Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis

Zhou, De; Lin, Zhulu; Liu, Liming

doi:10.1016/j.scitotenv.2012.09.013

Cited by 44 publications

(29 citation statements)

References 49 publications

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“…The eleven socio-industrial parameters were determined during the study of soil salinization in the Yellow River Delta [28]. The fourteen parameters as a composite risk index of Irrigation salinization was proposed and employed in the assessment of salinization in the Yinchuan Plain [8,24]. Although these studies have identified the main anthropogenic and natural causes of salinity and the mechanisms behind them [29].…”

Section: Composite Risk Index For Irrigation Salinity Hazardmentioning

confidence: 99%

Irrigation Salinity Risk Assessment and Mapping in Arid Oasis, Northwest China

Seydehmet

Abliz

et al. 2018

Water

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Irrigation salinity is a common environmental threat for sustainable development in the Keriya Oasis, arid Northwest China. It is mainly caused by unreasonable land management and excessive irrigation. The aim of this study was to assess and map the salinity risk distribution by developing a composite risk index (CRI) for seventeen risk parameters from traditional and scientific fields, based on maximizing deviation method and analytic hierarchy process, the grey relational analysis and the Pressure-State-Response (PSR) sustainability framework. The results demonstrated that the northern part of the Shewol and Yeghebagh village has a very high salinity risk, which might be caused by flat and low terrain, high subsoil total soluble salt, high groundwater salinity and shallow groundwater depth. In contrast, the southern part of the Oasis has a low risk of salinity because of high elevation, proper drainage conditions and a suitable groundwater table. This achievement has shown that southern parts of the Oasis are suitable for irrigation agriculture; for the northern area, there is no economically feasible solution but other areas at higher risk can be restored by artificial measures. Therefore, this study provides policy makers with baseline data for restoring the soil salinity within the Oasis.

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Section: Composite Risk Index For Irrigation Salinity Hazardmentioning

confidence: 99%

Irrigation Salinity Risk Assessment and Mapping in Arid Oasis, Northwest China

Seydehmet

Abliz

et al. 2018

Water

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“…The advantages of both models are integrated into a single and robust modeling technique called the CA‐Markov model by quantifying the probabilities of phenomenon dynamism via the Markov chain model and allocating the estimated changes through CA to predict the future evolution (Zhou et al. ). The CA‐Markov model is founded on an initial distribution of the dynamic phenomenon and a transition matrix, assuming that past driving forces will also operate in the future (Mondal and Southworth, ).…”

Section: Methodsmentioning

confidence: 99%

An Exploration of Future Patterns of the Contributions to OpenStreetMap and Development of a Contribution Index

Arsanjani

Mooney

Helbich

et al. 2015

Transactions in GIS

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OpenStreetMap (OSM) represents one of the most well‐known examples of a collaborative mapping project. Major research efforts have so far dealt with data quality analysis but the modality of OSM's evolution across space and time has barely been noted. This study aims to analyze spatio‐temporal patterns of contributions in OSM by proposing a contribution index (CI) in order to investigate the dynamism of OSM. The CI is based on a per cell analysis of the node quantity, interactivity, semantics, and attractivity (the ability to attract contributors). Additionally this research explores whether OSM has been constantly attracting new users and contributions or if OSM has experienced a decline in its ability to attract continued contributions. Using the Stuttgart region of Germany as a case study the empirical findings of the CI over time confirm that since 2007, OSM has been constantly attracting new users, who create new features, edit the existing spatial objects, and enrich them with attributes. This rate has been dramatically growing since 2011. The utilization of a Cellular Automata‐Markov (CA‐Markov) model provides evidence that by the end of 2016 and 2020, the rise of CI will spread out over the study area and only a few cells without OSM features will remain.

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“…In this study, risk factors such as lithologic unit texture, soil texture, and water stagnation were not considered due to lack of data. For example, the seepage effect and deep percolation from the vast network of irrigation and drainage channels on soil salinity were simply represented by the distance to irrigation channels and the distance to drainage ditches (see also Zhou et al 2012;2013). The irrigation canals are not lined and the canal efficiency is about 0.44 (Jia et al 2006).…”

Section: Methodsmentioning

confidence: 99%

Identifying and managing risk factors for salt-affected soils: a case study in a semi-arid region in China

Zhou

Wang

et al. 2015

Environ Monit Assess

Self Cite

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Soil salinization and desalinization are complex processes caused by natural conditions and human-induced risk factors. Conventional salinity risk identification and management methods have limitations in spatial data analysis and often provide an inadequate description of the problem. The objectives of this study were to identify controllable risk factors, to provide response measures, and to design management strategies for salt-affected soils. We proposed to integrate spatial autoregressive (SAR) model, multi-attribute decision making (MADM), and analytic hierarchy process (AHP) for these purposes. Our proposed method was demonstrated through a case study of managing soil salinization in a semi-arid region in China. The results clearly indicated that the SAR model is superior to the OLS model in terms of risk factor identification. These factors include groundwater salinity, paddy area, corn area, aquaculture (i.e., ponds and lakes) area, distance to drainage ditches and irrigation channels, organic fertilizer input, and cropping index, among which the factors related to human land use activities are dominant risk factors that drive the soil salinization processes. We also showed that ecological irrigation and sustainable land use are acceptable strategies for soil salinity management.

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Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis

Cited by 44 publications

References 49 publications

Irrigation Salinity Risk Assessment and Mapping in Arid Oasis, Northwest China

Irrigation Salinity Risk Assessment and Mapping in Arid Oasis, Northwest China

An Exploration of Future Patterns of the Contributions to OpenStreetMap and Development of a Contribution Index

Identifying and managing risk factors for salt-affected soils: a case study in a semi-arid region in China

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