Comparison of Different Vegetation Indices for Assessing Mangrove Density Using SENTINEL-2 Imagery (original) (raw)
Related papers
Comparison of Several Vegetation Indices for Mangrove Mapping using Remotely Sensed Data
The increasing application of remote sensing for mangrove mapping and monitoring is practically for sustainable management of the resources to the country. Over the past few decades, the emergence of several vegetation index (VI) on remotely sensed data has certainly give significant impacts on mapping of the natural resources such as mangrove. On the other hand, the vegetation index (VI) has been used over last decade for the most suitable vegetation index in remote sensing studies. In this study, the performance of the several VI’s were assess for the mapping of mangrove area using Landsat TM data. Each VIs can differentiate the mangrove classes based on their reflectance characteristics. In general the mangrove area was classified into five classes namely Avicennia, Avicennia-Sonneratia, Acanthus-Sonneratia, Mixed Sonneratia and Mixed Acrostichum. Results from several indices such as Normalized Difference Vegetation Index (NDVI), Infrared Percentage Vegetation Index (IPVI), Different Vegetation Index (DVI), Ratio Vegetation Index (RVI), Perpendicular Vegetation Index (PVI), Soil-Adjusted Vegetation Index (SAVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) were compared and evaluated.
Mangrove Leaf Area Index Estimation Using Sentinel 2A Imagery in Teluk Ratai, Pesawaran Lampung
IOP Conference Series: Earth and Environmental Science
Mangroves are estuary dominant vegetation in tropic and subtropic area which have an important role to balance the coastal ecosystem. One of the main mangrove biophysics parameters is leaf area index, which is defined as an unitless quantity from the area of one side of the leaf on each unit of ground surface area. LAI measurement using satellite imagery is more efficient than direct measurement because it covers the isolated area in the mangrove forest. This paper discussion focus on mangrove leaf area index estimation model comparison to obtain the best estimation model based on accuracy test value. The imagery used in this research is Sentinel 2A with 10 meters resolution and generic vegetation index (NDVI) compared with nongeneric index vegetation (EVI). Normalized vegetation index is chosen because its sensitivity to chlorophyll tissue of the leaves beside, Enhanced Vegetation Index is chosen because its sensitivity to vegetation canopy structure. The LAI measurement result in Teluk Ratai, Pesawaran, Lampung showing values range from 0.37 until 1.39. The correlation analysis result showing an adequate strong relationship between NDVI and EVI with LAI field measurement value. The correlation value between NDVI and the field measurement value of LAI is 0.779 also the correlation value between EVI and the field measurement value of LAI is 0.762. Based on those value both of vegetation index have a strong relationship with the field measurement result of LAI. From the standard error estimation value, the LAI estimation model accuracy using NDVI is 79.8% and 78.78% using EVI. Visual comparison also done by compare vegetation density pattern in Sentinel 2A with estimation classes in NDVI and EVI model. The best model to estimate leaf area index mangrove is EVI model based on visual comparison, accuracy test, and saturation effect from NDVI.
Mangrove Health Analysis Using Sentinel-2A Image with NDVI Classification Method
GeoEco, 2021
This study aims to determine 1) the mangrove vegetation density index, 2) the health of mangrove plants in Sungai Batang Village to Kuala Secapah. The data used in this study is the image of Sentinel-2A, dated June 8, 2020. The data taken are vegetation density (NDVI) and mangrove health. The method in this study uses the vegetation index transformation (NDVI). Data analysis used the supervised classification method and the vegetation density index (NDVI). The results showed that the NDVI value of -1 – 0.32 indicates a sparse vegetation density, a value of 0.33 – 0.42 indicates a medium density and 0.43 – 1 indicates a dense density. From this NDVI index value, it can be used as a basis for classifying the health of mangrove vegetation. The health of mangrove vegetation based on the vegetation index value of 0.43 – 1 (meet) indicates that the health of the mangrove vegetation is very good. Vegetation value 0.33 – 0.42 (moderate) indicates good health of mangrove vegetation and veget...
Estimating leaf area index of mangroves from satellite data
Aquatic Botany, 1997
The relationship between the normalised difference vegetation index (NDVI) and leaf area index (LAI) was modelled for mangroves growing on the Caicos Bank, Turks and Caicos Islands. NDVI values were used to predict LAI with this model and a thematic map of LAI produced from satellite data for the whole Bank. Mangrove LAI ranged between 0.8 and 7.0, with a mean of 3.96. LAI data, estimated from in situ measurements of canopy transmittance for a set of sites independent of those used to derive the LAI/NDVI model, were used to test the accuracy of this image. Accuracy was defined as the proportion of accuracy sites at which the LAI value (as estimated from field measurements) lay within the 95% confidence interval for the predicted value of LAI. The accuracy of this map was high (88%) and the mean difference between predicted and measured LAI was low (13%). Remote sensing is thus demonstrated as a powerful tool for estimating the spatial distribution of LAI for whole mangrove ecosystems. This information can be obtained rapidly compared to alternative methods of measuring LAI and can minimise the logistical and practical difficulties of fieldwork in inaccessible mangrove areas. © 1997 Elsevier Science B.V.
International Journal of Environment, Engineering and Education
The increasing applications of Geographic Information Systems (GIS) and Remote Sensing (RS) for mapping, predicting, and monitoring are practical for sustainable mangrove ecosystem management. This study evaluated various geospatial techniques for detecting healthy mangroves on the eastern coast of Sri Lanka, including single spectral indices, supervised/unsupervised classification, and developed methods using Landsat data. The use of medium-resolution satellite data and the uniqueness of the mangrove ecosystem are generally involved in discriminating healthy mangroves from non-mangrove areas. This study focused on detecting degraded narrow patches of mangroves on the Eastern coast of Sri Lanka using Landsat 8 remote sensing data and five vegetation indices. The accuracy of the results was assessed using randomly generated points. The study used ArcGIS Desktop software for processing, analyzing, and integrating spatial data to meet the research objectives. The mangroves were detecte...
Jurnal Ilmiah Perikanan dan Kelautan, 2024
Mangrove forests in Timbulsloko and Bedono have very dynamic conditions, due to tidal flooding and land subsidence that occur in these areas. Meanwhile, mangrove forests in the Timbulsloko and Bedono Village play an important role in preventing abrasion which often occurs in these areas. The importance of the mangroves function in this area makes it crucial to monitor their condition. Monitoring the condition of mangroves can be done by looking at their density through the vegetation index. Therefore, this study aimed to determine the best vegetation index to be used in the Timbulsloko and Bedono villages to monitor mangroves in 2016-2018, 2020, and 2022. The method in this research consisted of two stages, namely sentinel 2 image processing and the field survey. Image processing was used to determine the condition of mangroves based on several vegetation indices. Meanwhile, data collection in the field was utilized to validate several vegetation indices used in this study and conducted with the hemispherical photography method. Linear regression analysis was used to determine the most suitable vegetation index to be applied in the study area. The study found that NDVI vegetation index had the highest accuracy value, followed by SAVI, EVI, and MVI. The use of NDVI to see the changes in mangrove conditions showed an increase in the total area in each category. So, it can be concluded that the area and density of mangrove forests in the Bedono and Timbulsloko villages increased every year JIPK
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a <i>Rhizophoraceae</i>-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (Pl...
This research tested the use of geographic information systems using Sentinel 1 and Sentinel 2 satellite data to estimate biomass mangrove forest in Vinh Quang commune, Tien Lang district, Hai Phong province. 15 sample plots (10 m × 10 m) in the field were established for making models and evaluation, the satellite images for processing in 2017 were provided freely by ESA Corporation. The study created land cover and biomass maps from field allometric equations and estimated results from the model by maximum likelihood classification and the regression model, respectively. For land cover accuracy assessment, Kappa index was employed with 93% accuracy. NDVI, SAVI were representative indices of optical Sentinel 2 images, similarly, VV and VH backscatter, VV/VH and VH/VV from Sentinel 1A images. The study showed that Sentinel 1 backscatters were unable to generate model due to quite low R 2. Compare to optical images, the NDVI index was used for biomass estimating, the total biomass was about 67,983.12 tons, average: 153.94 ± 27.01 ton/ha, maximum: 223.14 ton/ha. By comparing real numbers and estimated numbers, the results were acceptable, 23.8% average. We conclude that the optical Sentinel 2 has been more suitable to make estimating the model for mangrove biomass at a small-scale level, especially for commune level.
Journal of Landscape Ecology
Mangroves critically require conservation activity due to human encroachment and environmental unsustainability. The forests must be conserving through monitoring activities with an application of remote sensing satellites. Recent high-resolution multispectral satellite was used to produce Normalized Difference Vegetation Index (NDVI) and Tasselled Cap transformation (TC) indices mapping for the area. Satellite Pour l’Observation de la Terre (SPOT) SPOT-6 was employed for ground truthing. The area was only a part of mangrove forest area of Tanjung Piai which estimated about 106 ha. Although, the relationship between the spectral indices and dendrometry parameters was weak, we found a very significant between NDVI (mean) and stem density (y=10.529x + 12.773) with R2=0.1579. The sites with NDVI calculated varied from 0.10 to 0.26 (P1 and P2), under the environmental stress due to sand deposition found was regard as unhealthy vegetation areas. Whereas, site P5 with NDVI (mean) 0.67 is ...
Modelling Above Ground Biomass of Mangrove Forest Using SENTINEL-1 Imagery
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23 cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75 cm to 7.5 cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a−c; and e) the identifie...