1887
PDF

Abstract

Summary

The unpredictable dynamics of military operations harm the environment and endanger human lives, health, and property. Agricultural activity and land use also face substantial risks. Hence, the goal of the research is to use remote sensing and GIS to assess and monitor the changes in land use, first of all agricultural lands, caused by hostilities. The research is aimed at monitoring the state of affected lands using remote sensing and artificial intelligence tools. We decided to use the NDVI index to determine agricultural land that is currently not cultivated due to hostilities. The non-cultivated agricultural land is mostly covered with wild grass and shrub vegetation, which we want to construct a NDVI profile for the 2023 growing season. By comparing it with the relevant crop profiles, we will be able to identify uncultivated agricultural land. To carry out our research, we used a series of Sentinel-2 satellite images for the period from March 20 to September 1, 2023. NDVI profile of wild grass and shrub vegetation is different with both winter and spring crop profiles, so NDVI can be used to identify non-cultivated agricultural lands, in particular affected by hostilities.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2023520194
2023-11-07
2025-02-13
Loading full text...

Full text loading...

/deliver/fulltext/2214-4609/2023/monitoring'2023/Mon23-194.html?itemId=/content/papers/10.3997/2214-4609.2023520194&mimeType=html&fmt=ahah

References

  1. Alcaraz-Segura, D., Cabello, J. & Paruelo, J. (2009). Baseline characterization of major Iberian vegetation types based on the NDVI dynamics.Plant Ecol., 202, 13–29. https://doi.org/10.1007/s11258-008-9555-2
    [Google Scholar]
  2. PangG., WangX. & YangM. (2017). Using the NDVI to identify variations in, and responses of, vegetation to climate change on the Tibetan Plateau from 1982 to 2012.Quaternary International, 444, Part A, 87–96. ISSN 1040–6182. https://doi.org/10.1016/j.quaint.2016.08.038
    [Google Scholar]
  3. GandhiG., ParthibanS., ThummaluN. & ChristyA. (2015). NDVI: Vegetation Change Detection Using Remote Sensing and Gis – A Case Study of Vellore District.Procedia Computer Science, 57, 1199–1210. ISSN 1877–0509. https://doi.org/10.1016/j.procs.2015.07.415
    [Google Scholar]
  4. IbatullinSh., DoroshY., SakalO., DoroshO., DoroshA. (2022). Crop Identification Using Remote Sensing Methods and Artificial Intelligence.International Conference of Young Professionals «GeoTerrace-2022», Lviv, October 2022, Volume 2022, p.1–5. https://openreviewhub.org/geoterrace/paper-2022/crop-identification-using-remote-sensing-methods-and-artificial-intelligence
    [Google Scholar]
  5. JakubauskasM., LegatesD. & KastensJ. (2002). Crop identification using harmonic analysis of time-series AVHRR NDVI data.Computers and Electronics in Agriculture, 37, 127–139. ISSN 0168–1699. https://doi.org/10.1016/S0168-1699(02)00116-3
    [Google Scholar]
  6. National Emergency Service of Ukraine (2023). Service of mine countermeasures of the State Emergency Service.https://mine.dsns.gov.ua/
    [Google Scholar]
/content/papers/10.3997/2214-4609.2023520194
Loading
/content/papers/10.3997/2214-4609.2023520194
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error