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Urban trees are the most important green infrastructure in cities to mitigate climate change, improve the urban environment, and promote human health and well-being by providing numerous critical ecosystem services. However, they experience various pressures, such as the urban heat island (UHI) effect, soil sealing, and air pollution, potentially affecting the tree health condition and thereby the quantity and quality of the ecosystem services they offer to humanity. Urban tree introduction and conservation initiatives are thus important, and various management strategies and policies have been formulated. In support of such efforts, it is essential to monitor the dynamics of important tree properties, in particular those related to tree functioning, across space. Moreover, the adaption capability of trees to environmental changes and how tree-provided ecosystem services influence human health and well-being need to be well understood. Based on these scientific outputs and insights, appropriate and targeted management activities can be implemented, and their effectiveness can be evaluated. However, conventional tree inventory is spatially and temporally constrained by a range of difficulties. Therefore, further study on promising solutions is required. In this dissertation, we explored and improved the potential of ground-based and airborne (hyperspectral and light detection and ranging (LiDAR)) remote sensing technology in achieving the abovementioned requirements of urban treescape analysis. Our study area was the Brussels Capital Region in Belgium. Driven by a lack of knowledge on the capability of trees to adapt to the urban environment, we investigated the within-species variation in leaf functional and optical traits (i.e., water- and pigment-related traits) and their phenology induced by the UHI effect and soil sealing using generalized additive models (Chapter 2). We focused on Tilia × euchlora trees and found that the intra-species trait variations among different environmental conditions ranged from 8% to 38%. The trees growing in sealed soils were observed to start the autumn downregulation of photosynthetic pigments earlier (up to 13 days) than those at unpaved sites. These intra-species variations indicate the leaf plasticity of Tilia × euchlora trees, enabling them to adapt to the fast-changing urban environment. We demonstrated that using leaf optical traits to act as a proxy of leaf functional traits is promising, and may allow for the examination of environmental impacts on trees at broader spatial scales using spectral sensors onboard airplanes or satellite platforms. Despite the adaptive capability, tree health conditions can to different extents be impaired by severe or chronic environmental stress, leading to degraded tree-provided ecosystem services. In Chapter 3, we therefore assessed the potential of airborne hyperspectral and LiDAR data with a Random Forest classifier to detect urban tree defoliation, discoloration, and a combination thereof at the individual tree crown level. We found that the fusion of hyperspectral and LiDAR features achieved the highest accuracies, with overall accuracies ranging from 0.81 to 0.89. The overall better performance of LiDAR features over hyperspectral features might be case-specific, needing further examination. Importantly, we demonstrated that a species-specific modelling approach should be adopted in mapping urban tree health.Tree species is an important tree property, determining the ability of trees to provide ecosystem services, and can serve as basic information to enhance the mapping of other tree properties (e.g., tree health) using remote sensing. Chapter 4 was therefore dedicated to assessing and improving the potential of airborne hyperspectral and bi-temporal LiDAR (leaf-on and leaf-off) data in urban tree species classification at the individual tree crown level. Additionally, we looked at how planting locations (i.e., streets vs parks) drive intra-species spectral and structural variations and in consequence affect classification accuracies. We found that the importance of hyperspectral and LiDAR features for species discrimination varied within species between street and park trees. The proportions of intra-species variation in spectral reflectance, leaf-on and leaf-off LiDAR features explained by planting locations were up to 40.6%, 63.9%, and 64.6% respectively. These results supported our finding that a planting location-specific modelling approach significantly improved urban tree species mapping, with the highest classification accuracies (85.1%) achieved by using the combined hyperspectral and leaf-on and leaf-off LiDAR data. Built upon these findings, we suggest integrating a step of semantic classification of trees into urban tree species discrimination. Central to the tree-provided ecosystem services is to improve human health and well-being. In Chapter 5, we explored how airborne LiDAR technology can be used to improve the quantification of exposure to trees and enhance our understanding of the associations with cardiovascular and mental health. We developed a complete workflow, including individual tree delineation, screening of incorrect trees, and estimation of tree traits, to map three-dimensional tree traits at the city level. We showed that medication sales for cardiovascular disease and mental disorders were negatively associated with crown volumes but positively associated with tree density in models including both exposure indicators. We thereby hypothesize that living in areas with larger crown volumes and lower tree densities may be more beneficial to cardiovascular and mental health, compared to living in areas with higher tree densities and smaller crown volumes. These findings underscore the need to conserve large trees in cities. The research conducted in this dissertation has confirmed the potential of ground-based and airborne (hyperspectral and LiDAR) remote sensing for a comprehensive and advanced analysis of urban treescape to support the assessment, conservation, and improvement of ecosystem services that trees provide to human society. Despite the operationality of the developed methodologies, future research should further address various uncertainties in urban tree monitoring by coupling a well-designed urban laboratory with multisource data (e.g., remote sensing, sensor networks, citizen science) and state-of-the-art techniques in order to better inform management and policy.

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Plantensoorten kunnen zich, door opzettelijk of onopzettelijk handelen van de mens, kilometers weg van hun normale leefgebied verspreiden. Planten en zaden worden over de continenten heen verspreid door bijvoorbeeld het invoeren van granen, het overbrengen van sierplanten voor tuinen, het importeren van hout en grond. In vele gevallen zijn deze uitheemse soorten slecht aangepast aan hun nieuwe omgeving en sterven snel uit. In andere gevallen kunnen ze overleven, reproduceren en zich vestigen. Wanneer deze uitheemse soorten zo succesvol worden dat ze een bedreiging vormen voor de oorspronkelijke biodiversiteit en functionering van ecosystemen, worden ze invasieve uitheemse soorten genoemd. Ze vormen niet enkel een bedreiging voor de natuur, maar ook voor landbouw, economie en soms menselijke gezondheid. Invasieve soorten zouden in kaart gebracht moeten worden om hun verspreiding te kennen, zodat beheer en beleid hierop gebaseerd kan worden. Het detecteren en karteren van invasieve soorten op basis van observaties en tellingen in het veld is een zeer tijdrovend en dus een kostelijk proces, waardoor deze methode zich beperkt tot een lokale schaal. Aangezien invasieve soorten zich echter over een groot gebied kunnen verspreiden, zou de kartering moeten opgeschaald worden. Hyperspectrale remote sensing is een beloftevolle techniek die de mogelijkheid biedt om plantinvasies te bestuderen op grote schaal. Deze relatief nieuwe techniek baseert zich op de wijze waarmee licht interageert met planten. De elektromagnetische straling van de zon wordt door de bladeren deels gereflecteerd, deels geabsorbeerd, deels doorgelaten of terug uitgezonden. De interactie van deze straling met het blad hangt af van zijn biochemische bladsamenstelling en bladstructuur. Aangezien elke plantensoort een unieke biochemische bladsamenstelling en plantenstructuur heeft, interfereert het licht voor elke soort anders. Doordat spectrometers de gereflecteerde straling kunnen registreren tussen 350 tot 2500 nanometer, bieden ze de mogelijkheid om de spectrale eigenschappen van de soort te meten. Alvorens invasieve soorten met behulp van vliegtuigen of satellieten te detecteren, zou er eerst uitgetest moeten worden of invasieve soorten gedetecteerd kunnen worden op basis van hyperspectrale remote sensing door spectrale metingen te nemen in het veld. In deze thesis wordt bekeken of invasieve soorten te onderscheiden zijn van de omliggende vegetatie op basis van specifieke spectrale eigenschappen en of deze eigenschappen behouden worden wanneer er opgeschaald wordt van bladmetingen naar metingen ter hoogte van de planten en de vegetatie. Aan de Dijle in Mechelen komen drie invasieve plantensoorten voor, namelijk Reuzenbalsemien, Reuzenberenklauw en Japanse duizendknoop. Ten eerste werden de soorten gekarakteriseerd in het gebied. Ten tweede werd bekeken of deze drie invasieve soorten te detecteren zijn. Ten derde werd nagegaan of de impact van invasieve soorten te detecteren is met hyperspectrale remote sensing. Er werd aangetoond dat Reuzenbalsemien te onderscheiden is van de omliggende vegetatie in het infrarode deel van het spectrum en Reuzenberenklauw in het visuele deel. Japanse duizendknoop is moeilijker te onderscheiden. Tot slot is de impact van de invasieve soorten niet gemakkelijk te bepalen. Om deze na te gaan was er een tekort aan data. Toch biedt deze thesis nuttige informatie om de invasieve soorten te gaan detecteren met vliegtuig- of satellietmetingen.

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Over the past decades the prevalence of pollen allergy has increased. This trend is expected to continue due to ongoing urbanization, climate change and increasing air pollution. In order to mitigate urban heat and improve air quality in the city, urban green space has been promoted. Urban green spaces have been associated with numerous health benefits. Contact with the natural environment strengthens the human microbiome and immune system. Green spaces promote an active lifestyle and provide mental health benefits. By improving the air quality, green spaces can also contribute to better respiratory health. However, urban vegetation is also a source of airborne pollen.The literature provides contradicting results regarding the effects of green space on the health of pollen allergy patients. These contradictions stem from the complex interactions between environmental factors, as well as from a heterogeneity in approaches when it comes to exposure studies. To properly study the health effects from exposure to green space on the health of people with pollen allergy we need detailed spatial data on green space and the allergenic trees within these greenspaces. In addition, we need insights in the effects of local vegetation on local pollen composition. Until now, the majority of exposure studies relied on exposure within various buffers around the residence. Nevertheless, a significant share of exposure takes place outside the residential area and pollen and air pollutant concentrations vary in time and space. In order to define exposure more realistically there is need for a method that can account for the spatiotemporal aspect of personal exposure.Nevertheless, the first step towards an exposure analysis is the generation of a distribution model of potential allergenic trees. For Flanders a database with validated observations of vascular plants at a 1 km resolution is available (Florabank). The observations can be combined with environmental covariates (soil characteristics, land use, habitat type) in a species distribution model. We modelled habitat suitability for 13 wind-pollinated tree genera. Genus-specific thresholds were used to obtain presence-absence maps. By summing the 13 presence-absence maps we obtained a tree diversity map at genus-level. We find that summing binary maps does not result in an overestimation of diversity when the study area is urbanized (Flanders) and the spatial resolution is coarse (1 km). The obtained diversity map can be used to determine exposure to biodiversity in health studies.In a second step, we studied the effects of local vegetation on the pollen composition. Although pollen can travel long distances, local vegetation contributes to local peak concentrations of pollen. Standardized pollen monitoring takes place at roof top level and measures a background level of pollen. Possible local peaks remain undetected, yet contribute to human pollen exposure. By mounting passive samplers at 2 m above the ground in 13 locations in Flanders we aimed to measure local pollen compositions during the tree pollen season (February-May) of 2017. The passive samplers successfully measured local pollen compositions characterized by 12 taxa. We used Non-metric Multidimensional Scaling (NMDS) to characterize each sampling site by its pollen composition. Then we use an indirect gradient analysis to associate the pollen composition with the land cover within a 20, 200, 500, 1000, 2000 and 5000 m radius around the sampling site. We found an urban-rural and wet-dry gradient associated with the first and second NMDS axis. Most of the associations were found for the land cover in a 1000-5000 m radius. This local scale effect is of importance for urban green management: urban forests at the edge of the city contribute to the pollen concentration within the city. In addition, we understand that environmental health studies require sufficiently large exposure radii.Thus, in the residential exposure study we determined garden cover, grassland cover and forest cover within a 1, 2 and 5 km radius around the residence of 157 adults with a tree pollen allergy. The density of allergenic trees (alder, hazel and birch) was derived from the regional forest inventories. We used a generalized linear models with a Poisson distribution to associate residential exposure to mental well-being (standardized questionnaires) and respiratory health (average symptom severity reported in a smartphone application). All green space types were protective for mental well-being, yet a risk factor for symptom severity. The density of allergenic trees in forests was a risk factor for mental well-being as well as symptom severity. We found that green space effects on health became smaller as the exposure radius increased.In an attempt to determine personal exposure more realistically, we used GPS data gathered by a smartphone application. We compared exposure between case-days with severe allergy symptoms and control-days without symptoms. We determined exposure by extracting green space cover, allergenic tree density, birch pollen levels and pollutant levels at the GPS point locations. For each day we could determine the average exposure taking into account spatiotemporal variability. Grassland cover, forest cover, alder density and hazel density protected against severe allergy symptoms. Birch density and birch pollen as well as pollutants (nitrogen dioxide (NO2), ozone (O3), and particulate matter < 10 µm (PM10)) were risk factors for severe allergy symptoms. For GPS tracks that were entirely within Flanders, we could calculate exposure to the tree diversity model at genus level. We found no associations between severe allergy symptoms and alpha-diversity of tree genera.This manuscript shows that adults with a tree pollen allergy experience mental and respiratory health benefits from exposure to green space, given that density of allergenic trees (birch) is low. GPS tracking allowed for a more realistic approximation of personal exposure. Although we did not identify a diversity effect, we do promote biodiverse urban green spaces in order to prevent domination of allergenic vegetation.