Modeling historical forest landscape in the county of Halland, Sweden using data from the first National Forest Inventory

Detta är en Master-uppsats från SLU/Dept. of Forest Resource Management

Författare: Cen Chen; [2012]

Nyckelord: landscape; regression; Kriging; k-Nearest Neighbor; kernel; R;

Sammanfattning: Spatial patterns is of core interest for landscape ecology, and tracking its temporal evolvement helps to attain a better understanding of the ecological effects of current ecosystems. The presence and recent digitization of the first Swedish National Forest Inventory data and the occurrence of a concurrent historical map offers a unique chance to take on this very challenging task. The objective was to describe and test a statistical model of the historical forest landscape for a study area in the county of Halland, Sweden during the 1920s by utilizing different spatial data sources in the model building towards a plausible methodological application of the model. Data from a detailed digital elevation model, thematic maps of soil type, and topographic maps were introduced into the modeling. Both deterministic and stochastic parts of response variables were extracted by combining Partial Least Squares and logistic regressions with ordinary Kriging. Conventional cross-validating was applied to judge the performance of the modeling, so were two compatible estimations, one made by applying k-Nearest Neighbor method, and the other origins from quartic kernel function. Both of them indicate the performance of the modeling is fairly good for the vegetation type part (with an accuracy no less than 0.75), and barely acceptable for the forest stand age and openness part (Root Mean Square Error is no bigger than 18.45 years for stand age, and 0.17 for stand openness). The modeling results agree poorly with information extracted from historical map. The nature of the data available is considered to be the determinant causal factor behind the results. Certainly, the modeling has the potential to be further improved methodologically.

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