaneously, different geometric-based scientific models have been widely applied in several studies to assess the land use/land cover transformation internationally, such as the multi-agent System [ 72 , 73 ], Cellular Automata (CA) Model [ 74 , 75 ], Markov chain analysis (also called transition matrix) [ 63 , 76 , 77 ], Expert Models [ [78] , [79] , [80] , [81] ], Land Change Modeler (LCM) [ 82 ], Logistic Regression [ [83] , [84] , [85] ], and Evolutionary Models [ [86] , [87] , [88] ], etc. However, an integrated Cellular Automata Markov (CA-Markov) Model [ 19 , 68 , [89] , [90] , [91] , [92] ] has been preferred by academia while conducting land use land cover (LULC) studies around the world due to virtue of its spatiotemporal attributes and perfect predictions [ 19 , 92 ].…”