Spatio-temporal variability in dune plant communities using UAV and multispectral data

O mapeamento da vegetação, através da identificação do tipo e distribuição das comunidades e espécies vegetais, é crucial para analisar a cobertura vegetal e os padrões espaciais. A compreensão das variabilidades espaciais e temporais das plantas dunares em ligação com a morfodinâmica permite uma maior compreensão do dinamismo e evolução dos ambientes costeiros. Tal análise pode contribuir para o desenvolvimento de planos de gestão costeira que ajudam a implementar a biodiversidade costeira e estratégias de protecção. Esta dissertação apresenta uma abordagem para avaliar a utilização de imagens multiespectrais e explorar a variabilidade da vegetação dunar costeira com dados recolhidos à distância por um Veículo Aéreo Não Tripulado (UAV). Foram escolhidas quatro zonas de estudo diferentes na parte oriental da Península de Ancao, distribuídas alongshore, e cobrindo a backhore e a crista das dunas até à base do lee das dunas. Foram utilizados dados de campo e de UAV, em diferentes épocas, nomeadamente ao longo de um período de dois anos. Foi utilizada uma abordagem de classificação em duas etapas, baseada num índice de vegetação de diferença normalizada e num classificador de Floresta Aleatória. Os resultados mostram desempenhos de classificação de alta precisão ao condensar a cobertura do solo em menos classes e também em áreas menos densamente vegetativas. As classificações resultantes foram posteriormente processadas em termos de alterações transfronteiriças e alterações sazonais. Estas técnicas mostram um elevado potencial futuro para avaliar a vegetação das áreas de dunas costeiras e para apoiar a gestão costeira.The mapping of vegetation, by identifying the type and distribution of plant communities and species, is crucial for analysing vegetation coverage and spatial patterns. Understanding dune plant spatial and temporal variabilities in connection with morphodynamics gives further insight in dynamism and evolution of coastal environments. Such analysis can contribute to the development of coastal management plans that helps to implement coastal biodiversity and protection strategies. This dissertation presents an approach to assess the use of multispectral imagery and explore the variability of coastal dune vegetation with remotely sensed data collected by an Unmanned Aerial Vehicle (UAV). Four different study zones were chosen at the eastern part of the Ancao Peninsula, distributed alongshore, and covering the backshore and the dune crest until the base of the dune lee. Field and UAV data were used, in different seasons namely over an extend of two years. A two-step classification approach, based on a normalized difference vegetation index and Random Forest classifier, was used. The Results show high accuracy classification performances when condensing the groundcover into fewer classes and also in less densely vegetated areas. Resulting classifications were further processed in terms of cross-shore changes and seasonal changes. These technics show a high future potential to assess the vegetation of coastal dune areas and to support coastal management

Monitoring dune vegetation changes and associated driving forces at Culatra Island, Algarve, Portugal

As dunas do sistemas de ilhas barreira suportam uma grande variedade de vegetação que estabilizam e promovem o crescimento das dunas protegendo as zonas costeiras. Os habitats de vegetação diferem entre dunas frontal, interdunar e posterior de acordo com a sucessão evolutiva e a exposição da vegetação a fatores naturais como o vento e ondas. Esses fatores podem ter um efeito significativo sobre a geomorfologia das dunas. Além disso, a geomorfologia das dunas pode influenciar a ecologia, devido a evolução eco geomorfológicos que existem nas dunas costeiras, tais como os fatores reguladores "top down", como a areia eólica, e "bottom up", como as condições para um crescimento ótimo de sucessão comunitária. O sistema de ilhas barreira da Ria Formosa é uma reserva natural protegida desde 1987, esse sistema é constituído por uma lagoa protegida por cinco ilhas barreira e duas penínsulas separadas canais de marés, denominados como barras. Dentro deste sistema de ilhas barreira, a Ilha da Culatra contém uma importante comunidade pesqueira, presença de trilhas e colônias locais da gaivota-de-patas-amarelas (Larus Michahellis). Visto que a Ilha da Culatra contém “dunas cinzentas” que estão listadas como um tipo de habitat prioritário, de acordo com a diretiva europeia, foi necessário realizar um estudo rentável sobre o estado da vegetação. Foram investigados fatores que possam contribuir para a perturbação do sistema de vegetação das dunas da ilha da Culatra. O estudo na presente dissertação, desenvolveu um índice para calcular a perturbação da vegetação dunar dentro da Ilha da Culatra utilizando técnicas de detecção remota com a utilização de Sistemas de Informação Geográfica (SIG). Os materiais utilizados para a análise foram ortofotografias e imagens do Google Earth. Estes materiais e ferramentas permitiram classificar a vegetação para completar um índice de perturbação da vegetação. O índice foi realizado através da composição colorida de ortofotografias a cores verdadeiras. Para complementar os dados, foi também utilizada a imagem do Google Earth. As fontes de perturbação foram identificadas pela sobreposição de elementos antropogénicos digitalizados. Este estudo identificou a mudança no estado da vegetação de 2005 para 2017, envolvendo o aumento da perturbação. Constatou-se que a principal causa desta perturbação estava relacionada com marcas causadas por veículos na ilha. O índice e os métodos destacaram a necessidade da utilizar imagens de melhor qualidade, que podem ser de grande utilidade para estudos futuros devido à simplicidade dos métodos e à sua abordagem rentável. Além disso, este método e índice pode ser altamente transferível para outros sistemas dunares e pode ser utilizado como base de referência para o estudo posterior da vegetação dunar na ilha da Culatra, chamando a atenção e importância para a conservação do sistema dunar.Barrier island sand dune systems support a high variety of plant communities, which stabilise and promote growth of dunes protecting inland areas. According to plant succession and exposure, to natural factors such as wind and ocean, vegetation habitats differ between foredune, interdune and backdunes. This can have a significant effect on the geomorphology of the dunes. Moreover, the geomorphology of dunes can influence the ecology, due to the eco-geomorphological feedbacks that exist in coastal dunes such as “top down” (for example aeolian sand) and “bottom up” (such as the conditions for optimal growth of community succession) regulating factors. A protected natural reserve since 1987, the Ria Formosa consists of a lagoon protected by five barrier islands and two peninsulas supported by tidal inlets. Within this barrier system, Culatra Island contains a significant fishing communities, the presence of local footpaths and colonies of the Yellow legged gull (Larus Michahellis). Since Culatra Island contains “grey dunes" which are listed as a priority habitat type according to the EU habitats directive, it was therefore necessary to conduct a cost-effective study on the state of the vegetation and whether any factors are contributing to the perturbation of the Culatra Island dune vegetation system. This study developed an index to calculate dune vegetation perturbation within Culatra island using remote sensing techniques within a Geographical Information Systems environment (GIS). The materials used for the analysis were orthophotos and Google Earth imagery. These materials and tools enabled the ability to classify vegetation to complete a vegetation perturbance index. The index was carried out by the classification of orthomosaicked true colour images. To complement the data, Google Earth imagery was also used. Sources of perturbation were identified by the superimposition of digitized anthropogenic elements. This study identified the change in vegetation state from 2005 to 2017 involving the increase of perturbation. It was found that the main cause of this perturbation was related to track marks caused by vehicles on the island. The index and methods highlighted the need to use better quality imagery but can be of great use for further studies due to the simplicity of the methods and its cost-effective approach. Furthermore, this method and index can be highly transferable to other dune systems and can be utilized as a baseline for further study of dune vegetation on Culatra island, drawing attention and importance to the conservation of the system

Using High-Spatial Resolution UAV-Derived Data to Evaluate Vegetation and Geomorphological Changes on a Dune Field Involved in a Restoration Endeavour

Nowadays, the employment of high-resolution Digital Surface Models (DSMs) and RGB orthophotos has become fundamental in coastal system studies. This work aims to explore the potentiality of low-cost Unmanned Aerial Vehicle (UAV) surveys to monitor the geomorphic and vegetation state of coastal sand dunes by means of high-resolution (2–4 cm) RGB orthophotos and DSMs. The area of study (Punta Marina, Ravenna, Italy), in the North Adriatic Sea, was considered very suitable for these purposes because it involves a residual coastal dune system, damaged by decades of erosion, fragmentation and human intervention. Recently, part of the dune system has been involved in a restoration project aimed at limiting its deterioration. RGB orthophotos have been used to calculate the spectral information of vegetation and bare sand and therefore, to monitor changes in their relative cover area extension over time, through the using of semi-automatic classification algorithms in a GIS environment. Elevation data from high-resolution DSMs were used to identify the principal morphological features: (i) Dune Foot Line (DFL); (ii) Dune Crest Line (DCL); Dune seaward Crest Line (DsCL); Stable Vegetation line (SVL). The USGS tool DSAS was used to monitor dune dynamics, considering every source of error: a stable pattern was observed for the two crest lines (DCL and DsCL), and an advancing one for the others two features (DFL and SVL). Geomorphological data, as well as RGB data, confirmed the effectiveness of planting operations, since a constant and progressive increase of the vegetated cover area and consolidation of the dune system was observed, in a period with no energetic storms. The proposed methodology is rapid, low-cost and easily replicable by coastal managers to quantify the effectiveness of restoration projects

A new index to assess the state of dune vegetation derived from true colour images

Vegetation on coastal dunes is a key element, as it promotes the growth and stabilization of these landforms while contributing significantly to biodiversity. Physical (e.g. impact of storms), ecological (e.g. animal grazing) and human-related (e.g. farming and recreation) factors may disturb coastal dune vegetation, changing dune dynamics and eventually inducing ecogeomorphic state shifts. Therefore, understanding vegetation dynamics and state turns crucial to predict dune evolution paths. The latter must be supported by observations combined with the development of tools (e.g. indexes) able to detect eventual changes and to automatically categorize the state of the vegetation. Here, a multi-step index to characterise the dune vegetation state (DUVES) was developed and tested in Barreta Island (South Portugal), where grey dune vegetation has declined in recent years. The index was computed using classified true colour orthophotos and orthomosaics derived from UAS (Unmanned Aerial Systems) surveys. Google Earth images were used as complementary data to analyse the evolution trends. The possible sources of disturbance (i.e. human-related activities and gull occupation) were also investigated by comparing their distribution with the vegetation changes. DUVES successfully identified different states of vegetation cover that expressed its stability, perturbation or growth based on temporal changes and allowed the analysis of their evolutionary trends. The distribution of perturbation was mostly associated with gull nesting areas, increasing over time, and to a less extent to human-related activities. The observed grey dune habitat loss was due to replacement of plants typical from this habitat by ruderal species promoted by the positive feedback established between gulls and vegetation. The developed index proved to be of great utility to define dune habitat evolution and understand the associated drivers, being a tool with a wide range of applications, namely for improving future coastal management actions aimed at conserving dune habitats. Moreover, DUVES is potentially transferable due to its easy adaptability depending on the particularities of each study site or goal.info:eu-repo/semantics/publishedVersio

Beached and Floating Litter Surveys by Unmanned Aerial Vehicles: Operational Analogies and Differences

The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and monitor local beached (BL) and floating (FL) marine litter items. In this work, the operational insights to carry out both BL and FL surveys using UAVs are detailly described. In particular, flight planning and deployment, along with image products processing and analysis, are reported and compared. Furthermore, analogies and differences between UAV-based BL and FL mapping are discussed, with focus on the challenges related to BL and FL item detection and recognition. Given the efficiency of UAV to map BL and FL, this remote sensing technique can replace traditional methods for litter monitoring, further improving the knowledge of marine litter dynamics in the marine environment. This communication aims at helping researchers in planning and performing optimized drone-based BL and FL surveys

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

Coastal Eye: Monitoring Coastal Environments Using Lightweight Drones

Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.Natural Environment Research Council (NERC