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首页> 外文期刊>Journal of Forest Planning >Development of a Prediction Probability Model to Monitor ARD Areas
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Development of a Prediction Probability Model to Monitor ARD Areas

机译：开发监测ARD区域的预测概率模型

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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, bandS, 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 toverify 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 yieldeda 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.
机译：本文讨论了使用联合条件概率的森林砍伐（D）预测模型的开发。在日本岐阜县东白河市确定了地面真理。四个相关因素，包括地理因素（坡度，距道路的距离以及与森林和非森林边界的距离）和三个植被变化检测（VCD）因子（NDVI，bandS或光谱形状分类（SSC））之一，分别在直接模型和贝叶斯模型中用于预测D。在构建预测模型时，我们测试了两种分区方法（半部分分区和系统网格分区）。在每种方法中，将研究区域分为两组进行训练和验证，然后反转以验证该分区方法。半部分分割的结果不一致，主要是因为半部分分割非常大（在一半的部分中发现了大约80％的D区域）。系统的网格划分比半部分的划分产生更好的结果。尽管直接模型和贝叶斯模型的精度相对接近，但是贝叶斯模型的结果更加一致。还可以构建类似的预测模型来监测《京都议定书》之下的其他活动，例如造林和重新造林。

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