Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

Abstract

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.

UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

“…Unoccupied Aerial Vehicles (UAVs) have become a widely applied tool in the field of spatial ecology [1,2], including the mapping and monitoring of shallow-water habitats [3][4][5][6][7][8][9]. Their ability to collect aerial imagery with a high spatial and temporal resolution over relatively large areas in a cost-and time-efficient way has allowed researchers to study ecosystems on new spatial scales by bridging the gap between traditional satellite and plane-based remote sensing and in-water techniques, including diver and vessel-based surveys [10].…”