The coastal area of Special Region of Yogyakarta (DIY) will experience rapid regional development after the functioning of the Southern Cross Roadway (JJLS) and New Yogyakarta International Airport (NYIA). In order to increase Regional Original Income (PAD) from the tourism sector, these developments must be welcomed with developments in the field of coastal tourism, especially in the form of ecotourism. The process of developing mangrove ecosystems for ecotourism in DIY must be synchronized with applicable government policies, especially policies that regulate spatial planning and development policies in the tourism sector. This study aims to analyze the existing conditions of mangrove ecosystems in the coastal areas of the Special Region of Yogyakarta. Mangrove mapping was carried out in four different locations, namely at Baros Beach, Kretek, Bantul Regency and Jangkaran Mangrove Forest Area, Kulonprogo Regency. Fieldwork consists of small format aerial photo data acquisition, Ground Control Point (GCP) and Independent Check Point (ICP) collection using Geodetic GPS, along with observations of mangrove conditions in the field. Post-field stages consist of various types of processes regarding processing field data and analysis, including orthophoto mosaics, accuracy calculations, and acquisitions of mangrove information such as area and distribution. The main benefit of this research is the availability of basic data on the existing conditions of mangrove areas in the Special Province of Yogyakarta. This basic data will then be used as a reference for managing the coastal environment and improving the economic conditions of the community through the development of mangrove ecotourism-based tourism. it also supports disaster risk reduction efforts through the development of mangrove ecosystems.
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.
Coral reef live percent cover (LPC) mapping has always been a challenging application for remote-sensing. The adoption of machine-learning algorithm in remote-sensing has opened-up the possibility of mapping coral reef at higher accuracy. This paper presents the application of machine-learning regression in the empirical modeling of coral reef LPC mapping. Stepwise regression, Support Vector Machine (SVM) regression, and Random Forest (RF) regression were used model the percentage of live coral cover in optically shallow water of Parang Island, Central Java, Indonesia using field photo-transect data to train the PlanetScope image. PlanetScope multispectral bands were transformed into water column corrected bands, Principle Component bands, and Cooccurrence texture analysis bands to be used as predictors in the regression process. The results indicate that the accuracy of machine learning algorithm to map coral reef LPC is relatively low due to the radiometric quality issue in the PlanetScope image (RMSE = 15.43%). We could not yet fairly justify the performance of machine learning algorithm until we applied the algorithms in other images.
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