A Bayesian approach to multi-source forest area estimation
A Bayesian approach to multi-source forest area estimation
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DOI：
10.1007/s10651-007-0049-5
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发表时间：
2008-06-01
影响因子：
3.8
通讯作者：
McRoberts, Ronald E.
中科院分区：
文献类型：
--
作者：
Finley, Andrew O.;Banerjee, Sudipto;McRoberts, Ronald E.
在土地利用变化监测、碳预算以及生态条件和木材供应预测等工作中，对描述森林覆盖的区域和国家数据层的需求不断增加。这些数据层必须允许对森林面积进行小面积估计，最重要的是，提供相关的误差估计。本文提出了一种基于模型的方法，将中分辨率卫星图像与基于地块的森林清查数据相结合，以产生像素级森林概率和相关误差的估计。所提出的贝叶斯分层模型提供了对每个像素的后验预测分布的访问，从而可以对感兴趣的像素和多像素区域进行高度灵活的分析。本文介绍了一项使用多个日期的陆地卫星图像和美国农业部林务局森林清查和分析图数据的试验。结果描述了试验地点内的空间依赖结构，提供了林地利用概率的像素和多像素摘要，并探索了森林和非森林类别的后验预测分布的离散化方案。保留集分析的模型预测结果表明，所提出的模型为试验站点提供了 88% 的高分类准确率。
In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel's posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.