Abstract Anthropogenic Marine Debris (AMD) is one of the most important pollutants in the oceans. Millions of tons of debris across oceans have created a critical environmental problem. This study presents a novel method aimed to improve the identification of macroplastics through remote sensing over beaches, combining AMD hyperspectral laboratory characterization and digital supervised classification in high spatial resolution imagery. Several samples were collected from the Chiloe Island beaches, Chile. Spectral signature samples and physical properties were assessed through laboratory work. HyLogger3® (CSIRO), PS-300 Apogee and ASD Field Spec hyperspectral systems were used to characterize each sample. Using those measurements, a spectral library was generated by processing, filtering and sorting the spectral data gathered, determining distinctive spectral bands for digital classification. By using this spectral library, a digital classification method was implemented over World-View 3 imagery, covering the three beaches selected as test sites. Distinct classification methods and geospatial analyses were applied to determine land cover composition, aimed for the detection of Styrofoam and the rest of anthropogenic marine debris. Four field campaigns were carried out to validate the AMD classification and mass retrievals, performed on >300 ground based points. The AMD hyperspectral library was successfully applied for an AMD digital classification in satellite imagery. Support Vector Machine method showed the best performance, resulting in an overall accuracy equivalent to 88% and over 50 tons of debris estimated on the pilot beaches. These results prove the feasibility of quantifying macro-AMD through the integration of hyperspectral laboratory measurements and remote sensing imagery, allowing to estimate anthropogenic influence on natural ecosystems and providing valuable information for further development of the methodology and sustainable AMD management.

中文翻译：

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海滩上的人为海洋垃圾：遥感应用的光谱特征

摘要 人为海洋垃圾（AMD）是海洋中最重要的污染物之一。数百万吨的海洋垃圾造成了严重的环境问题。本研究提出了一种新方法，旨在通过对海滩进行遥感，将 AMD 高光谱实验室表征和高空间分辨率图像中的数字监督分类相结合，提高对大塑料的识别。从智利奇洛埃岛海滩收集了几个样本。通过实验室工作评估光谱特征样本和物理特性。HyLogger3® (CSIRO)、PS-300 Apogee 和 ASD Field Spec 高光谱系统用于表征每个样品。使用这些测量，通过处理、过滤和分类收集的光谱数据生成光谱库，确定用于数字分类的独特光谱带。通过使用该光谱库，在 World-View 3 影像上实施了一种数字分类方法，涵盖了被选为测试地点的三个海滩。应用不同的分类方法和地理空间分析来确定土地覆盖组成，旨在检测聚苯乙烯泡沫塑料和其他人为海洋垃圾。进行了四次实地活动以验证 AMD 分类和大规模检索，在超过 300 个地面点上进行。AMD 高光谱库已成功应用于卫星图像中的 AMD 数字分类。支持向量机方法表现出最好的性能，在试点海滩上估计的整体精度相当于 88% 和超过 50 吨的碎片。