2. Materials and Methods
2.1. Airbone Laser Scanning Data and Derived Dataset
2.2. Principle of the Local Relief Model and of the SAILORE Method
2.2.1. The LRM Principles
2.2.2. The SAILORE Approach
2.3. Description of the SAILORE Algorithm
- Realization of a sliding average smoothing with a very large filter buffer (100 cells, or 50 m in our case, the DTM resolution being 0.5 m).
- Calculation of the slope of this global relief, from which will be determined the size of the filtering buffer applied to each zone. The slope is calculated with the default procedure proposed in ArcGis®, with a neighborhood of 3 × 3 cells. It was calculated from the previously smoothed DTM, and thus gives us information on the steepest slope around each pixel, expressed in degrees. This local slope value must be related to the size of the filtering kernel. The simplest way to proceed would be to adapt the size of the filtering area proportionally to the slope. If we look at the slope distribution from a statistical perspective (Figure 5), we can see that it logically follows a Weibull distribution, with a high occurrence of low slopes and very few values beyond 45°.
- Differential smoothing of the original DTM. For this phase, in order to reduce the complexity of the model, 5 thresholds were chosen (see Figure 4 and Figure 6). The maximum kernel size was set at 50 pixels (25 m), which corresponds to half of the kernel chosen in the first phase to restore the global relief of the site by removing all medium and high-frequency components. Values of 60 and 80 pixels, respectively, were tested, and they led to very similar results, which is logical because this kernel size will be used on very flat areas, for which the quality of the filtering was not very sensitive to the size of the kernel, the pixels having all a similar value. The interest of the 50-pixel kernel was then to be less demanding in terms of computing time. The minimum kernel size was set to 10 pixels (5m), which also corresponds to the values classically used to highlight micro-variations of the relief. Indeed, from a practical point of view, a sliding average filtering does not make sense if it is performed at the scale of a few pixels, knowing that for a structure to be identified, even by an expert eye, it must include several 10s of pixels. Finally, 3 intermediate filtering levels, corresponding, respectively, to 20, 30, and 40 pixels, were defined (10, 15, and 20 m, respectively). These values were chosen to allow for a gradual transition between minimum and maximum kernel sizes and to accommodate areas of intermediate slopes. In the absolute, we could consider 40 successive levels, allowing to go from the filtering on 10 pixels to the filtering on 50 pixels with a step of 1, but this configuration, which complicates the model, does not bring a significant gain in terms of resolution, as we could notice it in our tests. The step of 10 pixels was thus chosen as the best compromise between the resolution obtained and the necessary computing time. It is important to note that the choice of these thresholds was independent of the calculation principle of our Self-AdaptIve LOcal Relief Enhancer and that they can be adapted if particular study contexts require it.
- Finally, each pixel is associated with the filtering result of the threshold to which it corresponds, and the global filtered DTM is thus generated, pixel by pixel and then subtracted from the initial DTM, to provide the final visualization (Figure 4).
2.4. Testing the Performance of the SAILORE Approach
4. Discussion and Conclusions
Data Availability Statement
Conflicts of Interest
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