Analyzing Vegetation Cover Change in Dheerpur Wetland Restoration Site


The success of an ecological restoration project is based on several key attributes such as the progress/recovery of ecosystem function and structure. A timely assessment/evaluation of the progress of the restoration activities is an important step in measuring the progress. It further helps in monitoring the changes and emergent patterns that could be of significant value to help and guide further action. One of the evaluating strategies stated by the Society for Ecological Restoration (SER) Primer is the Attribute Analysis. Restoration progress of a wetland can be studied and evaluated by looking at the “changes in wetland’s extent and quality, wetland function, wetland and water body buffers, land use and land cover in a watershed, the extent of ditching, and water quality, such as turbidity and eutrophication” (Klemas, 2013). Vegetation cover and inundation changes over the period of restoration are perhaps the two most important indicators of the level of success/progress in wetland restoration (Skidmore et al. as cited in Klemas, 2013).

 “Vegetation phenology is a key indicator for observing changes in the natural environment” (Richardson as cited in Ekumah et al., 2020). The index used to measure the vegetation cover is NDVI (Normalised Difference Vegetation Index). It is the difference between the intensities of reflected light in the red and infrared range divided by the sum of these intensities. NDVI is calculated with the following formula. NDVI = NIR-RED/NIR+RED where NIR is a reflection in the near-infrared spectrum and RED is a reflection in the red range of the spectrum. The values lie between -1 to 1 in which negative values mainly represent clouds, water and snow; values close to zero are from rocks and bare soil, moderate values from 0.2 to 0.3 represent shrubs and meadows, and large values from 0.6 to 0.8 indicate temperate and tropical forests.

NDVI is regarded as positive sub-indicator which means that an increase in its value is read as improvement in the health of the wetland ecosystem (Ekumah et al., 2020). Calculating these indices and monitoring wetland ecosystem health at different temporal and spatial scales has become easier with the availability of remotely sensed data and different geospatial techniques (Kerr and Ostrovsky as cited in B. Ekumah et al., 2020).

In this article, I am going to analyse the Vegetation Cover Change using NDVI index with the help of the Landviewer platform about which I have discussed in my previous blog post (on Mapping Changes Using Landviewer).

The Dheerpur Wetland Restoration Project was inaugurated on 19 June 2015, and restoration work began almost immediately. Restoration of the Dheerpur wetland and creation of a wetland park is a collaborative project between Ambedkar University Delhi (AUD) and the Delhi Development Authority (DDA). The project is undertaken by the Centre for Urban Ecology and Sustainability (CUES) which is based in AUD.

The bounded area used for analysis is the area to which the Centre has permission to work on. Before beginning the analyses, I would like to present a visual representation of the changes that happened in the Wetland Restoration Site from 2015 to 2020. (Image Source: Google Earth Pro)

Image 1: Dheerpur Wetland Restoration Site in 2015

Image 2: Dheerpur Wetland Restoration Site in 2016

Image 3: Dheerpur Wetland Restoration Site in 2017

Image 4: Dheerpur Wetland Restoration Site in 2018

Image 5: Dheerpur Wetland Restoration Site in 2019

Change Detection

Satellite images from Sentinel Sensors were acquired to map the changes over a period of four years from 2016 (project beginning) to 2019. To calculate the changes in vegetation cover, NDVI index was used.  The change was calculated for a time interval of four years. The darkest green colour depicts the area of highest positive change that comprises an area of 59,600 metre square (5.96 hectares) with a maximum NDVI mean value of 0.3. Followed by it is a slightly less green colour with an NDVI mean value between 0.1-0.3 which comprises an area of the 60,800 metre square (6.08 hectares).

(Positive change means that the area with that particular mean value range has increased when compared to the previous year’s data)

Image 6: NDVI Change Detection. The legend shows the range of NDVI Values.

Use of Comparison Slider to depict NDVI change

With the help of the comparison slider, both images of different time periods are visualised on the map simultaneously. The left side of the slider is an imagery from the month of August 2016 and on the right side of the slider from the month of August 2019.

It can be seen in the two different images of the Dheerpur Wetland Restoration Site that the restoration area has undergone a significant change in terms of vegetation cover. The restoration efforts have helped in improving the wetland ecosystem health. The areas with no vegetation cover or sparse vegetation cover in 2016 have decreased in 2019. In the 2019 imagery, these areas are covered with vegetation which has led to an increase in the categories of moderate and dense vegetation areas from 30,400 metre square to 1,42,000 metre square (3.04 hectares to 14.2 hectares).

Image 7: Comparison between DWRS site in 2016 and 2019

Time Series Analyses

Time Series analyses are necessary to monitor temporal trends and variations in a restoration project (Klemas, 2013). “In particular, time-series NDVI datasets can provide a basis for describing the impact of restoration and its consequences with repeated monitoring at a fixed interval. Time series analysis of NDVI is considered fundamental for extracting numerical observations related to vegetation dynamics” (Kim et al., 2015).

Therefore, a time series analysis was also done to study and map the changes from 2016 to present. The graph shows month-wise yearly data on NDVI values for the wetland area. It can be clearly seen in the graph that NDVI values have significantly increased over the years.

Image 8: Time Series Analysis of NDVI Values from year 2016 to 2020

It is evident from the above analysis that vegetation change can be effectively monitored and evaluated using satellite-derived images. The increased availability and public accessibility of satellite images and remote sensing tools and techniques have made it easier and less time consuming to evaluate and effectively monitor restoration progress. Satellites which have been operational for a long time can even provide data for restoration sites for which no field monitoring data is available (Kim et al., 2015). Applying the tools of remote sensing in the field of restoration ecology can help in effective monitoring and timely evaluation of the progress made in the restoration project.


  • Klemas, V. (2013). Using remote sensing to select and monitor wetland restoration sites: an overview. Journal of Coastal Research, 29(4), 958–970
  • Ekumah, B., Armah, F., Afrifa, E., Aheto, D., Odoi, J., & Afitiri, A. (2020). Geospatial assessment of ecosystem health of coastal urban wetlands in Ghana. Journal of Ocean and Coastal Management, 193
  • Society for Ecological Restoration International Science & Policy Working Group. (2004). The SER International Primer on Ecological Restoration. & Tucson: Society for Ecological Restoration International.
  • Kim, J.Y., and Rastogi, G. (2015). Trends in a satellite-derived vegetation index and environmental variables in a restored brackish lagoon. Global Ecology and Conservation, 4, 614-624

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