**4. Applications of the monoplotting tool**

We present here selected examples of MPT applications in the field of natural hazards spanning from the reconstruction of historical and current damaging events to the verification of the efficiency of existent infrastructures as well as the related achieved precision and required workload.

> **Figure 3.** The Sasso Rosso slope failure (Airolo, 1898). (a) Original obliques images with control points. (b) Digitalization of the slide contour on the original obliques images. (c) Projection of the slide perimeter on a current orthophoto

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**Figure 4.** The Sasso Rosso slope failure (Airolo, 1898). Projection of the slide perimeter on an oblique terrestrial image of

(modified from Conedera et al. [21]).

the area prior to the event (source: Conedera et al. [21]).

#### **4.1. Reconstructing historical natural events**

A detailed reconstruction of past hazardous events may be of paramount importance for understanding related natural processes, defining danger zones, and planning protection infrastructures. Processing and evaluating pictures of past disastrous events by means of monoplotting is a highly efficient way of retrieving information contained in historical pictures, and transferring it to modern tools such as GIS.

#### *4.1.1. Example 1: the Sasso Rosso slope failure: 1898, Airolo*

The Sasso Rosso area above the village of Airolo (Canton of Ticino) turned into an unstable slope due to the decompression caused by post-glacial ice retreat. Local authorities were aware of this and put the area under surveillance. Initial significant slope movement began in the summer of 1898, followed by three slides of increasing size in December of the same year. Fortunately, the authorities decided at that time to close the school and evacuate the endangered part of the village. Just before midnight in the night of the December 27, 1898, a series of slides occurred, with a volume totaling 500,000 m3 , reaching part of the village and destroying a hotel, 11 houses, and 15 stables, while causing three deaths.

According to the reconstructed and digitalized perimeter of the debris deposit as reported in **Figure 3**, the area in question covered ca. 425,000 m2 . The MPT\_2.0 also enables the projection of the digitalized deposit perimeter on a historical picture prior to the debris flow (**Figure 4**), clearly identifying the area and the buildings destroyed by the event.

#### *4.1.2. Example 2: flood in Sommascona: 1927, Olivone*

During the autumn of 1927, southern Switzerland and northern Italy experienced a period of heavy precipitation that caused several flood events of varying severity. The worst occurred Using the Monoplotting Technique for Documenting and Analyzing Natural Hazard Events http://dx.doi.org/10.5772/intechopen.77321 113

returns the theoretical 3D errors, which correspond to the deviation between the real-world coordinates of each control point and the corresponding coordinates as calculated from the

It is important to note that shooting point reconstruction is implemented so as to optimize the overlap of the corresponding CPs in the image to be georeferenced and in the georeferenced map, respectively. Thus, the reconstructed theoretical shooting point may not necessarily correspond precisely to the real camera position, and the two may differ by a few centimeters to

We present here selected examples of MPT applications in the field of natural hazards spanning from the reconstruction of historical and current damaging events to the verification of the efficiency of existent infrastructures as well as the related achieved precision and required

A detailed reconstruction of past hazardous events may be of paramount importance for understanding related natural processes, defining danger zones, and planning protection infrastructures. Processing and evaluating pictures of past disastrous events by means of monoplotting is a highly efficient way of retrieving information contained in historical pic-

The Sasso Rosso area above the village of Airolo (Canton of Ticino) turned into an unstable slope due to the decompression caused by post-glacial ice retreat. Local authorities were aware of this and put the area under surveillance. Initial significant slope movement began in the summer of 1898, followed by three slides of increasing size in December of the same year. Fortunately, the authorities decided at that time to close the school and evacuate the endangered part of the village. Just before midnight in the night of the December 27, 1898, a series of

According to the reconstructed and digitalized perimeter of the debris deposit as reported in

of the digitalized deposit perimeter on a historical picture prior to the debris flow (**Figure 4**),

During the autumn of 1927, southern Switzerland and northern Italy experienced a period of heavy precipitation that caused several flood events of varying severity. The worst occurred

, reaching part of the village and destroying

. The MPT\_2.0 also enables the projection

calibration procedure of the monoplotting system.

112 Natural Hazards - Risk Assessment and Vulnerability Reduction

**4. Applications of the monoplotting tool**

**4.1. Reconstructing historical natural events**

tures, and transferring it to modern tools such as GIS.

*4.1.1. Example 1: the Sasso Rosso slope failure: 1898, Airolo*

slides occurred, with a volume totaling 500,000 m3

**Figure 3**, the area in question covered ca. 425,000 m2

*4.1.2. Example 2: flood in Sommascona: 1927, Olivone*

a hotel, 11 houses, and 15 stables, while causing three deaths.

clearly identifying the area and the buildings destroyed by the event.

several meters.

workload.

**Figure 3.** The Sasso Rosso slope failure (Airolo, 1898). (a) Original obliques images with control points. (b) Digitalization of the slide contour on the original obliques images. (c) Projection of the slide perimeter on a current orthophoto (modified from Conedera et al. [21]).

**Figure 4.** The Sasso Rosso slope failure (Airolo, 1898). Projection of the slide perimeter on an oblique terrestrial image of the area prior to the event (source: Conedera et al. [21]).

in Ticino (Switzerland) on Sunday afternoon on September 25, 1927, as a consequence of a strong and long-lasting waterspout above the mountain village of Olivone. This flood is still considered to be the most damaging natural event of the last century in the region. It destroyed part of the village of Campo Blenio and flooded the plain of Olivone over an area of at least 199,366 m2 (ArcGIS computation of the perimeter as digitalized with the monoplotting tool), affecting two sawmills and several private buildings (**Figure 5**). Due to the favorable weekday, no fatalities were recorded.

**4.2. Documenting natural events in real time**

of the remoteness or accessibility of the area in question.

*4.2.1. Example 3: avalanches in Hasliberg: 2018, Meiringen*

*4.2.2. Example 4: Spitzhorn rockslide: 2017, Gstaad*

In October 2017, a rockslide moved a volume of ca. 50,000 m3

**Table 1.** Achieved theoretical 3D-error for the presented study cases.

Real-time documentation of the fresh marks of hazardous events makes it possible to capture many important details that may enable a realistic reconstruction of the processes that occurred. Such improved understanding of underlying processes is, in turn, of paramount importance to improve the simulation and modeling of natural hazards as well as possible prevention and mitigation measures. Thanks to the monoplotting approach actual natural hazards or natural hazard-related situations can be easily processed and mapped, regardless

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For safety reasons, the security managers of the Hasliberg ski resort record all artificially provoked and natural occurring avalanches by means of SLF pro tools, which is a map-based information system (https://www.slf.ch/en/services-and-products/protools.html). For this purpose, they roughly estimate the avalanche contour in the field. Over time, a conspicuous number of events have been entered into the system, causing the cantonal forest service to migrate all data into the official historical avalanche cadaster. Before doing this, however, the reliability of the empirical approach used by the security managers required testing. To that end, an aerial recognition mission was organized on January 24, 2018, and a number of fresh

Despite the difficulty in identifying suitable CPs in a snowy landscape, the theoretical 3D-error achieved is more than acceptable (**Table 1**), and each single registered avalanche perimeter could be measured and compared with the estimated contours by the ski resort managers. The comparison revealed significant differences with a trend toward overestimating the avalanche size by the local ski managers (**Figure 6**). Single events should thus be verified by the mean of the MPT\_2.0 before their migration into the official historical avalanche cadaster.

the Spitzhorn mountain. The event took place in a single main slide preceded by individual

Flood (b) Good 13 0.2 0.9 0.4

**Study case Object Image quality Control points Theoretical 3D-error (m)**

1. Airolo Slope failure Medium 20 0.2 3.0 0.9 2. Olivone Flood (a) Good 8 0.1 2.4 0.9

3. Meiringen Avalanche Good 5 0.1 0.6 0.2 4. Gstaad Rockslide Good 6 0.1 2.3 0.5 5. Adelboden Snow bridges Good 6 0.1 1.0 0.3

downhill from the west slope of

**Min Max Mean**

avalanches were photographically documented to be processed with the MPT\_2.0.

**Figure 5.** Flood of the Sommascona plain (Olivone, 1927). (a + b) original oblique images with control points. (c + d) Digitalization of the flood contours on the original oblique image. (e) Projection of the digitalized flood contours on the current orthophoto. (f) Projection of the digitalized flood contours on the current oblique terrestrial image (modified from Conedera et al. [21]).

#### **4.2. Documenting natural events in real time**

Real-time documentation of the fresh marks of hazardous events makes it possible to capture many important details that may enable a realistic reconstruction of the processes that occurred. Such improved understanding of underlying processes is, in turn, of paramount importance to improve the simulation and modeling of natural hazards as well as possible prevention and mitigation measures. Thanks to the monoplotting approach actual natural hazards or natural hazard-related situations can be easily processed and mapped, regardless of the remoteness or accessibility of the area in question.

#### *4.2.1. Example 3: avalanches in Hasliberg: 2018, Meiringen*

For safety reasons, the security managers of the Hasliberg ski resort record all artificially provoked and natural occurring avalanches by means of SLF pro tools, which is a map-based information system (https://www.slf.ch/en/services-and-products/protools.html). For this purpose, they roughly estimate the avalanche contour in the field. Over time, a conspicuous number of events have been entered into the system, causing the cantonal forest service to migrate all data into the official historical avalanche cadaster. Before doing this, however, the reliability of the empirical approach used by the security managers required testing. To that end, an aerial recognition mission was organized on January 24, 2018, and a number of fresh avalanches were photographically documented to be processed with the MPT\_2.0.

Despite the difficulty in identifying suitable CPs in a snowy landscape, the theoretical 3D-error achieved is more than acceptable (**Table 1**), and each single registered avalanche perimeter could be measured and compared with the estimated contours by the ski resort managers. The comparison revealed significant differences with a trend toward overestimating the avalanche size by the local ski managers (**Figure 6**). Single events should thus be verified by the mean of the MPT\_2.0 before their migration into the official historical avalanche cadaster.

#### *4.2.2. Example 4: Spitzhorn rockslide: 2017, Gstaad*

In October 2017, a rockslide moved a volume of ca. 50,000 m3 downhill from the west slope of the Spitzhorn mountain. The event took place in a single main slide preceded by individual


**Table 1.** Achieved theoretical 3D-error for the presented study cases.

**Figure 5.** Flood of the Sommascona plain (Olivone, 1927). (a + b) original oblique images with control points. (c + d) Digitalization of the flood contours on the original oblique image. (e) Projection of the digitalized flood contours on the current orthophoto. (f) Projection of the digitalized flood contours on the current oblique terrestrial image (modified

in Ticino (Switzerland) on Sunday afternoon on September 25, 1927, as a consequence of a strong and long-lasting waterspout above the mountain village of Olivone. This flood is still considered to be the most damaging natural event of the last century in the region. It destroyed part of the village of Campo Blenio and flooded the plain of Olivone over an area of

tool), affecting two sawmills and several private buildings (**Figure 5**). Due to the favorable

(ArcGIS computation of the perimeter as digitalized with the monoplotting

from Conedera et al. [21]).

at least 199,366 m2

weekday, no fatalities were recorded.

114 Natural Hazards - Risk Assessment and Vulnerability Reduction

**Figure 6.** Avalanches in the Hasliberg ski area (Meiringen, 2018). (a) Original oblique image with control points. (b) Digitalization of the avalanche perimeter on the original image. (c) Map showing the area in question and avalanches over the years (blue), the avalanche perimeter as estimated by the ski resort managers (red), and as measured by the MPT\_2.0 (green).

rockfalls, which gave security managers the opportunity to block all vehicular and pedestrian access, thus preventing fatalities. Nevertheless, the rockslide caused significant damage to the forest, the electric line, and trails.

In order to assess the involved area precisely, we used the MPT\_2.0 to process an oblique picture taken during a field inspection. Despite the difficulty in selecting suitable CPs (**Figure 7**), the achieved precision (**Table 1**) and the related time investment (**Table 2**) for analyzing the image are acceptable.

#### **4.3. Verifying the efficiency of protection infrastructures**

Documenting protection infrastructures during particular events or climatic situations may also be very helpful when assessing their functional capability and reliability in preventing natural events. Our final example relates to the assessment of the dimensioning and efficiency of snow barriers as protection against avalanches under real conditions.

#### *4.3.1. Example 5: analyzing the functionality of snow bridges: 2018, Adelboden*

The functional capability of snow bridges against avalanche detachment highly depends on the correct dimensioning of the supporting structures. A prerequisite for snow bridge efficiency is that the snow never rises above the supporting area of the structure. When these conditions are not satisfied for a significant part of the snow bridge, avalanches can detach from the exceeding snow cover, damaging the snow bridges located downhill, and threatening the infrastructures in the danger zone.

Snow bridge reliability can be best assessed in real conditions during exceptional snow cover conditions. This was the case in Adelboden (Canton Berne, Switzerland) during the heavy snowfalls combined with strong snow blowing winds that repeatedly occurred in the winter **Figure 7.** Spitzhorn rockslide (Gstaad, 2017). (a) Original oblique images with control points. (b) Digitalization of the detachment and transit/deposit zone on the original oblique image. (c) Projection of the digitalized contours on the current orthophoto. (d) Projection and 3D viewing of the digitalized zones in ESRI ArcScene. Yellow contours = detachment

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zone; orange contours = transit and deposit zone.

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rockfalls, which gave security managers the opportunity to block all vehicular and pedestrian access, thus preventing fatalities. Nevertheless, the rockslide caused significant damage to the

**Figure 6.** Avalanches in the Hasliberg ski area (Meiringen, 2018). (a) Original oblique image with control points. (b) Digitalization of the avalanche perimeter on the original image. (c) Map showing the area in question and avalanches over the years (blue), the avalanche perimeter as estimated by the ski resort managers (red), and as measured by the

In order to assess the involved area precisely, we used the MPT\_2.0 to process an oblique picture taken during a field inspection. Despite the difficulty in selecting suitable CPs (**Figure 7**), the achieved precision (**Table 1**) and the related time investment (**Table 2**) for analyzing the

Documenting protection infrastructures during particular events or climatic situations may also be very helpful when assessing their functional capability and reliability in preventing natural events. Our final example relates to the assessment of the dimensioning and efficiency

The functional capability of snow bridges against avalanche detachment highly depends on the correct dimensioning of the supporting structures. A prerequisite for snow bridge efficiency is that the snow never rises above the supporting area of the structure. When these conditions are not satisfied for a significant part of the snow bridge, avalanches can detach from the exceeding snow cover, damaging the snow bridges located downhill, and threaten-

Snow bridge reliability can be best assessed in real conditions during exceptional snow cover conditions. This was the case in Adelboden (Canton Berne, Switzerland) during the heavy snowfalls combined with strong snow blowing winds that repeatedly occurred in the winter

forest, the electric line, and trails.

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**4.3. Verifying the efficiency of protection infrastructures**

ing the infrastructures in the danger zone.

of snow barriers as protection against avalanches under real conditions.

*4.3.1. Example 5: analyzing the functionality of snow bridges: 2018, Adelboden*

image are acceptable.

MPT\_2.0 (green).

**Figure 7.** Spitzhorn rockslide (Gstaad, 2017). (a) Original oblique images with control points. (b) Digitalization of the detachment and transit/deposit zone on the original oblique image. (c) Projection of the digitalized contours on the current orthophoto. (d) Projection and 3D viewing of the digitalized zones in ESRI ArcScene. Yellow contours = detachment zone; orange contours = transit and deposit zone.


distribution of the control points. The combination of highly resolved DEM and unambigu-

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Local significant deviations in the reconstructed position in monoplotting with respect to the real world may, in contrast, occur when there are objects in the background landscape of an image, or when areas in the landscape display a small incidence angle with respect to the camera ray. Here, small imprecisions in clicking on the corresponding image pixel may result in a great displacement of the corresponding real point where the ray intersects the DEM. In case of crests and ridges, the ray may even overshoot the DEM and hit the background landscape

Additional sources of error are changes in the terrain morphology that took place between the time the image was shot and the DEM measurement. These may have originated in anthropogenic mass movements or by natural events such as landslides. In the latter case, only the untouched margins of the terrain changes may be localized with the monoplotting technique

Similar achievement potential and limitations of the MPT\_2.0 in terms of usefulness and achievable precision have been reported in independent scientific studies in the field of his-

According to our accumulated experience utilizing the MPT\_2.0, the required time investment highly depends on (a) the epoch of the event (the further back in time, the more the landscape may have changed, and the fewer available control points), (b) the quality of the image regarding the extent and form of the object to be digitalized (from local land slide to regional floods), and (c) on the available information (shooting location in particular). Thus far, most oblique images with suitable CPs were successfully processed with the MPT\_2.0 in a short time. The most time-consuming steps relate to the preparation of basic information such as the DEM, maps, orthophotos, images of the area in question, and the definition of the

The main aim of using the WSL monoplotting tool (MPT\_2.0) is to document in real time and reconstruct the effects of natural events before damage and signs in the landscape are removed or disappear. In this respect, the tool was found to be very flexible, enabling the operator to combine images from different epochs, and points of view that describe the same event. The possibility of georeferencing such images by reconstructing the unknown shooting point enables the use of oblique aerial photographs taken from helicopters, which in turn, opens the door to documenting natural events in highly inaccessible sites (see Example 5,

When suitable photographic material exists, even events dating back more than a century can be reconstructed with very satisfactory precision (see Example 1, Sasso Rosso area, Airolo). Similarly, also the extent and the severity of past pest attacks, diseases or wildfires can be

torical landscape reconstructions [36] and treeline ecotone dynamics [37].

ous (e.g., constructions), well-distributed CPs easily result in sub-metric precision.

or get lost in the sky.

(see **Figure 4**).

CPs (**Table 2**).

**6. Conclusions**

blanketed snow bridges).

**Table 2.** Average time investment for individual working steps when processing an image with the WSL monoplotting tool.

**Figure 8.** Snow bridges (Adelboden, 2018). (a) Original oblique image of the snow bridge constructions with control points. (b) Digitalization of the snow bridges. (c) Projection of the digitalized bridges on the current orthophoto highlighting in blue the parts covered by snow.

season of 2017/2018, which caused a partial blanketing of the snow bridges in the upper part of the area in question. On January 24, 2018, which was 2 days after the end of a heavy snowfall event, a helicopter recognition mission was organized and the snow bridge area was photographed in order to document the blanketed parts (**Figure 8a**).

Through the georeferencing process of the MPT\_2.0 (**Figure 8b**), the bridges were digitalized and exported to a GIS file, making it possible to identify the blanketed snow bridge parts, which may fail in their protection function (**Figure 8c**).
