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

Abstract

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.

Soil Nutrient Status of Smallholder Farmers in Makurdi, Benue State, Nigeria

“…Similar CEC content has been reported in most studies conducted in the area [12]. The obtained low CEC could be due to the predominance of sesquioxides and kaolinite clays [40], over 2:1 clay mineral in the soil.…”

Pendeteksian Sebaran Kerapatan Vegetasi Dan Suhu Permukaan Menggunakan Citra Landsat Di Kabupaten Sabu Raijua

“…Nilai LST yang diekstraksi dari Landsat 9 tidak hanya untuk memperkirakan suhu permukaan namun lebih lanjut dapat melihat perubahan iklim. Hal ini juga terkait langsung dengan nilai indeks vegetasi (Aliyu et al 2022).…”

“…Nilai LST yang diekstraksi dari Landsat 9 tidak hanya untuk memperkirakan suhu permukaan namun lebih lanjut dapat melihat perubahan iklim. Hal ini juga terkait langsung dengan nilai indeks vegetasi(Aliyu et al 2022).Pada analisis suhu permukaan di Kabupaten Sabu Raijua, band 10 dan 11 digunakan untuk mendapatkan nilai suhu permukaan dari Landsat 9. Nilai DN pada gambar juga dikonversi ke suhu permukaan melalui langkah konversi DN ke suhu permukaan.…”