Classification of images provides useful information about land cover, based on the spectral radiometric response of the cover type.This is done by one of two basic methods: unsupervised or supervised classification. Unsupervised classification categorizes continuous raster data into discrete thematic groups having similar spectral-radiometric values. Supervised classification allows the analyst to define classes of interest. The computer then calculates training statistics based on the definitions found in signature files and assigns each pixel of the image to the class that it most closely resembles.
For unsupervised training and classification, ERDAS Imagine employs the (ISODATA) clustering technique which uses the statistics of the data to evaluate the similarities or differences of the pixel values then groups the pixels into separate classes. This process takes several passes, or iterations, until it reaches a convergence threshold. The groups are then defined by a signature file, which can be used to create a new raster layer of discrete class values.
For more, please download the attached tutorial manual.
Unsupervised Classification and Recode in Erdas Imagine@WEHA LABS - YouTube
A vegetation index is a transformation that can be used to map the “greenness” of vegetation.
The Normalized Difference Vegetation Index (NDVI) is a normalized ratio of two bands, NIR and red (typically, TM bands 4 and 3), where NIR and Red are the DN values in the near IR and red spectral bands.
The formula for NDVI is:
NDVI= (NIR-Red) / (NIR + Red)
Looking at your NDVI image result, note the NDVI values for different areas of the image:
Pixels with values less than 0 are usually water
Pixels with low values close to 0 are bare areas (soils, pavement, etc.)
Pixels with higher values are vegetation
How to calculate NDVI using Erdas Imagen - YouTube
Download link: one click will drop the following document on your PC
These vary spatially which suggests a spatial geographic approach to runoff estimation. Map algebra/Raster calculator is a powerful tool to calculate.
Map Algebra is a simple and powerful algebra with which you can execute all ArcGIS Spatial Analyst extension tools, operators, and functions to perform geographic analysis.
The Raster Calculator tool allows you to create and execute Map Algebra expressions in a tool. Like other geoprocessing tools, the Raster Calculator can be used in ModelBuilder, allowing the power of Map Algebra to be more easily integrated into your workflows.
Runoff Generation Raster Calculation In ArcGIS - YouTube
In this lab we will start using the ERDAS Imagine version 2013 image processing software package to learn some basic operations. We will also become familiar with the sentinel data to be used in future labs as well as gain experience using the USGS browsers to download Landsat satellite imagery(you can get this part in the attached tutorial document).
Google Chrome has been known to be incompatible with ERDAS help as well as the USGS Glovis site. Be prepared to try use of other browsers such Firefox or Internet Explorer if you experience unexplained problems.
Avoid having spaces in any input paths or output path names including the file name itself- some tools will not work properly if spaces are found. This has been known to affect ArcGIS products as well.
The European Space Agency (ESA), through the Copernicus program of the European Commission (http://www.copernicus.eu/main/overview), has launched three satellite sensors (Sentinels-1, 2, and 3A) providing free satellite images for the scientific community, government agencies, the private sector.Image downloaded from Sentinel-2 was used for layer stacking in this tutorial.
Landsat Enhanced Thematic Mapper (ETM+) data was used for introducing Erdas imagen software in the first part of the tutorial. The image used in this lab is a subset image cut from an entire Landsat “scene” acquired August 18, 2002 over southern Dakota County and northern Goodhue County. The subset images are roughly 666 pixels by 666 pixels, while an entire Landsat scene is about 6,500 by 6,500 pixels. The subset images are about 3 MB while a full scene is about 250 MB. These ETM+ data are 30 meter spatial resolution with six multispectral bands. The pan-sharpened ETM+ data are 15 meter spatial resolution with three multispectral bands. Later in the lab you will download another image from the USGS Glovis web site.
Different satellites have different band composites.
Band combinations for Sentinel-2. They can be found in SNAP menu, the RGB composite is as follows:
WEHA LABS was selected one of the Top 10 Hydrology blog & Top 60 GIS Blogs by Feedspot
I am just started WEHA LABS back in December of this year with the aim of creating a technical blog about software in water resources and numerical modeling for my students in Arba minch university, Ethiopia. There wasn’t any specific plan or strategy to increase the relevance of the blog globally, just the desire to create a space for promoting open source software on hydrology, hydrogeology and GIS. When time goes the external numBer of viewerS in my you tube channel is increasing which gives me a hope that my blog will become one of the best educational blog. The number of visits for my blog still its scary because the audience is so small.
Still, I can not declare that I have a huge blog, but its growing. There is a lot of tutorials to post in the future, and I will do it with passion. I receive this recognition by Feedspot with great humility and the compromise to improve our content everyday to provide a platform for the capacity building of professionals in water resources.
Please have a look on the complete post and the criteria used by Feedspot to create this ranking on this link:
Similar to other software GIS systems, QGIS allows users to create maps with many layers using different map projections. Maps can be assembled in different formats and for different uses. QGIS allows maps to be composed of raster or vectorlayers. Typical for this kind of software the vector data is stored as either point, line, or polygon-feature. Different kinds of raster images are supported and the software can perform georeferencing of images.
By manipulating imagery data values and positions, it is possible to see features that would not normally be visible and to locate geo-positions of features that would otherwise be graphical. The level of brightness, or reflectance of light from the surfaces in the image can be helpful with vegetation analysis, prospecting for minerals etc. Other usage examples include linear feature extraction, generation of processing work flows (“spatial models” in ERDAS IMAGINE), import/export of data for a wide variety of formats,orthorectification, mosaicking of imagery, stereo and automatic feature extraction of map data from imagery.
This tutorial shows the full installations of ERDAS IMAGEN 9. Thanks to AMANAT ALI