Kemujan Island Research Articles

Assessment of mangrove structures and biomass on islands along the Java Sea: a case study on Bawean Islands and Karimunjawa Islands

Mangroves on the islands along the Java Sea play a crucial role in supporting biodiversity, protecting coastlines, and sequestering carbon. However, there is a lack of information on mangrove structure and biomass in the area. This study aimed to assess the mangrove structures and biomass on Desa Daun, located on the Bawean Islands, and Kemujan Island, located on the Karimunjawa Islands, respectively. The study utilized a combination of line transect and quadrant plot methods with a size plot of 10x10 m. We established 7 line transects and 28 quadrant plots in the mangrove forest of Desa Daun and similarly set up 7 line transects and 21 quadrant plots on Kemujan Island. Within each quadrant, the mangrove species were identified, and their diameters at breast height (DBH) were recorded. To assess the relative significance of each species, the importance value index (IVI) was calculated, and allometric equations were applied to estimate biomass and carbon storage in the trees. PlanetScope imagery was also used to quantify the carbon stock of mangroves on those islands. In Desa Daun, 13 mangrove species were identified, while 12 species were recorded on Kemujan Island. The species Nypa fruticans and Excoecaria agallocha exhibited the highest IVI in Desa Daun and Kemujan Island, respectively. Notably, Rhizophora apiculata demonstrated the greatest biomass in both locations, with measurements of 43.08 Mg B/ha in Desa Daun and 102.22 Mg B/ha in Kemujan Island. Overall, mangrove stands on Desa Daun stored biomass of 113.60 Mg B/ha and carbon of 50.98 Mg C/ha, whereas mangrove stands on Kemujan Island stored biomass of 295.04 Mg B/ha and carbon of 131.95 Mg C/ha. Mangrove stands of the whole Bawean Islands sequestered 22,941 Mg C, while Karimunjawa Islands hold much higher C stocks, 79,451 Mg C. Despite being smaller in size, the Karimunjawa Islands have more extensive mangrove coverage compared to the Bawean Islands due to their geomorphological characteristics, which in turn influence their mangrove structures and carbon stocks. However, the loss of mangroves in the Karimunjawa Islands due to aquaculture expansions could increase carbon loss in the islands.

Rapid Bathymetry Mapping Based on Shallow Water Cloud Computing in Small Bay Waters: Pilot Project in Pacitan-Indonesia

Mapping coastal areas generally requires large data constellations in time series and requires analysis using complex mathematical and modeling approaches. In shallow-water bathymetric mapping, remote sensing plays an important role in supporting conventional bathymetric mapping, especially in areas that are difficult to access. This method called Satellite Derived Bathymetry (SDB). The cloud computing approach is a solution for mapping shallow water bathymetry rapid and effectively. This study using Google Earth Engine (GEE) to compute remote sensing data for produce near-shore bathymetry. The method of Li et al. (2021) performs bathymetric extraction without using depth samples but uses chlorophyll-A as input for depth extraction parameter calculations. This study examines a small bay in the waters of Pacitan, Anakan Bay, and the waters of Kemujan Island in the Karimunjawa Islands. Within this study area, significant differences in resulting depth are very limited, ranging from 0 to -17.8. The developed model, based on the algorithm proposed by Li et al. (2021), is estimated to be able to provide accurate predictions of up to around 90% in the waters studied, with a root mean error rate (RMSE) of 1.1 meters.

Preserving coastal ecosystem through micro-zonation analysis of Karimunjawa, Indonesia.

Small island ecosystems and their inhabitants face a significant threat from global warming, jeopardizing their sustainability. These communities are particularly vulnerable to the impact of climate change, as they heavily rely on natural resources for their livelihoods and are more vulnerable than mainland regions. Therefore, it is essential to take urgent action to address the challenges small island states face and promote their resilience in the face of climate change. To preserve the coastal ecosystems in Karimunjawa Islands, Indonesia, this study proposes an alternative spatial plan through micro-zonation analysis. The study conducted literature reviews and field surveys to collect data and develop recommendations for the current spatial plans through spatial, descriptive statistics, and comparative analysis. The findings show that the sea surface temperatures of Karimunjawa and Kemujan Island have increased by 1-2℃. Stress levels were found for coral reefs at the bleaching warning position for all Karimunjawa Island marine areas, including Kemujan Island. Legon Lele and Tanjung Gelam were found to have suspended sediment traces and indications of heavy metal contamination, making them the research focus. The Karimunjawa micro-zonation boundaries were obtained, especially in the Legon Lele and Tanjung Gelam areas, with an area of 640.63 and 817.45ha, respectively. The proposed micro-zonation for Karimunjawa National Park refers to watershed-sedimentary cells, making it an example of implementing integrated coastal management (ICM) spatial boundaries in Karimunjawa and other nations. By applying this micro-zonation, coastal ecosystem rehabilitation efforts can be carried out precisely.

Satellite-derived bathymetry from WorldView-2 based on linear and machine learning regression in the optically complex shallow water of the coral reef ecosystem of Kemujan island

Shallow coastal water bathymetry information is required for various navigation, management, conservation, and research purposes. Bathymetry data modeled through satellite remote sensing (SDB) is considered more effective and efficient because it is cheaper, can cover a large area, and can be done routinely. In this study, we assess the linear (LR) and machine learning regression performance of bathymetry mapping in the optically shallow water of a coral reef ecosystem. We selected random forest regression (RFR) and support vector machine regression (SVR) machine learning regression models. All three regression models were trained using field bathymetry data, and different tuning parameter scenarios were applied to each model to obtain the best bathymetry map. Independent field bathymetry data were used for the assessment, which included root mean squared error (RMSE), a plot between reference and predicted depths, and an underwater topographic profile in three unique areas. Each unique area represented different water depths, underwater topography, benthic cover variations, and water clarity. This highlights and provides a comprehensive insight into how each model performs under different underwater topographic conditions. Kemujan Island, which has various coral reef geomorphic classes and underwater topographic variations, was selected as the study site. The RMSE of the SDB mapping accuracy obtained in this research ranged from 0.82 to 0.87 m for RFR, 0.96–1.17 m for SVR, and 1.00–1.23 m for LR. Overall, RFR produced SDB with higher accuracy and precision compared to SVR and LR. RFR managed to estimate the depth that follows the underwater transverse profile accurately and precisely in three sites and does not require a band ratio to produce accurate SDB in areas with various benthic covers. LR competes well with RFR in predicting water depths of up to 6 m but cannot accurately predict depths beyond 6 m. The study results provide a comprehensive reference for developing robust automated mapping models for producing SDB, increasing the availability of optically shallow water bathymetry information worldwide while reducing the cost of conducting bathymetric surveys.

A comparative study on mangrove community structure at Karimunjawa, Indonesia: Kemujan Island and Jati Kerep aquaculture area

Mangrove forest habitats of great variety can be found in the Karimunjawa Islands, which is located in Indonesia. The Karimunjawa Islands, located in Jepara Regency, is a group of 27 islands in the Java Sea. Mangrove is a type of vegetation that protects coastal habitat. The ecosystem’s long-term sustainability will be determined by its regeneration condition. This study aimed to examine mangrove community structure and mangrove regeneration at Karimunjawa. The study area was determined by purposive sampling technique with plot at size of 10 x 10m for tree, 5 x 5m for sapling, and 1 x 1m for seedling. This study revealed 11 mangrove species with the highest IVI value of 63.60% for R. stylosa at Kemujan Island, and L. racemosa with an IVI value of 109.71% at Jati Kerep Aquaculture Area. The diversity index (H’) was ranging between 1.55 to 2.00 and both study areas shared high similarity (SI = 70.59%). It was revealed that mangrove species grow at a slightly slower pace in Karimunjawa, as indicated by the number of individual number of seedlings (179 ind/ha) < sapling (234 ind/ha) < tree (239 ind/ha). Further test using one-way analysis of variance (ANOVA) revealed that there was a non-statistically significant difference in mean between Kemujan Island and Jati Kerep Aquaculture Area (F(1,4) = [3.136], p = 0.151).

Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows

ABSTRACT Spatially explicit information on aboveground seagrass carbon stock (AGCseagrass) is required to understand the role of seagrass as a nature-based solution to mitigating and adapting to climate change. Remote sensing provides the most effective and efficient means to map AGCseagrass. This research aimed to assess the accuracy of multispectral images to map AGC in seagrass meadows with different characteristics. Kemujan Island in Indonesia was selected to represent patchy beds with relatively low seagrass percent cover and low water clarity, whereas Labuan Bajo of Indonesia served an example of continuous beds with a relatively high percent cover and water clarity. WorldView-2 is a high-spatial-resolution multispectral image with six visible bands, by which water can be penetrated. Several image processing techniques, including sunglint and water column correction, principal component analysis, and co-occurrence texture analysis, were applied to the atmospherically corrected WorldView-2 images. These datasets were used as inputs in the AGC empirical model using seven regression algorithms, namely, single-band linear regression, band-ratio linear regression, stepwise regression, random forest regression, support vector regression, extreme gradient boosting, and multivariate adaptive regression spline. Seagrass field data collected using photo quadrat technique were used to train the regression model and assess the accuracy of the resulting AGCseagrass maps. The results indicated that WorldView-2 can be used to map AGC in different seagrass meadows with a consistent accuracy. The most accurate AGCseagrass map for Kemujan Island had a root mean square error (RMSE) of 4.11 g C m−2 for the aboveground stock in the range of 0–28.70 g C m−2, and for Labuan Bajo, the RMSE of the map was 9.73 g C m−2, with aboveground stock range of 0.31–64.38 g C m−2. Model cross-validation revealed that the mapping model can be site-specific or robust depending on the characteristics of the field-derived AGCseagrass data used to train the regression algorithm. For example, the model developed for Labuan Bajo seagrasses, which had a higher AGC variance, can be successfully applied to Kemujan Island with its lower AGC variance, but not vice versa. This finding is a key factor in the future development of a robust AGCseagrass mapping model that is applicable to various seagrass meadows as a stepping stone to accelerating the filling of gaps in the global seagrass dataset.

Short communication: Algal genotypes in White Syndrome infected coral Acropora muricata from the Karimunjawa Islands, Indonesia

Abstract. Wijayanti DP, Nadia M, Indrayanti E, Haryanti D, Bachtiar M. 2021. Short communication: Algal genotypes in White Syndrome infected coral Acropora muricata from the Karimunjawa Islands, Indonesia. Biodiversitas 23: 415-423. The physiological equilibrium of the coral holobiont may help the corals to survive under various environmental threats. The symbiont genotype is thought to play an important role in the host's susceptibility to thermal stress and coral disease incidence. We identified the Symbiodiniaceae diversity of coral Acropora muricata infected by White Syndrome (WS). Samples were collected from Cemara Kecil Island and Barakuda Beach, Kemujan Island, Karimunjawa Islands. We applied DNA barcoding of 28S nuclear ribosomal RNA gene of the healthy colonies, healthy tissue, and lesions of diseased colonies to perform their symbiont type identification. There were two different symbiont types observed from the phylogenetic analysis, which formed two clades. A clade consisting all Cemara Kecil Island samples while the other with all Barakuda Beach samples except for one. All samples harbored symbiont types from the genus Cladocopium. The members of the genus were reported to have various physiological properties which may support the host resilience when facing the disease. A robust study is needed to reveal the influence of symbiont genotypes on the occurrence of coral disease and the susceptibility of the coral host.

Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

Reef fish diversity in the Legon Bajak Port, Kemujan Island, Karimunjawa National Park

Karimunjawa National Park provides substantial reef fish resources, leading to higher market demand for reef fish production. This study aims to determine the diversity of reef fish, particularly in regions where fishing boats dock, specifically in Legon Bajak Port, Kemujan Island, Karimunjawa National Park, Jepara, Central Java. The findings of this investigation should enrich previous studies. The study was conducted on the right and left sides of the Legon Bajak Port. The visual count of belt transects was used to obtain data on reef fish. The study results obtained the number of reef fish at the Legon Bajak Port was 2769 individuals/500 m2 on the right and 1809 individuals/500 m2 on the left side, representing 40 species, 26 genera, and 13 families. Istigobius rigilius had the largest density of individuals, with 233 individuals/500 m2. The fish diversity index (H') value on the right side of the Legon Bajak Port was 3.24, which includes the high category, and on the left side was 2.89, which includes the medium category. The dominance index (D) was between 0.05 and 0.06 (low), while the uniformity index (E) was between 0.74 and 0.83 (unstable in a stable community).

Diversity of sponge-associated fungi from a mangrove forest in Kemujan Island, Karimunjawa National Park, Indonesia

Abstract. Sibero MT, Pribadi R, Larasati SJH, Calabon MS, Sabdono A, Subagiyo S, Frederick EH. 2021. Diversity of sponge-associated fungi from a mangrove forest in Kemujan Island, Karimunjawa National Park, Indonesia. Biodiversitas 22: 5695-5605. Plenty of studies have reported interesting biological properties and bioactive compounds from sponge-associated fungi in Indonesia. However, only a few studies consider their biodiversity. Most of the studies collected the sponge from the coral reef ecosystem and barely reported from the mangrove ecosystem. This study aimed to discover the biodiversity of sponge-associated fungi from a mangrove ecosystem using a DNA barcoding approach. Five mangrove-associated sponges were collected from a tidal mangrove forest in Kemujan Island, Karimunjawa National Park, Indonesia. In total, we isolated 56 sponge-associated fungi, which consisted of seven genera in the phylum Ascomycota (77.78%), one genus in the phylum Basidiomycota (11.11%), and one genus in the phylum Mucoromycota (11.11%). Trichoderma (n: 15, 26.79%), Aspergillus (n: 13, 23.21%) and Fusarium (n: 12, 21.45%) were the top three dominant genera. In addition, based on the association of fungi to its sponge hosts, Aspergillus, Aureobasidium, Fusarium, and Trichoderma are sponge-generalists; Absidia and Pestalotiopsis are sponge-associates; while Lasiodiplodia, Montagnula, and Wallemia are sponge-specialist.

Hydro-Oceanographic Characteristics In Karimunjawa Coastal Waters During The 1st Transitional Season

This study aims to determine oceanographic characteristics such as tides, waves, and currents in Karimunjawa Coastal Waters during the 1st transitional season and to update the oceanographic database for spatial management evaluation in the region. The tidal characteristics were obtained from the least square method analysis using World Tide software based on Matlab programming language, while wave and current characteristics obtained from 2-dimensional numerical modeling using Mike 21 software on the flow model and spectral wave module. The primary data used were the significant wave height (Hs), wave peak period (Tp), and ocean current components (u and v velocity) on 13-26 May 2016 using the Sontex Argonaut XR type Acoustic Doppler Current Profiler (ADCP) equipment. Tide data were predicted for the Kemujan Islands station from 1-31 March 2020. Secondary data for additional numerical model input were obtained from ERA5-reanalysis in the form of Hs, Tp, u, and v wind velocity data for May 2020 with a temporal resolution of 20 minutes, while bathymetry data derived from GEBCO Satellite Derivated Bathymetry (SDB) data. The tide analysis results showed that Karimunjawa waters are a single daily mixed tidal type. The wave characteristic moves from east to west with high waves reached 0.9 meters and a peak period of 7 seconds. The eastern side of Karimunjawa Island, Kemujan Island, and the western area have a calmer wave. The current characteristic moves northeastward with a speed of 5-28 cm/s, which concludes that in several locations, such as the Menjangan Besar-Menjangan Kecil strait, the currents depend on the tidal conditions.

The anatomic response of the mangrove vegetation due to the changing in land functions

The mangrove forest in Indonesia have transformed into conservation area, tourist objects, and fishponds, causing the environmental changing. The purposes of this research are to find out the condition of the environment, the varieties of the species and the anatomy of the leaves. The locations of this research are determined based on the Karimun Java in December 2019. The locations are conservation area in Menjangan Besar Island, fishpond area in Kemujan Island and Mangrove Tracking area. The environment parameters such as temperature, pH, DO, TDS, and salinity. Photomicrograph is used to check stomata. The data are analyzed descriptively. The environmental conditions in those three locations have different condition, except temperature. The species found in Menjangan Island are A.marina and R.stylosa, the species found in Kemujan fishpond are C.tagal, R.apiculata and E.agallocha meanwhile the species found in tracking Kemujan are C.tagal, R.apiculata, R.stylosa and L.racemosa. The result of the observation towards the anatomy of the leaves in those three locations has not showed responses to the environment stress, but the condition of the environment in the fishpond showed the rising in salinity level. Therefore, it is suggested to grow Avicennia because it is more adaptable towards high level of salinity.

Distribution of DO (Dissolved Oxygen) and BOD (Biological Oxygen Demand) in the Waters of Karimunjawa National Park using Two-Dimensional Model Approach

Massive mangrove conversion into intensive pond farming has become environmental problem in Kemujan Island and Karimunjawa Island which affect the water quality. This research aimed to examine the dispersion of DO and BOD related to the current pattern in the seas west of Kemujan Island and Karimunjawa Island by using two-dimensional modelling simulations. Quantitative and descriptive methods were used to provide interpretation and analysis of the modelling simulation results. Modelling simulations were conducted in September 2019. The validation results show that the model and the field measurements has a very good. The results show that the current characteristics are dominated by tidal current, which moves westward with an average magnitude of 0.078 m/s. Furthermore, divergence, convergence and turbulence are also identified. Based on the simulation results, the prediction of DO and BOD concentrations fluctuate in Lagoon Mrican. During spring tide, the DO concentration changes from 7.95 - 8.1 mg/L into 8.55 - >9.45 mg/L and during neap tide, it changes from 8.55 - 8.7 mg/L into 9.15 - >9.45 mg/L. On the other hand, the BOD concentration increases from 0 - 0.08 mg/L to 0.88 - 0.96 mg/L during spring tide and neap tide.

BATHYMETRIC EXTRACTION USING PLANETSCOPE IMAGERY (CASE STUDY: KEMUJAN ISLAND, CENTRAL JAVA)

Bathymetry refers to the depth of the seabed relative to the lowest water level. Depth information is essential for various studies of marine resource activities, for managing port facilities and facilities, supporting dredging operations, and predicting the flow of sediment from rivers into the sea. Bathymetric mapping using remote sensing offers a more flexible, efficient,and cost-effective method and covers a largearea. This study aims to determine the ability of Planet Scope imagery to estimate and map bathymetry and to as certain its accuracy using the Stumpf algorithm on the in-situ depth data. PlanetScope level 3B satellite imagery and tide-corrected survey dataare employed; satellite images are useful in high-precision bathymetry extraction.The bathymetric extraction method used the Stumpf algorithm. The research location was Kemujan Island, Karimunjawa Islands, Central Java. The selection of this region wasbased on its water characteristics, which have a reasonably high variation in depth. Based on the results of the data processing, it was found that the PlanetScope image data were able to estimate depths of up to 20 m. In the bathymetric results, the R2 accuracy value was 0.6952, the average RMSE value was 2.85 m,and the overall accuracy rate was 71.68%.

Bathymetry mapping using PlanetScope imagery on Kemujan Island, Karimunjawa, Indonesia

Bathymetrical information plays an important role in the management of coastal resources such as fisheries and shipping lanes, and is basic information of coastal resources besides benthic habitat. The extraction of satellite data for bathymetry mapping or SDB (Satellite-Derived Bathymetry) has emerged as an effective method to obtain information in terms of cost and time. Kemujan Island is an island with unique geomorphology and thus selected as a study area for bathymetry mapping. The method applied single band and band ratio methods on PlanetScope imagery. PlanetScope was chosen because it has high spatial and temporal resolution. The image used was acquired on June 30, 2017. The results show that linear regression is more representative than SVR-based modeling, with the best single band modeling from the (ln) Green band and the best band ratio modeling from the ratio transformation of Blue (ln)/(ln) Green. Single band had higher correlation and accuracy than band ratio (0.89 and 0.66, respectively) and RMSE value of 0.78 and 0.96, respectively. However, based on bathymetry profiling, the results of band ratio modeling are more appropriate as the single band method cannot afford to minimize the effect of various bottom types.

Role of the Seagrass Bed at Kemujan Island, Karimunjawa Islands, Indonesia, as a Carbon Sink Area

AbstractThe Java Sea acts as a carbon source that contributes to the release of carbon dioxide into the atmosphere. The blue carbon ecosystems in the Karimunjawa Islands may absorb and store the carbon released from the Java Sea. The carbon stock in Kemujan Island (Karimunjawa Islands), one of these blue carbon ecosystems, was previously estimated using a remote sensing approach. In the present study, we investigated the carbon stock in the seagrass bed at Kemujan Island by conducting field observations and laboratory analysis. The result shows that there are only two seagrass species in the study area—‘Enhalus acoroides’ and ‘Thalassia hemprichii’—which are categorized as nearly dense and very dense, respectively. Regarding the carbon biomass, the carbon stock below the substrate is more than that above the substrate. However, the carbon stock in the seagrass biomass is less than in the sediment. The average carbon stock in the seagrass bed at Kemujan Island is 4901.91 g C/m2, which is much more than the amount estimated by means of remote sensing. Furthermore, this amount is much larger compared with the average carbon stock of other areas in the Karimunjawa Islands. The large amount of carbon stock stored in the seagrass bed at Kemujan Island denotes the role of this seagrass bed as a carbon sink area.

ESTIMASI STOK KARBON DI PADANG LAMUN PULAU NYAMUK DAN PULAU KEMUJAN, BALAI TAMAN NASIONAL KARIMUNJAWA, JEPARA

Blue carbon adalah karbon yang diserap dan disimpan di ekosistem pesisir dalam bentuk biomassa. Padang lamun memiliki fungsi utama sebagai penyimpan karbon biru. Penelitian ini bertujuan untuk mengetahui persentase karbon organik biomassa dan di sedimen lamun, serta total stok karbon di sebagian BTN Karimunjawa. Kerapatan dan tutupan lamun diakses melalui transek kuadran dan analisis kandungan karbon organik dilakukan dengan metode Loss on Ignition (LoI). Kandungan karbon organik rata-rata tertinggi di ketiga stasiun berada di Pulau Nyamuk (Stasiun 1) dengan persentase dari biomassa-bawah sebesar 33,13%, biomassa atas-sedimen 29,19% dan pada sedimen masing-masing sebesar 2,75% dan 2,82%. Berat karbon biomassa bawah lebih besar daripada biomassa atas kecuali pada genus Halophila. Berat karbon sedimen memiliki persentase kontribusi tertinggi terhadap total stok karbon di padang lamun yaitu 97,92-98,49%. Stok karbon tertinggi juga berada di Pulau Nyamuk (Stasiun 1) yaitu sebesar 13248,29 ton C/ha. Pantai Pokemon Pulau Kemujan (Stasiun 2) dengan luas padang lamun 7,68 ha menambat total stok karbon paling tinggi, yakni 83003,14 ton C.

DEPTH ESTIMATION OF SHALLOW WATER USING MULTISPECTRAL SATELLITE IMAGERY SENTINEL-2A

Shallow water bathymetry estimation from remote sensing data has been increasing widespread, as an alternative to traditional bathymetry measurement that has disturbed by technical and logistic problem. Deriving bathymetry data from Sentinel 2A images, at visible wavelength (blue, green and red) 10 meter spatial resolution was carried out around the waters of the Kemujan Island Karimunjawa National Park Central Java. Amount of 1280 points data are used as training data sets and 854 points data as test data set produced from sounding. Dark Object Substraction (DOS) has been to correct atmospherically the Sentinel-2A images. Several algorithm has been applied to derive bathymetry data, including: linear transform, ratio transform and support vector machine (SVM). The highest correlation between depth prediction and observe resulted from SVM algorithm with a coefficient of determination (R2) 0.71 (training data) and 0.56 (test data). The assessment of the accuracy of the three methods using RMSE and MAE values, the SVM algorithm has the smallest value (&lt; 1 m). This indicates that the SVM algorithm has a high accuracy compared to the other two methods. The bathymetry map derived from Sentinel 2A imagery cannot be used as a reference for navigation.

Analyses of inter-class spectral separability and classification accuracy of benthic habitat mapping using multispectral image

Theoretically, spectral separability will greatly affect the accuracy of multispectral classification. This study aims to understand the relationship between the inter-class spectral separability and the accuracy of benthic habitat classification using a WorldView-2 multispectral image. The study area for this research is Kemujan Island, Jepara Regency, Central Java Province, Indonesia. The datasets used are sunglint-corrected bands, Principle Component Analysis (PCA)-derived bands, vegetation indices, and filter occurrence bands. Benthic habitat field data were obtained through a photo-transect survey technique and were used to construct nine levels of benthic habitat hierarchical classification schemes. We used maximum likelihood (ML) and random forest (RF) as the classification algorithms. Spectral separability was calculated using the Jeffries-Matusita separability analysis algorithm. The results from both RF and ML showed that the increased number of class pairs with spectral separability less than 1.0 (S<1.0) decreased the OA and an increased number of class pairs with S>1.0-1.9 increased the OA. Especially for scheme Level 1 with the greatest number of classes, an increased number of class pairs with S>1.9 to is required to improve the OA. This has proven that the spectral separability between classes does affect the accuracy of benthic habitat classification and there is a significant relationship between spectral separability and the accuracy of benthic habitat classification.

Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.