There are numerous reasons why data sources need to be repaired or redirected to different locations. Using the connectionProperties dictionary.Using the updateConnectionProperties function.Sisense Bootcamp in Israel by Ellen Noone.Talend Engage Conference by Garrett Cronin.Ellen Noone, Business Development Representative, IMGS.FME 2020 by Gavin Park, Lead Solutions Consultant IMGS.Moving to the Cloud by Ciaran Kirk, Operations Director, IMGS.The Outputs: Two Categorised NDVI images, one for 2013 and one for 2015, and a vegetation change detection (2013 to 2015)image. The output image shows pixels in white (value of one) for areas where the software has detected a change in the NDVI output between 20. there is no change between the pixels), the pixel is assigned a value of zero. If the result of the subtraction is greater than zero, the pixel is assigned a value of one. The change detection section of the model takes the two georeferenced images and subtracts the value of each pixel in one image from the corresponding pixel in the second image. Now bare with me, we’re going to get a little maths-y, but I promise there’s no long division! The classes that I have created in the model are based on the following table which defines three classes of interest. To make the result more meaningful we can categorise the output into classes which will help to show us the areas that we are interested in. This is useful for monitoring green space and establishing a growth norm over time for a particular season, place or crop.Īs a result of the NDVI, the output for each pixel in the image will be a value between -1.0 to +1.0 where lower values are indicative of little or no photosynthetic activity (poorly vegetated areas or poor vegetation health). The workflow analyses Dublin City Council’s administration area for change in vegetation between 20. To give you an idea of some simple analysis that you can do with this software, I have added some screen shots further on in the post of an sample spatial model workflow created using the Spatial Modeller tool in ERDAS IMAGINE, but first let’s take a quick look at the model description. As mentioned above, it also has some tools that make it easy to build spatial modelling workflows that can be used time and time again. It allows the user to work with various bands recorded or analyse each pixel value, capabilities which cannot be found in a generic GIS. A major difference between a software like this and a standard GIS is that ERDAS IMAGINE has advanced capabilities in processing and analysing satellite imagery. ![]() ERDAS IMAGINE software is a versatile and comprehensive application that combines geospatial image processing, GIS tools, remote sensing, photogrammetry, LiDAR analysis and radar processing in one solution. I am going to show you an example of how you can use free data from USGS to analyse vegetation health in an urban area using this software. How can you process and analyse Spatial Imagery?įor this blog, I will be using ERDAS IMAGINE software to process and anayse the imagery. Have you seen our blog post on Finding Open Source Imagery in Ireland? Check it out to get some background info on this month’s Blog. This blog will provide an example of how you can work with open source imagery in ERDAS IMAGINE software and use the analysis across multiple applications. Welcome to this month’s blog post on spatial modelling of satellite imagery for Ireland. Author: Lauren Lucas, GIS Technician at IMGS.
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