Evaluation the Soil-Adjusted Vegetation Indices SAVI and MSAVI for Bristol City, United Kingdom Using Landsat 8-OLI Through Geospatial Technology

• Autorzy: Kareem Hayder H. , Attaee Muammar H. , Omran Zainab Ali

Identyfikatory

DOI

10.12912/27197050/169749

• Języki publikacji: EN

Abstrakty

EN

Soil moisture is highly variable in space and time; moreover, it has nonlinear effects on a wide variety of environmental systems. Understanding the multiple hydrological processes, developing more accurate models of those processes, and applying those models to conservation planning all benefit greatly from a better characterization of temporal and geographic variability in soil moisture. Vegetation indices (VIs) are used to assess vegetative coverings objectively and subjectively through spectral observations. The spectral responses of vegetated areas are influenced by many factors, including vegetation and soil brightness, environmental influences, soil color, and moisture. This research looked into the soil adjusted indices SAVI and MSAVI for the city of Bristol in the United Kingdom and assessed them. The Landsat 8 OLI of the research area was downloaded, whereas Bands 4 and 5 were processed in a geographic information system (GIS) to provide SAVI and MSAVI. The obtained values for the SAVI index are between -0.557 and 0.425, and the obtained values for the MSAVI index are between -1.183 and 0.441. The MSAVI is able to extract a thicker layer of vegetation than the SAVI. Similarly, MSAVI has revealed more non-vegetated locations compared to those extracted by SAVI. Since the MSAVI index provides reliable signals of land cover, it should be used in research applications. Technically, the work presented the GIS functionality of a raster calculator for processing Landsat 8 OLI data, and regionally, it added to the studies of Bristol City.

Słowa kluczowe

EN

soil-adjusted evaluation; SAVI; soil-adjusted vegetation index; MSAVI; modified soil-adjusted vegetation index; Landsat 8 OLI; GIS; geographic information system; Bristol City; United Kingdom

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2023
• Tom: Vol. 24, iss. 7
• Strony: 89--97
• Opis fizyczny: Bibliogr. 31 poz., rys.

Twórcy

autor

Kareem Hayder H.

• Structures and Water Resources Engineering Department, Faculty of Engineering, University of Kufa, Al-Najaf, Iraq

• https://orcid.org/0000-0003-3482-393X

autor

Attaee Muammar H.

• Department of Civil Engineering, College of Engineering, University of Misan, Misan, Amarah 62001, Iraq

• https://orcid.org/0000-0001-8036-2957

autor

Omran Zainab Ali

• Department of Civil Engineering, Faculty of Engineering, University of Babylon, Babil, Iraq

• https://orcid.org/0000-0001-5012-2047

Bibliografia

• 1. Abdollahnejad A., Panagiotidis D., Shataee Joybari S., Surový P. 2017. Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data. Forests, 8, 42.
• 2. Abdollahnejad A., Panagiotidis D., Surový P. 2018. Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data. Forests, 9, 85.
• 3. Ahmad F. 2012. Spectral vegetation indices performance evaluated for Cholistan Desert. J. Geogr. Reg. Plann., 5, 165–172.
• 4. Azadeh A., Dimitrios P., Lukáš B. 2019. An integrated GIS and remote sensing approach for monitoring harvested areas from very high-resolution, low-cost satellite images. Remote Sens., 1–18.
• 5. Bannari A., Morin D., Bonn F. 1995. A Review of vegetation indices. J. Remote Sens., 13, 95–120.
• 6. Basso B., Cammarano D., DeVita P. 2004. Remotely sensed vegetation indices: theory and applications for crop management. Rivista Italiana Di Agrometeorologia, 1, 36–53.
• 7. Bhambure T.V., Gharde K.D., Mahale D.M., Nandgude S.B., Mane M.S., Bhattacharyya T. 2018. Comparative Study of different vegetation Indices for Savitri Basin using remote sensing data. Advanced Agricultural Research & Technology Journal, 2(1), 69–75.
• 8. Chiteculo V., Abdollahnejad A., Panagiotidis D., Surový P., Sharma R.P. 2019. Defining Deforestation Patterns Using Satellite Images from 2000 and 2017: Assessment of Forest Management in Miombo Forests—A Case Study of Huambo Province in Angola. Sustainability, 11, 98.
• 9. Glene E.P., Huete A.R. 2008. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensos., 8, 2136–2160.
• 10. Haifa A.M., Hussam H., Hassan Y.A.S. 2018. Change detection and analysis of the vegetation cover using spectral indices in remote sensing, Wadi Al Arab’s case study. Studies, humanities and social sciences Journal, 45(1), 83–97.
• 11. Hawkins A.B. 1973. The geology and slopes of the Bristol region. Quarterly Journal of Engineering Geology and Hydrogeology, 6(3–4), 185–205.
• 12. Huete A.R. 1988. Soil adjusted vegetation index (SAVI). Remote Sens. Env., 25, 296–309.
• 13. Jassim K.S., Abas M., Abdul-kareem M.J. 2007. The use of normalized differences vegetation index in the determination and evaluation of degradation status of vegetation cover in Sinjar Mountain/Ninevah Governorate. Iraqi Journal for Earth Science, 7(2), 1–14.
• 14. Kayitakire F., Hamel C., Defourny P. 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens. Environ., 102, 390–401.
• 15. Lillesand T.M., Kiefer R.W. 1994. Remote Sensing and Image Interpretation. 3rd Edition, John Wiley and Sons, Inc., Hoboken, 750.
• 16. Lunetta R.S., Ediriwickrema J., Johnson D.M., Lyon J.G., McKerrow A. 2002. Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA. Remote Sens. Environ., 82, 258–270.
• 17. Manar M.A. 2008. Land cover study in Nablus using remote sensing technique. MSc. Thesis, An-Najah National University-Graduate School, Palestine.
• 18. Mehdi S. 2021. Land surface remote sensing. Package ‘LSRS’, 1–14.
• 19. National Meteorological Library and Archive Fact sheet 7 — Climate of South West England, Climate. http://www.metoffice.gov.uk/media/pdf/c/n/MetLIB_13_013_FactSheet_7_Final.pdf. [Accessed: June 22, 2023].
• 20. Ozdemir I., Karnieli A. 2011. Predicting forest structural parameters using the image texture derived from WorldView-2 multi-spectral imagery in a dryland forest, Israel. Int. J. Appl. Earth Obs. Geoinform., 13, 701–710.
• 21. Petursdottir T., Baker S., Aradottir A.L. 2020. Functional silos and other governance challenges of rangeland management in Iceland. Environmental Science and Policy, 105, 37–46.
• 22. Polina L. 2020. Hyperspectral vegetation indices calculated by Qgis using landsat Tm Image: a case study of Northern Iceland. Advanced Research in Life Sciences, 4, 70–78.
• 23. Qi J., Chetbouni A., Huete A.R., Kerr Y.H., Sorooshia S. 1994. A modified soil adjusted vegetation index. Remote Sens. Env., 48, 119–126.
• 24. Rouse J.W., Haas R.H., Schell J.A., Deering D.W. 1974. Monitoring vegetation system in the Great Plains with ERTS symposium. NSSA SP-351, 1, 309–351.
• 25. Saber M., Gonzalez J.R., Anderson R. 2020. Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest. Agricultural Water Management, 239, 106266.
• 26. Shimabukuro Y.E., Beachie R., Gracchi R.C., Achard F. 2014. Assessment of forest degradation in Brazilian Amazon due to selective logging and fires using time series of fraction images derived from Landsat ETM+ images. Remote Sens. Lett., 5, 773–782.
• 27. Singh V.K., Satpathy A., Parveen R. 2010. Spatial variation of vegetation moisture mapping using advanced space borne thermal emission and reflection radiometer (ASTER) data. J. Environ. Prot., 1, 448-455.
• 28. Stibig H., Achard F., Carboni S., Raši R., Miettinen J. 2014. Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences, 11, 247–258.
• 29. St-Onge B., Hu Y., Vega C. 2008. Mapping the height and above-ground biomass of a mixed forest using LiDAR and stereo Ikonos images. Int. J. Remote Sens., 29, 1277–1294.
• 30. Taylor J. 1872. A Book about Bristol: Historical, Ecclesiastical, and Biographical, from Original Research. Houlston and Sons, T. Kerslake & co., US.
• 31. The population of Bristol - bristol.gov.uk. www.bristol.gov.uk. [Accessed June 22, 2023].