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Analysis of building deformation in landslide area using multisensor PSInSAR ™ technique

View Article: PubMed Central - PubMed

ABSTRACT

We analyze ground deformation velocities of the buildings in San Fratello (Sicily, Italy).

We analyze satellite PSI data using different sensors, acquired from 1992 to 2012.

We performed a damages assessment map after the landslide occurred on the 14th February 2010.

The obtained data were compared to evaluate the residual risk.

The obtained data were compared to evaluate the residual risk.

No MeSH data available.


Percentages of buildings monitored with the available sensors considering the used velocity (in mm/y) classification described in the previous paragraphs. For C-band sensors: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −1.5; class 4: −1.49 to 1.5; class 5: 1.51–3.00; class 6: >3. For CSK: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −2.0; class 4: −1.99 to 2.0; class 5: 2.01–3.00; class 6: >3. For TSX_D: class 1: <−10; class 2: −9.99 to −8.00; class 3: −7.99 to −7.0; class 4: −6.99 to 7.0; class 5: 7.01–9.00; class 6: >9; for TSX_D_2: class 1: <−10; class 2: −9.99 to −7.00; class 3: −6.99 to −5.0; class 4: −4.99–5.0; class 5: 5.01–7.00; class 6: >7.
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fig0065: Percentages of buildings monitored with the available sensors considering the used velocity (in mm/y) classification described in the previous paragraphs. For C-band sensors: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −1.5; class 4: −1.49 to 1.5; class 5: 1.51–3.00; class 6: >3. For CSK: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −2.0; class 4: −1.99 to 2.0; class 5: 2.01–3.00; class 6: >3. For TSX_D: class 1: <−10; class 2: −9.99 to −8.00; class 3: −7.99 to −7.0; class 4: −6.99 to 7.0; class 5: 7.01–9.00; class 6: >9; for TSX_D_2: class 1: <−10; class 2: −9.99 to −7.00; class 3: −6.99 to −5.0; class 4: −4.99–5.0; class 5: 5.01–7.00; class 6: >7.

Mentions: Landslides may affect different kind of buildings in different ways. Construction methods, with regard to depth of foundations and used material, can counteract the development of the damages caused by ground displacement. This fact is fundamental to be taken into account in a town such as San Fratello, where very different kinds of buildings are present. San Fratello town is composed by historical buildings made of bricks, having very shallow foundations, as well as recent buildings with deeper foundations and a reinforced concrete structure. After the 2010 landslide the first ones show various degrees of damages, going from well-developed fractures on their walls and facades, distortion of windows and door frames, up to the structural partial or total collapse (Fig. 3). The more recent buildings stood up well to the landslide induced deformations, but in some cases, they show a rotation of the whole structure. The combined use of the buildings map and the PS radar datasets (Table 3) highlighted that the PS density is a fundamental parameter in order to evaluate the feasibility of this application for building monitoring activities. Only the X-band datasets, bearing a high target density (≫200 PS/km2), resulted useful to understand the displacement velocities of at least more than the half of the buildings of the San Fratello town. Anyway, some information were also retrieved by RADARSAT-1 datasets. The number of monitored buildings was sensibly improved considering a buffer of 2 m for each building. The size of the buffer was decided considering the proximity among the buildings. A wider buffer can lead to consider PS belonging to another building, especially in the city center, where several streets are very narrow. The use of a 2 m wide buffer allowed a considerable PS number improvement, which were used to calculate the buildings deformation velocity (Table 4). This improvement was observed for all the dataset even if the C-band sensors (ERS 1/2 and ENVISAT) were still characterized by a low percentage of monitored buildings. Only RADARSAT-1, among the C-band sensors, permitted the monitoring of a sufficient amount of buildings. When the percentages of monitored buildings were less than 40%, the use of the whole PS dataset was considered more appropriate in order to avoid the loss of information. The comparison between the building deformation velocity maps, highlights that before the 2010 event, all the C-band satellites indicate a substantial stability of the town (Fig. 13). In particular, up to the 96% of the monitored buildings with ERS 1/2 are characterized by velocities included within the stability range. Despite of the low number of monitored edifices, the ground deformation velocity maps for the eastern slope also confirm these results: no significant deformations were observed. The number of buildings affected by higher deformation velocities increases considering ENVISAT and RADARSAT-1. This increase can be related to the higher PS density. The ground deformation velocity maps obtained using ENVISAT and RADARSAT-1 suggest that the area corresponding to the 2010 landslide body was affected by ground deformation. Thanks to the higher PS density of RADARSAT-1, the instability can be also appreciated in the related building deformation velocity map. The number of buildings affected by deformation increases sensibly considering the X-band sensors, especially with regards to CSK, which highlights that only 60% of the monitored buildings are characterized by velocities within the stability range. These results are confirmed by the comparison between the building deformation velocity map and the damage assessment map. Buildings characterized by the highest residual velocities are located in the area affected by the 2010 landslide. A direct correlation between the deformation velocity and the degree of the damage was not observed because of the type of building affects their response to the deformation. For example, modern buildings having reinforced concrete foundations better resist to the stress with respect to the historical buildings. The X-band data highlighted the presence of considerably residual movements along the eastern slope even two years after the event. TSX data can be used with caution because of their high standard deviation, which makes PS velocities not reliable. Only a differential analysis can be performed to highlight moving areas with respect to stable areas. The TSX building deformation velocity map confirmed the same areas affected by ground deformation detected by CSK. An improvement in the measured velocities accuracy was obtained thanks to the reprocessing of the X-band datasets. Using these datasets, it was possible to reduce the standard deviation and to evaluate the deformation velocities more accurately. Interesting results were obtained also along the 1922 landslide crown. In this area, slow but continuous deformation was detected since 1992. In this sector, the correlation between the damaged buildings and the building deformation velocity map is very high. Some evidences of deformations of the edifices can be observed also using C-band sensors, but the lower PS density suggests that the use of the ground deformation velocity map is more suitable.


Analysis of building deformation in landslide area using multisensor PSInSAR ™ technique
Percentages of buildings monitored with the available sensors considering the used velocity (in mm/y) classification described in the previous paragraphs. For C-band sensors: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −1.5; class 4: −1.49 to 1.5; class 5: 1.51–3.00; class 6: >3. For CSK: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −2.0; class 4: −1.99 to 2.0; class 5: 2.01–3.00; class 6: >3. For TSX_D: class 1: <−10; class 2: −9.99 to −8.00; class 3: −7.99 to −7.0; class 4: −6.99 to 7.0; class 5: 7.01–9.00; class 6: >9; for TSX_D_2: class 1: <−10; class 2: −9.99 to −7.00; class 3: −6.99 to −5.0; class 4: −4.99–5.0; class 5: 5.01–7.00; class 6: >7.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5384476&req=5

fig0065: Percentages of buildings monitored with the available sensors considering the used velocity (in mm/y) classification described in the previous paragraphs. For C-band sensors: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −1.5; class 4: −1.49 to 1.5; class 5: 1.51–3.00; class 6: >3. For CSK: class 1: <−5; class 2: −4.99 to −3.00; class 3: −2.99 to −2.0; class 4: −1.99 to 2.0; class 5: 2.01–3.00; class 6: >3. For TSX_D: class 1: <−10; class 2: −9.99 to −8.00; class 3: −7.99 to −7.0; class 4: −6.99 to 7.0; class 5: 7.01–9.00; class 6: >9; for TSX_D_2: class 1: <−10; class 2: −9.99 to −7.00; class 3: −6.99 to −5.0; class 4: −4.99–5.0; class 5: 5.01–7.00; class 6: >7.
Mentions: Landslides may affect different kind of buildings in different ways. Construction methods, with regard to depth of foundations and used material, can counteract the development of the damages caused by ground displacement. This fact is fundamental to be taken into account in a town such as San Fratello, where very different kinds of buildings are present. San Fratello town is composed by historical buildings made of bricks, having very shallow foundations, as well as recent buildings with deeper foundations and a reinforced concrete structure. After the 2010 landslide the first ones show various degrees of damages, going from well-developed fractures on their walls and facades, distortion of windows and door frames, up to the structural partial or total collapse (Fig. 3). The more recent buildings stood up well to the landslide induced deformations, but in some cases, they show a rotation of the whole structure. The combined use of the buildings map and the PS radar datasets (Table 3) highlighted that the PS density is a fundamental parameter in order to evaluate the feasibility of this application for building monitoring activities. Only the X-band datasets, bearing a high target density (≫200 PS/km2), resulted useful to understand the displacement velocities of at least more than the half of the buildings of the San Fratello town. Anyway, some information were also retrieved by RADARSAT-1 datasets. The number of monitored buildings was sensibly improved considering a buffer of 2 m for each building. The size of the buffer was decided considering the proximity among the buildings. A wider buffer can lead to consider PS belonging to another building, especially in the city center, where several streets are very narrow. The use of a 2 m wide buffer allowed a considerable PS number improvement, which were used to calculate the buildings deformation velocity (Table 4). This improvement was observed for all the dataset even if the C-band sensors (ERS 1/2 and ENVISAT) were still characterized by a low percentage of monitored buildings. Only RADARSAT-1, among the C-band sensors, permitted the monitoring of a sufficient amount of buildings. When the percentages of monitored buildings were less than 40%, the use of the whole PS dataset was considered more appropriate in order to avoid the loss of information. The comparison between the building deformation velocity maps, highlights that before the 2010 event, all the C-band satellites indicate a substantial stability of the town (Fig. 13). In particular, up to the 96% of the monitored buildings with ERS 1/2 are characterized by velocities included within the stability range. Despite of the low number of monitored edifices, the ground deformation velocity maps for the eastern slope also confirm these results: no significant deformations were observed. The number of buildings affected by higher deformation velocities increases considering ENVISAT and RADARSAT-1. This increase can be related to the higher PS density. The ground deformation velocity maps obtained using ENVISAT and RADARSAT-1 suggest that the area corresponding to the 2010 landslide body was affected by ground deformation. Thanks to the higher PS density of RADARSAT-1, the instability can be also appreciated in the related building deformation velocity map. The number of buildings affected by deformation increases sensibly considering the X-band sensors, especially with regards to CSK, which highlights that only 60% of the monitored buildings are characterized by velocities within the stability range. These results are confirmed by the comparison between the building deformation velocity map and the damage assessment map. Buildings characterized by the highest residual velocities are located in the area affected by the 2010 landslide. A direct correlation between the deformation velocity and the degree of the damage was not observed because of the type of building affects their response to the deformation. For example, modern buildings having reinforced concrete foundations better resist to the stress with respect to the historical buildings. The X-band data highlighted the presence of considerably residual movements along the eastern slope even two years after the event. TSX data can be used with caution because of their high standard deviation, which makes PS velocities not reliable. Only a differential analysis can be performed to highlight moving areas with respect to stable areas. The TSX building deformation velocity map confirmed the same areas affected by ground deformation detected by CSK. An improvement in the measured velocities accuracy was obtained thanks to the reprocessing of the X-band datasets. Using these datasets, it was possible to reduce the standard deviation and to evaluate the deformation velocities more accurately. Interesting results were obtained also along the 1922 landslide crown. In this area, slow but continuous deformation was detected since 1992. In this sector, the correlation between the damaged buildings and the building deformation velocity map is very high. Some evidences of deformations of the edifices can be observed also using C-band sensors, but the lower PS density suggests that the use of the ground deformation velocity map is more suitable.

View Article: PubMed Central - PubMed

ABSTRACT

We analyze ground deformation velocities of the buildings in San Fratello (Sicily, Italy).

We analyze satellite PSI data using different sensors, acquired from 1992 to 2012.

We performed a damages assessment map after the landslide occurred on the 14th February 2010.

The obtained data were compared to evaluate the residual risk.

The obtained data were compared to evaluate the residual risk.

No MeSH data available.