| Research Abstract |
Recently, spatial ecological data have been accumulated from long-term ecological research sites or from remote censing techniques. The change in the spatial pattern of land use and land cover given as a series of maps. In this research project, I develop a new mathematical method to estimate the ecosystem process and the human impact from spatio-temporal data, based on spatial Markov model. By this way we might obtain a basic theoretical framework of ecosystem change.
[1] Estimating ecological process from map data.
BCI 50ha plot, large-area census of a neotropical forest has been carried out since early 1980's. In collaboration with Steve Hubbell and his colleagues, we analyzed times series of vegetation height spatial data over 2 years. The 50ha plot was separated into about 2000 sites each having 5x5m. I classified sites to gaps and non-gaps according to the maximum vegetation height. Then developed Markovian transition model describing the dynamics of spatial patterns. We observed that the transition rate from a canopy site to a gap site and the recovery rate (the opposite transition) depend strongly on the neighbors. As a consequence, the spatial pattern would become non-random with gap sites aggregated with each other.
We also recognized that the simple way of estimating transition rate parameter includes a large bias. This is because the transition at a site would change the rate of transition in its neighbors. To remove the bias we used a method named NCBC, which was developed by Dr Hakoyama and myself.
[2] Modeling based on land use change.
Sites are classified to forest, agricultural land, road etc., and then model the process of transition between these discrete states. From spatial data, we can know the effect of neighbors to modify the transition rate. We developed a Markovian spatial model that can predict future change in the loss of forests based on land use change.
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