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Application of Markov model in wetland change dynamics in Tianjin Coastal Area, China

TLDR
Wang et al. as mentioned in this paper used the Markov chain model to make quantitative comparisons of the wetland changes between discrete time periods extending from 1979 to 2008, and three main conclusions have been drawn from this model: (1) a continuing exchange of wetland area occurs between artificial wetlands and natural wetlands, which has little effects on the net amount of wetlands but could undermine the long-term ecological function of remaining natural wetland in this area.
Abstract
Wetland ecosystem is one of the most productive and most diverse ecosystems, which provides various important habitats for wildlife. However, the rapid urbanization caused wetland degradation. Thus, researchers all over the world pay attention to study on wetland dynamic changes in order to analyze the causes of wetland degradation. Tianjin Coastal Area is the center for the Bohai Bay. The government has prioritized integrating all the cities in the Bohai Bay Rim and fostering economic development in this area. Tianjin has various types of wetlands including coastal wetlands (estuarine waters, marshes, et al.), inland wetlands (rivers, lakes, et al.), and artificial wetlands (ponds, salt exploitation sites, et al.) according to classification in Ramsar Convention. The wetlands in Tianjin Coastal Area have high biodiversity and provide various ecological functions and values. With the development of this area, human disturbance have been increasing. The research on wetland change dynamics is the basic for wetland ecosystem protection and restoration. This area is the site of an intense land-use conflict among urbanization and natural protection. Large scale spatial and temporal land-use data were used to investigate the dynamics of wetland change in this area. Markov software was applied based on the support of GIS and RS from 1979 to 2008. The Markov chain was used as a stochastic model to make quantitative comparisons of the wetland changes between discrete time periods extending from 1979 to 2008. The wetland dynamic changes have been predicted according the Markov chain model in 2015, 2020 and 2050. Three main conclusions have been drawn from the Markov model about the wetland change dynamics in this area. (1)A continuing ‘exchange’ of wetland area occurs between artificial wetlands and natural wetlands categories that has little effects on the net amount of wetland but could undermine the long-term ecological function of remaining natural wetland in this area. (2) The human induced factors such as pollution and construction were the predominant causes for wetland changes. (3) The natural wetlands will be in great decline in 2020 and 2050 without enhancing wetland protection policy and increasing restoration technology. It is hoped that the dynamic model will serve as a laboratory to study the different features of the wetland problem in coastal area and to analyses different policy alternatives with an integrated, systemic approach.

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Citations
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Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques-A case study of a hilly area, Jiangle, China.

TL;DR: This study predicts the spatial patterns of land use in 2025 and 2036 based on the dynamic changes in land use patterns using remote sensing and geographic information system and can provide suggestions and a basis for urban development planning in Jiangle County.
Journal ArticleDOI

Urban expansion and wetland shrinkage estimation using a GIS-based model in the East Kolkata Wetland, India

TL;DR: In this paper, the authors analyzed the eco-social transformation of the East Kolkata Wetland (EKW) and examined the patterns and drivers of wetland change in the EKW.
Journal ArticleDOI

A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

TL;DR: In this article, the ability to assess the rate of change to wetland habitats has been discussed, and the authors propose a method to estimate the change rate of wetlands across Canada under both anthropogenic and natural activities.
Journal ArticleDOI

Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model

TL;DR: In this article, the authors used an integrated approach that combines remote sensing and GIS to simulate and predict plausible LULC changes for Dedza district in Malawi for the years 2025 and 2035 based on Cellular Automata (CA)-Markov Chain model embedded in IDRISI Software.
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Effects of seashore reclamation activities on the health of wetland ecosystems: A case study in the Yellow River Delta, China

TL;DR: In this paper, the authors evaluated the effects of seashore reclamation activities on the health of coastal wetland ecosystems and defined a comprehensive assessment index system based on the pressure-state-response model.
References
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Journal ArticleDOI

Statistical Inference about Markov Chains

TL;DR: In this article, the transition probabilities of a Markov chain of arbitrary order were obtained and their asymptotic distribution was obtained for a single observation of a long chain, and the relation between likelihood ratio criteria and contingency tables was discussed.
Journal ArticleDOI

Satellite remote sensing of wetlands

TL;DR: A review of the literature on satellite remotesensing of wetlands, including what classification techniques were most successful in identifying wetlands and separating them from other land cover types, is presented in this paper.
Journal ArticleDOI

A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada

TL;DR: In this article, a first order Markov chain was used as a stochastic model to make quantitative comparisons of the land-use changes between discrete time periods extending from 1935 to 1981.
Journal ArticleDOI

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

TL;DR: In this article, Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees.
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