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- INTRODUCTION
It will be the moment for all stakeholders to meet and co-create the strategic priorities for the European Commission's investment in research and innovation. At the same time, the event aims to mobilise EU citizens and increase awareness of how important research and innovation are in addressing the challenges that face society. It will include a free exhibition to showcase and celebrate the very best EU research and innovation has to offer. Mobilising and engaging different stakeholders will result in work programmes for Horizon Europe that will better reflect the views and needs of citizens.
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Background Topics For whom? Venue Creativity has no limits and in the sports ecosystem, innovation is constant, whether it is to improve the performance of athletes themselves, the stimulation of a healthy and active lifestyle or the fan experience. Organised by. We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you agree with it. Ok No. The resulting 20 misclassification out of bag errors OOB were averaged for each classification scenario.
Moreover, two additional investigations were realized.
First, we compared the performance of the RGB camera and CIR camera by undertaking classification scenario solely on the basis of individual flight metrics. Secondly, the added value of multitemporal data in species discrimination was evaluated by classifying tree crowns via survey pairs and trios. The 5 most efficient two-date and three-date combinations are compared and discussed. Replication of the classification approach : The use of the free and open source [R] statistical software enabled easy replication of the classification methods presented in this paper. A simplified dataset of the time series used in this research, as well as the [R] script, are available in S1 Appendix.
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Generation of the time series resulted in a co-registered collection of 10 RGB orthophotomosaics and 10 CIR orthophotomosaics. A visual inspection confirmed that the georeferencing is consistent for every image block. The time series is illustrated on Figs 4 and 5 , with superimposed tree crowns colored by species category. Orthophotomosaics of survey 5 and 9 appear locally less sharp than on the other surveys. For survey 5 in particular, such difference is probably due to the higher flight altitude of the survey, resulting in a lower spatial resolution.
Owing to the windy condition during the flight, raw images of survey 9 were affected by a smearing effect motion blur more pronounced than the other image block. As visible on Figs 4 and 5 , the presence of shadows on RGB orthophotomosaics of surveys 3, 6, 8 and 9 prevents the correct visualization of understory trees and forest gaps.
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The 6 first RGB orthophotomosaics are illustrated. Delineated trees are colored by species; English oak: green—poplars: orange—sycamore maple: blue—common ash: white—birches: purple. The 4 last RGB orthophotomosaics are illustrated. The most striking difference between the orthophotomosaics is the spectral variation among the images of a single block, caused by the rapid changes of luminosity conditions during a flight [ 31 ]. Orthophotomosaics affected by such issues are marked on Table 1 , and consist of 8 image blocks out of the total 20 image blocks. The spectral information of a given species varies across the study area.
As a consequence, the automatic classification of tree species is expected to show weak performances when luminosity conditions change across a single orthophotomosaic. Although the image blocks are distinct in terms of flight configuration, the photogrammetric workflow removes a major part of the heterogeneity among the surveys.
The resolution is the same for each orthophotomosaic of the time series and the georeferencing satisfactory. For flight 5-RGB, the root-mean-square error of residuals on the check point positions is 0. In addition, we used the same digital elevation model for the orthorectification process of all image blocks, and the resulting orthophotomosaics appear error free and present a good level of sharpness.
Hence, co-registration of all the surveys was a success. The classification results for monotemporal surveys, and for a single camera flight, reveal a strong trend Table 2. Monotemporal acquisitions enable a good discrimination of tree species, with an OOB error ranging from The best surveys are shown to be the survey 3, 4 and 2, which were all achieved at the end of the leaf-flushing event. Accordingly, spring and early summer are highlighted as the optimal time windows for the discrimination of broadleaved trees.
Surveys in spring and early summer gave the best results and the RGB camera clearly outperforms the color infra-red camera.
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The added value of multitemporal datasets is clearly significant: the lowest out of bag error for a two-date classification is For the three-date combinations, the accuracy of the classification increases, with a classification error of 8. Every date combination, illustrated in Table 3 , involves the survey number 3 which was demonstrated as the optimal single-date acquisition. Survey 4 is also repeatedly involved in date combinations. Indeed, the five best three-date combinations all consist of both surveys 3 and 4. Moreover, survey 4 which occurred in early summer is identified as the second optimal monotemporal survey Table 2.
In addition, the overall and by-class classification error is very similar for each combination, although the two-date and three-date combinations consist of various seasons. The mid-summer surveys number 5 and 6 are the only ones that do not appear among the five preferential two-date and three-date combinations. Finally, all the remaining surveys are involved at least once in the various date combinations Table 3. The 5 best two-date combinations and the 5 best three-date combinations. Survey 3 , highlighted in bold was present in all the combinations, and survey 4 , in italic writing was involved in all the three-date combinations.
Focus must be given to the different groups of species showing variable levels of separability. First, poplars and birches trees are the most spectrally separable categories.
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Such result is remarkable, given that the poplar category consists of two cultivars, both caracterized by different phenology timings. Sycamore maple is the most difficult species to discriminate, in accordance with the results on field maple of Hill et al. Similarly, common ash is hard to identify properly, since this tree species is often confused with sycamore maple confusion matrix is provided in S1 Appendix.
On the other hand, English oaks are intermediate in terms of separability. Finally, the confusion in species identification is most prononced between maple and ash. But the spectral response of English oaks is also overlapping the spectral response of maple trees and, to a lesser extent, the one of ash trees. On the basis of the survey rankings, we selected the most efficient orthophotomosaics for an additional and thorough visual inspection of the tree crowns. Orthophotomosaics of surveys 3 and 4 are far from being the most colorful in the time series. Visually, the more contrasted surveys are the ones performed at the very beginning of the vegetation growth period, and depicting a colorful autumn foliage.
By contrast, the spectral response of leaf-on trees during late spring and early summer is more homogenous, since all the individuals were green at these seasons.
The phenology status within every category of species is synchronized. In addition, the variability of phenology and spectral response within each group of species is less pronounced at the end of complete leafing, compared to autumn or early spring. Such results underline the importance of intra-species variations in phenology, for species discrimination.
By contrast, the disparity in phenology is at the lowest during summer. The end of leaf flushing, highlighted as the optimal monotemporal time windows, minimizes the spectral variation within tree species groups and, at the same time, maximizes the phenologic differences between species.
Some of the causes of phenology variability within a category of species are related to the forest sylviculture and the history of the study site. The complex vertical structure of the stands generates a micro-climate that can slightly impact the phenology timing.
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For instance, edges and gaps are numerous in the forest, and trees at these locations are less influenced by the below-canopy micro-climate. The micro-climate is thus more variable in the study site than in even-aged stands. In addition, an important number of common ash trees suffer from ash dieback disease Chalara fraxinea.