Abstract. We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼ 0.3 km 2 ) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to −0.073 m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ∼ 100 m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m. This provides a possible indication of sampling resolution in similar conditions.

The use of UAVs finds application in a variety of fields, among which are the small scale surveys for environmental protection application. In this frame some experimental tests were carried out at Politecnico di Milano to assess metric accuracies of images acquired by UAVs and derived photogrammetric products. A block of 5 strips and 49 photos was taken by fixed wing system SenseFly, carrying a camera Canon Ixus 220HS on a rural area included in an Italian Park. Images are processed through bundle adjustment, automatic DEM extraction and orthoimages production steps with several software packages, with the aim to evaluate their characteristics, capabilities and weaknesses. The software packages tested were Erdas-LPS, EyeDEA (University of Parma), Agisoft Photoscan, Pix4UAV, PhotoModeler Scanner. For the georeferencing of the block 16 pre-signalized ground control points were surveyed in the area through GPS (NRTK survey). Comparison of results is given in terms of differences among orientation parameters and their accuracies. Moreover, comparisons among different digital surface models are evaluated. Furthermore, exterior orientation parameters, image points and ground points coordinates, obtained by the various software packages, were used as initial values in a comparative adjustment made by scientific in-house software. Paper confirms that computer vision software are faster in computation and, even if their main goal is not to pursue high accuracy in points coordinates determination, they seems to produce results comparable to those obtainable with standard photogrammetric approach. Agisoft Photoscan seems in this case to yield the best results in terms of quality of photogrammetric products

Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20-0.30 m, but decreases to 0.06-0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

In the frame of project FoGLIE (Fruition of Goods and Landscape in Interactive Environment), UAS were used to survey a park area in its less accessible zones, for scenic and stereoscopic videos, 3D modeling and vegetation monitoring. For this last application, specifically, through the acquisition of very high resolution images taken with two UAS-borne compact cameras (RGB and NIR), a DSM of a small vegetated area and the corresponding orthoimages were produced and co-registered. Planimetric and height accuracies in block adjustments and orthophotos are in the range of 0.10 m horizontally and 0.15 m in height. Then, after the derivation of synthetic channels, both unsupervised classification and supervised one were performed in order to test the algorithms' ability to distinguish between different bushes and trees species: some of them were correctly classified by the latter method but misclassifications still remain. The overall accuracy for the unsupervised classification is about 50% while the supervised one yields an overall accuracy around 80%.

This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User's and Producer's Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.