利用指標克利金法和一般克利金法建立樹種的空間分佈

黃仁梅

doi:10.6342/NTU201803037

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College of Bio-resources and Agriculture > School of Forestry and Resource Conservation
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利用指標克利金法和一般克利金法建立樹種的空間分佈

Application of Indicator Kriging and Ordinary Kriging to Map Spatial Distribution of Trees Species

黃仁梅 , Masters　　Advisor：林增毅　　Co-advisor ：澤大衛

英文 DOI： 10.6342/NTU201803037

樹種空間分佈；半變異圖；克利金法；林分產生器；點過程；地理空間統計； Species spatial distribution ； Semivariogram ； Kriging ； Stand generator ； Point process ； Geospatial statistics

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Understanding species composition, size, and spatial distribution of trees are important for forest managers. Recent studies state that forest managers can use stand generators to generate stand structure information. However, method of species assignment of stand generators must be improved. Hershey et al. (1997) introduced indicator kriging to predict spatial distribution of species and suggested this method was a suitable tool for the tree species spatial distribution prediction. In this study, we used indicator kriging to predict spatial presence/absence and ordinary kriging to predict abundance of three tree species in Fushan Forest Dynamics Plot. To study influence of sampling intensity, cell size, and sampling method on prediction accuracy, four levels of sampling intensities (i.e. 5 %, 10 %, 20 %, and 0 % of total number of cells), two different cell sizes (i.e. 5×5 m and 10×10 m), and two different sampling methods (i.e. simple random sampling and systematic sampling) were included in the kriging estimation. Thus, there were a total of 16 combinations of cell size, sampling method and sampling intensity for indicator kriging and for ordinary kriging. Result of this study showed that indicator kriging and ordinary kriging were well behaved in predicting spatial distribution of the three tree species case studies. Sampling intensity significantly influenced the kriging prediction accuracy, by increasing the prediction accuracy as it increases. The influence of cell sizes and sampling methods on indicator kriging and ordinary kriging were smaller than sampling intensity and require further investigation. Forest managers can use these interpolation methods to simulate forest stand for decision making in forest management.

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Andersen, K.M., Turner, B.L., Dalling, J.W., 2010. Soil‐based habitat partitioning in understorey palms in lower montane tropical forests. Journal of Biogeography 37, 278-292.
Arslan, H., 2012. Spatial and temporal mapping of groundwater salinity using ordinary kriging and indicator kriging: The case of Bafra Plain, Turkey. Agricultural Water Management 113, 57-63.
Barreto, P., Amaral, P., Vidal, E., Uhl, C., 1998. Costs and benefits of forest management for timber production in eastern Amazonia. Forest Ecology and Management 108, 9-26.
Biging, G., Robards, T., Turnblom, E., Van Deusen, P., 1994. The predictive models and procedures used in the forest stand generator (STAG). Hilgardia 61, 1-36.
Bivand, R., Pebesma, E., Gómez Rubio, V., 2013. Applied spatial data analysis with R. Springer New York, New York.

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