Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

No abstract

The Modular Aerial Camera System (MACS) is a development platform for optical remote sensing concepts, algorithms and special environments. For Real-Time Services for Maritime Security (EMSec joint project) a new multi-sensor configuration MACS-Mar was realized. It consists of 4 co-aligned sensor heads in the visible RGB, near infrared (NIR, 700-950 nm), hyperspectral (HS, 450-900 nm) and thermal infrared (TIR, 7.5…14 µm) spectral range, a mid-cost GNSS/INS system, a processing unit and two data links. On-board image projection, cropping of redundant data and compression enable the instant generation of direct-georeferenced high resolution image mosaics, automatic object detection, vectorization and annotation of floating objects on the water surface. The results were transmitted over a distance up to 50 km in real-time via narrow and broadband data links and were visualized in a maritime situation awareness system. For the automatic onboard detection of objects a segmentation and classification workflow based on RGB, NIR and TIR information was developed and tested in September 2016. The completeness of the object detection in the experiment resulted in 95 %, the correctness in 53 %. Mostly bright backwash of ships led to overdetection of the number of objects, further refinement using water homogeneity in the TIR, as implemented in the workflow, couldn't be carried out due to problems with the TIR sensor. To analyze the influence of high resolution TIR imagery and to reach the expected detection quality a further experiment was conducted in August 2017. Adding TIR images the completeness was increased to 98 % and the correctness to 74 %.

The monitoring of worldwide ship traffic is a field of high topicality. Activities like piracy, ocean dumping, and refugee transportation are in the news every day. The detection of ships in remotely sensed data from airplanes, drones, or spacecraft contributes to maritime situational awareness. However, the crucial factor is the up-to-dateness of the extracted information. With ground-based processing, the time between image acquisition and delivery of the extracted product data is in the range of several hours, mainly due to the time consumed by storing and transmission of the large image data. By processing and analyzing them on-board and transmitting the product data directly as ship position, heading, and velocity, the delay can be shortened to some minutes. Real-time connections via satellite telecommunication services allow small packets of information to be sent directly to the user without significant delay. The AMARO (Autonomous Real-Time Detection of Moving Maritime Objects) project at DLR is a feasibility study of an on-board ship detection system involving on-board processing and real-time communication. The operation of a prototype system was successfully demonstrated on an airborne platform in spring 2018. The on-ground user could be informed about detected vessels within minutes after sighting without a direct communication link. In this article, the scope, aim, and design of the AMARO system are described, and the results of the flight experiment are presented in detail.Sensors 2020, 20, 1324 2 of 23 collision avoidance. Ships send out their identification, position, course, speed, and several other traffic-related data. This data is then received by other ships and ground stations in close range. Nowadays, to be able to track ships globally in real-time, satellites are also used to receive AIS data [7]. However, based on AIS data only, the detection of illegal activities like water pollution, illegal fishing, or smuggling is limited.To improve maritime domain awareness, Earth observation (EO) satellite data is a valuable source of information. Great efforts are made in researching the potential of vessel detection in optical and radar satellite images [8][9][10]. However, in most cases, these images are analyzed long after the data have been acquired [11]. To tackle this bottleneck, there is also promising progress in establishing near-real-time services on the ground, which today can provide information in, at best, the range of 15 min, measured from on-ground data reception [12,13]. However, the most significant time delay occurs between data acquisition on-board and data reception on-ground, since image data are comparatively huge and their downlink requires a direct contact to a ground station. This delay can amount to hours or even days [14].A second drawback of EO satellites for time-critical applications is their inability to continuously monitor a defined region of interest. Satellites with a reasonable spatial resolution for ship detection orbit in LEO (low Earth orbit) with speeds of a...

This article describes a new version of the Modular Airborne Camera System (MACS) [1], which has been made especially for maritime applications. The system is a successor to a High Altitude Long Endurance (HALE) MACS system which was operated in the Himalayas in 2014 [2]. Scenarios in the maritime environment require a different constellation of sensor heads, as shown in this paper. Furthermore, a method of supplying real-time capabilities to ensure flexibility in operation, as well as photogrammetric evaluation, is described. An overview of current and planned results will give an idea of the camera system's potential. The scene to be monitored is an unchanging surface; in general, nothing but water can be seen. Every change of this condition needs to be detected and classified automatically and in real-time. To ensure this, it is necessary to have more than just a camera capturing visible light, so a thermal infrared capturing camera and a hyperspectral sensor are also used. The classification results derived onboard are sent to a ground station by radio downlink. An operator in the command and control facility is then able to use this georeferenced semantic information to decide on the next steps.