Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China

Abstract

Decadal to centennial land use and land cover change has been consistently singled out as a key element and an important driver of global environmental change, playing an essential role in balancing energy use. Understanding long-term human-environment interactions requires historical reconstruction of past land use and land cover changes. Most of the existing historical reconstructions have insufficient spatial and thematic detail and do not consider various land change types. In this context, this paper explored the possibility of using a cellular automata-Markov model in 90 m × 90 m spatial resolution to reconstruct historical land use in the 1930s in Zhenlai County, China. Then the three-map comparison methodology was employed to assess the predictive accuracy of the transition modeling. The model could produce backward projections by analyzing land use changes in recent decades, assuming that the present land use pattern is dynamically dependent on the historical one. The reconstruction results indicated that in the 1930s most of the study area was occupied by grasslands, followed by wetlands and arable land, while other land categories occupied relatively small areas. Analysis of the three-map comparison illustrated that the major differences among the three maps have less to do with the simulation model and more to do with the inconsistencies among the land categories during the study period. OPEN ACCESSEnergies 2015, 8 3883Different information provided by topographic maps and remote sensing images must be recognized.

Analyzing historical land use changes using a Historical Land Use Reconstruction Model: a case study in Zhenlai County, northeastern China

“…Research regarding the spatial conversion rule module was comprehensively explained and discussed in the spatiotemporal changes’ analysis from 1954 to 2005 in the study area 47 48 . Results from our analysis ( Fig.…”

“…In order to make temporal comparisons, the maps also had to be thematically generalized. Considering both the local characteristics as well as the predominant land use classification system used in China 46 , the available land classes were aggregated into seven suitable land-use categories for this study: arable land, forestland, grassland, water, settlement (urban and rural construction), wetland and other unused land (including sand, saline-alkali soils and bare land) 47 48 .…”

Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

“…In this study, the grid of 30 m × 30 m was selected, and as many as ten factors that affect the land changes were analyzed for the simulation. The driving factor of the land use considered was more than other studies, even such as the factor of groundwater depth was assessed [14,45]. Many studies generally used the CA-Markov model based on the Analytic Hierarchy Process in operating of the MCE, which was been integrated in the IDRISI [22,41,46].…”

Historical Arable Land Change in an Eco-Fragile Area: A Case Study in Zhenlai County, Northeastern China

“…By applying the mutual relationship between the location of settlements and arable land, which can be reflected by the farming radius, historical arable land can be built, and analyzing historical changes in land cover/use is feasible. It is more common to obtain agricultural activity using population as a proxy, and most of the current studies focus on the relationship between cropland area and rate of population change [47]. This method enriches the current historical methods of arable land reconstruction by using settlement as a proxy based on other proxies that integrate multisource data.…”