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Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data...
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... heterogeneity of crowd-sourced photographs, we tested to only include photographs taken from distances or with angles that correspond to similar settings as perceived from the UAV perspective. The accuracy of the CNN-based regression for predicting angles and distances tested with independent test data resulted in a R 2 = 0.7 for both variables (Fig. 4 and Appendix Fig. A10). Using the predicted angle and distance values for all photographs and different thresholds, we filtered the training data prior to training the CNN models for plant species classification and tested the effect on the final plant segmentation accuracy in the orthoimages. The best results were obtained by filtering training photographs ...
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... did not result in increased accuracy. The low accuracy metrics for P. afra for some orthoimages (e.g., orthoimage 14 and 27) coincided with very low values for the average plant size and total cover of P. afra therein. Precision, Recall, and F 1 -score correlated with at least an R 2 of 0.5 with the average plant size and total cover (Appendix Fig. A11), meaning that the model did not perform well to detect very small P. afra plants (cf. Fig. ...
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... prediction of acquisition angles was relatively accurate over the range of possible angles (Appendix Fig. A10). However, for both case studies filtering photographs by acquisition angle did not improve the species classification (for detailed comparisons, see Appendix Table A2 to Table A4). ...
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... across heterogeneous illumination conditions. The latter may result from the fact that the iNaturalist data itself is very heterogeneous and facilities the transferability of the Fig. 6. Excerpts of the segmentation result of the target species of the case study P. afra. The segmentation results for all orthoimages are given in the Appendix in Fig. A1 to Fig. A8. From left to the right: UAV-based orthoimage, manually delineated reference data, prediction maps derived from the CNN models trained with crowd-sourced imagery. The last row (e), shows a close up. resulting models. The transferability across illumination conditions is further confirmed in the predictions of individual ...
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... from the expected canopy texture (we did not observe any training data with herbivory effects in the iNaturalist-derived training data). In very few cases, false positives of F. japonica were found for dried plants of the surrounding species. For P. afra, we found systematic missclassifications for very small plants (cf. Fig. 6 and Appendix Fig. A1 to Fig. A8), which was quantitatively confirmed by the high correlations of the estimated plant size and total cover of P. afra with segmentation accuracy results (Appendix Fig. A11). This may be a consequence of the fact that the already fewer image data for P. afra contain very few images for very small ...
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... F. japonica were found for dried plants of the surrounding species. For P. afra, we found systematic missclassifications for very small plants (cf. Fig. 6 and Appendix Fig. A1 to Fig. A8), which was quantitatively confirmed by the high correlations of the estimated plant size and total cover of P. afra with segmentation accuracy results (Appendix Fig. A11). This may be a consequence of the fact that the already fewer image data for P. afra contain very few images for very small ...
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... that the approach was tested with two case studies where the target species have quite different morphological characteristics compared to the surrounding species. For instance, P. afra has quite roundish leaves combined with a mostly star-shaped branching structure, while F. japonica differs from the surrounding vegetation through its large heart-shaped leaves ( Figs. 1 and 2). However, the success of such an approach not only depends on contrasting morphological properties between the target and the surrounding species but also if such properties are visible in both the citizen science photographs and the orthoimagery. ...
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... authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Fig. A1. Overview of segmentation result of the target species of the case study P. afra. From left to the right: UAV-based orthoimage, manually delineated reference data, prediction maps derived from the CNN models trained with crowd-sourced imagery. The row number on the left indicates the orthoimage of the corresponding site in this case ...
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... on all other orthoimages the histogram matching produre was applied. Fig. A10. Model accuracy assessment of the regression model for predicting acquisition angles from photographs (the blue smoothing line representing the fit was determined using a locally weighted least squares regression and the gray area shows its 95% confidence interval). Fig. A11. Comparison of plant size (top) and plant total cover (bottom) of P. afra with the map accuracy (Precision, Recall, F 1 -score). The total plant cover was calculated from the reference data (total cover of P. afra per plot). The average plant size was approximated by using the mean size of individual P. afra polygons in the respective ...
