Seasonal Assessment Of Surface Temperature With Normalized Vegetation Index And Surface Albedo Over Pampa Biome

Käfer, Pâmela Suélen; Rocha, Nájila Souza da; Diaz, Lucas Ribeiro; Kaiser, Eduardo André; Costa, S. T. L.; Hallal, G. P.; Veeck, Gustavo Pujol; Roberti, Débora Regina; Rolim, Silvia Beatriz Alves

doi:10.1109/lagirs48042.2020.9165660

Cited by 1 publication

(1 citation statement)

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“…There are rarely studies that compared the NDVI with other VIs. Rather, what is available in literature is the relationship between NDVI with some sort of climatic or biophysical variables such as Leaf Area Index (LAI), which can be found in Wang & Huang (2010); precipitation in Wang et al, (2020); temperature in Sun & Kafatos, (2007); evapotranspiration in Xiong & Qiu, (2011); and albedo in Kafer et al, (2020). Similarly, Lawrence & Ripple (1998) carried out a study on correlation of vegetation cover with some vegetation indices (NDVI, SAVI, OSAVI, MSAVI, Green Vegetation Index).…”

Section: Introductionmentioning

confidence: 99%

Relationship of the Seasonal Vegetation Indices against the NDVI and LST in the Region of Kamuku Game Reserve and Kwiambana National Park, Nigeria

Aliyu¹,

Youngu²,

Bala³

et al. 2022

Idn. J. Earth Sci.

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While it is anticipated that there would be some similarities amongst spectral Vegetation Indices (VIs) because the majority of the indices use the red and NIR bands, it is also expected that there would be some variances. The NDVI, derived earlier by Rouse (1973), and is the commonly used VI, there have meagre understanding of the relationship between the NDVI and another VIs. Similarly, investigations on the correlation between LST and other VIs (other than NDVI) in both dry and raining seasons have not been adequately explored. This motivated the study to determine the seasonal correlation of some spectral VIs against the NDVI and LST over the forest reserve area. The study investigated two categories of VIs: slope-based and distance-based. It derived spectral VIs from Landsat 8 images for dry (January) and raining (August) seasons; and estimated LST from MODIS. The findings showed that the ARVI, GNDVI and TVI not only showed resemblance in appearance with the NDVI in both seasons, but also had a high coefficient of correlation: ARVI = 0.973, 0.964; GNDVI = 0.919, 0.879; TVI = 0.779, 0.716. Based on this finding, the ARVI, GNDVI and TVI can be used to supplant the NDVI for biomass related studies in the study area. The study further revealed that the LST-VIs relationship was negative for both dry and rainy seasons, except for the distance-based VIs (DVI, SAVI, MSAVI) that specifically had a positive correlation with the LST. The LST was strongly correlated with the GNDVI, TVI, NDVI, ARVI (0.664 ? r ? 0.598). However, the strength of the correlation for the LST-VIs in the raining season was very weak (0.003 ? r ? 0.245). The study concluded that the correlation of the LST versus the ARVI, GNDVI, NDVI, and TVI can be used for climate related studies.

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