Quantifying error in aerial survey data
Quantifying error in aerial survey data
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DOI：
10.1080/00049158.2008.10675038
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发表时间：
2008-01
影响因子：
2.1
通讯作者：
Erik W. Johnson;Jen Ross
中科院分区：
文献类型：
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作者：
Erik W. Johnson;Jen Ross
航空测量，也称为航空草图测绘，是一种从飞机上观察森林损害症状并将信息人工转移到底图上的技术。最近美国西部的树皮甲虫死亡率很高，对航空测量数据产生了更大的需求和更不同的用途。虽然航空测量数据通常被认为是定性的，但最近对数据的应用引起了人们对定量评估其空间和分类准确性的兴趣。本文介绍了用于评估航空测量数据的精度的方法，并讨论了与误差结果相关的几个问题和应用。误差矩阵和kappa（k）统计，通常用于评估遥感图像分类的精度，被用来描述航空测量数据中存在的误差。实地工作人员收集了地面数据，用于验证1730万公顷233块土地的空中分类。从一个补充项目中将另外24个地块纳入验证，使地块总数达到257个。航空测量数据中的误差对于粗尺度分析来说是可以接受的，但对于细空间尺度的使用来说则过高。此外，本文还讨论了错误分析的好处，其中包括为报告目的量化错误，在元数据中包含错误率，以及通过突出错误最高的课程来集中培训计划和技术开发的能力。最后，精度评估实施相关的成本进行了描述，并权衡的好处。
Summary Aerial survey, also referred to as aerial sketchmapping, is the technique of observing symptoms of forest damage from an aircraft and transferring the information manually onto a base map. Recent high levels of bark beetle mortality across the western United States have generated greater demands for, and more disparate uses of, aerial survey data. While aerial survey data are typically considered to be qualitative in nature, the recent application of the data has driven an interest in assessing its spatial and categorical accuracies quantitatively. This paper describes methods for assessing the accuracy of aerial survey data and discusses several implications and applications related to the error results. The error matrix and kappa (k) statistic, commonly used to assess accuracies of image classifications in remote sensing, were used to describe errors present in the aerial survey data. Field crews collected ground data that were used to validate the aerial classifications on 233 plots across 17.3 million ha. An additional 24 plots were incorporated into the validation from a complementary project, bringing the total number of plots to 257. Errors within the aerial survey data were found to be acceptable for coarse-scale analyses but excessive for use at fine spatial scales. In addition, this paper discusses the benefits of error analysis which include the quantification of errors for reporting purposes, the inclusion of error rates in the metadata, and the ability to focus training programs and technology development by highlighting classes with the highest error. Finally, the cost associated with accuracy assessment implementation is described and weighed against the benefits.