Landscape Pattern and Economic Factors' Effect on Prediction
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Open Geosciences 2020; 12: 626–636 Research Article Wang Song, Zhao Yunlin, Xu Zhenggang*, Yang Guiyan, Huang Tian, and Ma Nan Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata- Markov chain model on county scale https://doi.org/10.1515/geo-2020-0162 Keywords: county scale, CA-Markov model, landscape received October 06, 2019; accepted May 26, 2020 indices, correlation analysis Abstract: Understanding and modeling of land use change is of great significance to environmental protec- tion and land use planning. The cellular automata- Markov chain (CA-Markov) model is a powerful tool to 1 Introduction predict the change of land use, and the prediction accuracy is limited by many factors. To explore the Land is one of the indispensable basic resources in impact of land use and socio-economic factors on the human life and production. Land use is a process in prediction of CA-Markov model on county scale, this which people use and renovate land by selecting ff ’ paper uses the CA-Markov model to simulate the land di erent natural attributes to meet people s needs for use of Anren County in 2016, based on the land use of life and society. The land carries human activities in ff 1996 and 2006. Then, the correlation between the land di erent time and space, thus creating an extremely use, socio-economic data and the prediction accuracy complex and varied land use pattern. The pattern of land was analyzed. The results show that Shannon’s evenness use, in turn, will have a positive or negative impact on index and population density having an important human activities. - - ( ) ff impact on the accuracy of model predictions, negatively The land use/land cover LULC change may a ect [ ] - correlate with kappa coefficient. The research not only climate, ecosystem processes and biodiversity 1 . There provides a reference for correct use of the model but also fore, understanding and modeling of LULC change is of fi helps us to understand the driving mechanism of great signi cance to environmental protection and land [ ] landscape changes. use planning 2 . A full understanding of the interaction between the driving forces of LULC change and a complete simulation is a prerequisite for accurately predicting future land use changes. * Corresponding author: Xu Zhenggang, Key Laboratory of Forestry The driving force problem has always been a Remote Sensing Based Big Data & Ecological Security for Hunan dominant aspect in the land use study [3]. There are Province, Central South University of Forestry and Technology, Changsha 410004, Hunan, P. R. China; College of Forestry, many factors that can drive changes in land use, Northwest A & F University, Yangling 712100, Shaanxi, P. R. China, including natural, economic, political, social, cultural, e-mail: [email protected] etc. [4]. All the above factors are often divided into two Wang Song: Key Laboratory of Forestry Remote Sensing Based Big categories: natural factors and socio-economic factors by Data & Ecological Security for Hunan Province, Central South the essential attributes [5,6]. Natural factors mainly University of Forestry and Technology, Changsha 410004, Hunan, include local climate, geological features and vegetation. P. R. China; Hunan Urban and Rural Ecological Planning and - Restoration Engineering Research Center, Hunan City University, The social and economic factors mainly include popula Yiyang 413000, Hunan, P. R. China tion change, economic development, policy and plan- Zhao Yunlin, Yang Guiyan: Key Laboratory of Forestry Remote ning [7]. Identifying the primary causes and estimating Sensing Based Big Data & Ecological Security for Hunan Province, the processes are crucial for land use planning, utiliza- Central South University of Forestry and Technology, Changsha tion of regional resources and environment manage- 410004, Hunan, P. R. China [ ] Huang Tian, Ma Nan: Hunan Urban and Rural Ecological Planning ment 8 . and Restoration Engineering Research Center, Hunan City Previous studies on the driving factors were mostly University, Yiyang 413000, Hunan, P. R. China concentrated in the field of topographic [9],climate Open Access. © 2020 Wang Song et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 Public License. Prediction effect on cellular automata-Markov chain model on county scale 627 changes [10], population growth [11], urbanization [12,13], At present, there are some studies on the prediction of changes in the direction of economic development [14], land use types at the county level [29,30]. Cellular borderline effect [15] and other fields. However, there are automata-Markov model is an important prediction model, few studies associating natural and socio-economic but there is no study on the affected factors at the county factors. On the other hand, the common points of the level. To explore the impact of land use and socio- above studies are large-scale, liking watershed or city, economic factors on the prediction of CA-Markov model which emphasizes a macro perspective of the land use on county scale, the paper uses the CA-Markov model to change. It lacks small-scale research, such as county scale. simulate the land use of Anren County, China, and To understand the process of LULC changes, LULC- analyzes the prediction accuracy of the model. change models are inevitable [16]. In recent years, many models have been used to simulate and predict land use change,suchasconversionoflanduseanditseffects on smallregionalextent(CLUE-S) model [17,18],whichisone 2 Methods of the most widely used land-use models, Markov model [ ] ( ) [ ] 19 ,multiagentsystems MAS model 20,21 , cellular 2.1 Study area automata (CA) model [22,23] and other models [24,25].In most cases, these models showed relatively good results. However, in fact, land use changes are always caused by Anren County is located in the south-east of Hunan complex factors. The neighborhood scale, conversion rules, Province (113° 05′ to 113° 36′ E, 26° 17′ to 26° 50′ N), and simulation mechanisms would influence the CA’s China. It consists of 17 rural communities, such as Yongle accuracy of the simulation [22].Markovmodelsare River Town, Anping Town, etc. (Table 1).YongleRiver susceptible to location factors and decision-making pro- Town is the political, economic and cultural center of Anren cesses. These models are all limited to theories and County. The total area coverage of the study area is methods [26]. So, there is a need to integrate these models 1,461 km2, and the elevation is descending from south-east to improve the effect of simulating land use, like CA-Markov to north-west. The annual precipitation average is model. 1,430 mm, and the annual temperature average is 17.7°C. Landscape prediction is the basis of urban planning. Anren County has convenient transportation adjacent to The simulation results can help to understand the current eight counties and cities. In 2013, Anren County had a total urban development trends and related land use issues and population of 4,47,800 and per capita income is to examine the consequences of urban evolution [27,28]. 5,448 yuan. Table 1: Population and economic situation of towns and townships in Anren County in 2013 Towns and townships Population (person) Population density (person/km2) Per capita income (yuan/person) Yongle River Town 1,39,178 397 9,530 Anping Town 41,197 812 4,914 Pingshang Township 16,336 226 3,111 Yangji Township 17,268 326 3,420 Pailou Township 33,433 401 3,823 Zhushan Township 14,502 447 3,188 Huawang Township 19,908 321 3,097 Longhai Town 19,267 272 4,880 Xinzhou Township 8,063 152 2,880 Dukou Township 22,994 432 2,889 Chengping Township 17,972 503 3,038 Longshi Township 15,650 222 2,969 Yangnao Township 9,918 97 3,206 Lingguan Town 22,372 324 3,882 Haoshan Township 12,319 93 3,387 Guanwang Town 16,147 128 3,306 Pingbei Township 21,271 471 3,147 Total 4,47,795 306 5,448 628 Wang Song et al. 2.2 Cellular automata-Markov chain land use in the future with Markov chain. Therefore, we can prediction effectively predict the quantity and spatial distribution of land use structure. The CA-Markov model has greater Cellular automata was originally proposed by Ulam and accuracy advantage than GIS-based technology in the Neumann in the 1940s, aiming to provide a formal frame- simulation of land use change [45]. work for studying the behavior of complex, self-reproducible systems [31].Itisadynamicsystemthatevolvesindiscrete space-time dimensions and has the ability to simulate the 2.3 Analysis process spatiotemporal evolution of complex systems. The cellular automaton consists of the following four parts: cell, lattice, This study firstly used ENVI 5.0 [46] for remote sensing neighborhoods, and rule. It should also include cell states images of Anren County in 1996, 2006 and 2016, included and discrete time. As a kind of grid dynamics model that can geometric correction, atmospheric correction, image crop- be used to process a large number of variables, the core ping, etc. The above images belonged to Landsat Thematic feature of the CA model is the generation of complex global Mapper (TM) or Operational Land Imager (OLI) and down- patterns by predefining a set of simple transformation rules loaded from Geospatial Data Cloud (http://www.gscloud.cn/). [32,33]. Cellular automata is a suitable method to simulate Then, land use raster data maps were extracted by the and predict the temporal and spatial evolution of complex supervised classification method. The land use map was geographical processes [34,35]. It is widely used in physical analyzed by ArcGIS [47,48], and the area and proportion of science, geography, ecology and mathematics [36]. land use types in Anren County were calculated.