Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network

Maxim Samarin,Volker Roth,Christine Alewell,Lauren Zweifel

doi:10.3390/rs12244149

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.

Highlights

Soil degradation is a major ecological threat which affects many areas of the world and can be accelerated by land-use management and changing climate parameters, such as precipitation and temperature [1,2,3,4,5]
High-quality segmentation results come at the expense of a lack of transferability of parameter settings from one input image to another, manual adjustments, and a need for expert knowledge in applying the method to the specific task which together lead to long processing times
The first aspect generally hinders object-based image analysis (OBIA) in a predictive setting for new images

Summary

Introduction

Soil degradation is a major ecological threat which affects many areas of the world and can be accelerated by land-use management and changing climate parameters, such as precipitation and temperature [1,2,3,4,5]. While soil erosion occurs naturally in these environments—in the form of landslides (triggered by snow gliding or heavy precipitation events) or sheet erosion (the process of the removal of topsoil caused by rain drops’ impacts and overland flow)—there are anthropogenic influences (e.g., agricultural activities) which can accelerate erosion rates [3,6,7]. Livestock keeping can lead to overgrazing and trampling in favoured grazing areas. Livestock trails develop and trampling and grazing can lead to a reduction in vegetation cover, which in turn is prone to sheet erosion [8,9]. Livestock keeping can cause instabilities on slopes and result in landslides [10]

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