Estimation for inaccessible, non-sampled forest areas using model-based inference and remotely sensed auxiliary information
Estimation for inaccessible, non-sampled forest areas using model-based inference and remotely sensed auxiliary information
复制标题
使用基于模型的推理和遥感辅助信息估计无法进入、未采样的森林面积
DOI：
10.1016/j.rse.2014.08.028
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发表时间：
2014
影响因子：
--
通讯作者：
T. Gobakken
中科院分区：
文献类型：
--
作者：
R. McRoberts;E. Næsset;T. Gobakken
对于偏远和交通不便的森林地区，缺乏足够的或可能的任何样本数据抑制人口参数的置信区间的估计和建设使用熟悉的概率或设计为基础的推理方法。虽然基于遥感数据的地图可提供关于资源分布的信息，但基于地图的估计会有分类和预测误差，而且地图准确性衡量标准并不直接反映估计的不确定性。基于模型的推断不需要概率样本，并且当与合成估计一起使用时，可以规避与基于概率的推断相关联的小样本或无样本困难。该研究的重点是估计比例森林面积使用陆地卫星数据的研究领域在明尼苏达州，美国和地上生物量使用机载激光扫描数据的研究领域在赫德马克县，挪威。对于这两个研究领域，基于模型的推断被用来估计构建非抽样地区人口平均值置信区间所需的组件。这些估计数与简单随机抽样、模型辅助和基于模型的估计数进行了比较，如果对这些地区进行了抽样，就可以获得这些估计数。所有估计数都在两个简单随机抽样标准误差之内，从而说明了基于模型的推断对非抽样地区的效用。
For remote and inaccessible forest regions, lack of sufficient or possibly any sample data inhibits estimation and construction of confidence intervals for population parameters using familiar probability- or design-based inferential methods. Although maps based on remotely sensed data may provide information on the distribution of resources, map-based estimates are subject to classification and prediction error, and map accuracy measures do not directly inform the uncertainty of the estimates. Model-based inference does not require probability samples and when used with synthetic estimation can circumvent small or no-sample difficulties associated with probability-based inference. The study focused on estimating proportion forest area using Landsat data for a study area in Minnesota, USA, and aboveground biomass using airborne laser scanning data for a study area in Hedmark County, Norway. For both study areas, model-based inference was used to estimate the components necessary for constructing confidence intervals for population means for non-sampled areas. The estimates were compared to simple random sampling, model-assisted, and model-based estimates that would have been obtained if the areas had been sampled. All estimates were within two simple random sampling standard errors of each other, thereby illustrating the utility of model-based inference for non-sampled areas.