Shiwani

Maps are extremely handy tools that can be used to visualize and illustrate a wide variety of complex data, that make interpretation of geographical information an engaging process. It helps us in making sense of comprehensive spatial relationships simultaneously with temporal changes. There was a time when mapping changes, accessing and studying remote areas was a difficult and time-consuming task. With the modern technology of satellites, drones, camera traps and interactive software, performing these tasks have become much easier and efficient.  These days there are a number of applications and software (like ArcGIS, QGIS, ERDAS, ENVIS) available to visualise and analyse spatial, geographical and demographic changes like that of land cover, vegetation, forest cover and melting of glaciers to name a few.  From accessing satellite imageries of different time periods using different sensors to using various analytical tools to perform simple tasks like calculating NDVI, these software and online applications can do it all. Moreover, they can also convey information regarding places and animals. For example, how a city grew over time, the migration journey of a bird, etc.

ArcGIS Online platform and Earth Observing System provides many such analytical tools which are quick and handy for basic mapping and image analysis. In this write-up, I attempt to demonstrate how to use one such popular online platform: Landviewer. 

About Landviewer

Landviewer is a very simple and easy-to-use web interface provided by EOS. It allows a user to search, compare, analyse and download data according to user preferences. The interface is divided into two sides, left and right panels. On the right panel, one can set search settings according to one’s preference and generate data (images) based on date, sensor type, cloud cover, etc.

The software provides explicit mapping features for users where one can compare images of the same area at two different time periods. The different tools available on the platform are area calculator, comparison slider, area of interest,  scene search, band combinations, change detection, clustering,  time series analysis, and stories. Along with these tools, there are different band combinations that are available for analysis.

In one of my previous blog posts,  I talked about visualising urban transformation through satellite imageries of IMT Manesar area, in Gurugram district of Haryana.  The area is undergoing rapid urban transformation, where landscapes can be observed changing from agricultural areas to high rise buildings and industries. As part of the Gurgaon Manesar Urban Complex Master plan, the area is going to be developed further in a planned manner to complement the pace of growth and urbanisation in this area. Using IMT, Manesar as an example, I have illustrated a step-by-step guide to how one can use Landviewer to detect and map changes (using NDVI as an index). 

Step 1: Define/Draw the area of interest (option available on the left side of the screen) and the desired satellite imagery (for this case Landsat, available on the right side), The area defined in this case represents the major area of the industrial model township which has changed over years. One can neatly define the area of interest as well as upload the Area Of Interest (or the AOI).

Step 2: Choose change detection box.  It will ask you to choose another imagery (different time period) to calculate change. It only has a limited set of satellite images that can be used for calculating the change and the two images chosen are of the same sensor (which can be slightly restricting). Make sure you have chosen two different images on the left and right side of the slider. I have selected an image of the year 2013 and an image of the year 2019 (Figure 1 and 2).

Step 3: After the two images appear on the screen, click change detection. You can also select the index which you want to use for the calculation. I chose NDVI. And then click Apply. After that click calculate change (Figure 3 and 4). 

Step 4: One can further go ahead and do a time series analysis. Click on the time series analysis box (on the right side of the screen). It will open up a box asking you to choose the time period. I have selected six years(2013-2019) for the example shown below. One also needs to set the index for which she/he wants to calculate the change. I chose the NDVI – Normalised Difference Vegetation Index for the present analysis.

Step 5: The graph is ready to be interpreted and used. One can download the graph, and data in excel and further use it in their study.

Step 6: Alternatively one can use a comparison slider if you have images from two different sensors to get an insight into how one particular area has changed over time. (Different band combinations can be used as per the requirements/interests). For example, I chose the same time period which I have chosen in my previous blog post which is the year 2002 to the year 2019. 

Step 7: I selected the comparison slider option. On the left, I chose an image of 2002 and selected the band combination of classic NDVI. On the right side of the slider, I chose an image of the year 2019 and selected the same band combination. And that’s it. I have graphics on my screen which allows me to see the change over these 17 years in this area. It even shows the amount of change in numbers happened over the years. In this case, one can see how areas of dense, moderate, sparse vegetation and open soil area have changed in these seventeen years. Sparse vegetation, open soil and no vegetation areas have increased over the years whereas dense vegetation and moderate vegetation areas have decreased.

There is a lot more to explore, and hopefully, in future blog posts, I will be able to introduce you to other mapping tools and techniques.

References: 

• Land Viewer. Earth Observing System. Retrieved from https://eos.com/lv/
• Land Viewer, https://eos.com/landviewer/?lat=28.6014&lng=77.1989&z=11