OR/15/001 Validation results - Revision history

Dbk: /* Digital Elevation Models */

2015-09-29T09:40:21Z

Digital Elevation Models

← Older revision		Revision as of 10:40, 29 September 2015
Line 1,183:		Line 1,183:
	The accuracy of the 30 DEMs was derived by comparing the elevation values in the DEMs to the most accurate ancillary elevation data available for that AOI using. For St. Lucia, the ancillary elevation data comprised 18 GPS control points. For Grenada, ancillary elevation data comprised a random sample of 500 LiDAR data points selected independently of those used in the vertical calibration. Accordingly, the vertical accuracy of the DEMs was computed as the root-mean-square (RMS) error:		The accuracy of the 30 DEMs was derived by comparing the elevation values in the DEMs to the most accurate ancillary elevation data available for that AOI using. For St. Lucia, the ancillary elevation data comprised 18 GPS control points. For Grenada, ancillary elevation data comprised a random sample of 500 LiDAR data points selected independently of those used in the vertical calibration. Accordingly, the vertical accuracy of the DEMs was computed as the root-mean-square (RMS) error:

	[[Image:15001equation.jpg\|~~thumb~~\|center\| 500px\| ]]		[[Image:15001equation.jpg\|frameless\|center\| 500px\| ]]

	where ''Z<sub>DEM</sub> ''and ''Z<sub>ref</sub>'' are the corresponding DEM and ancillary elevations, respectively, and ''n'' is the number of elevation values.		where ''Z<sub>DEM</sub> ''and ''Z<sub>ref</sub>'' are the corresponding DEM and ancillary elevations, respectively, and ''n'' is the number of elevation values.

Dbk: /* Digital Elevation Models */

2015-09-29T09:39:58Z

Digital Elevation Models

← Older revision		Revision as of 10:39, 29 September 2015
Line 1,183:		Line 1,183:
	The accuracy of the 30 DEMs was derived by comparing the elevation values in the DEMs to the most accurate ancillary elevation data available for that AOI using. For St. Lucia, the ancillary elevation data comprised 18 GPS control points. For Grenada, ancillary elevation data comprised a random sample of 500 LiDAR data points selected independently of those used in the vertical calibration. Accordingly, the vertical accuracy of the DEMs was computed as the root-mean-square (RMS) error:		The accuracy of the 30 DEMs was derived by comparing the elevation values in the DEMs to the most accurate ancillary elevation data available for that AOI using. For St. Lucia, the ancillary elevation data comprised 18 GPS control points. For Grenada, ancillary elevation data comprised a random sample of 500 LiDAR data points selected independently of those used in the vertical calibration. Accordingly, the vertical accuracy of the DEMs was computed as the root-mean-square (RMS) error:

	[[Image:15001equation.jpg\|thumb\|center\| ~~300px~~\| ]]		[[Image:15001equation.jpg\|thumb\|center\| 500px\| ]]

	where ''Z<sub>DEM</sub> ''and ''Z<sub>ref</sub>'' are the corresponding DEM and ancillary elevations, respectively, and ''n'' is the number of elevation values.		where ''Z<sub>DEM</sub> ''and ''Z<sub>ref</sub>'' are the corresponding DEM and ancillary elevations, respectively, and ''n'' is the number of elevation values.

Dbk at 09:39, 29 September 2015

2015-09-29T09:39:43Z

← Older revision		Revision as of 10:39, 29 September 2015
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	Nevertheless, observations both in the field and imagery confirm that this type of confusion has been reduced considerably in the new land use/land cover maps in comparison to the existing maps (Figure 45). However, it is worth noting that a relatively minor degree of confusion does still persist around the coastline, marking the transition from land to the sea. This is most likely the result of the colour similarities between roads, light grey buildings and a sub-pixel mixing of sand/rock and the wake caused by breaking waves on the coast.		Nevertheless, observations both in the field and imagery confirm that this type of confusion has been reduced considerably in the new land use/land cover maps in comparison to the existing maps (Figure 45). However, it is worth noting that a relatively minor degree of confusion does still persist around the coastline, marking the transition from land to the sea. This is most likely the result of the colour similarities between roads, light grey buildings and a sub-pixel mixing of sand/rock and the wake caused by breaking waves on the coast.

	[[Image:~~15001 fig45~~.jpg\|thumb\|center\|600px\| '''Figure 45''' Confusion between buildings, roads and bare ground. Left image is Pleiades satellite data, middle is the EO-RISC land cover map, right image is the 2001 land cover map. Includes material ©CNES 2014, Distribution Airbus DS/SPOT Image S.A. France, all rights reserved.]]		[[Image:15001fig45.jpg\|thumb\|center\|600px\| '''Figure 45''' Confusion between buildings, roads and bare ground. Left image is Pleiades satellite data, middle is the EO-RISC land cover map, right image is the 2001 land cover map. Includes material ©CNES 2014, Distribution Airbus DS/SPOT Image S.A. France, all rights reserved.]]

	{\| class="wikitable"		{\| class="wikitable"
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	The analysis of the full dataset (incorporating the dataset mapped at 1:10k) resulted in a cumulative frequency-area distribution shown in Figure 46, following an approach outlined by Guzzetti 2005<ref name="Guzzetti 2005">Guzzetti, F. (2005). Landslide hazard and risk assessment. Concepts, methods and tools for the detection and mapping of landslides, for landslide susceptibility zonation and hazard assessment, and for landslide risk evaluation (Doctoral dissertation, PhD Thesis, Bonn).</ref> and Malamud et al 2004<ref>Malamud, B. D., Turcotte, D. L., Guzzetti, F., & Reichenbach, P. (2004). Landslide inventories and their statistical properties. ''Earth Surface Processes and Landforms'', 29(6), 687–711.</ref> (see also Hurst et al. 2014<ref>Hurst, M D, Ellis, M A, Royse, K R., Lee, K A and Freeborough, K. (2013). Controls on the magnitude-frequency scaling of an inventory of secular landslides. ''Earth Surface Dynamics'', 1, 67–78.</ref>). It is interesting that spill-overs (i.e where the largest number of events are found of a particular size) depend on the nature of the survey. For quick reconnaissance surveys this spill-over lies at a characteristic size of about 104 to 105m<sup>2</sup> (i.e. landslide dimensions of approximately 100x100m to 300x300m). For more detailed geomorphological surveys greater detail can be captured and this results in a trend with a spill-over at about 103m<sup>2</sup> (indicative dimensions of about 30x30m) which is similar to what was found in our survey.		The analysis of the full dataset (incorporating the dataset mapped at 1:10k) resulted in a cumulative frequency-area distribution shown in Figure 46, following an approach outlined by Guzzetti 2005<ref name="Guzzetti 2005">Guzzetti, F. (2005). Landslide hazard and risk assessment. Concepts, methods and tools for the detection and mapping of landslides, for landslide susceptibility zonation and hazard assessment, and for landslide risk evaluation (Doctoral dissertation, PhD Thesis, Bonn).</ref> and Malamud et al 2004<ref>Malamud, B. D., Turcotte, D. L., Guzzetti, F., & Reichenbach, P. (2004). Landslide inventories and their statistical properties. ''Earth Surface Processes and Landforms'', 29(6), 687–711.</ref> (see also Hurst et al. 2014<ref>Hurst, M D, Ellis, M A, Royse, K R., Lee, K A and Freeborough, K. (2013). Controls on the magnitude-frequency scaling of an inventory of secular landslides. ''Earth Surface Dynamics'', 1, 67–78.</ref>). It is interesting that spill-overs (i.e where the largest number of events are found of a particular size) depend on the nature of the survey. For quick reconnaissance surveys this spill-over lies at a characteristic size of about 104 to 105m<sup>2</sup> (i.e. landslide dimensions of approximately 100x100m to 300x300m). For more detailed geomorphological surveys greater detail can be captured and this results in a trend with a spill-over at about 103m<sup>2</sup> (indicative dimensions of about 30x30m) which is similar to what was found in our survey.

	[[Image:~~15001 fig46~~.jpg\|thumb\|center\| 500px\| '''Figure 46''' a) Cumulative frequency-area distribution for landslide events in St Luci for the period 2010-2014 and b) an example of the different cumulative frequency-area distributions for reconnaissance and for detailed geomorphological surveys (Guzzetti 2005<ref name="Guzzetti 2005"></ref>).]]		[[Image:15001fig46.jpg\|thumb\|center\| 500px\| '''Figure 46''' a) Cumulative frequency-area distribution for landslide events in St Luci for the period 2010-2014 and b) an example of the different cumulative frequency-area distributions for reconnaissance and for detailed geomorphological surveys (Guzzetti 2005<ref name="Guzzetti 2005"></ref>).]]

	Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.		Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.
Line 1,183:		Line 1,183:
	The accuracy of the 30 DEMs was derived by comparing the elevation values in the DEMs to the most accurate ancillary elevation data available for that AOI using. For St. Lucia, the ancillary elevation data comprised 18 GPS control points. For Grenada, ancillary elevation data comprised a random sample of 500 LiDAR data points selected independently of those used in the vertical calibration. Accordingly, the vertical accuracy of the DEMs was computed as the root-mean-square (RMS) error:		The accuracy of the 30 DEMs was derived by comparing the elevation values in the DEMs to the most accurate ancillary elevation data available for that AOI using. For St. Lucia, the ancillary elevation data comprised 18 GPS control points. For Grenada, ancillary elevation data comprised a random sample of 500 LiDAR data points selected independently of those used in the vertical calibration. Accordingly, the vertical accuracy of the DEMs was computed as the root-mean-square (RMS) error:

	[[Image:~~15001 equation~~.jpg\|thumb\|center\| 300px\| ]]		[[Image:15001equation.jpg\|thumb\|center\| 300px\| ]]

	where ''Z<sub>DEM</sub> ''and ''Z<sub>ref</sub>'' are the corresponding DEM and ancillary elevations, respectively, and ''n'' is the number of elevation values.		where ''Z<sub>DEM</sub> ''and ''Z<sub>ref</sub>'' are the corresponding DEM and ancillary elevations, respectively, and ''n'' is the number of elevation values.

Dbk: /* Landslide inventories */

2015-09-25T13:57:50Z

Landslide inventories

← Older revision		Revision as of 14:57, 25 September 2015
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	Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.		Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.

	'''Minimum dimensions '''in the database are approximately 200 m<sup>2</sup> and many smaller events are known to have occurred. Very small (<5 m) and obscured (in the shade, on steep slopes, below overhanging vegetation) landslides are difficult to capture but are known to exist in substantial numbers. These small events have a significant impact on lives and livelihoods and recording these through different means will be important to add to the database. It is possible to interrogate the recent Pleiades images (at resolution of 0.5m) at a greater level of detail and it is therefore advantageous to carry out future assessments at scales of 1:5 000 or better. The case study of Chateau Belair (discussed in ~~'''section 2'''~~) highlights the restrictions in using satellite images for interpretation. Subtle changes in topography can be interpreted by skilled operators as signatures of landslides in a complex terrain. At high resolutions (1:10k and better) this can lead to very detailed maps showing geomorphology and engineering geology features relevant to slope instability.		'''Minimum dimensions '''in the database are approximately 200 m<sup>2</sup> and many smaller events are known to have occurred. Very small (<5 m) and obscured (in the shade, on steep slopes, below overhanging vegetation) landslides are difficult to capture but are known to exist in substantial numbers. These small events have a significant impact on lives and livelihoods and recording these through different means will be important to add to the database. It is possible to interrogate the recent Pleiades images (at resolution of 0.5m) at a greater level of detail and it is therefore advantageous to carry out future assessments at scales of 1:5 000 or better. The case study of Chateau Belair (discussed in the [[OR/15/001 Executive summary \| Executive summary]]) highlights the restrictions in using satellite images for interpretation. Subtle changes in topography can be interpreted by skilled operators as signatures of landslides in a complex terrain. At high resolutions (1:10k and better) this can lead to very detailed maps showing geomorphology and engineering geology features relevant to slope instability.

	It will remain very difficult to identify '''very large, relic and inactive landslides'''. This exercise has shown that very large, and mostly relic, landslides are often difficult to identify and interpret in the context of Caribbean Islands where a substantial vegetation cover masks topographic features.		It will remain very difficult to identify '''very large, relic and inactive landslides'''. This exercise has shown that very large, and mostly relic, landslides are often difficult to identify and interpret in the context of Caribbean Islands where a substantial vegetation cover masks topographic features.

Dbk: /* Landslide inventories */

2015-09-25T13:56:54Z

Landslide inventories

← Older revision		Revision as of 14:56, 25 September 2015
Line 1,168:		Line 1,168:
	To achieve an insight into the completeness of the landslide inventory of St Lucia, the complete multi-temporal dataset has been analysed. The extent of an individual landslide throughout the multi-temporal dataset is not constant. Deviations from this extent will occur in different years. This can occur through, for example, re-establishment of a vegetation cover leading to increasingly smaller areas remaining ‘active’. But it is also possible that slides extent, either downslope as a consequence of continuing displacement of a landslide mass, or upslope and laterally through retrogressive failure involving increasing large amounts of slope. For this exercise, landslide polygons offer an outline representing the maximum extent of an event throughout the time series. It is therefore unrealistic to construct area frequency diagrams for each year.		To achieve an insight into the completeness of the landslide inventory of St Lucia, the complete multi-temporal dataset has been analysed. The extent of an individual landslide throughout the multi-temporal dataset is not constant. Deviations from this extent will occur in different years. This can occur through, for example, re-establishment of a vegetation cover leading to increasingly smaller areas remaining ‘active’. But it is also possible that slides extent, either downslope as a consequence of continuing displacement of a landslide mass, or upslope and laterally through retrogressive failure involving increasing large amounts of slope. For this exercise, landslide polygons offer an outline representing the maximum extent of an event throughout the time series. It is therefore unrealistic to construct area frequency diagrams for each year.

	The analysis of the full dataset (incorporating the dataset mapped at 1:10k) resulted in a cumulative frequency-area distribution shown in Figure 46, following an approach outlined by Guzzetti 2005<ref name="Guzzetti 2005">Guzzetti, F. (2005). Landslide hazard and risk assessment. Concepts, methods and tools for the detection and mapping of landslides, for landslide susceptibility zonation and hazard assessment, and for landslide risk evaluation (Doctoral dissertation, PhD Thesis, Bonn).</ref> and Malamud et al 2004<ref>Malamud, B. D., Turcotte, D. L., Guzzetti, F., & Reichenbach, P. (2004). Landslide inventories and their statistical properties. ''Earth Surface Processes and Landforms'', 29(6), 687–711.</ref> (see also Hurst et al. 2014<ref>Hurst, M D, Ellis, M A, Royse, K R., Lee, K A and Freeborough, K. (2013). Controls on the magnitude-frequency scaling of an inventory of secular landslides. ''Earth Surface Dynamics'', 1, 67–78.</ref>). It is interesting that spill-overs (i.e where the largest number of events are found of a particular size) depend on the nature of the survey. For quick reconnaissance surveys this spill-over lies at a characteristic size of about 104 to 105m<sup>2</sup> (i.e. landslide dimensions of approximately 100x100m to 300x300m). For more detailed geomorphological surveys greater detail can be captured and this results in a trend with a spill-over at about ~~103 m~~<sup>2</sup> (indicative dimensions of about 30x30m) which is similar to what was found in our survey.		The analysis of the full dataset (incorporating the dataset mapped at 1:10k) resulted in a cumulative frequency-area distribution shown in Figure 46, following an approach outlined by Guzzetti 2005<ref name="Guzzetti 2005">Guzzetti, F. (2005). Landslide hazard and risk assessment. Concepts, methods and tools for the detection and mapping of landslides, for landslide susceptibility zonation and hazard assessment, and for landslide risk evaluation (Doctoral dissertation, PhD Thesis, Bonn).</ref> and Malamud et al 2004<ref>Malamud, B. D., Turcotte, D. L., Guzzetti, F., & Reichenbach, P. (2004). Landslide inventories and their statistical properties. ''Earth Surface Processes and Landforms'', 29(6), 687–711.</ref> (see also Hurst et al. 2014<ref>Hurst, M D, Ellis, M A, Royse, K R., Lee, K A and Freeborough, K. (2013). Controls on the magnitude-frequency scaling of an inventory of secular landslides. ''Earth Surface Dynamics'', 1, 67–78.</ref>). It is interesting that spill-overs (i.e where the largest number of events are found of a particular size) depend on the nature of the survey. For quick reconnaissance surveys this spill-over lies at a characteristic size of about 104 to 105m<sup>2</sup> (i.e. landslide dimensions of approximately 100x100m to 300x300m). For more detailed geomorphological surveys greater detail can be captured and this results in a trend with a spill-over at about 103m<sup>2</sup> (indicative dimensions of about 30x30m) which is similar to what was found in our survey.

	[[Image:15001 fig46.jpg\|thumb\|center\| 500px\| '''Figure 46''' a) Cumulative frequency-area distribution for landslide events in St Luci for the period 2010-2014 and b) an example of the different cumulative frequency-area distributions for reconnaissance and for detailed geomorphological surveys (Guzzetti 2005<ref name="Guzzetti 2005"></ref>).]]		[[Image:15001 fig46.jpg\|thumb\|center\| 500px\| '''Figure 46''' a) Cumulative frequency-area distribution for landslide events in St Luci for the period 2010-2014 and b) an example of the different cumulative frequency-area distributions for reconnaissance and for detailed geomorphological surveys (Guzzetti 2005<ref name="Guzzetti 2005"></ref>).]]

Dbk at 12:19, 25 September 2015

2015-09-25T12:19:27Z

← Older revision		Revision as of 13:19, 25 September 2015
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	==== Precise Digital Elevation Model ====		==== Precise Digital Elevation Model ====
	As discussed above, the precise 1m DEM for a ~~100km2~~ subset of Belize has not yet been produced due to a lack of suitable tri-stereo Pleiades imagery.		As discussed above, the precise 1m DEM for a 100km<sup>2</sup> subset of Belize has not yet been produced due to a lack of suitable tri-stereo Pleiades imagery.

	==Initial Validation ==		==Initial Validation ==

Dbk at 12:18, 25 September 2015

2015-09-25T12:18:47Z

← Older revision		Revision as of 13:18, 25 September 2015
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	==References==		==References==

	[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 01]]		[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 05]]

Dbk: /* Service 2: Hazard mapping to support landslide risk assessment */

2015-09-25T12:18:04Z

Service 2: Hazard mapping to support landslide risk assessment

← Older revision		Revision as of 13:18, 25 September 2015
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	Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.		Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.

	'''Minimum dimensions '''in the database are approximately 200 m2 and many smaller events are known to have occurred. Very small (<5 m) and obscured (in the shade, on steep slopes, below overhanging vegetation) landslides are difficult to capture but are known to exist in substantial numbers. These small events have a significant impact on lives and livelihoods and recording these through different means will be important to add to the database. It is possible to interrogate the recent Pleiades images (at resolution of 0.5m) at a greater level of detail and it is therefore advantageous to carry out future assessments at scales of 1:5 000 or better. The case study of Chateau Belair (discussed in '''section 2''') highlights the restrictions in using satellite images for interpretation. Subtle changes in topography can be interpreted by skilled operators as signatures of landslides in a complex terrain. At high resolutions (1:10k and better) this can lead to very detailed maps showing geomorphology and engineering geology features relevant to slope instability.		'''Minimum dimensions '''in the database are approximately 200 m<sup>2</sup> and many smaller events are known to have occurred. Very small (<5 m) and obscured (in the shade, on steep slopes, below overhanging vegetation) landslides are difficult to capture but are known to exist in substantial numbers. These small events have a significant impact on lives and livelihoods and recording these through different means will be important to add to the database. It is possible to interrogate the recent Pleiades images (at resolution of 0.5m) at a greater level of detail and it is therefore advantageous to carry out future assessments at scales of 1:5 000 or better. The case study of Chateau Belair (discussed in '''section 2''') highlights the restrictions in using satellite images for interpretation. Subtle changes in topography can be interpreted by skilled operators as signatures of landslides in a complex terrain. At high resolutions (1:10k and better) this can lead to very detailed maps showing geomorphology and engineering geology features relevant to slope instability.

	It will remain very difficult to identify '''very large, relic and inactive landslides'''. This exercise has shown that very large, and mostly relic, landslides are often difficult to identify and interpret in the context of Caribbean Islands where a substantial vegetation cover masks topographic features.		It will remain very difficult to identify '''very large, relic and inactive landslides'''. This exercise has shown that very large, and mostly relic, landslides are often difficult to identify and interpret in the context of Caribbean Islands where a substantial vegetation cover masks topographic features.

Dbk: /* Landslide inventories */

2015-09-25T12:17:44Z

Landslide inventories

← Older revision		Revision as of 13:17, 25 September 2015
Line 1,168:		Line 1,168:
	To achieve an insight into the completeness of the landslide inventory of St Lucia, the complete multi-temporal dataset has been analysed. The extent of an individual landslide throughout the multi-temporal dataset is not constant. Deviations from this extent will occur in different years. This can occur through, for example, re-establishment of a vegetation cover leading to increasingly smaller areas remaining ‘active’. But it is also possible that slides extent, either downslope as a consequence of continuing displacement of a landslide mass, or upslope and laterally through retrogressive failure involving increasing large amounts of slope. For this exercise, landslide polygons offer an outline representing the maximum extent of an event throughout the time series. It is therefore unrealistic to construct area frequency diagrams for each year.		To achieve an insight into the completeness of the landslide inventory of St Lucia, the complete multi-temporal dataset has been analysed. The extent of an individual landslide throughout the multi-temporal dataset is not constant. Deviations from this extent will occur in different years. This can occur through, for example, re-establishment of a vegetation cover leading to increasingly smaller areas remaining ‘active’. But it is also possible that slides extent, either downslope as a consequence of continuing displacement of a landslide mass, or upslope and laterally through retrogressive failure involving increasing large amounts of slope. For this exercise, landslide polygons offer an outline representing the maximum extent of an event throughout the time series. It is therefore unrealistic to construct area frequency diagrams for each year.

	The analysis of the full dataset (incorporating the dataset mapped at 1:10k) resulted in a cumulative frequency-area distribution shown in Figure 46, following an approach outlined by Guzzetti 2005<ref name="Guzzetti 2005">Guzzetti, F. (2005). Landslide hazard and risk assessment. Concepts, methods and tools for the detection and mapping of landslides, for landslide susceptibility zonation and hazard assessment, and for landslide risk evaluation (Doctoral dissertation, PhD Thesis, Bonn).</ref> and Malamud et al 2004<ref></ref> (see also Hurst et al. 2014<ref></ref>). It is interesting that spill- overs (i.e where the largest number of events are found of a particular size) depend on the nature of the survey. For quick reconnaissance surveys this spill-over lies at a characteristic size of about 104 to 105m<sup>2</sup> (i.e. landslide dimensions of approximately 100x100m to 300x300m). For more detailed geomorphological surveys greater detail can be captured and this results in a trend with a spill-over at about 103 m<sup>2</sup> (indicative dimensions of about 30x30m) which is similar to what was found in our survey.		The analysis of the full dataset (incorporating the dataset mapped at 1:10k) resulted in a cumulative frequency-area distribution shown in Figure 46, following an approach outlined by Guzzetti 2005<ref name="Guzzetti 2005">Guzzetti, F. (2005). Landslide hazard and risk assessment. Concepts, methods and tools for the detection and mapping of landslides, for landslide susceptibility zonation and hazard assessment, and for landslide risk evaluation (Doctoral dissertation, PhD Thesis, Bonn).</ref> and Malamud et al 2004<ref>Malamud, B. D., Turcotte, D. L., Guzzetti, F., & Reichenbach, P. (2004). Landslide inventories and their statistical properties. ''Earth Surface Processes and Landforms'', 29(6), 687–711.</ref> (see also Hurst et al. 2014<ref>Hurst, M D, Ellis, M A, Royse, K R., Lee, K A and Freeborough, K. (2013). Controls on the magnitude-frequency scaling of an inventory of secular landslides. ''Earth Surface Dynamics'', 1, 67–78.</ref>). It is interesting that spill-overs (i.e where the largest number of events are found of a particular size) depend on the nature of the survey. For quick reconnaissance surveys this spill-over lies at a characteristic size of about 104 to 105m<sup>2</sup> (i.e. landslide dimensions of approximately 100x100m to 300x300m). For more detailed geomorphological surveys greater detail can be captured and this results in a trend with a spill-over at about 103 m<sup>2</sup> (indicative dimensions of about 30x30m) which is similar to what was found in our survey.

	[[Image:15001 fig46.jpg\|thumb\|center\| 500px\| '''Figure 46''' a) Cumulative frequency-area distribution for landslide events in St Luci for the period 2010-2014 and b) an example of the different cumulative frequency-area distributions for reconnaissance and for detailed geomorphological surveys (Guzzetti 2005).]]		[[Image:15001 fig46.jpg\|thumb\|center\| 500px\| '''Figure 46''' a) Cumulative frequency-area distribution for landslide events in St Luci for the period 2010-2014 and b) an example of the different cumulative frequency-area distributions for reconnaissance and for detailed geomorphological surveys (Guzzetti 2005<ref name="Guzzetti 2005"></ref>).]]

	Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.		Even though from the above it appears that the established inventory provides a degree of confidence that it forms a fair representation of events there are known omissions from the database.

	'''Minimum dimensions '''in the database are approximately 200 m2 and many smaller events are known to have occurred. Very small (<5 m) and obscured (in the shade, on steep slopes, below overhanging vegetation) landslides are difficult to capture but are known to exist in substantial numbers. These small events have a significant impact on lives and livelihoods and recording these through different means will be important to add to the database. It is possible to interrogate the recent Pleiades images (at resolution of 0.5m) at a greater level of detail and it is therefore advantageous to carry out future assessments at scales of 1:5 000 or better. The case study of Chateau Belair (discussed in section 2) highlights the restrictions in using satellite images for interpretation. Subtle changes in topography can be interpreted by skilled operators as signatures of landslides in a complex terrain. At high resolutions (1:10k and better) this can lead to very detailed maps showing geomorphology and engineering geology features relevant to slope instability.		'''Minimum dimensions '''in the database are approximately 200 m2 and many smaller events are known to have occurred. Very small (<5 m) and obscured (in the shade, on steep slopes, below overhanging vegetation) landslides are difficult to capture but are known to exist in substantial numbers. These small events have a significant impact on lives and livelihoods and recording these through different means will be important to add to the database. It is possible to interrogate the recent Pleiades images (at resolution of 0.5m) at a greater level of detail and it is therefore advantageous to carry out future assessments at scales of 1:5 000 or better. The case study of Chateau Belair (discussed in '''section 2''') highlights the restrictions in using satellite images for interpretation. Subtle changes in topography can be interpreted by skilled operators as signatures of landslides in a complex terrain. At high resolutions (1:10k and better) this can lead to very detailed maps showing geomorphology and engineering geology features relevant to slope instability.

	It will remain very difficult to identify '''very large, relic and inactive landslides'''. This exercise has shown that very large, and mostly relic, landslides are often difficult to identify and interpret in the context of Caribbean Islands where a substantial vegetation cover masks topographic features.		It will remain very difficult to identify '''very large, relic and inactive landslides'''. This exercise has shown that very large, and mostly relic, landslides are often difficult to identify and interpret in the context of Caribbean Islands where a substantial vegetation cover masks topographic features.

Dbk: /* Initial Validation */

2015-09-25T12:14:55Z

Initial Validation

← Older revision		Revision as of 13:14, 25 September 2015
Line 1,200:		Line 1,200:
	Preliminary data have only recently been released to the ITC project therefore we do not have validation data from the users yet. A workshop/training exercise with the users is planned for end February to early March 2015 and BGS expects to gain more complete feedback from the users at that workshop/exercise.		Preliminary data have only recently been released to the ITC project therefore we do not have validation data from the users yet. A workshop/training exercise with the users is planned for end February to early March 2015 and BGS expects to gain more complete feedback from the users at that workshop/exercise.

			==References==

	[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 01]]		[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 01]]