Author:

Cheng-Liang Chang,

Jui-Chin Chang

Original Tittle:

Applications of Markov Model and Cellular Automata for Vegetation Recovery Simulation: A Case Study of Mount Jiou Jiou Area

Abstract:

The recovery of vegetation is the most significant landscape in the terrestrial globe. In recent years it has been changed rapidly due to human socio-economic growth and environmental impact. Therefore, many research methods have been applied so as to comprehend and forecast these dynamic changes.  This paper intends to apply some of theses methods and processes to quantify the change of vegetation recovery in Mount Jiou Jiou area. First of all, the graphic data of study area is collected by using the SPOT satellite images of four representative periods (Mar.1999, Oct.1999, Nov.2002 and Nov.2005).  Subsequently the vegetation types and their changes are examined through the Normalized Difference Vegetation Index (NDVI) analysis. Second, the Geographic Information System (GIS) technology is adopted to recognize the distributions of different vegetation types, characteristics of topography and their correlation. Finally, by combining the methods of Markov chains and Cellular automata this study models and simulates the spatio-temporal change of vegetation recovery. The results can be summarized as follow: (1) After 921 earthquake, 80% of vegetation on Mount Jiou Jiou area was destroyed seriously in 1999, therefore the index of NDVI was very low. After that, the vegetation is recovered gradually for recent years under stable weather conditions. (2) The healing situation of vegetation is intensely affected by geomorphologic factors, such as height, slope and aspect. (3).After comparing two models testing, the present study shows that CA-Markov model is more suitable for simulating the change trend of vegetation recovery.

Language:

Chinese

Publisher:

Journal of Geographical Research No.45, November 2006

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Author:

Antonius B. Wijanarto
Geomatics Researcher of BAKOSURTANAL-Indonesia

Abstarct:

Change detection is one of multi temporal analysis in remote sensing that is important for studying the dynamics of environment. Change detection is useful in many applications such as land use changes, habitat fragmentation, rate of deforestation, coastal change, urban sprawl, and other cumulative changes through spatial and temporal analysis techniques such as GIS and Remote Sensing along with digital image processing technique. Markov Change Detection is one
application of change detection that can be used to predict future changes based on the rates of past change. The method is based on probability that a given piece of land will change from one mutually exclusive state to another. These probabilities are generated from past changes and then applied to predict future change.

Publisher:

Jurnal Ilmiah Geomatika Vol. 12, No. 1 Agustus 2006

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Author:

Nelley Kovalevskaya and Vladimir Pavlov

Abstract:

Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Multiple schemes of image classification used in environmental mapping are either traditionally statistical or heuristic. While the former methods do not take account of spatial variability in space and aerial data, the latter ones does not lend themselves to optimal solutions we present.  Novel probabilistic models of piecewise-homogeneous images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Parameters of the model are estimated by using a stochastic approximation technique. Its convergence to the desired values is studied experimentally. Addition of spatial attributes appears to be necessary in most areas where the differences in spatial data between regions in the image occur. Experiments in generating the pairs of images and environmental maps and in segmenting the simulated as well as real images are discussed.

Publisher:

Journal of Environmental Quality 31:1462-1470 (2002)
© 2002

Article Credit:

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Author:

1. CLAUDIA MARIA DE ALMEIDA
2. ANTONIO MIGUEL VIEIRA MONTEIRO
3. GILBERTO CAMARA
4. BRITALDO SILVEIRA
5. SOARES-FILHO
6. GUSTAVO COUTINHO CERQUEIRA
7. CASSIO LOPES PENNACHIN
8. MICHAEL BATTY

Abstract:

This paper is concerned with building up methodological guidelines for modelling urban land-use change through Geographical Information Systems, remote sensing imagery and Bayesian probabilistic methods. A medium-sized town in the west of Sa˜o Paulo State, Bauru, was adopted as a case study. Its urban structure was converted into a 100m6100m resolution grid and transition probabilities were calculated for each grid cell by means of the ‘weights of evidence’ statistical method and upon the basis of the information related to the technical infrastructure and socio-economic aspects of the town. The probabilities obtained from there fed a cellular automaton simulation model—DINAMICA—developed by the Centre for Remote Sensing of the Federal University of Minas Gerais (CSR-UFMG), based on stochastic transition algorithms. Different simulation outputs for the case study town in the period 1979–1988 were generated, and statistical validation tests were then conducted for the best results, employing a multiple resolution fitting procedure.  This modelling experiment revealed the plausibility of adopting Bayesian empirical methods based on the available knowledge of technical infrastructure and socio-economic status to simulate urban land-use change. It indicates their possible further applicability for generating forecasts of growth trends both for Brazilian cities and cities world-wide.

Publisher:

International Journal of Remote Sensing
Vol. 26, No. 4, 20 February 2005, 759–774

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Author:

Ngyyen Kim Loi
Watershed and Environmental Management,
Nong Lam University (NLU), Ho Chi Minh City, VIETNA

Abstract:

Decision makers today need to be able to rapidly find good solutions to increasingly complex problems. Optimization based on decision support system (DSS) can help decision makers to meet this challenge. Building such systems, however, is expensive and time consuming.The use of decision support system (DSS), linear programming (LP), and geographic information system (GIS) for sustainable watershed management in Dong Nai watershed is presented. A general statement of system requirement for DSS has been conceptualized to provide a set of core requirement and behavior for DSS for mutil-criteria decision
making in sustainable watershed management. Classes of decision elements for the analysis of decision problems and of other DSS components are identified. This paper investigated also how demographic (socioeconomic) and land-use (physical and environmental) data can be integrated within a multi-scaled decision
support system framework to formulate and evaluate land-use planning scenarios. A case study approach is undertaken using ‘what-if’ planning scenarios for a Dong Nai Watershed, Vietnam. This paper has not only briefly outlined the three future land use scenarios comprised within the framework but also has generally methodology in developing decision support system (DSS) for sustainable land use allocation. Scenario A - ‘Future trend scenario’ is based on existing socio-economic trends. Scenario B – ‘Maximization land allocation scenario’ will be derived using maximizing modelling of land valuation data. Scenario C –
‘Sustainable Development’ will be derived using a number of environmental layers and assigning weightings of importance to each layer using a multiple criteria analysis (MCA) approach. The ‘what-if’ planning scenarios was expected through the use of maps and tables within a geographical information system (GIS), which delineate future possible land-use location - allocations. Each of the scenarios and their underlying model will be applied to the Dong Nai watershed, Vietnam.

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