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Open AccessArticle

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

1
Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, 4051 Basel, Switzerland
2
Department of Environmental Sciences, University of Basel, Bernoullistrasse 30, 4056 Basel, Switzerland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(24), 4149; https://doi.org/10.3390/rs12244149
Received: 25 November 2020 / Revised: 13 December 2020 / Accepted: 14 December 2020 / Published: 18 December 2020
(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)
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. View Full-Text
Keywords: deep learning; semantic segmentation; remote sensing; object-based image analysis; erosion mapping; landslides; livestock trails; sheet erosion deep learning; semantic segmentation; remote sensing; object-based image analysis; erosion mapping; landslides; livestock trails; sheet erosion
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MDPI and ACS Style

Samarin, M.; Zweifel, L.; Roth, V.; Alewell, C. Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network. Remote Sens. 2020, 12, 4149. https://doi.org/10.3390/rs12244149

AMA Style

Samarin M, Zweifel L, Roth V, Alewell C. Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network. Remote Sensing. 2020; 12(24):4149. https://doi.org/10.3390/rs12244149

Chicago/Turabian Style

Samarin, Maxim; Zweifel, Lauren; Roth, Volker; Alewell, Christine. 2020. "Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network" Remote Sens. 12, no. 24: 4149. https://doi.org/10.3390/rs12244149

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