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2 2023

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

Kareem Hayder H., Attaee Muammar H., Omran Zainab Ali

Ecological Engineering & Environmental Technology

2023

Vol. 24, iss. 7

89--97

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.

Fury of nature: evaluating the impact of hailstorm on vegetation using sentinel 2 data at Moran, India

Neog Rituraj

Acta Geophysica

2023

Vol. 71, no. 6

3025--3039

The primary objective of the study is to analyze the impact assessment of hailstorms on vegetation in the Moran region of Assam. The experiments employed sentinel-2A data of December, 2022 and January, 2023 for the computation of the NDVI, GNDVI, and MSAVI indices and their temporal dynamics. Further, LandScan gridded (1 k × 1 km) population data of 2021 have been used to portray the population affected in the study area. The result evidenced a significant decline in the mean NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), and MSAVI (Modified Soil Adjusted Vegetation Index) from the pre-hailstorm to the post-hailstorm period. The above indices declined from 0.270, 0279 and 0.416 in pre-hailstorm (24 December, 2022) period to 0.257, 0.269 and 0.410 in post-hailstorm period (3 January, 2023). Similarly, the area under healthy vegetation decreased from 72.06 and 103.55 sq km in 2022 to 60.74 and 96.35 sq km in 2023, based on GNDVI and MSAVI, respectively. The hailstorm affected the majority of villages as well as the population lying to the east of the NH-37, i.e., the Charaideo district of Assam. The Villages such Bagtali Sonowal, Demorukinar Changmai, Hatkhola gaon and Mout gaon experienced maximum damage to vegetation. Overall, 125.355 and 132.07 sq km of area considering both assessments (MSAVI & GNDVI with population) with a total population of about 131,342 are severely affected by hailstorm phenomena.