Multi-temporal high-resolution marsh vegetation mapping using unoccupied aircraft system remote sensing and machine learning

Anna E. Windle, L. Staver, A. Elmore, Stephanie Scherer, Seth Keller, Ben Malmgren, G. Silsbe
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Abstract

Coastal wetlands are among the most productive ecosystems in the world and provide important ecosystem services related to improved water quality, carbon sequestration, and biodiversity. In many locations, wetlands are threatened by coastal development and rising sea levels, prompting an era of tidal wetland restoration. The creation and restoration of tidal marshes necessitate the need for ecosystem monitoring. While satellite remote sensing is a valuable monitoring tool; the spatial and temporal resolution of imagery often places operational constraints, especially in small or spatially complex environments. Unoccupied aircraft systems (UAS) are an emerging remote sensing platform that collects data with flexible on-demand capabilities at much greater spatial resolution than sensors on aircraft and satellites, and resultant imagery can be readily rendered in three dimensions through Structure from Motion (SfM) photogrammetric processing. In this study, UAS data at 5 cm resolution was collected at an engineered wetland at Poplar Island, located in Chesapeake Bay, MD United States five times throughout 2019 to 2022. The wetland is dominated by two vegetation species: Spartina alterniflora and Spartina patens that were originally planted in 2005 in low and high marsh elevation zones respectively. During each survey, UAS multispectral reflectance, canopy elevation, and texture were derived and used as input into supervised random forest classification models to classify species-specific marsh vegetation. Overall accuracy ranged from 97% to 99%, with texture and canopy elevation variables being the most important across all datasets. Random forest classifications were also applied to down-sampled UAS data which resulted in a decline in classification accuracy as spatial resolution decreased (pixels became larger), indicating the benefit of using ultra-high resolution imagery to accurately and precisely distinguish between wetland vegetation. High resolution vegetation classification maps were compared to the 2005 as-built planting plans, demonstrating significant changes in vegetation and potential instances of marsh migration. The amount of vegetation change in the high marsh zone positively correlated with interannual variations in local sea level, suggesting a feedback between vegetation and tidal inundation. This study demonstrates that UAS remote sensing has great potential to assist in large-scale estimates of vegetation changes and can improve restoration monitoring success.
基于无人飞机系统遥感和机器学习的多时相高分辨率沼泽植被制图
沿海湿地是世界上最具生产力的生态系统之一,并提供与改善水质、碳封存和生物多样性有关的重要生态系统服务。在许多地方,湿地受到沿海开发和海平面上升的威胁,引发了潮汐湿地恢复的时代。潮汐沼泽的形成和恢复需要对生态系统进行监测。虽然卫星遥感是一种宝贵的监测工具;图像的空间和时间分辨率通常会对操作造成限制,特别是在小型或空间复杂的环境中。无人飞机系统(UAS)是一种新兴的遥感平台,它以比飞机和卫星上的传感器大得多的空间分辨率,以灵活的按需能力收集数据,并且通过运动结构(SfM)摄影测量处理,可以很容易地呈现三维图像。在这项研究中,在2019年至2022年期间,在位于美国马里兰州切萨皮克湾的Poplar岛的一个工程湿地上收集了五次分辨率为5厘米的无人机数据。湿地以互花米草和花米草两种植被为主,它们分别是2005年在沼泽低洼带和沼泽高洼带种植的。在每次调查中,提取UAS的多光谱反射率、冠层高程和纹理,并将其作为监督随机森林分类模型的输入,对特定物种的沼泽植被进行分类。总体精度在97%到99%之间,纹理和冠层高度变量在所有数据集中是最重要的。随机森林分类也被应用于下采样的UAS数据,导致分类精度随着空间分辨率的降低(像素变大)而下降,这表明使用超高分辨率图像准确和精确地区分湿地植被的好处。高分辨率植被分类图与2005年建成的种植计划进行了比较,显示了植被的显著变化和潜在的沼泽迁移实例。高沼泽区植被变化量与当地海平面年际变化呈正相关,表明植被与潮汐淹没之间存在反馈关系。该研究表明,无人机遥感在协助大规模估算植被变化和提高恢复监测成功率方面具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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