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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Download Satellite Images and DEM for Free

Most vital part of any GIS and Remote Sensing work is satellite data. Buying a satellite image for research purpose is quite expensive. However there are many online resources those provide data for this purpose free of cost. All that you may need is to credit them for using data from their archive. 

earthexplorer: Is a place where you will find satellite images, DEM and many other related data for any area around the globe. You get data right from 1972. You get data from various sensors like LANDSAT series, ASTER, SRTM etc. This is an amazing service provided by NASA. Do remember to credit NASA in your research work.

bhuvan: If you are from India, you would consider NRSC (National Remote Sensing Centre) data first. It is also observed that, bhuvan data scores better than earthexplorer for Indian region. You get data primarily from LISS and CAROSAT. But note that you get data only for Indian subcontinent. Also to mention that data is available after 1990s.

Following is the tutorial illustrating the procedure to download satellite Images and DEM for free.

Notes and Tips: 

• You will need to register to both sites as a user first in order to download data.

• Your any query related to NASA products can directly be sent to the mail ID provided. You can expect an early and prompt reply (personal experience)

• Both in earthexplorer and bhuvan, do explore all the options for all available data, you will get many more data products that would be useful for your work. So just do not log out once you get the data, you are looking for, explore more.

• Download natural look like data before downloading the image to check for quality of image. Primarily to have a look at the cloud condition.

Calculation of NDWI (Normalised Differential Water Index) in ERDAS

The normalized difference water index (NDWI) is uses similar principles as Normalized Difference Vegetation Index (NDVI) to enhance the reflectance values of surface water bodies. NDVI was introduced in 1973 by Rouse et al.  Differences of two bands, red and near-infra-red (NIR) is carried out suing the formula. NDVI=  ((NIR-RED))/((NIR+RED)), As a result the Spectral Reflectance values of the image will be highlighted. Using similar principles in 1996 GAO derived NDWI technique. The primary idea was to assess the liquid water content of vegetation canopy. Formula used is NDWI=  ((NIR-SWIR))/((NIR+SWIR)) . Where SWIR is Short Wave Infrared. This was further modified by McFeeters in 1996 to enhance the spectral reflectance values of surface waterbodies in an image. The modified formula is NDWI=((Green-NIR))/((Green+NIR)) .

There is readymade model available in ERDAS on NDVI. All we need to do is just replace the relevant bands as required. Primary concept involved is the arithmetic operation as per the formula on each pixel of one band with the corresponding pixel in the bother band.

Here is the video on calculating NDWI in the simplest way using ERDAS Imagine.

Notes and Tips:

• There will be slight changes in carrying out the process depending on the version of the software. 

• Logically and Theoretically the pixel values of NDWI and NDVI result shall be varying from -1 to +1. In ERDAS, it is stretched to 255 values for easy comprehension. So don't be under doubt on the accuracy of the result seeing the values beyond the range of -1 to +1.

• NDVI is explained in another post.

• You can try these only with proper satellite images or UAV images having Infra Red band. This can not be done without IR band as you see in the formula, so do not try with Google Earth images. 

Changing Coordinate System and Projection

Coordinate system and Projections are most important part of any Map. This helps in in determining the location of any object on the map. There are various coordinate systems and projections. Even if the files are having geographical reference and they are of same region, they may not overlap due to different projection.  Simplest way to definition as follows.

Coordinate System: A reference system with which every location(point) on the surface of the earth can be located using intersection of a latitude(easting) and a longitude(northing). 

Projection: It is the method of representing 3D earth surface on 2D paper.

You can further study about Coordinate System and Projections at your free time to get a detailed understanding.

It is very likely that various maps, satellite images, DEMs, GPS coordinates be in different projections and hence they do not overlap. So it is always advisable to have all the files of a project in the same Coordinate System and Projection. So there would be a need to change Coordinate system or Projection of one file into another. Below is the video which illustrates on how to change projection from one to another.

Notes and Tips:

• Just try to understand the basics of the projection and coordinate system you are going to retain in your project, that will help you in taking the best decision to select the projection and coordinate that suits best to your work.

• It is always better to import the projection from another file than defining on your own, as there are possibilities of human errors while you define on your own.

• in ArcMap version above 10 importing Projection from another file is slightly different than in 9.3. In the window there is a small down arrow next to the globe symbol in the search row. When you click on that, you will get the option "Import"