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.

This paper presents the first assessment of PlanetScope image for benthic habitat and seagrass species mapping in optically shallow water. PlanetScope image is equipped with ideal resolutions for benthic habitat and seagrass mapping including high-spatial resolution (3 m), high radiometric resolution (12-bit), sufficient water penetration bands (Visible-Near-infrared) and very high temporal resolution (almost daily), which distinguishes it from other high spatial resolution images. It is necessary to assess the accuracy of this ideal system in a real-world benthic habitat and seagrass species mapping application. The optically shallow water of Karimunjawa Islands was selected as the study area. Two PlanetScope images acquired on 17 May 2017 and 15 August 2017 were tested as a control for the consistency of PlanetScope image accuracy. Several treatments were applied to both PlanetScope images including atmospheric correction, sunglint correction, Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and Linear Spectral Unmixing (LSU). Per-pixel classification algorithms (including Maximum Likelihood – ML, Support Vector Machine – SVM, and Classification Tree Analysis – CTA) and Object-based Image Analysis (OBIA) were used to perform benthic habitat and seagrass species classification. Spectra-based classifications were also applied to classify seagrass species using seagrass species spectra as input endmember. The results indicated that PlanetScope images produced 47.13–50.00% overall accuracy (OA) for benthic habitat mapping consist of five classes (coral reefs, macroalgae, seagrass, bare substratum, dead coral) and 74.03–74.31% OA for seagrass species mapping consist of five seagrass species classes. The accuracy of PlanetScope images for benthic habitat and seagrass species mapping was comparable to other high spatial resolution images. The performance of PlanetScope images was also consistent, shown by the similar accuracy obtained from May and August image. The concern regarding PlanetScope image was the low Signal-to-Noise Ratio (SNR) over homogeneous areas such as optically deep water, which led to the failure of performing sunglint correction and obtaining higher accuracy. To conclude, with the very high temporal resolution, PlanetScope image is promising for monitoring the dynamics and changes of benthic habitat and seagrass species composition, and rapid assessment of extreme events impacts, especially in coastal areas with limited accessibility.

Among vegetated coastal habitats, mangrove forests are among the densest carbon pools. They store their organic carbon in the surrounding soil and thus the sequestered carbon stays in the sediment for a long time and cannot be easily returned to the atmosphere. Additionally, mangroves also provide various important ecosystem services in coastal areas and surroundings. Accordingly, it is important to understand the distribution of biomass carbon stock in mangrove habitats in a spatial and temporal context, not only to reduce CO₂ concentrations in the atmosphere, but also for their sustainability. The objectives of this research are to map the mangrove carbon stock and estimate the total biomass carbon stock sheltered by mangrove forests, with the Karimunjawa Islands as a study site, using the widely available passive remote sensing system ALOS AVNIR-2. The modelling and mapping of mangrove carbon stock incorporates the integration of image pixel values and mangroves field data via empirical modelling. Vegetation indices and PC bands at different levels of radiometric corrections were all used as the input in the mangrove carbon stock modelling so that the effectiveness and sensitivity of different image transformations to particular radiometric correction levels could be analysed and understood. Afterward, the accuracy and effectiveness of each mangrove carbon stock-mapping routine was compared and evaluated. The accuracy of the best mangrove above-ground carbon stock (AGC) map modelled from vegetation index is 77.1% (EVI1, SE 5.89 kg C m⁻²), and for mangrove below-ground carbon stock (BGC) it is 60.0% (GEMI, SE 2.54 kg C m⁻²). The mangrove carbon stock map from ALOS AVNIR-2 PC bands showed a maximum accuracy of 77.8% (PC2, SE 5.71 kg C m⁻²) and 60.8% (PC2, SE 2.48 kg C m⁻²) for AGC and BGC respectively. From the resulting maps, the Karimunjawa Islands are estimated to shelter 96,482 tonnes C of mangroves AGC with a mean value of 21.64 kg C m⁻² and 24,064 tonnes C of mangroves BGC with a mean value of 5.39 kg C m⁻². Potentially, there are approximately 120,546 tonnes C of mangrove biomass carbon stock in the Karimunjawa Islands. Remote-sensing reflectance can successfully model mangrove carbon stock based on the relationship between mangrove canopy properties, represented by leaf area index (LAI) and the tree or root biomass carbon stock. The accuracy of the mangrove carbon stock map is subject to errors, which are sourced mainly from: (1) the absence of a species-specific biomass allometric equation for several species present in the study area; (2) the generalized standard conversion value of mangrove biomass to mangrove carbon stock; (3) the relationship between mangrove reflectance and mangrove LAI; (4) the relationship between mangrove reflectance and above-ground mangrove biomass and carbon stock due to its relationship with LAI; (5) the relationship between mangrove LAI and mangrove below-ground parts; (6) the inability to perform mangrove carbon stock modelling at the species level due to the complexities of the mangrove forest in the study area; (7) background reflectance and atmospheric path radiance that could not be completely minimized using image radiometric corrections and transformations; and (8) spatial displacement between the actual location of the mangrove forest in the field and the corresponding pixel in the image. The availability of mangrove biomass carbon stock maps is beneficial for carrying out various management activities, and is also very important for the resilience of mangroves to changing environments.

This research address several issues related to mangrove above-ground carbon stock (AGC) mapping using the integration of remote-sensing images and field data. These issues are (1) remote-sensing image availability for specific mangrove AGC mapping scale and precision, (2) the impact on mangrove AGC modelling due to the difference between images spatial resolution and plot size of field mangrove AGC measurement, which follow the standardized procedure and not specially developed to be integrated with remote-sensing data, and (3) the accuracy of performing mangrove AGC mapping using image at different spatial resolutions using similar field size mangrove AGC data. Four multispectral data sets, namely Worldview-2, Advanced Land Observation System Advanced Visible and Near-Infrared Radiometer-2 (ALOS AVNIR-2), Advanced Spectral and Thermal Radiometer (ASTER) Visible Near-Infrared (VNIR) and Landsat 8 Operational Land Imager (OLI), and a Hyperion hyperspectral image were tested for their performance for mangrove AGC mapping. These images represent various spatial, spectral, and radiometric resolutions of remote-sensing data available to date. The mapping was performed using their original spatial resolution, and for Worldview-2 the mapping was also conducted using 10 m spatial resolution. Image radiometric corrections, vegetation indices, principal component analysis and minimum noise fraction were applied to each image. These were used as input in the empirical modelling of mangrove AGC. The results indicate that (1) it is not possible to perform empirical modelling of mangrove AGC using image with sub-canopy spatial resolution, (2) decreasing the spatial resolution may be beneficial to obtaining a significant correlation with mangrove AGC, and (3) it is possible to perform empirical modelling of mangrove AGC mapping using field data not specially intended to be integrated with remote-sensing data, along with some adjustments. This opens up the possibility of utilizing the available field mangrove AGC data collected by stakeholders, that is, government institutions, NGOs, academics, private sector, to assist mangrove AGC mapping across the nation.

Coastal regions are one of the most vulnerable areas to the effects of global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, the socioeconomic importance of coastal regions where the most populated cities are located is high. However, shoreline changes in Indonesia are relatively understudied. In particular, detailed monitoring with remote sensing data is lacking despite the abundance of datasets and the availability of easily accessible cloud computing platforms such as the Google Earth Engine that are able to perform multi-temporal and multi-sensor mapping. Our study aimed to assess shoreline changes in East Java Province Indonesia from 2000 to 2019 using variables derived from a multi-sensor combination of optical remote sensing data (Landsat-7 ETM and Landsat-8 OLI) and radar data (ALOS Palsar and Sentinel-1 data). Random forest and GMO maximum entropy (GMO-Maxent) accuracy was assessed for the classification of land and water, and the land polygons from the best algorithm were used for deriving shorelines. In addition, shoreline changes were quantified using Digital Shoreline Analysis System (DSAS). Our results showed that coastal accretion is more profound than coastal erosion in East Java Province with average rates of change of +4.12 (end point rate, EPR) and +4.26 m/year (weighted linear rate, WLR) from 2000 to 2019. In addition, some parts of the shorelines in the study area experienced massive changes, especially in the deltas of the Bengawan Solo and Brantas/Porong river with rates of change (EPR) between −87.44 to +89.65 and −18.98 to +111.75 m/year, respectively. In the study areas, coastal erosion happened mostly in the mangrove and aquaculture areas, while the accreted areas were used mostly as aquaculture and mangrove areas. The massive shoreline changes in this area require better monitoring to mitigate the potential risks of coastal erosion and to better manage coastal sedimentation.