Spatial Structure Analysis of Benthic Ecosystem Based on Geospatial Approach at Parang Islands, Karimunjawa National Park, Central Java, Indonesia

Helmi, Muhammad; Aysira, Afrina; Munasik, Munasik; Wirasatriya, Anindya; Widiaratih, Rikha; Ario, Raden

doi:10.14710/ijoce.v2i1.7284

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Similar features are proposed by the Pleiades-1 satellites, from the Optical and Radar Federated Earth Observation program, also present in the literature [97,98]. An even higher accuracy can be found with GeoEye-1 satellite, providing images at a resolution of less than 1m, making it particularly useful to study coral reefs [99].…”

Section: Satellite Datasupporting

confidence: 53%

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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Section: Satellite Datasupporting

confidence: 53%

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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“…The DOS method assumes that there are other objects in the images produced by satellites, this is caused by atmospheric scattering (Rumora et al, 2020). The Dark Object Subtraction (DOS) method can eliminate or correct additive fog components in each band (Helmi et al, 2018). This method was chosen because it was relatively simple and did not require a lot of input, because it only uses the approach that the reflectance value of the entire image pixels was reduced by the reflectance value of the darkest object.…”

Section: Sentinel 2 Satellite Image Processingmentioning

confidence: 99%

The Use of Vegetation Indices on Temporal Mangrove Condition: A Case Study on Timbulsloko and Bedono, Demak

Aulia,

Pribadi,

Helmi

2023

JIPK

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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. Highlight Research Mangroves in each region have different canopy density values. The use of the mangrove vegetation index will produce different accuracy values in different areas. LAI has a very strong relationship with the NDVI. The addition of area in the sparse mangrove category can be an indication of mangrove planting at the most recent time. In general, the research outcome will be valuable recommendation for mangrove rehabilitation in current target area.

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“…The spatial, spectral and temporal resolution of remote sensing data products will determine the seascape metrics that can realistically be measured [121]. Seascape metrics such as patch shape, area and edge length have been measured using aerial platforms [122][123][124] and patch complexity, connectivity and diversity have been quantified using high spatial resolution platforms, including GeoEye-1, IKONOS and SPOT 7 [125][126][127]. Studies have demonstrated the importance of ultra-high spatial resolution imagery for mapping patch level fragmentation [128,129], which may otherwise be masked in measures of overall seagrass density based on coarser spatial resolution imagery (Figure 2).…”

Section: Landscape Ecology Metrics Applied To Seagrass Meadowsmentioning

confidence: 99%

A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies

et al. 2022

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Seagrass ecosystems sequester carbon at disproportionately high rates compared to terrestrial ecosystems and represent a powerful potential contributor to climate change mitigation and adaptation projects. However, at a local scale, rich heterogeneity in seagrass ecosystems may lead to variability in carbon sequestration. Differences in carbon sequestration rates, both within and between seagrass meadows, are related to a wide range of interrelated biophysical and environmental variables that are difficult to measure holistically using traditional field surveys. Improved methods for producing robust, spatially explicit estimates of seagrass carbon storage across large areas would be highly valuable, but must capture complex biophysical heterogeneity and variability to be accurate and useful. Here, we review the current and emerging literature on biophysical processes which shape carbon storage in seagrass beds, alongside studies that map seagrass characteristics using satellite remote sensing data, to create a blueprint for the development of remote sensing-enabled proxies for seagrass carbon stock and sequestration. Applications of satellite remote sensing included measuring seagrass meadow extent, estimating above-ground biomass, mapping species composition, quantifying patchiness and patch connectivity, determining broader landscape environmental contexts, and characterising seagrass life cycles. All of these characteristics may contribute to variability in seagrass carbon storage. As such, remote sensing methods are uniquely placed to enable proxy-based estimates of seagrass carbon stock by capturing their biophysical characteristics, in addition to the spatiotemporal heterogeneity and variability of these characteristics. Though the outlined approach is complex, it is suitable for accurately and efficiently producing a full picture of seagrass carbon stock. This review has drawn links between the processes of seagrass carbon sequestration and the capabilities of remote sensing to detect and characterise these processes. These links will facilitate the development of remote sensing-enabled proxies and support spatially explicit estimates of carbon stock, ensuring climate change mitigation and adaptation projects involving seagrass are accounted for with increased accuracy and reliability.

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Spatial Structure Analysis of Benthic Ecosystem Based on Geospatial Approach at Parang Islands, Karimunjawa National Park, Central Java, Indonesia

Cited by 4 publications

References 11 publications

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

The Use of Vegetation Indices on Temporal Mangrove Condition: A Case Study on Timbulsloko and Bedono, Demak

A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies

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