title: Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis.
abstract: Land salinization and desalinization are complex processes affected by both biophysical and human-induced driving factors. Conventional approaches of land salinization assessment and simulation are either too time consuming or focus only on biophysical factors. The cellular automaton (CA)-Markov model, when coupled with spatial pattern analysis, is well suited for regional assessments and simulations of salt-affected landscapes since both biophysical and socioeconomic data can be efficiently incorporated into a geographic information system framework. Our hypothesis set forth that the CA-Markov model can serve as an alternative tool for regional assessment and simulation of land salinization or desalinization. Our results suggest that the CA-Markov model, when incorporating biophysical and human-induced factors, performs better than the model which did not account for these factors when simulating the salt-affected landscape of the Yinchuan Plain (China) in 2009. In general, the CA-Markov model is best suited for short-term simulations and the performance of the CA-Markov model is largely determined by the availability of high-quality, high-resolution socioeconomic data. The coupling of the CA-Markov model with spatial pattern analysis provides an improved understanding of spatial and temporal variations of salt-affected landscape changes and an option to test different soil management scenarios for salinity management.
authors.affiliation: Department of Land Resources Management, China Agricultural University, 2 Yuanmingyuan Road W., Haidian District, Beijing 100193, China.
authors.forename: De
authors.initials: D
authors.lastname: Zhou
authors.forename: Zhulu
authors.initials: Z
authors.lastname: Lin
authors.forename: Liming
authors.initials: L
authors.lastname: Liu
mesh_terms: D002681:China; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods / Q000706:statistics & numerical data*; D008390:Markov Chains; D054712:Salinity; D012987:Soil / Q000737:chemistry / Q000592:standards; D012989:Soil Pollutants / Q000032:analysis*; D062206:Spatial Analysis
chemical_list: D012987:Soil; D012989:Soil Pollutants