No abstract

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25–0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F1-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method suitable to efficiently and successfully capture the temporal trends and spatial heterogeneity of degradation in alpine grasslands. Additionally, U-Net is a powerful and robust tool to map erosion sites in a predictive manner utilising large amounts of new aerial imagery.

Abstract. Mountainous grassland slopes can be severely affected by soil erosion. To better understand the regional differences of soil erosion patterns, we determine the locations of shallow landslides across different sites and aim at identifying their triggering causal factors. Ten sites across Switzerland located in the Alps (8 sites), in foothill regions (1 site), and the Jura mountains (1 site) were selected for statistical evaluations. For the shallow landslide inventory, we used aerial images (0.25 m) with a deep learning approach (U-Net) to map the locations of eroded sites. We used logistic regression with a Group Lasso variable selection method to identify important explanatory variables for predicting the mapped shallow landslides. The set of variables consists of traditional susceptibility modelling factors and climate-related factors to represent local as well as cross-regional conditions. This set of explanatory variables (predictors) are used to develop individual site models (regional evaluation) as well as an all-in-one model (cross-regional evaluation) using all shallow landslide points simultaneously. While the local conditions of the ten sites lead to different variable selections, consistently slope and aspect were selected as the essential explanatory variables of shallow landslide susceptibility. Accuracy scores range between 70.2 and 79.8 % for individual site models. The all-in-one model confirms these findings by selecting slope, aspect as well as roughness as the most important explanatory variables (Accuracy = 72.3 %). Our finding suggest that traditional susceptibility variables describing geomorphological and geological conditions yield satisfactory results for all tested regions. However, for two sites with lower model accuracy, important processes may be under-represented with the available explanatory variables. The regression models for sites with an east-west oriented valley axis performed slightly better than models for north-south oriented valleys, which may be due to the influence of exposition related processes. Additionally, model performance is higher for Alpine sites, suggesting that core explanatory variables are understood for these areas.

<p>Understanding the occurrence of soil erosion phenomena is of vital importance for ecology and agriculture, especially under changing climate conditions. In Alpine grasslands, susceptibility to soil erosion is predominately due to the prevailing geological, morphological and climate conditions but is also affected by anthropogenic aspects such as agricultural land use. Climate change is expected to have a relevant impact on the driving factors of soil erosion like strong precipitation events and altered snow dynamics. In order to assess spatial and temporal changes of soil erosion phenomena and investigate possible reasons for their occurrence, large-scale methods to identify different soil erosion sites and quantify their extent are desirable.</p><p>In the field of remote sensing, one such semi-automatic method for (semantic) image segmentation is Object-based Image Analysis (OBIA), which makes use of spectral and spatial properties of image objects. In a recent study (Zweifel et al.), we successfully employed OBIA on high-resolution orthoimages (RGB spectral bands, 0.25 to 0.5 m pixel resolution) and derivatives of digital elevation models (DEM) of a study site in the Swiss Alps (Urseren Valley). The method provides high-quality segmentation results and an increasing trend of total area affected by soil erosion (+156 +/- 18%) is shown over a period from 2000 to 2016. However, using OBIA requires expert knowledge, manual adjustments, and is time-intensive in order to achieve satisfying segmentation results. In addition, the parameter settings of the method cannot be easily transferred from one image to another.</p><p>To allow for large-scale semantic segmentation of erosion sites, we make use of fully convolutional neural networks (CNNs). In recent years, CNNs proved to be very performant tools for a variety of image recognition tasks. While training CNNs might be more time demanding, predicting segmentations for new images and previously unseen regions is usually fast. For this study, we train a U-Net with high-quality segmentation masks provided by OBIA and DEM derivatives. The U-Net segmentation results are not only in good agreement with the OBIA results, but also a similar trend for the increase of total area affected by soil erosion is observed.</p><p>In order to have a natural understanding of what in the input is “relevant” for the segmentation result, we make use of methods which highlight different regions of the input image, thereby providing a visually interpretable result. We use different approaches to identify these relevant regions which are based on perturbation of the input image and relevance propagation of the output signal to the input image. While the former approach identifies the relevant regions by modifying the input image and considering the changes in the output, the latter approach tracks the dominant signal from the segmentation output back to the input image, highlighting the relevant regions. Although both approaches attempt to attain the same goal, differences in the relevant regions of the input images for the segmentation results can be observed.</p><p><span>Zweifel, L., Meusburger, K., and Alewell, C. Spatio-temporal pattern of soil degradation in a Swiss Alpine grassland catchment. Remote Sensing of Environment, 235, 2019.</span></p>

The supplemental material contains boxplots showing the results for all ten study sites showing the estimated coefficient ranges after 100 repetitions of the model. Numbers above variable names indicate the number of times it was selected for the model.