In the context of marine litter monitoring, reporting the weight of beached litter can contribute to a better understanding of pollution sources and support clean -up activities. However, the litter scaling task requires considerable effort and specific equipment. This experimental study proposes and evaluates three methods to estimate beached litter weight from aerial images, employing different levels of litter categorization. The most promising approach (accuracy of 80 %) combined the outcomes of manual image screening with a generalized litter mean weight (14 g) derived from studies in the literature. Although the other two methods returned values of the same magnitude as the groundtruth, they were found less feasible for the aim. This study represents the first attempt to assess marine litter weight using remote sensing technology. Considering the exploratory nature of this study, further research is needed to enhance the reliability and robustness of the methods.

The abundance of beach litter has been increasing globally during the last decades, and it is an issue of global concern. A new survey strategy, based on uncrewed aerial vehicles (UAV, aka drones), has been recently adopted to improve the monitoring of beach macro-litter items abundance and distribution. This work identified and analysed the 15 studies that used drone for beach litter surveys on an operational basis. The analysis of technical parameters for drone flight deployment revealed that flight altitude varied between 5 and 40 m. The analysis of final assessments showed that, through manual and/or automated items detection on images, most of studies provided litter bulk characteristics (type, material and size), along with litter distribution maps. The potential standardization of drone-based litter survey would allow a comparison among surveys, however it seems difficult to propose a standard set of flight parameters, given the wide variety of coastal environments, the different devices available, and the diverse objectives of drone-based litter surveys. On the other hand, in our view, a set of common outcomes can be proposed, based on the grid mapping process, which can be easily generated following the procedure indicated in the paper. This work sets the ground for the development of a standardized protocol for drone litter data collection, analysis and assessments. This would allow the provision of broad scale comparative studies to support coastal management at both national and international scales.

Unmanned Aerial Systems (UAS, aka drones) are being used to map marine macro-litter on the coast. Within the UAS4Litter project, the application of UAS has been applied on three sandy beach-dune systems on the wave-dominated North Atlantic Portuguese coast. Several technical solutions have been tested in terms of drone mapping performance, manual image screening and marine litter map analysis. The conceptualization and implementation of a multidisciplinary framework allowed to improve and making more efficient the mapping of marine litter items with UAS on coastal environment. The location of major marine litter loads within the monitored areas were found associated to beach slope and water level dynamics on the beach profiles. Moreover, the abundance of marine pollution was related to the geographical location and level of urbanization of the study sites. The testing of machine learning techniques underlined that automated technique returned reliable abundance map of marine litter, while manual image screening was required for a detailed categorization of the items. As marine litter pollution on coastal dunes has received limited scientific attention when compared with sandy shores, a novel non-intrusive UAS-based marine litter survey have been also applied to quantify the level of contamination on coastal dunes. The results showed the influence of the different dune plant communities in trapping distinct type of marine litter, and the role played by wind and overwash events in defining the items pathways through the dune blowouts. The experiences on the Portuguese coast show that UAS allows an integrated approach for marine litter mapping, beach morphodynamic and nearshore hydrodynamic, setting the ground for marine litter dynamic modelling on the shore. Besides, UAS can give a new impulse to coastal dune litter monitoring, where the long residence time of marine debris threat the bio-ecological equilibrium of these ecosystems.

Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.

This introductory chapter provides a synopsis of the underlying factors that enable the existence of the mobility as a service (MaaS) concept and its role regarding the improvement of the performance of transportation systems. Therefore, several considerations are made to contextualize MaaS in modern societies explaining not only its main functions and advantages but also the challenges facing its widespread implementation.

Beliefs, expectations and values are often assumed to drive decisions about climate change adaptation. We tested hypotheses based on this assumption using survey responses from 508 European forest professionals in ten countries. We used the survey results to identify communication needs and the decision strategies at play, and to develop guidelines on adequate communications about climate change adaptation. We observed polarization in the positive and negative values associated with climate change impacts accepted by survey respondents. We identified a mechanism creating the polarization that we call the ‘blocked belief’ effect. We found that polarized values did not correlate with decisions about climate change adaptation. Strong belief in the local impacts of climate change on the forest was, however, a prerequisite of decision-making favoring adaptation. Decision-making in favor of adaptation to climate change also correlated with net values of expected specific impacts on the forest and generally increased with the absolute value of these in the absence of ‘tipping point’ behavior. Tipping point behavior occurs when adaptation is not pursued in spite of the strongly negative or positive net value of expected climate change impacts. We observed negative and positive tipping point behavior, mainly in SW Europe and N-NE Europe, respectively. In addition we found that advice on effective adaptation may inhibit adaptation when the receiver is aware of effective adaptation measures unless it is balanced with information explaining how climate change leads to negative impacts. Forest professionals with weak expectations of impacts require communications on climate change and its impacts on forests before any advice on adaptation measures can be effective. We develop evidence-based guidelines on communications using a new methodology which includes Bayesian machine learning modeling of the equivalent of an expected utility function for the adaptation decision problem.

Non-destructive testing (NDT) techniques play an important role in the characterization and diagnosis of historic buildings, keeping in mind their conservation and possible rehabilitation. This paper presents a new approach that merges building information modeling (BIM) with environment geospatial data obtained by several non-destructive techniques, namely terrestrial laser scanning, ground-penetrating radar, infrared thermography, and the automatic classification of pathologies based on RGB (red, green, blue) imaging acquired with an unmanned aircraft system (UAS). This approach was applied to the inspection of the Monastery of Batalha in Leiria, Portugal, a UNESCO World Heritage Site. To assess the capabilities of each technique, different parts of the monastery were examined, namely (i) part of its west façade, including a few protruding buttresses, and (ii) the masonry vaults of the Church (nave, right-hand aisle, and transept) and the Founder’s Chapel. After describing the employed techniques, a discussion of the optimization, treatment and integration of the acquired data through the BIM approach is presented. This work intends to contribute to the application of BIM in the field of cultural heritage, aiming at its future use in different activities such as facility management, support in the restoration and rehabilitation process, and research.

Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.

The environmental concerns together with social inclusion issues and the need to promote economic equity in the society have profound implications regarding the sustainable mobility concept. This allied to a technological (r)evolution leads to the path of the internet of mobility (IoM). On the other hand, we are witnessing the prosperity of mobility associated with services, mobility as a service (MaaS), which also aims at the integration of different transport modes. Linking together IoM and MaaS, the internet of mobility as a service (IoMaaS) concept is introduced, which can learn from the end user experiences and behaviors, enabling the reduction of ease of use and sustainable mobility, while supporting a much-needed cultural shift regarding mobility habits.

The role of values in climate-related decision-making is a prominent theme of climate communication research. The present study examines whether forest professionals are more driven by values than scientists are, and if this results in value polarization. A questionnaire was designed to elicit and assess the values assigned to expected effects of climate change by forest professionals and scientists working on forests and climate change in Europe. The countries involved covered a north-to-south and west-to-east gradient across Europe, representing a wide range of bio-climatic conditions and a mix of economic–social–political structures. We show that European forest professionals and scientists do not exhibit polarized expectations about the values of specific impacts of climate change on forests in their countries. In fact, few differences between forest professionals and scientists were found. However, there are interesting differences in the expected values of forest professionals with regard to climate change impacts across European countries. In Northern European countries, the aggregated values of the expected effects are more neutral than they are in Southern Europe, where they are more negative. Expectations about impacts on timber production, economic returns, and regulatory ecosystem services are mostly negative, while expectations about biodiversity and energy production are mostly positive.

The environmental concerns together with social inclusion issues and the need to promote economic equity in the society have profound implications regarding the sustainable mobility concept. This allied to a technological (r)evolution leads to the path of the internet of mobility (IoM). On the other hand, we are witnessing the prosperity of mobility associated with services, mobility as a service (MaaS), which also aims at the integration of different transport modes. Linking together IoM and MaaS, the internet of mobility as a service (IoMaaS) concept is introduced, which can learn from the end user experiences and behaviors, enabling the reduction of ease of use and sustainable mobility, while supporting a much-needed cultural shift regarding mobility habits.

Background. Uncertainty about climate change impacts on forests can hinder mitigation and adaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet different uncertainty components emerge in different studies. Consequently, inconsistent understanding of uncertainty among different climate impact studies (from the impact analysis to implementing solutions) can be an additional reason for delaying action. In this review we (a) expanded existing uncertainty assessment frameworks into one harmonised framework for characterizing uncertainty, (b) used this framework to identify and classify uncertainties in climate change impacts studies on forests, and (c) summarised the uncertainty assessment methods applied in those studies. Methods . We systematically reviewed climate change impact studies published between 1994 and 2016. We separated these studies into those generating information about climate change impacts on forests using models –‘modelling studies’, and those that used this information to design management actions—‘decision-making studies’. We classified uncertainty across three dimensions: nature , level , and location , which can be further categorised into specific uncertainty types. Results . We found that different uncertainties prevail in modelling versus decision-making studies. Epistemic uncertainty is the most common nature of uncertainty covered by both types of studies, whereas ambiguity plays a pronounced role only in decision-making studies. Modelling studies equally investigate all levels of uncertainty, whereas decision-making studies mainly address scenario uncertainty and recognised ignorance. Finally, the main location of uncertainty for both modelling and decision-making studies is within the driving forces—representing, e.g. socioeconomic or policy changes. The most frequently used methods to assess uncertainty are expert elicitation, sensitivity and scenario analysis, but a full suite of methods exists that seems currently underutilized. Discussion & Synthesis. The misalignment of uncertainty types addressed by modelling and decision-making studies may complicate adaptation actions early in the implementation pathway. Furthermore, these differences can be a potential barrier for communicating research findings to decision-makers.

The aim of this article is to assess if the data provided by soft classifiers and uncertainty measures can be used to identify regions with different levels of accuracy in a classified image. To this aim a soft Bayesian classifier was used, which enables the assignment of classifications confidence levels to all pixels. Two uncertainty measures were also used, namely the Relative Maximum Deviation (RMD) uncertainty measure and the Normalized Entropy (NE). The approach was tested on a case study. A multispectral IKONOS image was classified and the classification uncertainty and confidence where computed and analysed. Regions with different levels of uncertainty and confidence were identified. Reference datasets were then used to assess the classification accuracy of the whole study area and also in the regions with different levels of uncertainty and confidence. A comparative analysis was made on the variation of accuracy and classification uncertainty and confidence along the map and per class. The results show that for the regions with more uncertainty or less confidence the spatially constrained confusion matrices always generate lower values of global accuracy than for global accuracy of the regions with less uncertainty or more confidence. The analysis of the user’s and producer’s accuracy also shows the same general tendency. Proposals are then made on methodologies to use the information provided by the uncertainty and confidence to identify less reliable regions and also to improve classification results using fully automated approaches.

O acumulo de animais tem sido estudado pioneiramente nos Estados Unidos e recentemente no Brasil, sendo relacionado como Transtorno de Acumulacao de Animais. O tema e pouco conhecido e precisa ser apresentado, de forma mais clara, a comunidade academica e ao futuro medico-veterinario. O acumulador de animais e o individuo que tem um numero exagerado de animais, em um local com deficiencia de saneamento, espaco, alimento disponivel, cuidados veterinarios e que nao atende as necessidades basicas dos animais. Quando o numero de animais em uma residencia unica passa a ser problematico, isso se torna uma psicopatologia psiquiatrica chamada Hoarding, sendo o portador desse transtorno incapaz de reconhecer os efeitos deleterios ao bem- -estar a que os animais estao sendo submetidos. Diante do tema, o trabalho tem o objetivo de capacitar futuros medicos-veterinarios em como abordar o assunto e tomar iniciativas que possibilitem estrategias de tratamento adequadas, diferenciando acumuladores de protetores de animais. Para a realizacao do trabalho, foi elaborada uma palestra em PowerPoint, que foi apresentada aos alunos do 4o semestre do curso de Medicina Veterinaria, a fim de revelar o perfil de um acumulador. O porque de se ter a consciencia e clareza de suas limitacoes nem sempre e facil, muito menos quando isso passa a se tornar problematico. Os resultados do trabalho foram obtidos durante a execucao das atividades, na observacao da receptividade e interacao dos alunos com o tema, da preocupacao com a ausencia de bem-estar quando o numero de animais e excessivo, na observacao do desconhecimento do perfil de acumuladores de animais, assim como quais iniciativas devem ser tomadas para reverter ou auxiliar na solucao desse disturbio. Dessa maneira, conclui- -se a importância do medico-veterinario ter conhecimento do problema e ter uma visao clara e sensivel para os sinais apresentados pelos acumuladores de animais, para que assim sejam efetuadas intervencoes interdisciplinares destinadas a proporcionar uma vida mais tranquila ao tutor e de contribuir para o bem-estar dos animais.

This chapter presents the application of multispectral images of very large resolution, from different years, for the automatic detection of time changes of buildings' roofing materials and nonstructural anomalies using a hybrid methodology. This is an evolution of the method developed by Gonçalves, the development of a 3D-GIS model that incorporates a geodatabase to store the information related to the history and the pathologies of each building of the model. The 3D-GIS city model of the historical city centre of Leiria was enriched with a collection of pathological information. The final model enables an interactive system for the management, integration and presentation of historical urban geoinformation. The development of Web services for the model's contents could extend the ways in which the city model can be accessed by the municipality technicians and other partners contributing to the preservation and promotion of the built capital, heritage identity and memory.

The urban expansion over the last century in Lisbon has created a lot of pressure in the suburbs leading to construction taking place in less favourable terrains. These areas were also occupied by an extensive mining industry, for the extraction of massive limestones for building and ornamental stone that had an important role for the reconstruction of Lisbon after the earthquake of 1755. Due to its heavily urbanized landscape in the last century, the terrain morphology is often changed by non-natural processes such as excavations and landfill deposits and nowadays the exact location where these exploitations took place is unknown leading to potential risk situations for buildings, infrastructures and the local population. The aim of this study was to perform a detailed subsurface dynamic analysis to locate the quarries and their landfill material thickness. For that, cartographic and topographic information dating from 1911, 1950, 1970, 2012 were used to create digital terrain models which allowed the terrain morphology changes to be identified and the volume of landfill materials to be obtained. This study enables the Town Hall to create constraints in land use in their Master Plan and other planning instruments to reduce cost increases and the risk of hazards.