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Mangrove ecosystems at the estuary of Teleng River, Pacitan Bay, call for preservation, protection, and development as a natural vegetative defense against tsunami impacts. Limited availability of potential land for mangrove growth poses a challenge to sustainable mangrove management. To anticipate land acquisition for another use, it is necessary to study land suitability for mangrove and mangrove conservation priorities in the Pacitan Bay. The land suitability parameters consisted of landform, slope, soil texture, and tidal fluctuation, while the mangrove conservation priority was determined based on several aspects: mangrove land cover reduction, mangrove damage level, beach ridge and riparian zone, and land suitability. The results indicate that lands with high potential for mangroves are distributed along Teleng and Grindulu Rivers but not precisely at the estuary. Based on the suitability results, highly potential and potential lands are proposed for mangrove planting, while existing mangroves are for protection and preservation zones, especially on the sides where relatively young mangroves grow for their roots are not strong enough to withstand river currents.

Shoreline dynamics naturally occur in coastal areas, and over time, are also influenced by anthropogenic processes taking place both on-site and upstream. Bordering the Indian Ocean, the coastal area of Bantul Regency in the Special Region of Yogyakarta is faced with typical strong and high waves that induce changes in its shoreline dynamics and activities. Consequently, tourism, a leading economic sector in the area, often needs to adjust to such changes. Here, shorelines were extracted from the spatial data of time-series Sentinel 2A imagery (2015, 2016, 2017, 2018, 2019, and 2020) using water index transformation, MNDWI, while the land cover changes were analyzed using the Decision Tree classification. Based on the results, accretion appeared most significant from 2016 to 2017, creating an additional 22.32 ha. In contrast, shoreline change from 2019 until 2020 indicated the most severe abrasion that led to a loss of 34.89 ha. The highest rate of landward shoreline change was 41.58 m/year.

The growth of human occupations in coastal areas and climate change impact have changed the dynamics of seagrass cover and accelerated the damage to coral reefs globally. For these reasons, coastal management measures need to be developed and renewed to preserve the state of seagrass beds and coral reefs. An example includes the improvement of spatial and multitemporal analyses. This study sought to analyze changes in seagrass cover and damages to coral reefs in Gili Sumber Kima, Buleleng Regency, Bali based on multitemporal Sentinel 2A-MSI imagery. The algorithms of a machine learning, Random Forest (RF), and a Support Vector Machine (SVM) were used to classify the benthic habitats (seagrass beds and coral reefs). Also, a change detection analysis was performed to identify the pattern and the extent to which seagrass beds had changed. The multispectral classification of, particularly, coral reefs was used to explain the condition of this benthic habitat. The results showed +-70% to +-83% accuracies of estimated seagrass cover, and the change detection analysis revealed three directions of change, namely an increase of 27.9 ha, a decrease by 86 ha, and a preserved state in 157 ha of seagrass cover. The product of coral reefs mapping had an accuracy of 42%, and the coral reefs in Gili Sumber Kima were split almost equally between the good (1505 ha) and damaged ones (1397 ha). With the spatial information on seagrass beds and coral reefs in every region, the ecological functions of the coast can be assessed more straightforwardly and appropriately incorporated as the basis for monitoring the dynamics of resources and coastal area management.

Biodiversity of benthic habitats is among the highest of all ecological communities. This study was conducted to model benthic habitat biodiversity indices using a remote sensing approach in optica...

There are not many discussion or previous works that specifically address the issue of integrating small field plot size (1 m2) and image at different spatial resolutions in the seagrass percent cover (PC) mapping using remote sensing. This is important to determine the spatial resolution of image that can still be effectively integrated with 1 × 1 m plot size field data. This research aimed at assessing the accuracy and spatial distribution of seagrass PC map modelled from image at different spatial resolutions, using seagrass field data measure at 1 m2 plot size. Two multispectral satellite images namely WorldView-2 (2 m) and PlanetScope (3 m) were used for this research and simulated to 5 m, 10 m, 15 m, and 30 m. Kemujan and Lombok Island were selected as the study area, and seagrass beds in each island have different characteristics. Machine learning random forest regression was used to perform empirical modelling and the mapping accuracy was assessed using independent seagrass PC samples. The results indicated that 1 m2 plot size is still effective to be integrated with image up to 30 m spatial resolution, where the RMSE and overall seagrass PC pattern is relatively similar but the level of information precision is reduced at lower spatial resolution. Furthermore, we found out that the main factor that strongly determines the success use of 1 m2 plot size and the mapping accuracy is the configuration of the seagrass bed in the study area. Seagrass PC in the more continuous seagrass bed can be mapped with higher accuracy than in patchy seagrass bed.

Mapping of the distribution of individual seagrass species is essential for any attempts to manage seagrass ecosystems. It is therefore important to understand how the spectra of different ...

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 b...

The green mussel cultivation by fishermen in Pasaran Island is influenced by nature and uses simple technology without regarding water conditions. In fact, site selection considering the water condition is one of the important factors in determining the success of quality green mussel cultivation. High market demand but not supported by modern technology, good marketing strategies, price stability, and appropriate cultivation site can reduce the production of green mussels. This research was conducted to determine the optimal location for the green mussel cultivation around Pasaran Island, in Lampung Bay and to formulate a management strategy based on the map. Modeling parameters measured on the field include depth, salinity, pH, temperature, current velocity, dissolved oxygen, water clarity, and chlorophyll-a. Data processing methods include inverse distance weighted (IDW) interpolations and fuzzy overlay. The study result in the form of raster-based physical water suitability maps for green mussel cultivation are intended to refine the uncertainties in the vector-based data presentation on water quality data so that it is expected to provide additional information to avoid a less optimal cultivation environment so it maintain the quality of green mussel products and support to accelerate aquaculture production raising program (minapolitan) in Lampung Bay.

Mangrove ecosystem is one of coastal resources that have many benefits for coastal communities. The mangrove ecosystem has very high economic and ecological functions if it is developed and preserved properly. Nowadays, many mangrove ecosystems are threatened by human activities. It is necessary to preserve and to develop the mangrove ecosystems to avoid the impact of human activity and to increase their usefulness. In the process of developing the mangrove ecosystems, detailed data are required to provide a comprehensive overview of the environmental and physical conditions of the mangrove ecosystems. The study aims at identifying and making the inventory of the existing condition of the mangrove ecosystems related to mangrove cover and biodiversity. The data are collected using aerial photography and UAVs, observation and field measurement. The data inventory making is the first step in the process of developing and preserving the mangrove ecosystems. It finds that the use of the UAVs for the mangrove ecosystem data inventory making can give high accuracy data. The mangrove cover can easily be identified using image segmentation or onscreen digitization analysis. Finally, the UAVs can be a promising technology in the management and the monitoring of the mangrove ecosystems.

We presented the use of Random Forest (RF) classification to map benthic habitat in two small islands using different parameter scenario, which are the number of trees, the function to determine the number of randomly selected features, and the function to determine the impurity in a node. The results indicated that RF is a consistent performer with accuracy difference of 1.3% and 5.8% for the two Islands. Furthermore, different scenarios also producing similar benthic habitat spatial distribution and misclassification pattern.

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.

The availability of frequently updated maps has always been the ultimate goal of remote sensing in benthic habitat data acquisition. Although the techniques offer cost and time effectiveness, the lack of consistency of inputted images can cause issues in successfully performing accurate multi-temporal mapping with minimum effort. This research aimed to assess the consistency of Sentinel-2 images for benthic habitat mapping in the waters of Labuan Bajo, East Nusa Tenggara, Indonesia. We employed two approaches: 1) assessing the consistency of Sentinel-2 image used to map four classes of benthic habitat from 2017 to 2019 and 2) an in-depth analysis of three images in May 2019 to assess the consistency of four-class benthic habitat mapping (coral reef, seagrass, macroalgae and bare substratum). This research incorporated atmospheric, sunglint and water column corrections and four classification algorithms: Isodata, Random Forest, Support Vector Machine, and Maximum Likelihood. The consistency of Sentinel-2 images was assessed using overall accuracy (OA), agreement percentage (AP) and total classification performance (TCP). Our results indicate that Sentinel-2 images are reliable enough for accurate benthic habitat mapping with OA >80% and consistent with agreement >80% in both approaches given the right image conditions, which are minimum cloud cover, haze, and sunglint. These factors strongly contribute to the consistency of the resulting benthic habitat classification from the Sentinel-2 images. Also, to maximize the rich image availability in the Sentinel-2 archive database, we suggest selecting images with minimal atmospheric and sunglint disturbances instead of performing image corrections that may introduce noise, leading to lower accuracy and consistency. Finally, we encourage the use of Sentinel-2 images in benthic habitat monitoring since the long-term plan of the Sentinel-2 mission guarantees the availability of these datasets.

Ecotourism is a practice of utilizing natural ecosystems to support education and conservation efforts. In the Special Region of Yogyakarta (SRY), Indonesia, two mangrove ecosystems have been designated as protected areas and ecotourism sites. However, it is undeniable that the need for space for infrastructure development has been detrimental to these areas. This research set out to identify changes occurring nearby mangrove ecosystems and the impacts they would potentially have in the future. It used a triangulation method that combined secondary data analysis, observation, and primary data collection through in-depth interviews. The observed coastal environments experienced, among others, extensive conversion of agricultural land for the Yogyakarta International Airport (YIA) construction, increased tourism growth and pressures, and varying artificial modifications in parts of mangrove ecosystems utilized for mass tourism; all of which could disrupt sustainability and reduce the ecological functions of mangroves. Intensive aquaculture and iron sand mining in the vicinity could also negatively affect the mangrove ecosystem and ecotourism. Within a certain period, these anthropogenic activities are most likely to pose significant threats to the preservation of mangrove ecotourism. Regional spatial plans are an example of instruments required to regulate sustainable spatial planning and protect mangrove ecosystems.

Coral reefs and seagrass are critical coastal resources due to their role in the ecosystem benefits for the coastal environment in terms of biodiversity, coastal protection, fisheries, and tourism. It is therefore important to preserve and protect these species. Coral and seagrass percent cover mapping is a simple approach to assess coral and seagrass condition. The application of remote sensing of coral and seagrass percent cover mapping is very challenging with respect to performance and accuracy. This research aims to utilize remote sensing data for coral and seagrass percent cover mapping. Linear and machine learning regressions (RF and SVM) were used to develop a coral and seagrass percent cover model from a Sentinel-2 MSI images. The Sentinel-2 MSI images were transformed into deglint, water column (DII), principal component, and mean texture analysis as input bands for the model. The results showed that coral percent cover mapping accuracy is relatively low (RMSE = ±17%) due to various problems, limitations, and an inaccurate model, whereas the results of the seagrass percent cover map had higher accuracy, with RMSE ±11%. The results obtained indicate that the seagrass percent cover map is suitable for use as basic information to support coastal management. However, the coral percent cover map is not an optimal information source due to its low accuracy.

Mangrove ecosystem is a natural resource that can be beneficial for coastal community. One of its benefits is it can be utilized as an ecotourism spot such as Baros Mangrove Conservation Area in Kretek, Bantul. Hence, mangrove ecosystem must be conserved or expanded if possible. The first step to conserving mangrove ecosystem is to map its existing condition. UAV can be used to acquire a high-resolution imagery quickly. With modified near-infrared (NIR) sensor from a pocket camera can produce three band i.e. NIR, green, and blue that can be used to calculate several vegetation indices such as Simple Ration (SR), Normalized Difference Vegetation Index (NDVI), and Enhanced Difference Vegetation Index (ENDVI). Those bands and vegetation indices then used to map mangrove using Geographic Object-Based Image Analysis approach. The result indicated that high-resolution imagery from modified NIR camera sensor can be used to detect mangrove and distinguish it from other object using supervised classification. The digitally classified imagery then compared to manually digitized mangrove map to calculate the total error of the map.

Perairan laut Lampung sebagai bagian kecil dari ekosistem terumbu karang Indonesia terindikasi memiliki tren penurunan kualitas karena aktivitas pelayaran dan pariwisata yang ekstensif khususnya di Pulau Pahawang. Kontrol kondisi terumbu karang pada wilayah ini menjadi kegiatan vital dalam rangkaian konservasi sumber daya laut. Sayangnya, pemetaan kesehatan terumbu karang memerlukan survei detail yang memakan banyak waktu, biaya, dan tenaga. Citra sebagai produk data penginderaan jauh hadir sebagai solusi monitoring terumbu karang secara cepat, murah, dan dalam jangkauan wilayah yang relatif luas. Tujuan dari penelitian ini adalah untuk memetakan kesehatan terumbu karang melalui citra WorldView-2 (WV-2) serta menguji akurasi peta yang dihasilkan. Metode yang digunakan untuk memetakan kesehatan terumbu karang adalah transformasi nilai pixel pada band-band WV-2 menjadi nilai original objek dengan urutan: 1) koreksi atmosfer (Top of Atmospheric Reflectance), 2) koreksi kilap air (sun glint), dan 3) koreksi kolom air (metode lyzenga) menghasilkan 15 band DII (depth invariant bottom index). Kelima belas band DII tersebut diubah menjadi nilai kesehatan terumbu karang dengan cara regresi antara nilai pixel pada band DII dengan nilai rasio kesehatan terumbu karang aktual yang diperoleh dari proses kalkulasi acak titik foto transek di lapangan. Tiga tipe regresi (linier, eksponen, dan polinomial) dilakukan untuk melihat persamaan terbaik yang bisa digunakan untuk mentransformasi nilai pixel ke nilai kesehatan terumbu karang. Persamaan terbaik kemudian diimplementasikan menjadi model kesehatan terumbu karang untuk kemudian diuji akurasi menggunakan metode Standard Error Estimation. Hasil terbaik diperoleh pada regresi antara rasio kesehatan terumbu karang dengan band DII Coastal Red-Edge dengan koefisien determinasi (R2) sebesar 0,6553 dan akurasi pemetaan sebesar 70,191%. Nilai tersebut menunjukan bahwa citra WV-2 memiliki potensi untuk menjadi instrumen monitoring ekosistem marine yang layak.Kata Kunci: Depth Invariant Bottom Index, Kesehatan Terumbu Karang, Lyzenga, Regresi, WorldView-2