Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network
"> Figure 1
<p>The Urseren Valley is located in the Central Swiss Alps in the Canton of Uri. The left map contains the topographic map of Switzerland (from low elevations in green to high elevations in brown to white). The right image contains an aerial image of the Urseren Valley overlaid on a hill-shade map of the area.</p> "> Figure 2
<p>Training (9 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>) and testing (17 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>) areas are marked on the aerial image with examples of OBIA training labels for 2000 (map on the left). On the right-hand side is an overview of all available years and the sections used for training and testing. All training areas contain OBIA training labels (not shown) for the respective years (2000–2013). Training labels vary for each year due to the continuous evolution of soil erosion sites. The entire area of the image taken in 2016 was used only for testing.</p> "> Figure 3
<p>Examples for the labels used for training the U-Net model. From left to right: shallow landslides, livestock trails, sheet erosion, management effects.</p> "> Figure 4
<p>An overview of the developed workflow on the basis of U-Net showing examples of input files for training and prediction purposes. The output shows one of four erosion classes, namely, shallow landslides, with four different probability thresholds.</p> "> Figure 5
<p>The employed U-Net architecture: In the first (upper) part, the input is contracted into a compressed representation (right). In the second (lower) part, the compressed representation is expanded into a segmentation map with pixel-wise class probabilities. The input consists of the inpuxt RGB image (three channels) and the DTM derivative maps for the aspect, curvature, and slope (one channel each). The resulting output provides a segmentation map for each considered class: Shallow landslides (indicated by 1 in the output), livestock trail (2), sheet erosion (3), management effects (4), and a class for non-assignable pixels (5).</p> "> Figure 6
<p>Example of input RGB images for training for the years 2000, 2004, 2010, and 2013 with a size of 194 × 176 m (corresponding to 388 × 352 pixels at 0.5 m resolution). The images show examples of eroded area on grassland slopes (livestock trails, shallow landslides). Below, the corresponding aspect, curvature, and slope maps are displayed (for all years the same DTM information is used). To obtain the samples, the aerial images of the respective years (<a href="#remotesensing-12-04149-f002" class="html-fig">Figure 2</a>) and the DTM derivatives were divided into smaller tiles.</p> "> Figure 7
<p>Visualisation of U-Net mapped shallow landslides (<b>left</b>) and livestock trails (<b>right</b>) for 2016. The lower panel shows segmentation results with different probability thresholds: the lighter colour indicates a lower probability threshold (0.2) and the darker colour indicates a higher probability threshold (0.8). Lower thresholds lead to larger and more numerous segments. For the same region (background omitted for better visualisation), the upper panel shows the full-probability heatmap output of U-Net: darker colours indicate higher probabilities.</p> "> Figure 8
<p>Comparison of segmentation results of OBIA and U-Net (probability threshold of 0.3) for the aerial image of the year 2016. This aerial image was not used during training of the U-Net model and depicted sections are located in the held-out test area. Lighter colours show OBIA results; darker colours (shaded) are results of U-Net.</p> "> Figure 9
<p>Examples of two different types of false positives: On the left-hand side, U-Net identifies some rock surfaces as sheet erosion (yellow) and shallow landslides (purple). For both erosion classes, thresholds of 0.2 and 0.8 are shown. Lower threshold choices are linked to more of such false positives. Depicted on the right-hand side are livestock trails with OBIA and U-Net (threshold of 0.2). Here, U-Net is capable of identifying more livestock trails correctly compared to OBIA.</p> "> Figure 10
<p>Linear trend of the total degraded area in the held-out test region (see <a href="#remotesensing-12-04149-f002" class="html-fig">Figure 2</a>) as obtained with the OBIA and U-Net approaches. On the left, the results for a range of different threshold values are displayed; on the right the results for the suitable threshold value 0.3 and the full-probability results are given. Qualitatively, a similar increase or decrease of degraded soil in the individual years is retained in all models. The linear interpolation provides a similar temporal trend of increase in degraded soil in all cases. In particular, the full-probability and threshold 0.3 results of the U-Net approach show good agreement with the OBIA baseline. The linear trends with lower and higher thresholds surround the OBIA result. The years 2000 to 2013 provide a result on the spatial generalisation of U-Net (years used for training), while the result for 2016 (shaded column) in addition provides a temporal generalisation result (aerial image of 2016 was not used for training). Note that the OBIA approach needs to be trained on all aerial images.</p> "> Figure 11
<p>Comparison of total degraded area in years 2000 and 2016 for the baseline (OBIA) and the U-Net approach with different thresholds. The total degraded area was obtained from the interpolation results of each year (<b>top panel</b>). In all approaches, an increase of degraded area in the Urseren Valley is observed with threshold-specific differences in the total extent. However, the relative increase in degraded area (<b>bottom panel</b>) shows that assessing the trend of soil degradation can be done independently of the threshold, as all results fall within the statistical uncertainty of the linear regression fit. Note that the statistical uncertainty for U-Net 0.8 increases due to the comparably small total degraded area detected. The error bars depict the statistical uncertainty of one standard deviation.</p> "> Figure 12
<p>Mapped degraded area in the test region by erosion class for both the OBIA and U-Net methods (full-probability results and threshold value 0.3). Comparing the two methods, class-specific differences for the yearly degraded area and linear trends can be observed. Moreover, by selecting appropriate thresholds for each erosion class, similar linear trends in both methods can be attained (see <a href="#app1-remotesensing-12-04149" class="html-app">Supplementary Figure S2</a>). The years 2000 to 2013 provide a result on the spatial generalisation of U-Net (years used for training), while the result for 2016 (shaded column) in addition provides a temporal generalisation result (aerial image of 2016 was not used for training).</p> ">
Abstract
:1. Introduction
2. Study Area
3. Data Sets
3.1. Aerial Imagery
3.2. Digital Terrain Model
3.3. Training Data
3.3.1. Training Labels
4. Methodology
4.1. Object-Based Image Analysis
4.2. Neural Network Architecture
4.3. Training Process
4.4. Details on the Evaluation
5. Results and Discussion
5.1. Segmentation of Soil Erosion Sites
5.2. Threshold Selection
5.3. Trend Analysis of Soil Erosion Sites
5.4. Deep Learning and OBIA
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
OBIA | Object-based image analysis |
DTM | Digital terrain model |
RGB | Red, Green and Blue spectral bands |
CNN | Convolutional Neural Networks |
U-Net | Name of Convolutional Neural Network architecture |
GPU | Graphics Processing Unit |
UAV | Unmanned Aerial Vehicle |
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Data Set | Derivative | Spectral Bands | Spatial Res. | Recording Date | |
---|---|---|---|---|---|
Aerial Image | Red, Green, Blue | 0.5 m | 24 August | 2000 | |
Red, Green, Blue | 0.5 m | 9 September | 2004 | ||
Red, Green, Blue | 0.25 m | 20 July | 2010 | ||
Red, Green, Blue | 0.25 m | 1 August | 2013 | ||
Red, Green, Blue | 0.25 m | 20 July | 2016 | ||
Digital Terrain | Slope | 2 m | |||
Model (DTM) | Aspect | 2 m | |||
Curvature | 2 m |
Scores | U-Net |
---|---|
Recall | 84% |
Precision | 73% |
F | 78% |
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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
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 StyleSamarin, Maxim, Lauren Zweifel, Volker Roth, and Christine Alewell. 2020. "Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network" Remote Sensing 12, no. 24: 4149. https://doi.org/10.3390/rs12244149
APA StyleSamarin, M., Zweifel, L., Roth, V., & Alewell, C. (2020). Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network. Remote Sensing, 12(24), 4149. https://doi.org/10.3390/rs12244149