We present the discovery of the optical transient of the long-duration gamma-ray burst GRB 000630. The optical transient was detected with the Nordic Optical Telescope 21.1 hours after the burst. At the time of discovery the magnitude of the transient was . The transient displayed a power-law decline characterized by a decay slope of . A deep image obtained 25 days after the burst shows no indication of a contribution from a supernova or a host galaxy at the position of the transient. The closest detected galaxy is a galaxy 2.0 arcsec north of the transient. The magnitudes of the optical afterglows of GRB 980329, GRB 980613 and GRB 000630 were all less than 24 hours from the burst epoch. We discuss the implications of this for our understanding of GRBs without detected optical transients. We conclude that i) based on the gamma-ray properties of the current sample we cannot conclude that GRBs with no detected OTs belong to another class of GRBs than GRBs with detected OTs and ii) the majority (75% ) of GRBs for which searches for optical afterglow have been unsuccessful are consistent with no detection if they were similar to bursts like GRB 000630 at optical wavelengths.

Unmanned aerial vehicles (UAVs) are becoming increasingly accessible tools with widespread use as environmental monitoring systems. They can be used for anthropogenic marine debris survey, a recently growing research field. In fact, while the increasing efforts for offshore investigations lead to a considerable collection of data on this type of pollution in the open sea, there is still little knowledge of the materials deposited along the coasts and the mechanism that leads to their accumulation pattern. UAVs can be effective in bridging this gap by increasing the amount of data acquired to study coastal deposits, while also limiting the anthropogenic impact in protected areas. In this study, UAVs have been used to acquire geo-referenced RGB images in a selected zone of a protected marine area (the Migliarino, Massacciuccoli, and San Rossore park near Pisa, Italy), during a long-term (ten months) monitoring programme. A post processing system based on visual interpretation of the images allows the localization and identification of the anthropogenic marine debris within the scanned area, and the estimation of their spatial and temporal distribution in different zones of the beach. These results provide an opportunity to investigate the dynamics of accumulation over time, suggesting that our approach might be appropriate for monitoring and collecting such data in isolated, and especially in protected, areas with significant benefits for different types of stakeholders.

Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing this task. Students from Italian secondary schools (in this work, the CSO) were invited to identify, mark, and classify stranded litter items on a UAV orthophoto collected on an Italian beach. A specific training program and working tools were developed for the aim. The comparison with the standard in situ visual census survey returned a general underestimation (50%) of items. However, marine litter bulk categorisation was fairly in agreement with the in situ survey, especially for sources classification. The concordance level among CSO ranged between 60% and 91%, depending on the item properties considered (type, material, and colour). As the assessment accuracy was in line with previous works developed by experts, remote detection of marine litter on UAV images can be improved through citizen science programs, upon an appropriate training plan and provision of specific tools.