Evaluation of Multi-temporal Imagery and Vegetation Indices for Mangrove Mapping (Case Study of The East Coast of Lampung)

Abstract

Mangrove ecosystem monitoring aims to provide information on a regular basis to be used as a reference in sustainable management activities in an effort to maintain the functions and services of the mangrove ecosystem. Mangroves have a very high level of threat to exploitation, namely logging and land conversion which can cause increasingly damaged conditions. This study aims to map changes in mangrove density by comparing several methods using multi-temporal data. The satellite image data used are Landsat images in 2010, 2015 and 2020. The research method used is the transformation of the vegetation indices ARVI, DVI, EVI, GNDVI, MSAVI, NDVI, RVI and TSAVI. Based on the results of the analysis carried out, the identification of mangrove density shows that the index value is in the range -1 – 18.31 with the division of density classes into 5 classes, namely very low, low, medium, high and very high. The mangrove class is in the range of values from 0.4 to 0.8 with a medium to very high density class. The results of the map accuracy test which were validated using 30 samples in the field obtained an accuracy of 84.38%. This accuracy value is quite good with the multi-temporal image used is of medium resolution.

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

“…GNDVI was created to assess the level of the vegetation's greenness using values generated by digital signal processing of brightness data from many satellite sensor data channels. 60 Conversely, in densely vegetated areas with good environmental conditions, the ratio of the two channels is exceptionally high (maximum) in the NIR band, whereas vegetation reflectance drops in the red band. The water spectral reflectance pattern decreases in the infrared (IR) and red light bands.…”

Vegetation Indices for Spatio-Temporal Analysis of the Quality of Can Gio Mangrove for Biodiversity and Conservation

“…These images were freely acquired from the USGS website to calculate the average annual and season indices of the years 2010, 2015, and 2020. Most of the chosen images of the study area had land cloud cover less than 20 percent, and they were described in the Table 1 below [2,7,18]. NDVI is commonly used for vegetation segregation, plant growth, and vegetation cover estimation [9].…”

“…Connecting land and marine environments, it protects coastlines, enriches eco-diversity and sediments, and interacts with natural stressors. It has important roles as a freshwater resource (riverine inputs of freshwater and nutrients), in reducing soil salinity, and as a coastal environmental defence [1][2][3]. In terms of climate change mitigation, mangrove forests act as carbon sinks in the world carbon cycle.…”

“…IOP Publishing doi:10.1088/1755-1315/1247/1/012003 2 Tracking the mangrove morphological processes and damages can be conducted through temporal and spatial analyses and measurements. These can serve as grounds for construction of a database for management and monitoring, as well as for data inventory for development [2]. There are many vegetable indices that can be used to examine mangrove health, its morphology, its density and distribution [5].…”

“…The leaf area index (LAI) is another important index to analyse vegetation structure and density [9][10][11]. Other indices such as PTC (percentage tree cover), SAVI (soil adjusted vegetation index), MSAVI (modified soil adjusted vegetation index), OSAVI (optimized soil adjusted vegetation index), TSAVI (transformed soil adjusted vegetation index), FAPAR (fraction of absorbed photosynthetically active radiation), etc., can well support analyses of vegetation quality and health [2,7,10,12]. In the mangrove forest management, the Normalized Difference Water Index (NDWI) can be used for monitoring water bodies and water content in vegetation canopies [7,13].…”