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One may consider the application of remote sensing as a trade-off between the imaging platforms, sensors, and data gathering and processing techniques. This study addresses the potential of hyperspectral imaging using ultra-light aircraft for vegetation species mapping in an urban environment, exploring both the engineering and scientific aspects related to imaging platform design and image classification methods. An imaging system based on simultaneous use of Rikola frame format hyperspectral and Nikon D800E adopted colour infrared cameras installed onboard a Bekas X32 manned ultra-light aircraft is introduced. Two test imaging flight missions were conducted in July of 2015 and September of 2016 over a 4000 ha area in Kaunas City, Lithuania. Sixteen and 64 spectral bands in 2015 and 2016, respectively, in a spectral range of 500–900 nm were recorded with colour infrared images. Three research questions were explored assessing the identification of six deciduous tree species: (1) Pre-treatment of spectral features for classification, (2) testing five conventional machine learning classifiers, and (3) fusion of hyperspectral and colour infrared images. Classification performance was assessed by applying leave-one-out cross-validation at the individual crown level and using as a reference at least 100 field inventoried trees for each species. The best-performing classification algorithm—multilayer perceptron, using all spectral properties extracted from the hyperspectral images—resulted in a moderate classification accuracy. The overall classification accuracy was 63%, Cohen’s Kappa was 0.54, and the species-specific classification accuracies were in the range of 51–72%. Hyperspectral images resulted in significantly better tree species classification ability than the colour infrared images and simultaneous use of spectral properties extracted from hyperspectral and colour infrared images improved slightly the accuracy over the 2015 image. Even though classifications using hyperspectral data cubes of 64 bands resulted in relatively larger accuracies than with 16 bands, classification error matrices were not statistically different. Alternative imaging platforms (like an unmanned aerial vehicle and a Cessna 172 aircraft) and settings of the flights were discussed using simulated imaging projects assuming the same study area and field of application. Ultra-light aircraft-based hyperspectral and colour-infrared imaging was considered to be a technically and economically sound solution for urban green space inventories to facilitate tree mapping, characterization, and monitoring.

International audience; Few airborne aerosol research experiments have deployed N 2-Raman Lidar despite its capability to retrieve aerosol optical properties without ambiguity. Here, we show the high scientific potential of this instrument when used with specific flight plans. Our demonstration is based on (i) a field-experiment conducted in June 2015 in southern France, involving a N 2-Raman Lidar embedded on an ultra-light aircraft (ULA); and (ii) an appropriate algorithmic approach using two-level flight levels, aiming to solve the notorious instability of the airborne Lidar inversion for the retrieval of aerosol optical properties. The Lidar measurements include the determination of the aerosol extinction coefficient along~500 m horizontal line of sight, and this value is used as a reference to validate the proposed algorithm. The Lidar-derived vertical profiles obtained during the flights are used as an input in a Monte Carlo simulation in order to compute the error budget in terms of biases and standard deviations on the retrieved aerosol extinction coefficient profile, as well as the subsequent optical thickness. The influence of the Lidar ratio (i.e., between aerosol extinction and backscatter) on the error budget is further discussed. Finally, from this end-to-end modeling, an optimal N 2-Raman Lidar is proposed for airborne experiments, adapted to both small and large carriers.

International audience; Imaging system based on simultaneous use of Rikola hyperspectral and RGB/NIR cameras installed on a manned ultra-light aircraft is introduced in this study. Simultaneously acquired hyperspectral and color- infrared (CIR) images were tested for their potential to identify deciduous tree species and estimate tree health in Kaunas city, Lithuania. Six urban deciduous tree species were separated using tree crown level statistics, extracted from 16 visible-near infrared spectral band hyperspectral images, and discriminant analyses with an overall classification accuracy of 63.1 %. Classification accuracy increased by 3 percent when hyperspectral images were integrated with simultaneously acquired CIR images. The accuracy in identifying tree health using fused hyperspectral and CIR images, ranged from poor to moderate.

We developed and installed a scientific payload on-board an ultra-light aircraft dedicated to ozone, nitrogen oxides and aerosol-scattering profilers to study the spatial distribution of pollution in the Chamonix and Maurienne valleys (French Alps). Meteorological parameters (temperature, pressure and relative humidity) and altimetry (GPS measurements) were recorded simultaneously. A synergy of measurements has been ensured with a ground mobile station equipped with a new lidar emitting at 532 nm. The experiment undertaken in January 2003 during the daytime allowed us to document, for the first time, the pollution level on horizontal and vertical scales during typical winter situations in the Alps valleys. Our results show a strong inversion of temperature up to about 150±50 m above the valley bottom, in the Chamonix valley, which traps the pollutants close to the snow-covered surface. Their concentrations are thus closely linked to the proximity of their sources and seem to have a local influence. In the Maurienne valley, with a bottom surface mostly free of snow, more efficient mixing due to convection is observed and a multi-layer structure between the planetary boundary layer and the free troposphere is detected on lidar vertical profiles. These lidar measurements allow a characterization of valley aerosols, with a backscatter to extinction ratio of not, vert, similar0.028 sr−1 very close to the value retrieved during the INDian Ocean EXperiment for a mixing of aerosols from biomass and fossil fuel burning.