Although we designed three LULC change scenarios, No LULC change scenario is actually using the LULC map in 2015. Thus, LULC change scenario modelling focused on the BAU and protection scenario. The Markov- Cellular Automata (CA) model, which incorporates the theories of Markov and CA, is about the time series and space for LULC forecasting. The model’s input includes a base-year LULC map, the transition matrix produced by the Markov chain, and a collection of suitability map for each land use type. The multiple criteria evaluation (MCE) is widely used to score the suitability of each affecting factor and produce the input as transition rule for the CA model (Eastman 2006).
3.5.2.1 Markov chain for LULC temporal change
In Markov chain model, LULC changes are thought of as a stochastic process in which the probability distribution of the current state is conditionally independent of the path of past states. It is a model of the system where the next state is solely depending on the current state (Myint and Wang 2006). The state at a particular time t is dependent exclusively on the state at previous time step t-1. The transition probability equation can be written as:
(29) where S(t) and S(t-1) are the system status at the time of t and t-1. Pij is the transition probability matrix in a state.
3.5.2.2 Suitability maps for LULC change
MCE is a multi-attributes decision-making method for land suitability analysis in land use planning, environmental hazards and sustainable development (Agarski et al., 2012). MCE combines map overlay and user preferences that can be divided into two main parts – factor selection and suitability score assignment. Factors used in MCE usually include socio-economic and environmental dimensions, and translate their information into measurable parameters. In this study, typical socio-economic and environmental factors, including population, and the GDP, slope, elevation, distance to roads and distance to river, and protection area were selected to calculate transition potential maps of LULC. The details of factor selection are as follows :
Slope: According to the GGP, the croplands more than 25 degrees were directly converted to woodlands and grasslands. Thus, the slope which is more than 25 degrees in the study site will be used as one of the constraint factors in the BAU scenario. It means the croplands more than 25 degrees will not be converted to woodlands and grasslands under this scenario. Moreover, slop will be used as one of the constraint factors in the protection scenario. The croplands more than 25 degrees will be converted to woodlands and grasslands. The Boolean method is used to create slope constraint map.
Population and the GDP: Although economic development in northern Shaanxi has lagged far behind progress on the national average, the GDP in this region in recent years increased fast (Figure 3-11a). Moreover, the population in northern Shaanxi
)
1
(
)
(t
=P
×S
t−
S
ijshowed an increase tendency from 1988 to 2013. Based on previous studies, the accelerated industrialization and urbanization following population growth in China have greatly affected LULC change through increasing built-up areas and urban sprawl (Wu et al., 2004). With the continuous growth of China’s economy, massive croplands converting to non-agricultural lands may occur without appropriate planning and management of existing land resources (Long et al., 2007). Thus, the GDP and population were considered as the social-economic driving factors in this study, and they will be used for both scenarios.
Elevation: LULC changes caused by human activities are limited by the elevation factor in the Loess Plateau (Fu et al., 2006). For example, croplands abandonment often occurred in the highest or lowest regions as elevation differences between ridges and valley bottoms can influence climate variations (Shrestha and Zinck 2001). Moreover, built-up lands are often located in the flat regions with good traffic condition and water supply. We considered the elevation factor as a driving force factor in both scenarios. Distance to roads and river: Due to natural (e.g. topographic) constraints, land use
activities have focused on riverside areas, and these regions usually have more opportunities to develop agricultural and industrial facilities compared with mountain areas. Moreover, many studies have shown that transportation networks affect LULC change (Frazier and Kockelman 2005). In this study, distance to roads and distance to river factors will be used in both scenarios and they were created as raster maps by using Euclidean distance method.
Protection areas: In all scenarios, woodlands and grasslands will be protected, which means they will not be allowed to convert to another LULC type. The Boolean method is used to create protection area maps.
3.5.2.3 Model implementation
Model implementation consisted of two steps, namely model validation and model prediction. For model validation, only the BAU scenario is validated due to data limitation. In particular, the Markov_CV model firstly was used to simulate LULC change in 2015. LULC maps for the years 1988 and 2000 were entered into a Markov chain to calculate the land transition possibility matrix for the year 2015. Secondly, the suitability maps for each LULC were produced using the MCE method. Then, these LULC demands were translated
into spatial allocation using the CA model to simulate 2015 LULC changes. The main steps include determining CA filters and selecting iteration number. CA filters can produce a clear sense of the space weighting factor, which can be changed according to the current adjacent cellular state.
In this study, the validation results were assessed to measure the goodness of fit between the observed and the simulated LULC maps. It means that simulated LULC map for 2015 was compared with the satellite-derived map for 2015 which is provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (DCRES, CAS). Using an error matrix to represent accuracy has been recommended by many researchers (Rwanga and Ndambuki 2017). It is computed by dividing the total correct by the total number of pixels in the error matrix. The equation can be expressed as:
(30)
Where K overall is the overall accuracy; Ncorrectis the number of the correct pixel; and Ntotal is the total number of pixel.
3.5.3 Statistical downscaling of the RCPs 4.5 scenario