OR/15/001 Products description - Revision history

Ajhil at 14:36, 29 November 2019

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	[[Image:15001fig11.jpg\|thumb\|center\|600px\|'''Figure 11''' Landslide inventory maps of St Lucia showing the distribution of active landslides (NB identification is affected by cloud cover and a poor quality SE quadrant for 2013; see Table 3).]]		[[Image:15001fig11.jpg\|thumb\|center\|600px\|'''Figure 11''' Landslide inventory maps of St Lucia showing the distribution of active landslides (NB identification is affected by cloud cover and a poor quality SE quadrant for 2013; see Table 3).]]

	'''Field checking '''involved a six-day field investigation in the first week of October 2014. The identification of routes and areas to concentrate on was facilitated by the ECLAC (2011)<ref name="ECLAC">ECLAC Report (2011). Saint Lucia: Macro socio-economic and environmental assessment of the damage and losses caused by Hurricane Tomas: a geo-environmental disaster. Towards Resilience. Economic Commission for Latin America and the Caribbean. 183pp.</ref> report. This report provided excellent guidance in terms of the impact of Hurricane Tomas in 2010.		'''Field checking '''involved a six-day field investigation in the first week of October 2014. The identification of routes and areas to concentrate on was facilitated by the ECLAC (2011)<ref name="ECLAC"></ref> report. This report provided excellent guidance in terms of the impact of Hurricane Tomas in 2010.

	While in the field, the team was able to access all available digital information on a GPS-enabled laptop. The routes taken are shown in the figure below. The aim was to see as many potential landslide sites as possible in this short period of time with a focus on the following landslides sites (Figure 12):		While in the field, the team was able to access all available digital information on a GPS-enabled laptop. The routes taken are shown in the figure below. The aim was to see as many potential landslide sites as possible in this short period of time with a focus on the following landslides sites (Figure 12):

Dbk: /* National Digital Elevation Model */

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National Digital Elevation Model

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	where ''H<sub>cal</sub> ''are the vertically calibrated ASTER elevation values and ''x ''are the original ASTER elevation values. The quality of the calibrated DEM was further enhanced by applying a 3×3 pixel moving average filter. This filter helped to reduce “noisy” data values as well as notable 'spike' artefacts in the DEM. The 20m and 30m DEMs generated for Belize are shown in Figure 42in the form of a map also produced as part of the project.		where ''H<sub>cal</sub> ''are the vertically calibrated ASTER elevation values and ''x ''are the original ASTER elevation values. The quality of the calibrated DEM was further enhanced by applying a 3×3 pixel moving average filter. This filter helped to reduce “noisy” data values as well as notable 'spike' artefacts in the DEM. The 20m and 30m DEMs generated for Belize are shown in Figure 42in the form of a map also produced as part of the project.

	[[Image:~~15001fig421~~.jpg\|thumb\|center\|600px\|'''Figure 42''' The national 30m DEM and 20m DEM (in blue window) of Belize.]]		[[Image:15001fig42.jpg\|thumb\|center\|600px\|'''Figure 42''' The national 30m DEM and 20m DEM (in blue window) of Belize.]]

	==== Precise Digital Elevation Model ====		==== Precise Digital Elevation Model ====

Dbk at 09:38, 29 September 2015

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Dbk at 08:33, 29 September 2015

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	[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 04]]		[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 03]]

Dbk at 13:55, 25 September 2015

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Dbk at 13:20, 25 September 2015

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Dbk at 13:12, 25 September 2015

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	The basis for each land use/land cover map was the result of supervised per-pixel classification of the imagery according to the land use/land cover classes outlined in Table 5. Due to their inherent similarities, it was anticipated that some of the classes — in particular some forest types, and 'Bare ground', 'Roads and other built-up surfaces', 'Pastures, cultivated land and herbaceous agriculture' — could be particularly difficult to discriminate using the limited spectral information contained in only the blue, green, red and near-infrared bands of the Pleiades imagery. Therefore, textural information was also incorporated in the form of the Grey-Level Co- occurrence Matrix (GLCM) parameters of entropy, dissimilarity, second moment and homogeneity (Haralick et al., 1973 and Herold et al., 2003). These parameters were derived from the Pleiades green band in the ENVI 4.8 software package (Research Systems, Inc.) for a 3 × 3 pixel (i.e. 6m × 6m) window and a co-occurrence window shift of 4 pixels (i.e. 8m) in both the x- and y-direction. These 4 textural bands were merged with the 4 Pleiades multispectral bands to create an 8-band spectral-textural dataset for each AOI for input to the classification.		The basis for each land use/land cover map was the result of supervised per-pixel classification of the imagery according to the land use/land cover classes outlined in Table 5. Due to their inherent similarities, it was anticipated that some of the classes — in particular some forest types, and 'Bare ground', 'Roads and other built-up surfaces', 'Pastures, cultivated land and herbaceous agriculture' — could be particularly difficult to discriminate using the limited spectral information contained in only the blue, green, red and near-infrared bands of the Pleiades imagery. Therefore, textural information was also incorporated in the form of the Grey-Level Co-occurrence Matrix (GLCM) parameters of entropy, dissimilarity, second moment and homogeneity (Haralick et al., 1973 and Herold et al., 2003). These parameters were derived from the Pleiades green band in the ENVI 4.8 software package (Research Systems, Inc.) for a 3 × 3 pixel (i.e. 6m × 6m) window and a co-occurrence window shift of 4 pixels (i.e. 8m) in both the x- and y-direction. These 4 textural bands were merged with the 4 Pleiades multispectral bands to create an 8-band spectral-textural dataset for each AOI for input to the classification.

	Classification of the datasets was performed using a supervised neural network (NN) classification algorithm. The NN used in this case was a Multi-Layered Perceptron NN with a back-propagation learning algorithm for supervised learning (Richards and Jia, 2006). Using a three-layered NN (i.e., input, output and one hidden layer), land use/land cover classifications were performed in ENVI 4.8 with the default training parameters. Each classification was supervised with the aid of a set of training pixels that were carefully selected to represent the spectral and textural characteristics of each of the land use/land cover classes. These training pixels were identified in Pleiades imagery by using the existing maps as a guide.		Classification of the datasets was performed using a supervised neural network (NN) classification algorithm. The NN used in this case was a Multi-Layered Perceptron NN with a back-propagation learning algorithm for supervised learning (Richards and Jia, 2006<ref></ref>). Using a three-layered NN (i.e., input, output and one hidden layer), land use/land cover classifications were performed in ENVI 4.8 with the default training parameters. Each classification was supervised with the aid of a set of training pixels that were carefully selected to represent the spectral and textural characteristics of each of the land use/land cover classes. These training pixels were identified in Pleiades imagery by using the existing maps as a guide.

	The classified images did not initially provide full areal coverage of the AOIs owing to the obscuring effects of cloud cover and associated shadowing in the imagery. Accordingly, areas with missing land use/land cover information in the classified image were first patched using the results of RapidEye image classifications achieved using the same approach as outline above. Any remaining unclassified areas were then patched using the existing land use/land cover information (Figure 4). The St. Vincent & the Grenadines classification was patched entirely using the existing land use/land cover maps due to the poor quality of the RapidEye imagery.		The classified images did not initially provide full areal coverage of the AOIs owing to the obscuring effects of cloud cover and associated shadowing in the imagery. Accordingly, areas with missing land use/land cover information in the classified image were first patched using the results of RapidEye image classifications achieved using the same approach as outline above. Any remaining unclassified areas were then patched using the existing land use/land cover information (Figure 4). The St. Vincent & the Grenadines classification was patched entirely using the existing land use/land cover maps due to the poor quality of the RapidEye imagery.

Dbk: /* Metadata */

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Metadata

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	\| 9.8m (RMS)		\| 9.8m (RMS)
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			==References==


	[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 04]]		[[category: OR/15/001 Risk information services for Disaster Risk Management (DRM) in the Caribbean \| 04]]

Dbk at 12:36, 25 September 2015

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	Existing land use/land cover maps from 2001 were available for the three AOIs following their production by the various partners of The Nature Conservancy’s Mesoamerica and Caribbean Region project (Helmer et al., 2007; 2008). These maps were produced at a spatial resolution of 30m through a combination of automated imagery classification of Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery and manual delineation of IKONOS satellite imagery. The mapping was augmented with extensive field knowledge and observation. Accordingly, these maps provide excellent baseline data to build upon for the land use/land cover mapping under this service.		Existing land use/land cover maps from 2001 were available for the three AOIs following their production by the various partners of The Nature Conservancy’s Mesoamerica and Caribbean Region project (Helmer et al., 2007<ref name="Helmer 2007">Helmer, E H, Schill, S, Pedreror, D H, Kennaway, T, Cushing, W M, Coan, M J, Wood, E C, Ruzycki, T and Tieszen, L L. (2007). Forest formation and land cover map series-Caribbean Islands. U.S. Geological Survey/Earth Resources Observation and Science (EROS), Land Cover Applications and Global Change, International Land Cover and Biodiversity, Caribbean Land Cover Analyses.</ref>; 2008<ref name="Helmer 2008">Helmer E H, Kennaway T A, Pedreros, D H, Clark, M L, Marcano-Vega, H, Tieszen, L L, Ruzycki, T R, Schill, S R and Carrington, C M. 2008. Land cover and forest formation distribution for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from Decision Trees classification of cloud-cleared satellite imagery. ''Caribbean Journal of Science'', 44, 175–189.</ref>). These maps were produced at a spatial resolution of 30m through a combination of automated imagery classification of Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery and manual delineation of IKONOS satellite imagery. The mapping was augmented with extensive field knowledge and observation. Accordingly, these maps provide excellent baseline data to build upon for the land use/land cover mapping under this service.

	The Pleiades multispectral imagery was selected as the basis for the three new land use/land cover maps because its higher spatial resolution enables the most detail to be resolved. With a spatial resolution (pixel size) of 2m for the multispectral bands, mapping can theoretically be undertaken at a scale equivalent to 1:10 000. Given the areal extent of the AOIs, the spatial resolution and the resources available, it was decided that the most efficient mapping strategy was to utilise a largely automated approach with subsequent refinement and manual digitisation.		The Pleiades multispectral imagery was selected as the basis for the three new land use/land cover maps because its higher spatial resolution enables the most detail to be resolved. With a spatial resolution (pixel size) of 2m for the multispectral bands, mapping can theoretically be undertaken at a scale equivalent to 1:10 000. Given the areal extent of the AOIs, the spatial resolution and the resources available, it was decided that the most efficient mapping strategy was to utilise a largely automated approach with subsequent refinement and manual digitisation.

Dbk: /* Landslide inventory establishment — Grenada */

2015-09-25T10:46:05Z

Landslide inventory establishment — Grenada

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	For St. Lucia, ancillary elevation data derived from contour maps was made available by the Physical Planning Office and University of the West Indies. In an attempt to increase the accuracy of the ASTER-derived elevation data, a vertical calibration approach utilising the contour data was implemented. To achieve this, 32 000 corresponding ASTER- and contour-derived elevation points were extracted and modelled (''R''2=0.97) using regression analysis (Figure 39). All ASTER-derived elevation values were subsequently vertically calibrated using:		For St. Lucia, ancillary elevation data derived from contour maps was made available by the Physical Planning Office and University of the West Indies. In an attempt to increase the accuracy of the ASTER-derived elevation data, a vertical calibration approach utilising the contour data was implemented. To achieve this, 32 000 corresponding ASTER- and contour-derived elevation points were extracted and modelled (''R''2=0.97) using regression analysis (Figure 39). All ASTER-derived elevation values were subsequently vertically calibrated using:

	: ''H<sub>cal</sub>''= (-9×10<sup>-5</sup>) ''x<sup>2</sup>'' + 1.01x - 6.475,(1)		: ''H<sub>cal</sub>''= (-9×10<sup>-5</sup>) ''x<sup>2</sup>'' + 1.01x - 6.475, (1)

	where ''H<sub>cal</sub> ''are the vertically calibrated ASTER elevation values and ''x ''are the original ASTER elevation values. Next, the calibrated ASTER-derived elevation point data were merged with the contour heights to create a single x-y-z dataset. These data points were then gridded using a Triangular Irregular Network with linear interpolation algorithm to generate a 30m DEM.		where ''H<sub>cal</sub> ''are the vertically calibrated ASTER elevation values and ''x ''are the original ASTER elevation values. Next, the calibrated ASTER-derived elevation point data were merged with the contour heights to create a single x-y-z dataset. These data points were then gridded using a Triangular Irregular Network with linear interpolation algorithm to generate a 30m DEM.