Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States
Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States
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DOI：
10.1016/j.foreco.2018.08.020
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发表时间：
2018-12-15
影响因子：
3.7
通讯作者：
Ryerson, Daniel
中科院分区：
文献类型：
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作者：
Coleman, Tom W.;Graves, Andrew D.;Ryerson, Daniel
昆虫和疾病每年造成数百万公顷的落叶和死亡的森林景观在美国监测这些干扰事件，美国农业部林务局，森林健康保护和合作的国家合作伙伴进行年度空中检测调查。这些数据是将森林干扰与特定昆虫和疾病联系起来的最大和最具历史意义的遥感数据集。然而，这些数据的正式准确性评估在规模和范围上都非常有限。我们的两个主要目标是：（1）评估航空探测调查数据的准确性，以描绘森林干扰归因于昆虫，疾病，和非生物事件在多个生态区域和（2）评估航空测量员的可重复性，以定位和观察落叶和死亡率的森林景观。我们研究中使用的方法代表了全国范围内航空探测调查采用的程序。我们在分析中使用了逐步下降的方法，以显示从广泛到特定类别的航空测量观测的准确性，例如从摄食公会到特定的损害因子。我们使用误差矩阵来比较空中探测事件和地面观测的准确性。所有误差矩阵的总体准确度为> 70%的空中测量观测地面收集的数据进行比较。树木死亡率和落叶的损害类型的意见有很高的准确性，只有2%和5%的佣金误差，分别。超过一半的地面收集数据验证了小蠹引起的树木死亡率，代表低佣金误差（9%）。准确性下降的观察的特异性从属到树种和破坏剂的物种水平，但许多突出的树种和破坏剂的零和低佣金的错误。在小的多边形（< 2公顷），航空测量员准确地观察到的树木损伤和死亡率的计数比高估和低估的观察更频繁，而树木英亩（-1）的观察更常见高估比正确的树木英亩（-1）的观察。在重复性测试中，与航空探测调查期间相比，在高分辨率图像上绘制树木损伤和死亡率时，多边形的一致性更高，但使用类似的点和多边形来注意两种调查技术之间的损伤。此外，米平均公顷映射的所有多边形和落叶多边形之间的调查技术。这项研究的结果改善了全国范围内的昆虫和疾病调查技术，并增加了这些数据的使用，同时承认其准确性和局限性。
Insects and diseases annually cause millions of hectares of defoliation and mortality to forested landscapes in the U.S. To monitor these disturbance events, the USDA Forest Service, Forest Health Protection and cooperating state partners conduct annual aerial detection surveys. These data represent the largest, and most historical, remote sensing dataset linking forest disturbances to specific insects and diseases. However, formal accuracy assessments of these data are exceptionally limited in scale and scope. Our two primary objectives were to (1) assess the accuracy of aerial detection survey data for delineating forest disturbances attributed to insects, diseases, and abiotic events across multiple ecological regions and (2) assess the repeatability of aerial surveyors to locate and observe defoliation and mortality on a forested landscape. The methodology used in our study represents the procedures adopted nationwide for aerial detection surveys. We used a stepdown approach in our analyses to show the accuracy of aerial survey observations from broad to specific categories, such as from feeding guilds to specific damage agents. We used error matrices to compare the accuracy of aerially detected events and ground observations. Overall accuracy in all the error matrices was > 70% for comparisons of aerial survey observations to ground-collected data. Damage type observations for tree mortality and defoliation had high-levels of accuracy with only 2% and 5% commission error, respectively. More than half of the ground collected data verified bark beetle-caused tree mortality, representing low commission error (9%). Accuracy declined as the specificity for observations went from genera to species level for tree species and damaging agents, but many of the prominent tree species and damaging agents had zero and low commission errors. In small polygons (< 2 ha), aerial surveyors accurately observed counts for tree injury and mortality more frequently than over- and underestimated observations, whereas trees acre(-1) observations were more commonly overestimated than correct trees acre(-1) observations. Greater agreement of polygons occurred when tree injury and mortality were mapped on high-resolution imagery than during aerial detection surveys in the repeatability test, but similar usage of points and polygons were used to note injury between the two survey techniques. In addition, m ean hectares mapped were comparable for all polygons and defoliation polygons between the survey techniques. The results from this study improve insect and disease survey techniques nationwide and increase the use of these data while acknowledging its accuracies and limitations.