Markov chain models for vegetation dynamics
By H. Balzter. 2000
243 citations recorded by the source corpus.
A theoretical implementation of Markov chain models of vegetation dynamics is presented. An overview of 22 applications of Markov chain models is presented, using data from four sources examining different grassland communities with varying sampling techniques, data types and vegetation parameters. For microdata, individual transitions have been observed, and several statistical tests of model assumptions are performed. The goodness of fit of the model predictions is assessed both for micro- and macrodata using the mean square error, Spearman’s rank correlation coefficient and Wilcoxon’s signed-rank test. It is concluded that the performance of the model varies between data sets, microdata generate a lower mean square error than aggregated macrodata, and time steps of one year are preferable to three months. The rank order of dominant species is found to be the most reliable prediction achievable with the models proposed.
Subfields: Nature and Landscape Conservation, Plant Science
View Paper: https://www.semanticscholar.org/paper/75f4774183faeee8c2ae8d3b3c36cbc219634c8c
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In Nature and Landscape Conservation
In Plant Science
Citations in the corpus
- Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh (2013, methodology): “In this research, the combination of Markov Chain [59] and MLP modeling techniques [60] were applied to project the future land cover patterns due to their accuracy in outcomes and wide acceptability [61,62].”
- Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “MCs are stochastic processes whose parameters can be obtained by empirically estimating the transition probabilities between discrete states in the studied systems [31] .”