Operational Monitoring of Illegal Fishing in Ghana through Exploitation of Satellite Earth Observation and AIS Data.

Over the last decade, West African coastal countries, including Ghana, have experienced extensive economic damage due to illegal, unreported and unregulated (IUU) fishing activity, estimated at about USD 100 million in losses each year. Illegal, unreported and unregulated fishing poses an enormous threat to the conservation and management of the dwindling fish stocks, causing multiple adverse consequences for fisheries, coastal and marine ecosystems and for the people who depend on these resources. The Integrated System for Surveillance of Illegal, Unlicensed and Unreported Fishing (INSURE) is an efficient and inexpensive system that has been developed for the monitoring of IUU fishing in Ghanaian waters. It makes use of fast-delivery Earth observation data from the synthetic aperture radar instrument on Sentinel-1 and the Multi Spectral Imager on Sentinel-2, detecting objects that differ markedly from their immediate background using a constant false alarm rate test. Detections are matched to, and verified by, Automatic Identification System (AIS) data, which provide the location and dimensions of ships that are legally operating in the region. Matched and unmatched data are then displayed on a web portal for use by coastal management authorities in Ghana. The system has a detection success rate of 91% for AIS-registered vessels, and a fast throughput, processing and delivering information within 2 h of acquiring the satellite overpass. However, over the 17-month analysis period, 75% of SAR detections have no equivalent in the AIS record, suggesting significant unregulated marine activity, including vessels potentially involved in IUU. The INSURE system demonstrated its efficiency in Ghana’s exclusive economic zone and it can be extended to the neighbouring states in the Gulf of Guinea, or other geographical regions that need to improve fisheries surveillance

Remote sensing of fish-processing in the Sundarbans Reserve Forest, Bangladesh: an insight into the modern slavery-environment nexus in the coastal fringe

© 2020, The Author(s). Land-based fish-processing activities in coastal fringe areas and their social-ecological impacts have often been overlooked by marine scientists and antislavery groups. Using remote sensing methods, the location and impacts of fish-processing activities were assessed within a case study of Bangladesh’s Sundarbans mangrove forests. Ten fish-processing camps were identified, with some occurring in locations where human activity is banned. Environmental degradation included the removal of mangroves, erosion, and the destruction of protected areas. Previous studies have identified cases of labour exploitation and modern slavery occurring within the Sundarbans, and remote sensing was used to triangulate these claims by providing spatial and temporal analysis to increase the understanding of the operational trends at these locations. These findings were linked to the cyclical relationship between modern slavery and environmental degradation, whereby environmental damage is both a driver and result of workers subjected to modern slavery. Remote sensing can be used as an additional methodological tool to support the achievement of the Sustainable Development Goals (SDGs) and provide evidence to support the promotion of the “freedom dividend” which would have far-reaching economic, social, cultural, and environmental benefits. Satellite remote sensing is likely to play an important role going forward for understanding these issues but should be augmented with ground-based data collection methods

Slavery from space: an analysis of the modern slavery-environmental degradation nexus using remote sensing data

Modern slavery has been connected to degradation of the environment, and has been found to contribute to anthropogenic climate change. Three sectors have been investigated using satellite Earth Observation (EO) data in order to provide a unique insight into the modern slavery-environmental degradation nexus. Remote sensing affords a unique ability to measure and understand these ecological changes over large timescales, and vast geographical areas. A local, regional, and global assessment of sectors known to heavily use modern slavery practices within their workforce has been undertaken using a variety of remotely sensed data sources and products. Fish-processing, brick kilns, and tree loss associated with multiple sectors, have all been analysed. Levels of environmental damage in the affected sectors have been noted, and measured using satellite EO data. These effects have included: tree loss of mangroves and tropical forests for fish-processing camps and oil palm plantations; the emission of pollutants which contribute to atmospheric climate change; the extraction of resources, such as groundwater and good-quality topsoil; and changes to landcover and land-use in areas that are important for production of food and economic support for large populations. Over the course of this investigation, ten post-harvest fish-processing camps have been located, and the first replicable methodology for estimating the number of brick kilns in the South Asian ‘Brick Belt’ region has been provided – where open access satellite EO data enabled the estimation of 55,387 brick kilns. The latter has since enabled machine learning methodologies to provide accurate locations and kiln ages which have assisted in the environmental assessment of this large-scale transnational industry. Furthermore, if modern slavery practices were eliminated from this industry, the environmental impact of the brick-making could be reduced by the equivalent of almost 10,000 kilns. Finally, tree loss has been quantified and the policy implications of deforestation and forest degradation as a result of modern slavery have been explored in four countries. Ultimately, there are a large variety of environmentally degrading activities known to use modern slavery practices that may be explored using satellite EO data. Remote sensing throughout this thesis has enabled the exploration of these implications for some sectors, and proved the proof of concept that additional data acquisition from remotely sensed sources, can support in the overall goal of assisting in the understanding and eradication of modern slavery. Satellite EO is an underutilised methodology within the antislavery community and, as shown within this thesis, there is the power to investigate the environmental implications of these sectors which have had numerous documented cases of modern slavery. In order to achieve the Sustainable Development Goals (SDGs) – particularly target 8.7 which aims to end modern slavery by 2030 – multiple avenues of investigation are required to understand, locate, and eradicate modern slavery. Applying remote sensing to assess the ecological impact of these cases is one such avenue that can provide information to assist in this achievement, and support the success of multiple SDGs. The author would like to acknowledge that they have written the thesis from the starting point of being a non-survivor

훈련 자료 자동 추출 알고리즘과 기계 학습을 통한 SAR 영상 기반의 선박 탐지

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 2021. 2. 김덕진.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vessel’s movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner. As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval. Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.전천후 지구 관측 위성인 SAR를 통한 선박 탐지는 해양 자원의 확보와 해상 안전 보장에 매우 중요한 역할을 한다. 기계 학습 기법의 도입으로 인해 선박을 비롯한 사물 탐지의 정확도 및 효율성이 향상되었으나, 이와 관련된 다수의 연구는 탐지 알고리즘의 개량에 집중되었다. 그러나, 탐지 정확도의 근본적인 향상은 정밀하게 취득된 대량의 훈련자료 없이는 불가능하기에, 본 연구에서는 선박의 실시간 위치, 속도 정보인 AIS 자료를 이용하여 인공 지능 기반의 선박 탐지 알고리즘에 사용될 훈련자료를 자동적으로 취득하는 알고리즘을 제안하였다. 이를 위해 이산적인 AIS 자료를 SAR 영상의 취득시각에 맞추어 정확하게 보간하고, AIS 센서 자체가 가지는 오차를 최소화하였다. 또한, 이동하는 산란체의 시선 속도로 인해 발생하는 도플러 편이 효과를 보정하기 위해 SAR 위성의 상태 벡터를 이용하여 위성과 산란체 사이의 거리를 정밀하게 계산하였다. 이렇게 계산된 AIS 센서의 영상 내의 위치로부터 선박 내 AIS 센서의 배치를 고려하여 선박 탐지 알고리즘의 훈련자료 형식에 맞추어 훈련자료를 취득하고, 어선에 대한 위치, 속도 정보인 VPASS 자료 역시 유사한 방법으로 가공하여 훈련자료를 취득하였다. AIS 자료로부터 취득한 훈련자료는 기존 방법대로 수동 취득한 훈련자료와 함께 인공 지능 기반 사물 탐지 알고리즘을 통해 정확도를 평가하였다. 그 결과, 제시된 알고리즘으로 취득한 훈련 자료는 수동 취득한 훈련 자료 대비 더 높은 탐지 정확도를 보였으며, 이는 기존의 사물 탐지 알고리즘의 평가 지표인 정밀도, 재현율과 F1 score를 통해 진행되었다. 본 연구에서 제안한 훈련자료 자동 취득 기법으로 얻은 선박에 대한 훈련자료는 특히 기존의 선박 탐지 기법으로는 분별이 어려웠던 항만에 인접한 선박과 산란체 주변의 신호에 대한 정확한 분별 결과를 보였다. 본 연구에서는 이와 함께, 선박 탐지 결과와 해당 지역에 대한 AIS 및 VPASS 자료를 이용하여 선박의 미식별성을 판정할 수 있는 가능성 또한 제시하였다.Chapter 1. Introduction - 1 - 1.1 Research Background - 1 - 1.2 Research Objective - 8 - Chapter 2. Data Acquisition - 10 - 2.1 Acquisition of SAR Image Data - 10 - 2.2 Acquisition of AIS and VPASS Information - 20 - Chapter 3. Methodology on Training Data Procurement - 26 - 3.1 Interpolation of Discrete AIS Data - 29 - 3.1.1 Estimation of Target Interpolation Time for Vessels - 29 - 3.1.2 Application of Kalman Filter to AIS Data - 34 - 3.2 Doppler Frequency Shift Correction - 40 - 3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 - 3.2.2 Mitigation of Doppler Frequency Shift - 48 - 3.3 Retrieval of Training Data of Vessels - 53 - 3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 - Chapter 4. Methodology on Object Detection Architecture - 66 - Chapter 5. Results - 74 - 5.1 Assessment on Training Data - 74 - 5.2 Assessment on AIS-based Ship Detection - 79 - 5.3 Assessment on VPASS-based Fishing Boat Detection - 91 - Chapter 6. Discussions - 110 - 6.1 Discussion on AIS-Based Ship Detection - 110 - 6.2 Application on Determining Unclassified Vessels - 116 - Chapter 7. Conclusion - 125 - 국문 요약문 - 128 - Bibliography - 130 -Maste

Slavery from space: an analysis of the modern slavery-environmental degradation nexus using remote sensing data

Modern slavery has been connected to degradation of the environment, and has been found to contribute to anthropogenic climate change. Three sectors have been investigated using satellite Earth Observation (EO) data in order to provide a unique insight into the modern slavery-environmental degradation nexus. Remote sensing affords a unique ability to measure and understand these ecological changes over large timescales, and vast geographical areas. A local, regional, and global assessment of sectors known to heavily use modern slavery practices within their workforce has been undertaken using a variety of remotely sensed data sources and products. Fish-processing, brick kilns, and tree loss associated with multiple sectors, have all been analysed. Levels of environmental damage in the affected sectors have been noted, and measured using satellite EO data. These effects have included: tree loss of mangroves and tropical forests for fish-processing camps and oil palm plantations; the emission of pollutants which contribute to atmospheric climate change; the extraction of resources, such as groundwater and good-quality topsoil; and changes to landcover and land-use in areas that are important for production of food and economic support for large populations. Over the course of this investigation, ten post-harvest fish-processing camps have been located, and the first replicable methodology for estimating the number of brick kilns in the South Asian ‘Brick Belt’ region has been provided – where open access satellite EO data enabled the estimation of 55,387 brick kilns. The latter has since enabled machine learning methodologies to provide accurate locations and kiln ages which have assisted in the environmental assessment of this large-scale transnational industry. Furthermore, if modern slavery practices were eliminated from this industry, the environmental impact of the brick-making could be reduced by the equivalent of almost 10,000 kilns. Finally, tree loss has been quantified and the policy implications of deforestation and forest degradation as a result of modern slavery have been explored in four countries. Ultimately, there are a large variety of environmentally degrading activities known to use modern slavery practices that may be explored using satellite EO data. Remote sensing throughout this thesis has enabled the exploration of these implications for some sectors, and proved the proof of concept that additional data acquisition from remotely sensed sources, can support in the overall goal of assisting in the understanding and eradication of modern slavery. Satellite EO is an underutilised methodology within the antislavery community and, as shown within this thesis, there is the power to investigate the environmental implications of these sectors which have had numerous documented cases of modern slavery. In order to achieve the Sustainable Development Goals (SDGs) – particularly target 8.7 which aims to end modern slavery by 2030 – multiple avenues of investigation are required to understand, locate, and eradicate modern slavery. Applying remote sensing to assess the ecological impact of these cases is one such avenue that can provide information to assist in this achievement, and support the success of multiple SDGs. The author would like to acknowledge that they have written the thesis from the starting point of being a non-survivor

Enhancing Satellite Oceanography-Driven Research in West Africa: a Case Study of Capacity Development in an Underserved Region

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168391/1/rsess_2021_nyadjro_etal_remotesensing_GhanaSchool.pdfSEL

Water Quality Status Within The Anchorage Space of Tema Harbour, Ghana

Marine pollution is attributable to anthropogenic introductions of contaminants above their natural background levels and being dispersed by ocean forcing. Assemblages of vessels within offshore platforms and seaport terminals could also be potential sources for marine water contamination. As such, nearshore perimeters of the Tema Port were assessed to review the vessel register and the seawater quality through Automatic Identification System (AIS), in-situ and laboratory analysis. The results of analysed satellite data suggested ~1,600 commercial vessels of over 50 flag states including Ghana were present in the West Africa territorial waters between 2016 and 2020. Bacterial load shows the following order: total heterotrophic bacterial [THB] (364-468 cfu/mL) > total coliform [TC] (26-73 cfu/100 mL) > faecal coliform [FC] (1-13 cfu/100 mL). Phytoplankton species abundances were in order Ceratium spp. (31.8%) >Protoperidinium spp. (30.1%) > Dinophysis spp. (9.3%) > Coscinodiscus sp. (7.3%) > Lingulodinium polyedra (6.9%) = Nitzschia sp. (6.9%). Water temperature ranged between 23.9 and 27.5 oC (surface to 25.4 m depth), salinity 36.03 ± 0.51‰, dissolved oxygen 6.54 ± 0.94 mg/L and pH 8.18 ± 0. 06. Phosphate, ammonia, Cd, As, and Pb levels were low (0.01 to 0.153 mg/L). Nitrate, silicate and Mg were relatively high (0.7 - 2.18 mg/L). Pearson correlation coefficient displayed 0.05 and 0.01 significant levels between total dissolved solids (TDS) and electrical conductivity and salinity, and dissolved oxygen and temperature and arsenic levels. Normalization physicochemical data suggested thermal stratification at 15 m depth. Nutrient and biological results indicated normal water quality conditions, however, relatively high levels of phytoplankton including harmful and toxic species suggested excess nutrient contamination in the study area. Further assessment is recommended to ascertain the link between phytoplankton and nutrient load at the anchorage space.&nbsp

Design in Engineering: An Evaluation of Civilian and Military Unmanned Aerial Vehicle Platforms, Considering Smart Sensing with Ethical Design to Embody Mitigation Against Asymmetric Hostile Actor Exploitation

This report is written in part-fulfilment of personal output criteria for the Visiting Research Fellowship (Sir Richard Grenville Fellowship) at the Changing Character of War Centre, Pembroke College, Oxford, and the Centre for Sea Power and Strategy, Britannia Royal Naval College, Plymouth University at BRNC, Dartmouth. In this report I undertook an extensive analysis of the maritime UAV platform systems sector of a wide range of upstream manufacturing industry and downstream end user stakeholders. I consulted a global range of military and civilian users, to inform discussions around civilian UAV platforms which could be modified by hostile non-state actors, with emphasis on the littoral maritime region. This has strategic relevance to the United Kingdom, being an island-state with over 10,000 miles of coastline, c. 600 ports, and nearly 300 off-shore oil and gas platforms. In addition the UK has 14 dependencies together with a combined EEZ of 2.5 million square miles, the fifth largest in the world

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews