Development of a Prediction Probability Model to Monitor ARD Areas

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This paper discusses the development of a deforestation (D) prediction model using joint conditional probability. Ground truth was determined in Higashi-Shirakawa city, in the Gifu prefecture of Japan. Four related factors, consisting of geographic factors (slope, distance from the road, and distance from the forest and nonforest boundary) and one of three vegetation change detection (VCD) factors (NDVI, band3, or spectral shape classification (SSC)), were used in direct and Bayes models to predict D. We tested two partitioning approaches, half-portion partitioning and systematic grid partitioning, in constructing the prediction models. In each approach, the study area was partitioned into two groups for training and validation and then reversed to verify the partitioning approach. The results of the half-portion partitioning were inconsistent, primarily because the half-portion partition is very large (about 80% of the D areas were found in one half portion). The systematic grid partition yielded a better result than the half-portion partition. Although the accuracies of the direct and Bayes models were relatively close, the results of the Bayes model were more consistent. Similar prediction models could also be constructed to monitor other activities under the Kyoto Protocol, such as afforestation and reforestation.
森林計画学会の論文