Application of remote sensing for critical damage assessment and strong motion analysis in the Baghjan oil blowout disaster, Tinsukia, Assam

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Article title: "Application of remote sensing for critical damage assessment and strong motion analysis in the Baghjan oil blowout disaster, Tinsukia, Assam"
Authors: SANGEETA SHARMA , BHAGYA PRATIM TALUKDAR, SAURABH BARUAH, ASHIM GOGOI, UMESH KALITA and KAPIL MALLIK
Source: https://doi.org/10.1007/s12040-025-02705-z
Date: 2 January 2026

Fig 1.1Land use and land cover map in an around blowout site (BGN-5) for the time periods: (A) 26/04/2020 (pre-blowout) (B) 29/06/2020 (post-blowout)(C) 29/12/2020 (six months post-blowout) (D) 29/04/2021 (E) 13/10/2021.

Fig 2.Normalized differential vegetation index (NDVI) map for the time periods (A) 26/04/2020 (pre-blowout) (B) 29/06/2020 (post-blowout) (C) 29/12/2020 (six months post-blowout) (D) 29/04/2021 and (E) 13/10/2021.

Summary and Introduction

In 2020 there was a blowout disaster in the village of Baghjan in India. This blowout led to the uncontrolled released of crude oil and natural gas into the village and surrounding ecosystems leading to a massive fire. Since then, analysis has shown high levels of hydrocarbon contamination, especially by polycyclic aromatic hydrocarbons (PAH) which lead to bioaccumulation. Additionally, locals have demonstrated adverse health effects such as respitory and gastrointestinal symptoms, likely due to exposure from VOCs and particulate matter. This paper conducted a remote sensing analysis using Landsat imagery and GIS in order to determine and quantify the spatial and temporal damage. Major findings include a significant increase in contaminated water areas, barren land, a decline in vegetation health, and elevated temperatures. However fortunately, strong motion analysis indicated very little structural risk to the land, meaning there is little risk of infrastructure collapsing from ground sinks or earthquakes.

Methods

The study area was 78.5 km^2 with an emphasis on the area that is a 5 km radius around the oil well. Prior to the disaster the area had high levels of vegetation, biodiversity, and dense forest. Landsat 8 imagery was used to assess the environmental damage using two instruments, Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). These have a spatial resolution of 30m and 100m, respectively. The images were processed using ArcGIS and Google Earth Engine. Five time periods were analyzed correlating to before the blowout, right after the blowout, post-control, and two recovery phases which are about a 1 and 1.5 years after the disaster. Land surface subsidence was evaluated with Sentinel 1A datasets from three dates, one before the blowout, one after blowout but before the fire, and one after the fire.
The impact of the blowout was assessed by creating a land use land cover (LULC) map by running an unsupervised maximum likelihood classification technique. This technique takes the pixel values and assesses which category they are most likely to belong to. Additionally, a normalized difference vegetation index (NDVI) was calculated to observe the impact on vegetation. The ground motion and seismic activity was monitored using a strong motion accelerograph (SMA). This study used recordings of 20-minute long ambient noises and an earthquake event that occurred on July 14th, 2020 to do the analysis through a horizontal-to-vertical spectral ratio (HVSR) technique.

Results

Figure 1 depicts the land use and land cover map for five different points in time at the study area. At the center, depicted by a dot, is the site of the blowout. Fig1a shows the land use map prior to the incident while the remainder is after. Barren land was originally only 0.83% but jumped to 14.33% after the blowout in June of 2020, likely due to the fire. Subsequently, this number decreased 11.03% by December, exhibiting some recovery but not nearly as low as it was pre-blowout. Vegetation decreased by 3% from April to June 2020. Furthermore, the quantity of filthy water, which refers to chemically contaminated and stagnant water) increased from 3.01% to 11.2% and then a decrease to 6.19%

The NDVI results showed that vegetation was highly impacted by the blowout, as shown in figure 2. The green resembles low NDVI while the red is high NDVI, signaling higher vegetation. The large green line is the river so it makes sense that the portion had low NDVI values. Figure 2b which shows the NDVI immediately after the blowout, depicts a much larger area of green in the top left area and below the river. The green area below the river seems to correlate well with the accumulation of filthy water as shown in the land use map. Figure 2c shows an increase in NDVI (from -0.18 to 0.71 in the area by the oil well). The last two figures depict a stabilization in NDVI values. The land surface temperature map showed that in the months of December 2020 (pre-blowout), April 2021, and October 2021 the temperatures were 15–19, 26–31 and 23–28C, respectively. This shows an overall rise in temperatures following the incident.

Surface deformation was also analyzed in the study. The reason for this is because following the devastation, sub-surface water resources were used to manage the damages and fires. This excessive extraction of water has the potential to deform the surface so it was important to measure the impact of this as well. Interferograms were created for two time periods: May 23–June 4, 2020, and June 4–June 16, 2020, but they fortunately showed no particular shifting of the ground. The researchers accredit this to the fact that the area has sediments of with high hydraulic conductivity which is versatile to the extraction of subsurface water. Microearthquakes were recorded at the blowout site by the strong motion network within a distance of 1.5 to 3.0 km, indicated the blowout was responsible.

Conclusion

This study used remote sensing tools to analyze the effects of the Baghjan oil blowout on the area. The land usage was found by conducting an unsupervised classification, while the vegetative health was found by calculating the NDVI. Furthermore, ground motion changes were found using a strong motion accelerograph. The results indicate a significant increase in the vegetation cover, as well as an increase in the quantity of filthy water and barren land. Furthermore, ground temperature has increased but little structural risk was found following the hyper extraction of ground water. Overall, this paper highlights the effectiveness of remote sensing and GIS tools for the understanding and analysis in environmental assessments.