Identification of roofing material is an important issue in the urban environment due to hazardous and risky materials. We conducted an analysis with Discriminant Function Analysis (DFA) and Random Forest (RF) on WorldView-2 imagery. We applied a three-and a six-class approach (red tile, brown tile and asbestos; then dividing the data into shadowed and sunny roof parts). Furthermore, we applied pan-sharpening to the image. Our aim was to reveal the efficiency of the classifiers with a different number of classes and the efficiency of pan-sharpening. We found that all classifiers were efficient in roofing material identification with the classes involved, and the overall accuracy was above 85 per cent. The best results were gained by RF, both with three and with six classes; however, quadratic DFA was also successful in the classification of three classes. Usually, linear DFA performed the worst, but only relatively so, given that the result was 85 per cent. Asbestos was identified successfully with all classifiers. The results can be used by local authorities for roof mapping to build registers of buildings at risk.

The extensive destruction of arable lands by the process of lateral bank erosion is a major issue for the alluvial meandering type of rivers all around the world. Nowadays, land managers, stakeholders, and scientists are discussing how this process affects the surrounding landscapes. Usually, due to a land mismanagement of agroforestry activities or urbanization plans, river regulations are designed to reduce anthropogenic impacts such as bank erosion, but many of these regulations resulted in a degradation of habitat diversity. Regardless, there is a lack of information about the possible positive effects of meandering from the ecological point of view. Therefore, the main aim of this study was to investigate a 2.12 km long meandering sub-reach of Sajó River, Hungary, in order to evaluate whether the process of meander development can be evaluated as a land degradation processes or whether it can enhance ecological conservation and sustainability. To achieve this goal, an archive of aerial imagery and UAV (Unmanned Aerial Vehicle)-surveys was used to provide a consistent database for a landscape metrics-based analysis to reveal changes in landscape ecological dynamics. Moreover, an ornithological survey was also carried out to assess the composition and diversity of the avifauna. The forest cover was developed in a remarkable pattern, finding a linear relationship between its rate and channel sinuosity. An increase in forest areas did not enhance the rate of landscape diversity since only its distribution became more compact. Eroding riverbanks provided important nesting sites for colonies of protected and regionally declining migratory bird species such as the sand martin. We revealed that almost 70 years were enough to gain a new habitat system along the river as the linear channel formed to a meandering and more natural state.

Gullies reduce both the quality and quantity of productive land, posing a serious threat to sustainable agriculture, hence, food security. Machine Learning (ML) algorithms are essential tools in the identification of gullies and can assist in strategic decision-making relevant to soil conservation. Nevertheless, accurate identification of gullies is a function of the selected ML algorithms, the image and number of classes used, i.e., binary (two classes) and multiclass. We applied Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Random Forest (RF) on a Systeme Pour l’Observation de la Terre (SPOT-7) image to extract gullies and investigated whether the multiclass (m) approach can offer better classification accuracy than the binary (b) approach. Using repeated k-fold cross-validation, we generated 36 models. Our findings revealed that, of these models, both RFb (98.70%) and SVMm (98.01%) outperformed the LDA in terms of overall accuracy (OA). However, the LDAb (99.51%) recorded the highest producer’s accuracy (PA) but had low corresponding user’s accuracy (UA) with 18.5%. The binary approach was generally better than the multiclass approach; however, on class level, the multiclass approach outperformed the binary approach in gully identification. Despite low spectral resolution, the pan-sharpened SPOT-7 product successfully identified gullies. The proposed methodology is relatively simple, but practically sound, and can be used to monitor gullies within and beyond the study region.

Unoccupied aerial systems (UASs) are frequently used in the field of fluvial geomorphology due to their capabilities for observing the continuum rather than single sample points. We introduce a (semi-)automatic workflow to measure river bathymetry and surface flow velocities of entire river reaches at high resolution, based on UAS videos and imagery. Video frame filtering improved the visibility of the riverbed using

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