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Housekeeping slide

What is this webinar about

We’re going to examine some problems customers have run into with solutions that customers have crafted, in some cases going to expand on their solutions to give additional advice- 5 different scenarios, each case, dig into - Goal: well rounded overview of usings

FME’s raster capabilities to the full for solving common raster scenarios

Customer Story 1: Reprojecting tiles

Demo/Solution 1: http://blog.websoftdev.com/fme/making-reprojected-raster-tiles-work-with-fme

Reprojection/Mosaicing using NOdata/Alpha for

Problem we’re solving

Using Nodata values in raster processing to avoid gaps in reprojected mosaics.

The Solution

Demo/Solution 1

Key FME Functionality Used

Apply nodata and or Alpha before any reprojections or clips

Customer story 2: Image Download

http://impossibleaddress2find.blogspot.ca/2014/03/featching-all-of-ahn2-raster-data-with.html

**make intro page with this blog

Problem we’re solving

Utilizing a remote raster dataset

The Solution

Key FME Functionality Used

Federating GeoRSS and Shape data to get required tiles.

Raster downloading and mosaicking.

Reading from URL and Zip file.

Midway Q&A

Customer stories 3: Raster Arithmetic to the rescue

Solving problems with raster arithmetic

http://blog.safe.com/2013/09/a-perfect-pairing-fme-and-rapideye-imagery-in-la-rioja-spain/

Problem we’re solving

Generate land cover classification from RapidEye multispectral images for agricultural analysis:

● Step 1 – Convert each pixel’s Digital Number (DN) to a radiance value by multiplying the DN by

the radiometric scale factor.

● Step 2 – Convert radiance values to ToA (top of atmosphere) reflectance values, taking into

consideration variables such as:

o distance from the sun

o angle of incoming solar radiation.

The variables, different for each scene, are defined as parameters in the workspace.

Calculate the solar azimuth angle from the attributes created with a ParameterFetcher

Calculate the distance between the sun and earth.

RasterExpressionEvaluator performs ToA calculations in each band.

Use another RasterExpressionEvaluator to calculate vegetation indexes:

● NDVI (Normalized Difference Vegetation Index)● TCARI (Transformed Chlorophyll Absorption in Reflectance

● OSAVI (Optimized Soil Adjusted Vegetation Index)

The Solution

Write final result to TIFF file.

Customer story 3b:

Extract dangerous vegetation heights from Lidar

Solution

pic of workspace

Key FME Functionality Used

RasterExpressionEvaluator

Customer story 4: Rapid response Flood mapping

Gavin Park's FME UC presentation (featuring Mapnik and Excel) -

http://www.safe.com/fmeuc/erdas-imagine-spatial-modeller/

Problem we’re solving

Using a combination of FME functionality and external tools to create Flood maps

SOlution: Flooding Proof of Concept

Choose Image Scene

Processing Workflow

Flood Detection

Download and write to disk

Vectorize Raster Output

Remove Existing

Water Bodies

Identify Impacted Assets

WCS

Pan Sharpen Image

Image MapFME Processing

ERDAS Processing

FME Driven Workflow

Access Web Coverage Service

Mid Translation Read / Write

● Need to pass Raster Image between FME and Imagine● Use FME to write the data to local disk for Imagine to pick up

and use within the Spatial Modeller

Flood Detection Spatial Model

Cloud Free

Cloud Cover

Running Spatial Models in FME

Reading Raster Back into FME

Running Erdas Pan-Sharpening within FME

Generate Raster Output

Flood Extents Overlay

Automatic Excel Reports

● Real Time River Flows

● Automatically graph flows

● Identify affected assets / property

● Embed Image Map

Key FME Functionality Used

FME’s ability to utilize other applications through the Python APIs and the PythonCaller .

Bonus customer story 5 - gowing trees with FME (Owen Powell)

Blog ** add link**, FME trees, Mapnik splashscreen

Owen’s splashscreen

Poll question

Which solution was the most relevant to the challenges you face?

Wrap-up slides

Midway Q&A

Contact slide