Burrowing mammals such as European sousliks are widespread and contribute significantly to soil ecosystem services. However, they have declined across their range and the non-invasive estimation of their actual population size has remained a challenge. Results support that the number of burrow entrances is positively correlated with population abundance, and burrow locations indicate the occupied area. We present an imagery-based method to identify and count animals’ burrows semi-automatically by combining remotely recorded red, green, and blue (RGB) images, pixel-based imagery, and random forest (RF) classification. Field images were collected for four colonies, then combined and processed by histogram matching and spectral band normalization to improve the spectral distinctions among the categories BURROW, SOIL, TREE, and GRASS. The accuracy indexes of classification for BURROW kappa (κ) were 95% (precision) and 90% (sensitivity). A 10-iteration bootstrapping of the final model resulted in coefficients of variation (CV%) of BURROW κ for sensitivity and precision lower than 5%; moreover, CV% values were not significantly different between those scores. The consistency of classification and balanced precision and sensitivity confirmed the applicability of this approach. Our approach provides an accurate, user-friendly, and relatively simple approach to count the number of burrow openings, estimate population abundance, and delineate the areas of occupancy non-invasively.

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Abstract. Recent advancements of technology resulted in greater knowledge of the Solar System and the need for mapping small celestial bodies significantly increased. However, creating a good map of such small objects is a big challenge for the cartographer: they are usually irregular shaped, the usual reference frames like the ellipsoid of revolution is inappropriate for their approximation.A method is presented to develop best-fitting irregular surfaces of revolution that can approximate any irregular celestial body. (Fig. 1.) Then a simple equal-area map projection is calculated to map this reference frame onto a plane. The shape of the resulting map in this projection resembles the shape of the original celestial body.The usefulness of the method is demonstrated on the example of the comet 67P/Churyumov-Gerasimenko. This comet has a highly irregular shape, which is hard to map. Previously used map projections for this comet include the simple cylindrical, which greatly distorts the surface and cannot depict the depressions of the object. Other maps used the combination of two triaxial ellipsoids as the reference frame, and the gained mapping had low distortion but at the expense of showing the tiny surface divided into 11 maps in different complicated map projections (Nyrtsov et. al., 2018). On the other hand, our mapping displays the comet in one single map with moderate distortion and the shape of the map frame suggests the original shape of the celestial body (Fig. 2. and 3.).

Burrowing mammals are widespread and contribute significantly to soil ecosystem services. However, how to conduct a non-invasive estimation of their actual population size has remained a challenge. Results support that the number of burrow entrances is positively correlated with population abundance and burrows’ location indicates their area of occupancy consequently it provides a benchmark for estimating population size. European souslik is an endangered burrowing species in decline across its range. We present an imagery-based method to identify and count animals’ burrows semi-automatically by combining remotely recorded RGB images, pixel-based imagery (PBI) and Random Forest (RF) classification. Field images recorded in four colonies were collected, combined and then processed by histogram matching and spectral band normalisation to improve the spectral distinction between the categories BURROW, SOIL, TREE, GRASS. Raw or processed images were analysed by RF classification to compare the change in accuracy metrics as a result of processing. From accuracy metrics kappa of precision (κBURROWP) and sensitivity (κBURROWS) for BURROW were 95 and 90% respectively. A 10-time bootstrapping of the final model resulted in coefficients of variation (CV%) of κBURROWS and κBURROWP lower than 5%, moreover CV% values were not significantly different between precision and sensitivity scores. The consistency of classification results and balanced precision and sensitivity confirmed the applicability of this approach. Our method provides an accurate and user-friendly tool to count the number of burrow openings and delineate the areas of occupancy as compared to traditional, more invasive approaches or other computer capacity and end-user expertise demanding methods.

<p>Sustainable agriculture is seriously threatened by severe soil erosion, which is also occurred in the Neszmély Wine Region, in the northern part of the Gerecse Hills in Hungary. In the region, three vineyards with visible signs of erosion were chosen to quantify the amount of eroded soil. The empirically based Universal Soil Loss Equation (USLE) model was utilized first to determine the soil loss. The study sites were monitored with an unmanned aerial vehicle (UAV) to create high-resolution models of seasonal and annual soil loss. After the empirically based, spatially detailed quantification of erosion, we have tested the applicability of machine learning methods to predict soil erosion for the selected parcels during the same time period. The primary concept was to use the empirically inferred erosion values as observation data to construct parcel-specific prediction models and test them on the remaining two parcels. In the model we have used (i) Sentinel 2 satellite data in the form of both native spectral bands and its derived spectral indices; (ii) terrain features derived from digital surface model created and aggregated from the UAV flights and (iii) formerly elaborated digital soil property maps as auxiliary data. Various machine learning methods (ranger, ridge, xgbLinear, enet, pls, brnn) have been tested to find the best performing predictions. Observation data were generated in the form of random points, in 100 representations. Model performances have been tested by proper measures to evaluate the applicability of the applied machine learning techniques for soil erosion mapping.</p> <p> </p> <p>Acknowledgment: Our research has been supported by the Hungarian National Research, Development and Innovation Office (NRDI; Grant No: K 131820).</p>