Modelling NDVI, NDWI and Other indices in ArcGIS

Indices are very good techniques for analysing remotely sensed data. Especially the indices like NDVI, NDWI, NDBI, NDSI etc. are quite often used in Geospatial Science.  This tutorial explains the procedure to model these indices in ArcGIS.

Once the concept and the method are understood any indices can be modelled if the formula is known.  Watch the video till end to understand the compete concept and to understand the places where caution is to be exercised to avoid errors.

View other posts in the blog to model these indices in ERDAS Imagine

See other posts to carryout these indices in ERDAS Imagine.

Comprehensive Guide to Geo-Referencing

The fact that you can pin point the location of everything is the most vital part of geospatial technologies. Though the satellite images that we download from internet (say earthexplorer or bhuvan) have a predefined coordinate system, there many other things things that are not georeferenced. We can quote examples like scanned toposheets, guide maps, Images obtained from Google Earth etc. Here is a comprehensive set of videos to carry out the georeferencing process with different cases.

Georeferencing a Satellite Image Using another Georeferenced Image:
Say you want to georeference an image, and you have another image that is already georeferenced (say a satellite image downloaded from earthexplorer) and that covers the first image either wholly or part.

Now you will be taking the reference from the second image (that is already georeferenced) and georeference the first image.

Carry out this process in ERDAS

Same Process in ArcMap

Georeference Satellite Image Using Available Latitude and Longitude (GCPs):
It is quite possible that there is no reference image available or the resolution of the two images are so different that you find it difficult to precisely identify common points. In such a case if you have GCPs (latitudes and Longitudes of any point on the image), may be collected from Google Earth or using a GPS or any other means, you can correlate those GCPs with the points on the image and georeferenced the image.

Carrying out this process in ERDAS Imagine

Same Process in ArcMap (This process is also a method of downloading and Georeferencing images from Google Earth)

Georeference an Image from Google earth:

For a free high resolution image, Google Earth is a very good solution. However whenever the image downloaded from Google Earth will not have spatial references. So it is virtually unusable for any geospatial project. Following is a method to download and georeference the image from Google Earth. Here both image and reference is taken directly from Google Earth.

Carrying out the process in ArcMap

Another Method

You can try the similar process in ERDAS Imagine by following the similar idea and using the technique shown in the previous section (georeference using Latitude and Longitude in ERDAS Imagine)

Georeferencing A vector File:
There are cases where we end up digitizing an image and then realise that the image is not georeferenced. In such a case we will be needed to georeferenced the vector file, which is not as straight forward as a Raster file. Here we will be needed using spatial adjustment tool to carry out the process. Here we will be using another georeferenced file as reference.
Carrying out georeferencing of Vector File in ArcMap.

Changing the Projection of A georeferenced file:
There are many projection system, each suitable for various areas and various projects. Dpending on the requirement, there might be need to change the projection from one to another. Here is the tutorial in ArcMap to change the projection of a georeferenced file.

Increase or Enhance the Spatial Resolution of DEM using Spatial Interpolation Technique in ArcMap

Digital Elevation Model is very important dataset for carrying out many geospatial analysis, especially in processes like Hydrology. There are free DEMs available online for the benefits of researchers thanks to organizations like NASA and ISRO. However the freely available DEMs generally have a spatial resolution of 30 meters or more. For many applications we might need higher resolution.

Interpolation predicts values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data: elevation, rainfall, chemical concentrations, noise levels, and so on.

Here I have just interpolated the values of the DEMs and created a higher resolution DEM. In this example I have increased the resolution of the DEM from 30 meter to 10 meter. You can follow the video and get your high resolution DEM. However there are certain disadvantages, to do watch the video completely to understand the theory behind it and also the disadvantages.

Here is the video

Note and Tips:

• The accuracy of the result will not be as good as the accuracy of the original 10 meter resolution DEM.

• Try to understand different types of interpolation techniques, so that you can choose the best interpolation technique suitable to you.

Gapfilling or Destriping Landsat 7 Image for Scientific Analysis - ERDAS Imagine

Almost everyone in the field of Remote Sensing and GIS would have felt thezcpinch of stripes in Landsat 7 image one or other time in their career. In 2003 Landsat 7 has developed a technical snag which called SLC (Scan Line Correlator) off, which has resulted in stripes or gaps throughout the image. Using these images for Scientific Analysis were virtually impossible.

NASA has developed a few techniques to destripe these images. Here is a method using ERDAS imagine to Gapfill Landsat 7 image as suggested by NASA for scientific analysis. Though this problem can never be addressed perfectly as the problem is with the sensor, this is probably the best available solution.

Here you will need a historical images. Basically means to say, you will have an image with stripes for which you will considering for gapfilling, and another image (either without gaps or with gaps) from which the data will be extracted and filled in the gaps of the first image. 

A simpler method for gapfilling using Focal Analysis method in ERDAS imagine is also available in my channel, however that method is best suited for display purpose and not for scientific analysis.

Do watch video completely as I have also briefly explained as to how to select the images for carrying out this work. If not understood completely, you might end up selecting wrong images resulting in accurate and unsuitable result.

Following is the video.

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Notes and Tips:

• While selecting historical images for gap filling, select images which are temporally and seasonally close to each other.

• Do remember to layer stack all the bands once you have completed the gapfilling process for individual bands

• If you are looking for an easy process for display purpose, you can use focal analysis in ERDAS Imagine. Look for the other post for the video tutorial on this.

• Do try to select cloud free data to the extent possible.

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