Cities across the globe recognise their role in climate mitigation and are acting to reduce carbon emissions. Knowing whether cities set ambitious climate and energy targets is critical for determining their contribution towards the global 1.5°C target, partly because it helps to identify areas where further action is necessary. This paper presents a comparative analysis of the mitigation targets of 327 European cities, as declared in their local climate plans. The sample encompasses over 25% of the EU population and includes cities of all sizes across all Member States, plus the UK. The study analyses whether the type of plan, city size, membership of climate networks, and its regional location are associated with different levels of mitigation ambition. Results reveal that 78% of the cities have a GHG emissions reduction target. However, with an average target of 47%, European cities are not on track to reach the Paris Agreement: they need to roughly double their ambitions and efforts. Some cities are ambitious, e.g. 25% of our sample (81) aim to reach carbon neutrality, with the earliest target date being 2020. 90% of these cities are members of the Climate Alliance and 75% of the Covenant of Mayors. City size is the strongest predictor for carbon neutrality, whilst climate network(s) membership, combining adaptation and mitigation into a single strategy, and local motivation also play a role. The methods, data, results and analysis of this study can serve as a reference and baseline for tracking climate mitigation ambitions across European and global cities. Highlights• 78% of cities have a mitigation plan with targets (avg. 47% GHG reduction)• Only 25% of cities strive for carbon neutrality, most by 2050, avg. by 2045 • 90% of cities striving for carbon neutrality are members of a climate network • Ambition is driven by city size, climate networks, M-A combination, local motivation • European cities must double their ambitions to meet the aims set by the Paris Agreement

Citizen science is proliferating in the water sciences with increasing public involvement in monitoring water resources, climate variables, water quality, and in mapping and modeling exercises. In addition to the well-reported scientific benefits of such projects, in particular solving data scarcity issues, it is common to extol the benefits for participants, for example, increased knowledge and empowerment. We reviewed 549 publications concerning citizen science applications in the water sciences to examine personal benefits and motivations, and wider community benefits. The potential benefits of involvement were often simply listed without explanation or investigation. Studies that investigated whether or not participants and communities actually benefitted from involvement, or experienced negative impacts, were uncommon, especially in the Global South. Assuming certain benefits will be experienced can be fallacious as in some cases the intended benefits were either not achieved or in fact had negative impacts. Identified benefits are described and we reveal that more consideration should be given to how these benefits interrelate and how they build community capitals to foster their realization in citizen science water projects. Additionally, we describe identified negative impacts showing they were seldom considered though they may not be uncommon and should be borne in mind when implementing citizen science. Given the time and effort commitment made by citizen scientists for the benefit of research, there is a need for further study of participants and communities involved in citizen science applications to water, particularly in low-income regions, to ensure both researchers and communities are benefitting.

Commission VI, WG VI/4KEY WORDS: Stress detection, unmanned aerial vehicle, unmanned aerial system, UAV, UAS, camera calibration. ABSTRACT:Climate change has a major influence on forest health and growth, by indirectly affecting the distribution and abundance of forest pathogens, as well as the severity of tree diseases. Temperature rise and changes in precipitation may also allow the ranges of some species to expand, resulting in the introduction of non-native invasive species, which pose a significant risk to forests worldwide. The detection and robust monitoring of affected forest stands is therefore crucial for allowing management interventions to reduce the spread of infections. This paper investigates the use of a low-cost fixed-wing UAV-borne thermal system for monitoring disease-induced canopy temperature rise. Initially, camera calibration was performed revealing a significant overestimation (by over 1 K) of the temperature readings and a non-uniformity (exceeding 1 K) across the imagery. These effects have been minimised with a two-point calibration technique ensuring the offsets of mean image temperature readings from blackbody temperature did not exceed ± 0.23 K, whilst 95.4% of all the image pixels fell within ± 0.14 K (average) of mean temperature reading.The derived calibration parameters were applied to a test data set of UAV-borne imagery acquired over a Scots pine stand, representing a range of Red Band Needle Blight infection levels. At canopy level, the comparison of tree crown temperature recorded by a UAV-borne infrared camera suggests a small temperature increase related to disease progression (R = 0.527, p = 0.001); indicating that UAV-borne cameras might be able to detect sub-degree temperature differences induced by disease onset.

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Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements. and angle in the form of 3D points [9,10]. They are usually mounted on airplanes for a large coverage while maintaining a good (cm) level of accuracy. The point density is dependent upon a few factors, e.g., scanner measurement rate and scanning mechanism, flight height and speed, swath width, and strip overlaps, hence it may vary from less than 1 point per m 2 to more than 50 points per m 2 . But in general, the maximum point density is getting higher with the development of airborne laser scanners.Early studies have mostly focused on the characteristics at stand-level, such as canopy cover and height, from airborne lidar data, due to limited point density [11][12][13]. Now the point density is high enough to capture a sufficient number of points on each individual tree, so that individual tree detection or delineation (ITD), including tree location, size, shape and number, has drawn considerable attention [5,[14][15][16]. Vertical distribution, above ground biomass and other secondary properties, can be derived from those accurate delineation parameters. Therefore, ALS has been increasingly used for precise forest mapping and monitoring at landscape or regional scale [10].Although ITD from airborne lidar is an important research topic for forest studies, it still remains as a challenge due to the complexity and heterogeneity of the forest structure and its composition. The main difficulty of ITD is tree segmentation, a step to segment the overall points into clusters that represent individual trees. There are two main strategies for tree segmentation: Raster-based and point-based [17,18]. Earlier methods mostly adopted the first strategy, converting the 3D point clouds into canopy height models (CHMs), a raster image, then detecting tree tops using 2D image processing techniques such as local maxima, region growing and watershed [5]. The second strategy segments the trees based directly on 3D points [14,19]. Exam...

Tree growth and survival predominantly depends on edaphic and climatic conditions, thus climate change will inevitably influence forest health and growth. It will affect forests directly, for example, through extended periods of drought, and indirectly, such as by affecting the distribution and abundance of forest pathogens and pests. Developing ways of early detection and monitoring of tree stress is crucial for effective protection of forest stands. Thermography is one of the techniques that can be used for monitoring changes in the physiological state of plants; however, in forestry, it has not been widely tested or utilized. The main challenge rises from the need for high spatial resolution data. Newly emerging technologies, such as unmanned aerial vehicles (UAVs) could aid in provision of the required data. However, their main constraint is the limited payload, requiring the use of miniature sensors. This paper investigates whether a miniature microbolometer thermal camera, designed for a UAV platform, can provide reliable canopy temperature measurements of conifers. Furthermore, it explores whether there is a distinction in whole canopy temperature between the control and the stressed trees, assessing the potential of low-cost thermography for investigating stress in conifers. Two experiments on young larch trees, with induced drought stress, were performed. The plants were imaged in a greenhouse setting, and readings from a set of thermocouples attached to the canopy were used as a method of validation. Following calibration and a basic normalization for background radiation, both the spatial and temporal variation of canopy temperature was well characterized. Very mild stress did not exhibit itself, as the temperature readings for both stressed and control plants were similar. However, with a higher stress level, there was a clear distinction (temperature difference of 1.5 • C) between the plants, showing potential for using low-cost sensors to investigate tree stress.

Hedgerows are an abundant and ecologically important feature of many rural areas. Their biodiversity value depends on composition, structure and availability of food resources, which can be significantly impacted by poor management. However, information about hedgerow condition is very limited due to field surveys being costly and labour-intensive. Unmanned aerial vehicles (UAVs) equipped with miniaturized cameras could prove a more cost-effective and time-efficient hedgerow surveying solution while preserving a high level of detail unattainable with airborne or satellite sensors. This study explored whether UAV remote sensing is a viable alternative for performing hedgerow condition surveys at local scale, focusing on hedgerow structure and flowering abundance. We acquired UAV Red, Green and Blue (RGB) and multispectral nadir and oblique imagery of structurally different hedgerows and used them to generate 3D point clouds and models with SfM workflow. Height thresholding allowed extraction of hedgerow extents, with root-mean-square error (RMSE) of height and width ranging from 0.11 to 0.23 m. RGB flower classification showed poor relationship with ground measurements (R 2 = 0.31-0.42) due to confusion with woody material of hedgerows. Inclusion of a near-infrared channel in multispectral imagery significantly improved the relationship (R 2 = 0.68-0.75, RMSE = 10%). Our study shows UAV remote sensing has high potential for performing detailed surveys of hedgerows, providing better characterization of structural variations and distribution of flowers than traditional ground surveys due to larger coverage. More comprehensive understanding of hedgerow, or other vegetated buffer strips, conditions offered by UAV surveys can enable better informed decisions on habitat management and biodiversity conservation in rural areas. Acquisitions over larger areas, potentially integrated with satellite remote sensing, can allow assessment of hedgerow connectivity over farm to landscape scales, contributing to better understanding of the hedgerow network and its role as a wildlife corridor.

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