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An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

机译:利用高分辨率LiDAR数据和航空影像检测潜在林地春季库的有效方法

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Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts.
机译:林地春季水池的有效保护-区域两栖动物多样性和生态系统服务的重要组成部分-取决于准确定位和绘制这些水池。当前识别潜在春季库的方法主要基于视觉解释和航拍照片的数字化,准确性可变且重复性低。在本文中,我们提出了一种有效且有效的方法,该方法使用随机凹陷分析与其他地理空间分析来检测和映射潜在的春季库。我们的方法旨在利用高分辨率光检测和测距(LiDAR)数据的优势,这些数据变得越来越可用,尽管在春季库研究中并未经常使用。我们在马萨诸塞州东部〜150 km 2 研究区成功检测出2000多个潜在的春季池。我们研究中的准确性评估表明,佣金率在2.5%至6.0%之间,而代理人遗漏率是8.2%,该比率远低于美国东北部先前进行的春季库研究报告的误差。我们的半自动方法用于春季池识别的一个重要优点是,它可以减少与手动照片解释方法相关的不一致性并减轻可重复性问题。我们策略的另一个优势是,除了可以检测清单的基于点的春季池位置之外,还可以将春季池的边界提取为多边形特征以表征其几何属性,这在当前的州级春季池中是不可用的马萨诸塞州的数据库。

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