Derivation of a yearly transition probability matrix for land-use dynamics and its applications

Takada, Tappei; Miyamoto, Asako; Hasegawa, Shigeaki F.

doi:10.1007/s10980-009-9433-x

Cited by 116 publications

(95 citation statements)

References 19 publications

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“…Such comparisons allow for estimating annual rates of change during the analyzed period, frequently by means of a Markov matrix. A Markov matrix expresses the proportion of a given land cover that would be transformed to another land cover type, assuming that the observed trends will remain the same (stationary process), thus allowing their projection to the future [48]. Furthermore, a statistical analysis allows for relating factors of change (such as slope or accessibility) to the observed changes.…”

Section: Training or Calibration Of The Modelmentioning

confidence: 99%

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Chang-Martínez

Mas

Valle

et al. 2015

IJGI

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Spatially-explicit land cover land use change (LCLUC) models are becoming increasingly useful tools for historians and archaeologists. Such kinds of models have been developed and used by geographers, ecologists and land managers over the last few decades to carry out prospective scenarios. In this paper, we review historical models to compare them with prospective models, with the assumption that the ample experience gained in the development of models of prospective simulation can benefit the development of models having as their objective the simulation of changes that happened in the past. The review is divided into three sections: in the first section, we explain the functioning of contemporary LCLUC models; in the second section, we analyze historical LCLUC models; in the third section, we compare the former two types of models, and finally, we discuss the contributions to historical LCLUC models of contemporary LCLUC models.

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Section: Training or Calibration Of The Modelmentioning

confidence: 99%

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Chang-Martínez

Mas

Valle

et al. 2015

IJGI

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“…The algorithm proposed by Urban and Wallin (2002) could not always be validated (data not shown). The determination of these annual probabilities seems more difficult than expected, and requires a profound mathematical analysis, as shown by Takada et al (2010).…”

Section: Measuring Anthropogenic Patternsmentioning

confidence: 99%

Anthropogenic Effects in Landscapes: Historical Context and Spatial Pattern

Bogaert

Vranken

André

2014

Biocultural Landscapes

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Bio-cultural landscapes are characterized by anthropogenic pattern features, of which the measurement constitutes a key step in landscape analysis. Metrics and strategies for this measurement of anthropogenic patterns and their dynamics are discussed, considering the pattern/process paradigm, the patchcorridor-matrix model and the complementarity of landscape composition and configuration as conceptual benchmarks. Historically, noticeable anthropogenic effects are accepted to have appeared in landscapes after the invention of agriculture and further trends of landscape change could be linked to the development of agriculture. Through time, a sequence of landscape dynamics with three stages is expected, in which a natural landscape matrix is initially substituted by an agricultural one; urban patch types will later on dominate the matrix as a consequence of ongoing urbanization. The importance of the development of agriculture and its productivity for the evolution of settlements, villages and cities is emphasized. Anthropogenic change of landscapes confirms the status of geographical space as a limited resource.

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“…If the date being projected in the future is not an even multiple of the training period, the derivation of a yearly transition probability matrix is required. Previous studies used a linear algebraic formula of the power root of matrices to generate this annual matrix, but several difficulties arise by this approach as discussed in the study of Takada, Miyamoto, and Hasegawa (2010).…”

Section: Stochastic Modelling and Future Projectionsmentioning

confidence: 99%

Historical and future land-cover changes in the Upper Ganges basin of India

Tsarouchi

Mijic

Moulds

et al. 2014

International Journal of Remote Sensing

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The green revolution represents one of the greatest environmental changes in India over the last century. The Upper Ganges (UG) basin is experiencing rapid rates of change of land cover and irrigation practices. In this study, we investigated the historical rate of change and created future scenario projections by means of 30 m-resolution multitemporal Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus data of the UG basin. Post-classification change analysis methods were applied to Landsat images in order to detect and quantify land-cover changes in the UG basin. Subsequently, Markov chain analysis was applied to project future scenarios of land-cover change. Fifteen different scenarios were generated based on historic land-cover change. These scenarios diverged in terms of future projections, highlighting the dynamic nature of the changes. This study has shown that between the years 1984 and 2010 the main land-cover change trends are conversion from shrubs to forest (+4.7%), urbanization (+5.8%), agricultural expansion (+1.3%), and loss of barren land (-9.5%). The land-cover change patterns in the UG basin were mapped and quantified, showing the capability of Landsat data in providing accurate land-cover maps. These results, in combination with those derived from the Markov model, provide the necessary evidence base to support regional land-use planning and develop future-proof water resource management strategies.

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Derivation of a yearly transition probability matrix for land-use dynamics and its applications

Cited by 116 publications

References 19 publications

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Anthropogenic Effects in Landscapes: Historical Context and Spatial Pattern

Historical and future land-cover changes in the Upper Ganges basin of India

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