Open-Source Data in Ukraine

Examining the use of open-source environmental spatial data in Mariupol, Ukraine.

Austin Bullock

Background: The Russo-Ukrainian War is now on its 15th month. The amount of destruction through war has brought to the people of Ukraine is insurmountable. In addition to its impacts on people, the war also has serious negative consequences for our planet. The city of Mariupol, Ukraine is a large industrial center and played a key role throughout the last century in soviet industrialization. The city is a large exporter of Ukrainian steel, coal, and corn (Gardner, 2022). Damage to the industrial infrastructure in Mariupol has led to the possible contaminate of water and soil sources through chemical leakage. Mariupol has been devastated, and leakage of industrial due to artillery shelling and explosions may have also severely affected the surrounding agriculture (Rawtani et al., 2022). The potential impacts of the war in Ukraine has already prompted the use of environmental spatial data by some scientists. In one paper, scientists use satellite imagery to provide information on the direct damage to agricultural fields, classify crop cover, and computer Normalized difference Vegetation Index (NDVI) for crop yields (Deininger, 2023). My work in this story map will also use environmental spatial data to examine aspects about the Russo-Ukrainian War. Specifically, using fire (J1 VIIRS C1) and spectral (Landsat 8-9) to determine specific ground damage changes in the industrial city of Mariupol, Ukraine.

Objective: Use open-source environmental spatial data to help determine damaged ground areas from before the Russo-Ukrainian War to months after the war began.

Hypothesis: By calculating and mapping Surface Temperature (C) and Greenness Index (% Reflectance) you will be able to determine the location of damaged ground areas in Mariupol, Ukraine.

Methods: The data used to create the maps and images that appear later in this story map are from NASA FIRMS Fire data (J1 VIIRS C1) and USGS Earth Explorer spectral data (Landsat 8-9). I collected this open-source data (In Citations Section) to then interpret it in a variety of ways in ERSI ArcGIS. In ArcGIS, my goal was to use the fire data to determine a suitable area of Ukraine that has been impacted by war. the fire data is produced by an orbital sensor collecting infrared hot spot data. The fire data shows each data point from February 1, 2022 to April 2023, 2022, essentially the beginning of the War to the present. Searching through this fire data help determine Mariupol, Ukraine as a suitable candidate due to its many infrared hot-spots over the last year.

After Mariupol was determined as the location, I used spectral data from the USGS Earth Explorer website's (In Citations Section) Landsat 8-9 data. I collected data from prior to the war on November 11, 2021 and after the start of the war on March 30, 2022. This data was then synthesized into True and False color images, and Surface Temperature (C) and Greenness Index (% Reflectance) maps with the goal of using this open-source environmental spatial data to determine ground damage changes over time due to the war.

GIS Work Flow Diagram:

Results, Interpretation & Discussion:

The maps above both display Fire Data from the J1 VIIRS C1 orbital sensor collected from February 1, 2022 to April 20, 2023. The first map presents the data collected in the Russian Annexed Regions of Ukraine. These fire data points are at 40% transparency, this allows a better look at the density of the fire data. The second map presents the fire data collected within the city of Mariupol. Ultimately, this data led me to choose Mariupol as my location of interest.

The four above maps present true and false color images of Mariupol, Ukraine. The two above maps show Mariupol through open-source USGS Landsat 8 spectral bands in November, 2021. The two below maps show Mariupol through USGS Landsat 9 spectral bands in March, 2023. These maps were created to show use of the open-source data in creating images that could give insight to any drastic differences or stand-out details that have to do with ground destruction. In these images, I was unable to determine any particular differences that would indicate large scale ground damage that is actually present, but invisible to the untrained eye. One difference I noticed between the maps themselves is the Landsat 8 data (November, 2021) is surprisingly clearer than the newer Landsat 9 data 9 (March, 2020).

The two maps above present Surface Temperature Images (C) of Mariupol, Ukraine. These images were created with Landsat 8-9 spectral data to determine if there are any stand-out differences between the November 2021 and March 2022 images that may help determine ground damage changes. However, the above images present details that do not help determine changes in ground damage due to the war, negating my hypothesis.

The two maps above present Greenness Index Images (% Reflectance) of Mariupol, Ukraine. These images were created with Landsat 8-9 spectral data to determine if there are any stand-out differences between the November 2021 and March 2022 images that may help determine ground damage changes. It was my hypothesis that Greenness Index would help determine changes in ground damage due to the war. I believed that Greenness Index especially would help bring out signs of destruction because the satellite imagery shown in the beginning of this Story Map shows a drastic loss of color in some areas. Ultimately, Greenness Index does not help present any specific details relating to ground damage changes, negating my hypothesis.

The two maps above present Greenness Index Images (% Reflectance) of Mariupol, Ukraine. These images were created with Landsat 8-9 spectral data. These maps contain the same images as the previous section just at a more detailed look with a greater zoom. Unfortunately, even more focused look does not bring out details of ground damage.

Conclusion: This story map presented a multitude of different maps and images created with open source Fire (J1 VIIRS C1)and Spectral (Landsat 8-9) data. This data was collected to produce maps of Surface Temperature (C) and Greenness Index (% Reflectance) to try and gain a more detailed assessment of ground damage changes from before the Russo-Ukrainian War (Nov. 2021) and months into the war (Mar. 2022). I hypothesized that creating these maps and images will help determine the location of damaged ground areas in Mariupol, Ukraine. This hypothesis was negated in practice. However, it would be interesting to retest this hypothesis using higher-resolution data. Ultimately, there is no clear evidence that these maps and images provide any details that can help determine ground damage.

Citations:

Text:

Gardner, F. (2022, March 21). Mariupol: Why Mariupol is so important to Russia's plan. BBC News. Retrieved May 7, 2023, from https://www.bbc.com/news/world-europe-60825226

Deepak Rawtani, Gunjan Gupta, Nitasha Khatri, Piyush K. Rao, Chaudhery Mustansar Hussain, Environmental damages due to war in Ukraine: A perspective, Science of The Total Environment, Volume 850, 2022, 157932, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.202

Klaus Deininger, Daniel Ayalew Ali, Nataliia Kussul, Andrii Shelestov, Guido Lemoine, Hanna Yailimova, Quantifying war-induced crop losses in Ukraine in near real time to strengthen local and global food security, Food Policy, Volume 115, 2023, 102418, ISSN 0306-9192, https://doi.org/10.1016/j.foodpol.2023.102418.

Data:

USGS. Landsat Collection 2 Level-1, Landsat 8 OLI/TIRS C2 L1. Mariupol, Ukraine. November 11, 2021. USGS Earth Explorer.  https://earthexplorer.usgs.gov/  Date accessed, May 3, 2023.

USGS. Landsat Collection 2 Level-1, Landsat 9 OLI/TIRS C2 L1. Mariupol, Ukraine. March 30, 2022. USGS Earth Explorer.  https://earthexplorer.usgs.gov/  Date accessed, May 3, 2023.

NASA. Fire Information for Resource Management System (FIRMS). Ukraine. February 1, 2022 - April 20, 2023. NASA FIRMS.  https://firms.modaps.eosdis.nasa.gov/ . Date accessed, May 3, 2023.

Images: Images provided by Google Earth Pro Software from Maxar Technologies Satellites, Hamilton College 3019 Lab Computer.

We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Earth Observing System Data and Information System (EOSDIS).

The two maps above present Surface Temperature Images (C) of Mariupol, Ukraine. These images were created with Landsat 8-9 spectral data to determine if there are any stand-out differences between the November 2021 and March 2022 images that may help determine ground damage changes. However, the above images present details that do not help determine changes in ground damage due to the war, negating my hypothesis.

The two maps above present Greenness Index Images (% Reflectance) of Mariupol, Ukraine. These images were created with Landsat 8-9 spectral data to determine if there are any stand-out differences between the November 2021 and March 2022 images that may help determine ground damage changes. It was my hypothesis that Greenness Index would help determine changes in ground damage due to the war. I believed that Greenness Index especially would help bring out signs of destruction because the satellite imagery shown in the beginning of this Story Map shows a drastic loss of color in some areas. Ultimately, Greenness Index does not help present any specific details relating to ground damage changes, negating my hypothesis.

The two maps above present Greenness Index Images (% Reflectance) of Mariupol, Ukraine. These images were created with Landsat 8-9 spectral data. These maps contain the same images as the previous section just at a more detailed look with a greater zoom. Unfortunately, even more focused look does not bring out details of ground damage.