Post Event Processing Scenarios and Productsfaculty.nps.edu/scrunyon/DHS/Monterey_Earthquake_Project_Playbooks/NPS-DHS_Playbook...Post Event Processing Scenarios and Products Playbook

Post Event Processing Scenarios and Products Playbook # RSC-07

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Post Event Processing Scenarios and Products

Naval Postgraduate School

Earthquake Response Project

Playbook #: RSC-07

Revised – 10/15/2013

Approved for public release; distribution is unlimited


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Playbook Table of Contents

Executive Summary……………….………………………………………... iii Overview……………….……………………………………………………... 1 Purpose/Objectives……………….………………………………………… 1 Hardware Required……………….………………………………………….. 1 Software Required……………….………………………………………….. 1 Data Overview……………….……………………………………………….. 1 Product Descriptions…………….…………………………………………. 2 Instructions……………….………………………………………………...... 3

Open ArcCatalog and create a geodatabase…………...……….. 3

Load LiDAR points data into ArcCatalog………………..……..... 5

Create Digital Surface Model (DSM) or Digital Elevation Model (DEM) from LiDAR data………..……………………..…. 7

Load the Digital Elevation Model…...……………………….……... 8

Create “Heights of Objects” product from DEM and DSM…….. 11

Find and extract above-ground objects……………….….………. 12

Create DEM and DSM height change detection products……… 14

Find vegetation in a spectral image/orthophoto….…….……….. 15

Spectral classification and change detection………...…………. 23

Combined analysis of spectral and LiDAR data….......…............ 27

Create a map……………………………………………......…............. 31

Overlay spectral/orthophoto on DEM/DSM….……………………. 32 Playbook Directory……………….………………………………………… 35 Additional Scientific Background Additional Notes…………………… 36 References………………………….……………………………………….. 36


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Executive Summary This Playbook provides a description of products and methods prepared by the NPS/DHS Earthquake Response Project to provide sample post-earthquake event scenarios and information to 1st responders and emergency managers. Many of the products described are “change detection” products for damage assessment. Changes are measured by comparing post event data to pre-event remote sensing imagery and other datasets prepared by the NPS/DHS Earthquake Response Project. Instructions and samples of specific products are provided as examples. The pre-event baseline datasets are described in Playbook # RSC-02. The Playbook Directory near the end of this document shows the NPS Earthquake Response Playbook sequence to help put this Playbook into perspective and give an overview of products resulting from this research and some aspects of practical implementation. The content of each Playbook is briefly described; however, users are referred to the specific named and numbered Playbooks for full product descriptions. These provide additional detailed product information, instructions on how to separately utilize the individual products, and how to combine them into an integrated system for improved earthquake response.


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Overview

Sponsored by the Department of Homeland Security (DHS) Science & Technology Directorate, the Remote Sensing Center (RSC) at the Naval Postgraduate School (NPS) has developed a series of instructional playbooks designed to assist 1st responders and emergency managers with the use of remote sensing technology for improving earthquake response.

This playbook (Playbook #: RSC-07) describes products expected to be useful in a post event scenario. Specifically, products that enable the user to perform “change detection” are useful in performing post-earthquake damage assessments, and determining the effect of damages on emergency response plans and operations. This playbook is designed to familiarize the reader with the technologies, terminologies, and capabilities of the remote sensing products.

Purpose/Objectives

The objective of this playbook is to familiarize the reader with remote sensing products that may be useful in a post disaster event scenario. Specific examples are provided of change detection products derived from LiDAR and imagery datasets. In a post event scenario, similar change detection products would enable wide area damage assessment. Descriptions of how these products are derived, as well as instructions for creating the products, are provided to assist emergency responders with pre-earthquake planning and as guidelines for generation of new products after an earthquake occurs.

Hardware Required

Data used in these exercises are provided on a USB external disk drive compatible with MS-Windows (XP, VISTA, Windows-7) operating systems. Users should be able to plug this device directly into an USB port, have the drive recognized by the system, and be able to see the directory structure as described below. Data can then be used directly on the drive, or copied to another EOC resource. They can also be supplemented by existing imagery and geographic information developed by the EOC. The exercises can optionally be run using other similar datasets.

Software Required

ArcGIS products: ArcCatalog, ArcMap, ArcScene version 10.0 or higher.

Data Overview

Attention is focused on three products expected to be useful in a post event scenario: 1) Digital Elevation Models (DEM) and Digital Surface Models (DSM) – DEMs and DSMs are 3D digital models of the Earth’s surface. They are slightly different products – a DEM excludes any above ground objects such as buildings and vegetation, and is sometimes referred to as a “bare earth model”. A DSM is created from the elevation values of all the top surfaces of objects in the scene, including building, trees and other


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objects. In the examples described, both the DEM and DSM were created utilizing Light Detection and Ranging (LiDAR) data. The DEM and DSM are created from elevation values derived from the LiDAR data. To find the heights of objects in the scene, the DEM can be subtracted from the DSM. 2) Orthophotos/Spectral Imagery – Orthophotos are aerial images that have been geometrically corrected to match map coordinates. Streets that are straight on the ground will be straight in the orthophoto; road intersections will align properly with GIS layers, etc. Spectral imagery is similar to a photograph, but may also provide information about object reflectivity outside of the human visual range. The spectral data can be used to create ground cover maps, or to automatically identify specific objects of interest. 3) Fused data products – Spectral and LiDAR – Fusing height information with spectral or color information allows for more in-depth analysis. Fusing separate datasets can be a challenging task due to differences in collection geometry, spatial extent, time of collection, etc. The benefits of combined datasets, however, often make dealing with the challenges of fusing the data worthwhile. Change detection products useful for wide area damage assessment can be created from fused DEMs/DSMs and orthophotos/spectral imagery. Change detection products created from LiDAR, spectral, and fused datasets are described in the next section.

Product Descriptions

DEM/DSM change detection A comparison of pre- and post-event DEMs/DSMs can highlight any changes in structures, such as collapsed buildings or downed trees. Changes in the Earth’s surface, such as a ground rupture due to an earthquake or changes in the coastline due to water inundation can also be detected. Spectral/Orthophoto change detection Surface changes shown in pre- and post-event spectral data have many applications. Spectral data are very useful for image classification, or for detection of specific items of interest, such as a scenario where structures with specific roofing materials (E.g. cedar shingles=high fire hazard) need to be located. Combined Spectral/DEM/DSM change detection Combining the spectral data with the information present in a DEM/DSM enhances the amount and quality of information extraction. For example, spectral data can be used to identify tar shingle roofs in an image. Because tar shingles are very spectrally similar to paved tar roads, there will be confusion between the two classes. By including a LiDAR DEM/DSM, tar shingles could be distinguished from paved tar roads based on the objects height above ground level.


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Instructions

Instructions are provided here to create DEM/DSM products using the Association of Monterey Bay Area Governments (AMBAG) LiDAR data. Processing of spectral data is introduced using 8-band spectral data from the WorldView-2 sensor. These datasets are described further in Playbook#RSC-02: Monterey County Baseline Products and Pre-Event Data Processing. The instructions are meant to be followed in order, with later steps relying on files created in earlier steps. ArcGIS Helpful Hints: - The processes outlined here for working with LiDAR data make use of the ArcGIS “3D

Analyst” and “Spatial Analyst” extensions. Ensure these extensions are loaded by going to “Customize -> Extensions” on the main menu bar in ArcMap or ArcCatalog. In the pop-up dialog, make sure the “3D Analyst” and “Spatial Analyst” extensions are selected.

- The easiest way to find tools is use the “Search” feature. To access the “Search”

feature, on the main menu bar click “Windows -> Search”. Clicking on the small pin

icon in the “Search” window will create a tab on the right-hand side of the ArcCatalog window for easy access to the “Search” feature. The “Search” feature can also be accessed by using the keyboard shortcut Ctrl+F.

- Avoid using numerals to start a filename. Ex: “3AV13_points” = bad! “Points_3AV13”

is ok. Open ArcCatalog and create a geodatabase and feature dataset

-> Start ArcCatalog by double clicking the program icon for ArcCatalog or by selecting the program from the Windows Start menu

-> Connect to the folder containing your geospatial data. On the main

ArcCatalog menu bar, click “File -> Connect To Folder”. -> In the pop-up menu, navigate to the location of the data you would like to work

with. For the Monterey County data, go to: \Monterey_Image_Baseline_Data

Once connected, the folder will appear in the list of “Folder Connections” in the

Catalog Tree on the left-hand side of the ArcCatalog window (see Figure 1).


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Figure 1: The ArcCatalog main window. The “Monterey_Image_Baseline_Data” folder appears in the list of “Folder Connections” once it has been connected.

-> Create a new “File Geodatabase” in the data directory to organize your data. - Highlight the Monterey_Image_Baseline_Data folder you just connected to by

clicking on the filename in the Folder Connections list - Go to “File -> New -> File Geodatabase”. - Name this file geodatabase “Monterey_geodatabase.gdb”. -> Create a new “Feature Dataset” to hold the LiDAR data within the

geodatabase. - Expand the Monterey_Image_Baseline_Data folder by clicking on the small

“+” sign next to the folder name in the Catalog Tree. - Click on the “Monterey_geodatabase” in the catalog tree to highlight it. - On the main menu bar, go to “File -> New -> Feature Dataset”. - Name the new feature dataset “LiDAR_Points”. - Click “Next”. -> Choose the coordinate system for the XY coordinates of the data. The XY

coordinate system for the Monterey County AMBAG LiDAR data is the Projected Coordinate System -> State Plane -> NAD 1983 (US Feet) -> NAD 1983 StatePlane CA IV FIPS 0404 (US Feet). Click “Next”.

-> Choose the coordinate system for the Z coordinates of the data. The vertical

coordinate system for the AMBAG LiDAR data is North America -> NAVD 1988. Click “Next”.

-> Leave the XY Tolerance, Z Tolerance, and M Tolerance as the default values.

Click “Finish”. The Monterey_geodatabase.gdb will appear in the Catalog Tree.


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Load LiDAR points data into ArcCatalog -> Using the “Search” feature, find the “LAS to Multipoint” tool (Figure 2). To

access the “Search” feature, on the main menu bar click “Windows -> Search”, or use the keyboard shortcut “Ctrl+F”.

Figure 2: Using the “Search” feature to find the “LAS to Multipoint” tool.

-> Click on the within the “Search” feature window to open the tool.

The LAS to Multipoint dialogue will open as shown in Figure 3.

Figure 3: The LAS to Multipoint tool.


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-> Define the Input file. In the LAS to Multipoint tool, click the icon to browse to find tile 3AV-13.las of the AMBAG LiDAR data. The data is located here:

\Monterey_Image_Baseline_Data\ Monterey_County_Data-06_LiDAR_Data\

Monterey_County_AMBAG_LiDAR_Block_Data_Archive\ BLOCK_3_LAS_CLASSIFIED

-> Define the “Output Feature Class” by clicking on the icon next to the “Output Feature Class” box. The “Output Feature Class” dialog will appear (Figure 4).

- Browse to the “LiDAR_Points” feature dataset created in above

\Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\LiDar_Points - Enter the name “Pts_3AV13” for the name of the feature class and click “Save”.

Figure 4: Create the “Pts_3AV13” feature class within the “LiDAR_Points” feature dataset.

-> Update the Average Point Spacing to 10. -> Leave all other boxes as default and click “OK”. After processing for a minute,

the Pts_3AV13 feature class will appear under the LiDAR_Points feature dataset in the Catalog Tree.


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Create Digital Surface Model (DSM) from LiDAR data. The method for creating DSM products outlined in this document makes use of the “Point To Raster geoprocessing tool”.

-> Use the “Search” feature to find and open the “Point to Raster” tool. The tool will open a dialogue box as shown in Figure 5. Follow the instructions below Figure 5 to enter the appropriate settings for processing an AMBAG LiDAR tile using the “Point to Raster” tool.

Figure 5: The “Point to Raster” tool with appropriate settings for the AMBAG LiDAR data.

-> Browse to the “Pts_3AV13” feature class LiDAR points imported above. \Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\LiDAR_Points\ Pts_3AV13

-> Change the “Value field” to “Shape.Z” to create a raster based on elevation

values. -> For the “Output Raster Dataset” field, browse to the “Monterey_geodatablase”

and type in the name “Max_3AV13” for the DSM. -> Change the “Cell assignment type” value to “MAXIMUM”.. -> Leave the “Priority field (optional)” box as the default setting. -> Change the “Cellsize” to 10 to match the DEMs in the AMBAG LiDAR data. -> Click “OK”.

When the processing completes, a layer named “Max_3AV13” will appear under the “Monterey_geodatabse.gdb” in the catalog tree.


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-> (Optional) Click on the “Max_3AV13” DSM layer in the Catalog Tree to select. Preview the DSM by clicking on the “Preview” tab near the top of the ArcCatalog window (Figure 6). When finished viewing, click on the “Contents” tab.

Figure 6: Select the DSM layer and click on the “Preview” tab to view the results of the

“Point to Raster” process. Note: Advanced ArcGIS users may consider using the Terrain Wizard to build a terrain dataset. This process is more in-depth, but also enables more advanced editing options. This may be required for datasets that contain significant blunders - either false high or false low points. Load the Digital Elevation Model

-> In the Catalog Tree on the left-hand side of the main ArcCatalog window, click on the “Monterey_geodatabase.gdb” folder previously created to highlight the name of the geodatabase file.

-> Right-click to access the pop-up menu. -> Click “Import -> Raster Datasets…” (Figure 7). Note: If the “geodatabase”

folder is not highlighted, the option to import a raster dataset will not be available. After selecting “Import -> Raster Datasets…”, the “Raster to Geodatabase” dialogue will appear (Figure 8).


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Figure 7: Menu option to import a raster dataset into the geodatabase. Note: This

option only appears when the geodatabase folder is selected in the Catalog Tree.

Figure 8: The “Raster to Geodatabase” dialogue box.


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-> In the “Raster to Geodatabase” dialogue box, click on the folder icon next to the “Input Rasters” box to browse for the appropriate DEM file:

Monterey_Image_Baseline_Data\

Monterey_County_AMBAG_LiDAR_Block_Data_Archive\BLOCK_3_DEM\ 3AV-13.img

-> Verify that the “Output Geodatabase” is correct:

Monterey_Image_Baseline_Data\ Monterey_geodatabase.gdb. -> Click “OK”. The file will load and appear in the Catalog Tree as an item within

the geodatabase with a default name of “AV_13”. -> Rename the layer to “DEM_3AV13”. Click on “AV_13” to select it, and right-

click to access the pop-up menu. Select “Rename”, and enter the name “DEM_3AV13”.

-> (Optional) Click on the “DEM_3AV13” layer in the Catalog Tree to select.

Preview the DEM by clicking on the “Preview” tab near the top of the ArcCatalog window (Figure 9). When finished viewing, click on the “Contents” tab.

Figure 9: Digital Elevation Model (DEM) for AMBAG LiDAR tile 3AV-13.


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Create “Heights of Objects” product from DEM and DSM Use the ArcGIS “Raster Calculator” to subtract the DEM from the DSM.

-> Use the “Search” feature to find the “Raster Calculator” tool. -> From the Catalog Tree, drag and drop the DSM file into the expression box in

the middle of the raster calculator (Figure 10). The full file path and name of the DSM image will appear in quotes. Type a minus “-“ sign, and then drag and drop the DEM file. The full expression may be too long to show in the expression box, but should be as follows:

"Z:\Monterey_Image_Baseline_Data\ Monterey_geodatabase.gdb\Max_3AV13"-"Z:\Monterey_Image_Baseline_Data\ Monterey_geodatabase.gdb\DEM_3AV13"

-> Click on the folder icon next to the “Output raster” box to browse to the Monterey_geodatabase.gdb, and type the name “height_raster” for the output raster.

-> Click “OK”.

When processing completes, the “height_raster” will appear under the Monterery_geodatabase.gdb in the catalog tree.

-> (Optional) Click on the “height_raster” layer in the Catalog Tree to select. Preview the raster by clicking on the “Preview” tab (Figure 7) to display the image (Figure 11). When finished viewing, click on the “Contents” tab.

Note: Distance units for the “height raster” product are determined by the projection system of the input data. The AMBAG LiDAR data are in units of feet.

Figure 10: The “Raster Calculator”


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Figure 11: The “height_raster” product created by subtracting the DSM from the DEM for the 3AV_13 AMBAG LiDAR tile.

Find and extract above-ground objects This capability is useful for finding objects that may appear visually similar, but can be distinguished based on differences in object heights (for example, consider tar shingle roofs and paved tar roads). The ArcGIS “Extract by Attributes” tool can be used to create a mask based on a threshold applied to the height raster.

-> Use the “Search” feature to find the “Extract by Attributes” tool. -> In the “Extract by Attributes” pop-up dialogue (Figure 12), click on the folder

icon to browse to select the “height_raster” previously created as the input raster - select:

Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\height_raster

-> In the “Where clause” box, type the expression “VALUE” > 10. -> Change the name of the “Output raster” to “Hts_GT10” -> Click “OK”.


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Figure 12: The “Extract by Attributes” tool with settings to extract points having a height

greater than 10 feet. This process will create a raster image with 1’s where the values in “height raster” are greater than 10, and “NO DATA” values elsewhere.

-> (Optional) Click on the “Hts_GT10” layer in the Catalog Tree to select. Preview the raster by clicking on the “Preview” tab (Figure 7) to display the image (Figure 13). When finished viewing, click on the “Contents” tab.


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Figure 13: The “Hts_GT10” product created by extracting points from the height_raster

that are greater than 10 ft above ground. Create DEM and DSM height change detection products When a pre- and post-event DEM or DSM exist, the ArcGIS “Raster Calculator” can be used to determine amount of change, and to apply a threshold to the change raster. Pre- and post-event DEMs do not exist within the Monterey_Image_Baseline_Data, so this step will not be illustrated. A text description of the process is given for future use. To find locations where objects in the DSM have changed by more than 1 ft (higher or lower), enter the following expression in the “Raster Calculator”: Abs(DEM1 – DEM2) > 1

Note: “DEM1” and “DEM2” are the file names of the DEMs from time 1 and time 2. Drag and drop the file from the Catalog Tree into the expression box of the Raster Calculator tool to avoid typos. Note: Distance units are defined by the projection system of the LiDAR data. The AMBAG LiDAR data are in units of feet.


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Find vegetation in a spectral image/orthophoto -> Load the spectral data into the geodatabase. - Right-click on the name of the geodatabase in the catalog tree:

Monterey_geodatabase.gdb.

- In the pop-up menu, select “Import -> Raster Datasets” (Figure 7). - After selecting “Import -> Raster Datasets…”, the “Raster to Geodatabase”

dialogue will appear (Figure 8).

-> In the “Raster to Geodatabase” dialogue, click on icon next to the “Input Rasters” box to browse and select the spectral data file:

Monterey_Image_Baseline_Data\ Monterey_County_Data-01_DigitalGlobe_Worldview-2_Data\

Monterey_County_Mosaic_8_Band_CorrectedFLAASH

-> Make sure the “Output geodatabase” is correct (Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb),

and hit “OK”.

Note: Individual bands of spectral data can be accessed by double-clicking on the spectral data file when browsing for data. Once the spectral data have been added to the geodatabase using ArcCatalog, it is more convenient to view the data in ArcMap.

-> Open ArcMap by double clicking the program icon or by selecting the program from the Windows Start menu.

-> When ArcMap opens, a pop-up dialogue will appear (see Figure 14), click on

“New Maps” on the left-hand side of the dialog and select “Blank Map”. -> Change the default database for the map to the Monterey_geodatabase.gdb.

(Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb) and click “OK”.


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Figure 14: ArcMap opening dialogue box.

-> Add the spectral data to the map by clicking “File -> Add Data -> Add Data…”

on the main menu bar, or by clicking on the add data icon. Either option will open the “Add Data” dialogue box (Figure 15).

Figure 15: The “Add Data” dialogue box for adding data to an ArcMap document.


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-> In the “Add Data” dialogue box, browse to the location of the spectral data within the geodatabase:

\Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\

Monterey_County_Mosaic_8_Band_CorrectedFLAASH

-> Single-click on the Monterey_County_Mosaic_8_Band_CorrectedFLAASH

filename to highlight it and then click “Add”.

Once finished loading, the spectral data will appear in list of “Layers” on the left-hand side of the ArcMap window, and the image will display in the main window (Figure 16).

Figure 16: After loading the spectral data into the ArcMap document, the file will appear

in the list of layers on the left-hand side, and the image will display in the main window with default RGB layers.

The bands that are being used to display the RGB composite are shown under the spectral file name in the list of layers.

-> Change the bands used to display the RGB composite to a “True Color” configuration. Double-click on the: “Monterey_County_Mosaic_8_Band_CorrectedFLAASH”

spectral layer in the list of layers to access the “Layer Properties” dialogue (Figure 17).


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-> Under the “Symbology” tab, use the drop down arrows to select the appropriate bands.

- To create a False-color Infrared display of the Worldview-2 8-band data,

choose Red = Band 7, Green = Band 5 and Blue = Band 3. Click “Apply” to see what the new RGB composite looks like, and click “OK” if satisfied with the combination. The False-color Infrared display highlights vegetation in red.

- To display a true-color version of the Worldview-2 image, try Red = Band 5,

Green = Band 3 and Blue = Band 2.

Figure 17: The “Layer Properties” dialogue. Click on the drop-down boxes next to the band numbers to choose different bands for the Red, Green or Blue channels.

Once the image has been added to ArcMap, the Image Analysis toolbar can be used to access some built-in image analysis tools, such as the Normalized Difference Vegetation Index (NDVI).


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-> Access the “Image Analysis” toolbox by clicking on the main menu “Windows -> Image Analysis”. The Image Analysis toolbar will appear as shown in Figure 18.

-> Click on the icon at the top-left corner of the Image Analysis dialogue to access the “Image Analysis Options” dialogue (Figure 19).

Figure 19: Image Analysis Options dialogue.

-> Under the NDVI tab, update the Red and

Infrared bands as needed. For the 8-band WV-2 data, the Infrared Band is band 7, and the Red Band is band 5. Click “OK” to close the Image Analysis Options dialogue.

-> Make sure the layer is highlighted in the Image Analysis , and run the NDVI by

clicking the leaf icon in the “Processing” block of the Image Analysis dialogue. Note: If the NDVI icon is grayed-out, double check to make sure the spectral raster is selected at the top of the Image Analysis dialogue.

Figure 18: Image Analysis toolbox

A temporary layer will be added to the list of layers with a filename that begins with “NDVI_”. In this case, the filename will be: NDVI_Monterey_County_Mosaic_8_Band_CorrectedFLAASH.


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The NDVI product is scaled from 0 to 255, with higher values being healthier (greener) vegetation. The NDVI process creates a temporary layer. To work with it further, it needs to be exported to a regular raster layer.

-> Right-click on the NDVI result in the list of layers.

(NDVI_Monterey_County_Mosaic_8_Band_CorrectedFLAASH

-> In the pop-up menu, select “Data -> Export Data…” (Figure 20).

Figure 20: Access the Export Data menu option by right-clicking on the file name in the list of layers.

-> In the “Export Raster Data” dialogue (Figure 21), verify the “Location” is the

correct geodatabase: Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb.


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Figure 21: The Export Data Raster dialogue.

-> Update the “Name” if desired. The default name adds a “1” to the end of the input filename. In this case, the name will be

NDVI_Monterey_County_Mosaic_8_Band_CorrectedFLAASH1

-> Click “Save”. -> When the processing is finished, a pop-up box will ask if you would like to add

the exported data to the map as a layer. Click “Yes” to continue working with the data, and the raster will appear in the list of layers.

-> Remove the temporary NDVI result by right-clicking on the filename

NDVI_Monterey_County_Mosaic_8_Band_CorrectedFLAASH in the list of layers and selecting “Remove” on the pop-up menu.


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Finally, apply a threshold to the NDVI result and display the vegetation as a layer.

-> Open the layer properties for the NDVI raster by double-clicking on the filename in the list of layers.

NDVI_Monterey_County_Mosaic_8_Band_CorrectedFLAASH1

-> In the “Layer Properties” dialogue, choose the “Symbology” tab. -> On the left-hand side, under “Show:”, select “Classified”. -> Set the number of classes to 1. -> To display only healthy vegetation, click the “Classify…” button, and then the

“Exclusion…” button. In the “Excluded values” box, enter 1-150. Click “Apply” and “OK”. Click ‘OK’ to close the Classification window. Click “Apply” again in the “Layer Properties” dialogue to preview the results. If desired, click “Classify…” and “Exclusion…” again and adjust the excluded values as needed

-> To change the color of the class, double-click on the black box under

“Symbol”, and choose a color. -> Click “Apply” to preview, and “OK” when finished to exit the Layer Properties

dialogue. The result of this process will look something like Figure 22. In this case, a green color was selected to color the healthy vegetation locations. The NDVI result is overlaid on top of the WorldView-2 spectral image,


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Figure 22: Healthy vegetation areas in green overlaid on top of the WorldView-2 spectral image.

Spectral classification and change detection To perform spectral change detection, two spectral images are classified, and then a comparison is made between the image classification products. The process can be broken into three steps: Step 1: Create a “signature file” containing spectral signatures of classes Step 2: Perform supervised classification of Image1 and Image2 Step 3: Examine changes between the classification of Image1 and Image2 Each of these steps is explained in detail below. To begin, add the “Image Classification” toolbar to the ArcMap window.

-> On the main menu bar, click “Customize -> Toolbars -> Image Classification”. A toolbar as shown in Figure 23 will appear.

Figure 23: Image Classification toolbar.


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Step 1: Create a “signature file” containing spectral signatures of classes -> On the “Image Classification” toolbar, make sure the “Layer:” box

contains the correct spectral layer. Use the drop-down arrows to select the spectral image if needed. In this case, choose the Monterey_County_Mosaic_8_Band_CorrectedFLAASH layer.

-> Click on the icon on the far right tool of the toolbar to select one of the drawing tools. Draw the shape around a known area of interest, double clicking to finish drawing.

-> Click on the “Training Sample Manager” tool (next to the layer name) to see a list of all the created training samples (Figure 24).

-> Click on the “Class Name” to change the default sample name. -> Create training samples for all of the classes you wish to classify, such as:

lake, parking lot, trees, road, red-tile roof, bright roof, tar roof, and grass.

Figure 24: Training Sample Manager -> Once the training samples have been created, click on the “Create signature

file” icon on the far right-hand side of the “Training Sample Manager” toolbar. Save the training sample signatures to a file named

“Monterey_County_Mosaic_8_Band_CorrectedFLAASH_training_sample.gsg”


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Step 2: Perform supervised classification of Image1 and Image2 -> On the Image Classification toolbar, click on the “Classification” drop-down

menu to access the classification methods available in ArcMap (Figure 25).

Figure 25: Image classification methods available in ArcGIS.

-> Select the “Maximum Likelihood Classification” tool. The “Maximum Likelihood Classification” dialogue will appear (Figure 26).

Figure 26: The “Maximum Likelihood Classification” dialogue.


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-> The Monterey_County_Mosaic_8_Band_CorrectedFLAASH image should already be added by default when the tool is opened. It will be listed under the “Input raster bands”

If not, choose the spectral image as the “Input raster bands” by clicking on the

to browse and select the file. -> Choose the signature file created in step 1 as the “Input signature file”: Monterey_County_Mosaic_8_Band_CorrectedFLAASH_training_sample.gsg

-> Choose a name and location for the “Output classified raster”: Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\MLClass_Image1

-> Click “OK”. After processing, the MLClass_Image1 will be added to the list of

Layers. The classification result should look something like Figure 27. If needed, add more training classes and repeat the classification process until the classification map accurately represents the objects in the image.

Figure 27: An example Maximum Likelihood classification result.


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-> Repeat Step 2 to perform a supervised classification of the second spectral image. Use the same signature file for each image.

Step 3: Examine changes between the classification of Image1 and Image2

using the ArcGIS “Raster Calculator”. A text description of the process is given here, but the process will not be illustrated with example files.

-> Use the “Search” function to find the “Raster Calculator” tool. -> To find regions that have changed, enter the following expression in the

Raster Calculator: “MLClass_Image1” != “MLClass_Image2”

-> In the “Output Raster” field, enter the name of the output file “mask”. Regions

that have changed will have a value of 1; regions that are the same in both images will have a value of 0.

-> To create an image showing only the regions that have changed, enter the

following expression into the “Raster Calculator”:

“Image1” * “mask”

The output image will contain Image1 values where the classification changed between Image1 and Image2, and 0 values where the classification remained the same.

Combined analysis of spectral and LiDAR data Spectral classification is discussed in the “Spectral classification and change detection” section. Creation of the “Heights Raster” from the LiDAR data is discussed in the “Create “Heights of Objects” product from DEM and DSM” section. These products can be combined for further analysis. As an example, consider finding building locations using the combined LiDAR and Multspectral datasets above. In the spectral classification result, there are many objects that are classified as buildings, but some are actually some other material. Often, buildings with tar roofs are confused with spectrally similar tar roads. Buildings with red-tile roofs may be confused with red clay tennis courts. The “Heights of Objects” raster created from the LiDAR data can be used to refine the spectral classification results by separating ground and above-ground objects. The ArcGIS “Extract by Attributes” tool can be used to find locations that are classified as buildings AND have a height of greater than 10 feet above ground level (Figure 28). First, extract the buildings class(es) from the Maximum Likelihood classification result “MLClass_Image1”.


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-> Use the “Search” feature to find and open the “Extract by Attributes” tool. -> Select the Maximum Likelihood classification result “MLClass_Image1” as the input raster.

-> Choose the class(es) to be extracted. If buildings are represented by a single

class, say Class 1, enter the following expression in the “Where clause” box:

“VALUE” = 1

If multiple classes need to be extracted, use the OR statement. For example,

if Classes 3, 5 and 6 represent different roof materials, extract these points using this statement in the “Where clause” box:

“VALUE” = 3 OR “VALUE” = 5 OR “VALUE” = 6

This will create a classification result that only contains the building classes (Figure 27).

-> Update the “Output raster” to: Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\MLclass_bldgs

-> Click “OK”. A new classification result that only contains the building classes

is added to the list of layers.


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Figure 28: The “Extract by Attributes” tool is used to create a layer containing only the

regions classified as buildings from the MLClass_Image1. Next, extract objects from the height_raster that have a height above ground level of greater than 10 feet.

-> Add the height_raster to the ArcMap document by clicking “File -> Add Data ->

Add Data…” on the main menu bar, or by clicking on the add data icon. -> Select the file: Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\height_raster

and click “Add”. The height_raster will appear in the list of layers.

-> Use the “Search” feature to find and open the “Extract by Attributes” tool

again. -> Select the “height_raster” Raster as the input raster. -> Enter the expression “VALUE” > 10 in the “Where clause” box to extract

locations where the value of “height_raster” is greater than 10.


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-> Update the “Output raster” to: Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\Height_GT10

-> Click “OK”.

Finally, combine the two results by using the “Raster Calculator” tool to multiply the extraction results.

-> Use the “Search” feature to find and open the “Raster Calculator” tool. -> From the Catalog Tree, drag and drop “MLclass_bldgs” into the expression

box in the middle of the raster calculator (Figure 5). The full file path and name of the file will appear in quotes. Type the multiplication “*“ sign, and then drag and drop the “Height_GT10” raster into the expression box.

-> Click on the folder icon next to the “Output raster” box to browse to Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb

and name the output raster “Combo_bldgs”. -> Click “OK”. The “Combo_bldgs” layer will appear in the list of layers. Deselect

all of the other layers. -> Change the symbology of the layer to display all building locations as a solid

color. - Double-click on the “Combo_bldgs” name in the list of layers to access the

“Layer Properties” dialogue. - Under the “Show:” heading, select “Classified”. - Change the number of classes to 1. This will cause all of the objects in the

“Combo_bldgs” raster layer to display as black (Figure 29). - If desired, click on the colored box under “Symbol” to choose a different

color. -> Click “Apply” and “OK” when finished.


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Figure 29. “Combo_bldgs” result created by combining the spectral and LiDAR building

extraction results. Create a Map ArcMap can be used to create a printable map from any of the products created above.

-> Within ArcMap, on the main menu, click “File -> Add Data -> Add Data…” or

click on the add data icon to access the “Add Data” dialogue. -> Select desired layers to add to the map from the geodatabase Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb

-> Once all desired layers have been added, on the main menu bar, click “View -

> Layout View” to see the layout of the map on paper.

-> Click on the “Change Layout” icon on the “Layout” toolbar (Figure 30) to change the paper size. Standard letter paper is found under the “North American (ANSI) Page Sizes)” tab, as “Letter (ANSI A)”.


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Figure 30: The Layout toolbar. -> To insert a Title, Legend, Scale Bar, and North Arrow, select “Insert” on the

main menu bar, and choose the desired object(s). -> Finally, export the map by selecting “Export Map” under “File” on the main

menu bar. In the “Export Map” dialogue pop-up, choose the location to save the file, enter a file name for the map, and choose the appropriate type (PDF, JPEG, etc.)

Overlay spectral/orthophoto on DEM/DSM This procedure will be accomplished using ArcScene.

-> Open ArcGIS ArcScene by double clicking the program icon or by selecting from the Windows Start menu.

-> On the “ArcScene – Getting Started” dialogue (Figure 31), click on “New

Scenes” and select the Blank Scene. Update the default geodatabase to the Monterey_geodatabase.gdb. Click “OK” to open a blank scene.

Figure 31: The ArcScene – Getting Started dialogue appears when ArcScene is first opened.


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-> Add the spectral imagery by going to “File -> Add Data -> Add Data…” on the

main menu bar, or by clicking on the add data icon. -> Select the file “Monterey_County_Mosaic_8_Band_CorrectedFLAASH” within the

Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb. Click “Add”. Once loaded, the image will be listed under “Scene layers” on the left-hand side of the ArcScene window. The bands being used to display the RGB composite are shown under the file name in the list of scene layers.

-> Change the bands being used to create RGB composite. - Right-click on the image name and select “Properties…”.

- Choose the “Symbology” tab, and use the drop down arrows to select the appropriate bands. For a true-color display of the WorldView-II spectral data, choose Red – Band 5, Green – Band 3, and Blue – Band 2.

- Click “Apply” and “OK”.

In the ArcScene viewer, the image will appear at lower resolution than if the image was viewed in ArcMap. The lower resolution display allows faster zooming and scrolling within the ArcScene viewer.

-> (Optional) If a higher level of detail is needed, right-click on the orthophoto/spectral imagery name in the list of Scene Layers, and select “Properties” from the pop-up menu.

-> (Optional) Click on the “Rendering” tab (Figure 32). Move the slider all the

way to the right for “Quality enhancement for raster imagers”. -> (Optional) Click “Apply”, and wait for the process to finish. This could take a

long while. The higher resolution will also cause the re-draw time to be slower.


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Figure 32: Layer Properties dialogue “Rendering” tab. If a high-resolution display of the spectral imagery is needed, move the slider for “Quality enhancement for raster images” all the way to the right.

-> Create the 3D view (Figure 33). Right-click on the orthophoto/spectral

imagery name in the list of Scene Layers, and select “Properties” from the pop-up menu.

-> Select the “Base Heights” tab within the “Layer Properties” dialogue. ->The radio button next to “Floating on a custom surface” will be grayed-out, but

click on the folder icon to browse to the location of the DEM or DSM surface:

Monterey_Image_Baseline_Data\Monterey_geodatabase.gdb\DEM_3AV13

-> Click the “Add” button. -> Click “Apply” and “OK”.


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Figure 33: WorldView-2 image draped over an AMBAG DEM Note: ArcScene will only display the heights of one DEM or DSM at a time. If you would like to view a larger area, you will need to mosaic the DEMs or DSMs together, and repeat the steps above.

Playbook Directory

This Playbook is one of a series of Playbooks designed to cover the technical breadth of the NPS-DHS Earthquake Response Project. Each Playbook describes one series of products and its use. These Playbooks can be printed, transmitted electronically as Portable Document Format (PDF) documents, or stored locally on existing emergency management networks, workstations, or mobile devices. The following summarizes the individual Playbooks developed as part of this project and available to emergency responders. See the listed Playbook for specifics and details.

Playbook#RSC-01: NPS-DHS Remote Sensing Project/Products Overview

Playbook documenting project and scope and big picture for other Playbooks

Playbook#RSC-02: Monterey County Baseline Products and Pre-Event Data Processing

Playbook documenting baseline data, preprocessing, use/analysis of basic products

Playbook#RSC-03: Monterey (City) Infrastructure Products

Critical Infrastructure data (location, description, pre-event photos, geolocated imagery

frames and metadata)


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Playbook#RSC-04A: Airborne Imagery Change Detection Products (SDSU) Monterey baseline imagery of critical infrastructure, Camp Roberts imagery, and selected

change detection example products. Full-Resolution NEOS imagery

Playbook#RSC-04B: NOAA Night Lights/Power Change Detection and Fire Detection Products Night lights/power and fire detections (NOAA)

Playbook#RSC-05A: Social Networking Products (Ushahidi)

Ushahidi implementation and instructions for Monterey City/County

Playbook#RSC-05B: Social Networking Products (Twitter)

Twitter implementation and instructions for Monterey City/County

Playbook#RSC-06: Mobile Application Damage Assessment Product Lighthouse damage assessment application download, install, configure, execute

Playbook#RSC-07: Post Event Processing Scenarios Products

LiDAR DEM, DSM, derived products, NAIP/WV-2 Change Detection Examples

Playbook#RSC-08: Soft and Hardcopy Output Products and Distribution

GeoPDF Products, Monterey Map Books, w/National Grid Index, PDF and Printed

Playbook#RSC-09: Common Operating Picture (COP) Products

Sensor Island Common Operating Picture, UICDS to WebEOC Link

Playbook#RSC-10: Systems Integration, Transition, and Training

Hardware/Software Installation Details, Coordination, and Integration

Additional Scientific Background and Notes

Additional information on the methods used here is available in the published literature (see References section below). No additional specific information is needed to use this playbook. ArcGIS 10.0 help files are available online at: http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#

References

Kim, A.M., Kruse, F.A., Olsen, R.C., and Clasen, C.C., 2012, Extraction of Rooftops from LiDAR and Multispectral Imagery: in Imaging and Applied Optics Technical Digest, 24 June - 28 June 2012, Monterey, CA, USA, Optical Society of America (paper RTu2E.1.pdf on CD-ROM).

Kruse, F.A., Clasen, C.C., Kim, A.M., and Carlisle, S.C., 2012, Use of Imaging Spectrometer Data and Multispectral Imagery for Improved Earthquake Response: in Imaging and Applied Optics Technical Digest, 24 June - 28 June 2012, Monterey, CA, USA, Optical Society of America (paper RM2E.1.pdf on CD-ROM).

http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html

Documents

Post Event Processing Scenarios and Productsfaculty.nps.edu/scrunyon/DHS/Monterey_Earthquake_Project_Playbooks/NPS-DHS_Playbook...Post Event Processing Scenarios and Products Playbook