Analysis Of Vegetation Index For Ndvi, Evi-2, And Savi For Mangrove Forest Density Using Google Earth Engine In Lembar Bay, Lombok Island

Abstract

The mangrove forest is one of the most productive ecosystems in the world. Mangrove forests have benefits such as flood control, groundwater conservancy, shoreline and storm protection, biodiversity conservation, mitigation, and adaptation to climate change. Remote sensing for monitoring and mapping natural ecosystems such as mangrove forests has increased recently. The advantage of using remote sensing data for mapping mangrove forests lies in the remote sensing imagery that provides a comprehensive view compared to land-based measurements. The easily-recognizable appearance of mangrove vegetation in the image is because mangrove vegetation lives between land and sea transitions; thus, the mangrove vegetation has a darker color appearance. The vegetation index algorithm is an algorithm that can see the condition and density of mangrove forests. Therefore, the Google Earth Engine platform can be utilized. It has a very large remote sensing data set; thus, it can process and discover the density value of mangrove forests the results of the vegetation index values with the NDVI, EVI-2, and SAVI methods. The location of this research is in Lembar Bay. The results of this study indicate that sentinel imagery 2A level 2A can be used to generate vegetation index data using the NDVI, EVI-2, and SAVI algorithms. Based on the vegetation index classification with NDVI, EVI-2, and SAVI, it is known that the dominant density level is a very high density, with an area of 56,66 ha for NDVI, 50,24 ha for EVI-2, and 56,65 ha for SAVI. Then the correlation between NDVI, EVI-2, and SAVI with water and soil parameters that have the most influence on mangrove vegetation density is water pH with a correlation coefficient value of NDVI 0,464, EVI-2 0,469, and SAVI 0,464, showing that water pH and vegetation index have a strong enough correlation.