Predicting Barrier Island Shrub Presence Using Remote Sensing Products and Machine Learning Techniques

IF 3.5 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Benton Franklin, Laura J. Moore, Julie C. Zinnert
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Abstract

Barrier islands are highly dynamic coastal landforms that are economically, ecologically, and societally important. Woody vegetation located within barrier island interiors can alter patterns of overwash, leading to periods of periodic-barrier island retreat. Due to the interplay between island interior vegetation and patterns of barrier island migration, it is critical to better understand the factors controlling the presence of woody vegetation on barrier islands. To provide new insight into this topic, we use remote sensing data collected by LiDAR, LANDSAT, and aerial photography to measure shrub presence, coastal dune metrics, and island characteristics (e.g., beach width, island width) for an undeveloped mixed-energy barrier island system in Virginia along the US mid-Atlantic coast. We apply decision tree and random forest machine learning methods to identify new empirical relationships between island geomorphology and shrub presence. We find that shrubs are highly likely (90% likelihood) to be present in areas where dune elevations are above ∼1.9 m and island interior widths are greater than ∼160 m and that shrubs are unlikely (10% likelihood) to be present in areas where island interior widths are less than ∼160 m regardless of dune elevation. Our machine learning predictions are 90% accurate for the Virginia Barrier Islands, with almost half of our incorrect predictions (5% of total transects) being attributable to system hysteresis; shrubs require time to adapt to changing conditions and therefore their growth and removal lags changes in island geomorphology, which can occur more rapidly.

利用遥感产品和机器学习技术预测壁垒岛灌木存在情况
屏障岛是高度动态的海岸地貌,在经济、生态和社会方面都具有重要意义。位于屏障岛内部的木本植被会改变冲刷模式,导致屏障岛周期性后退。由于岛屿内部植被与屏障岛迁移模式之间的相互作用,更好地了解控制屏障岛上木本植被存在的因素至关重要。为了对这一主题提供新的见解,我们利用激光雷达、LANDSAT 和航空摄影收集的遥感数据,测量了美国大西洋中部沿岸弗吉尼亚州一个未开发的混合能源障碍岛系统的灌木存在情况、沿海沙丘指标和岛屿特征(如海滩宽度、岛屿宽度)。我们采用决策树和随机森林机器学习方法来确定岛屿地貌与灌木存在之间的新经验关系。我们发现,在沙丘海拔高于 1.9 米且岛屿内部宽度大于 160 米的区域,灌木极有可能出现(90% 的可能性),而在岛屿内部宽度小于 160 米的区域,无论沙丘海拔如何,灌木都不可能出现(10% 的可能性)。我们的机器学习预测对弗吉尼亚障碍群岛的准确率为 90%,几乎一半的错误预测(占总横断面的 5%)可归因于系统滞后;灌木需要时间来适应不断变化的条件,因此它们的生长和移除滞后于岛屿地貌的变化,而后者可能发生得更快。
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来源期刊
Journal of Geophysical Research: Earth Surface
Journal of Geophysical Research: Earth Surface Earth and Planetary Sciences-Earth-Surface Processes
CiteScore
6.30
自引率
10.30%
发文量
162
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