Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation

Abstract

Discriminating between vegetation and non-vegetation areas is of crucial importance in the study of mangroves. This will help mangrove monitoring and management since losses and degradation of the mangroves are reported to be substantial in recent years. This study investigates the integration of Normalised Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) as a tool to discriminate vegetation covers in the mangrove forest. High resolution remotely sensed images from Satellite Pour l'Observation de la Terre (SPOT-6 and SPOT-7) satellite, with 1.5 m spatial resolution over the Matang Mangrove Forest Reserve (MMFR) was acquired. A complete ground-truthing was conducted at the preliminary stages of land cover classification. A Moran's I analysis shows a random pattern of groundtruthing; (Kuala Sepetang; p-value = 0.219; Kuala Trong, p-value = 0.163; Sungai Kerang, pvalue = 0.159). Since SAVI requires a suitable L-factor to be used to distinguish the vegetation areas, four different L-factors viz. 0.1, 0.25, 0.5 and 0.75 were tested, and the multiple linear regressions, using the stepwise regression method of backward elimination, found that the Lfactor 0.75 was significant to be used for MMFR. A correlation analysis conducted between the results of NDVI, SAVI and supervised classification shows a high significant relationship, especially between NDVI and SAVI (0.991) at 99.99% level. This shows that NDVI and SAVI are useful analyses that can be employed to improve the accuracy of classification in the mangroves.

A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images

“…10-15), and waterproof-coated (Nos. [16][17][18] areas were all lower than the vegetation index of lower-vitality grass (Nos. [22][23][24].…”

Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery

“…In previous studies, VIs, including NDVI, EVI, NDII, CVI, SAVI and NDMI, were widely used to represent forest damage in response to typhoon/hurricanes ( [6,9,10,[21][22][23]37,40]). Therefore, we also used these VIs as inputs to the RF algorithm.…”

Estimación de Biomasa Aérea y Carbono con Teledetección en Bosques Alto-Andinos de Boyacá, Colombia. Estudio de caso: Santuario de Fauna y Flora Iguaque

“…Los IV seleccionados en este estudio fueron implementados en Colombia para la estimación y monitoreo de AGB en bosques naturales empleando sensores remotos (Galindo et al, 2011). El Índice de Diferencia Normalizada (NDVI) se expresa como la diferencia entre las bandas infrarrojas cercanas y rojas normalizadas, es muy sensible a identificar vegetación y presenta valores entre -1 y 1 donde los valores positivos constituyen vegetación (Silleos et al, 2006;Rhyma et al, 2020). Asimismo, el Índice de Vegetación Ajustado al Suelo (SAVI) disminuye los efectos provocados por el suelo principalmente, trabaja igual que el NDVI incluyendo un factor de ajuste L, el cual puede tomar valores de entre 0 y 1 según la densidad de la vegetación (Eastman, 2006).…”