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 Procurement of Public Green Open Space (RTH) in DKI Jakarta Province is carried out by the DKI Jakarta Forestry Service based on the land status of the BPN. The procurement of green open space is passive based on requests from the community. The DKI Jakarta Forestry Service data shows West Jakarta's public green open space has only fulfilled 8.8%. This study aims to assist stakeholders in the procurement of green open space in terms of ease of procurement. The ease aspect is compiled from land-use parameters, BPN land status, spatial pattern zoning, SIPPT, RTH Assets, and raw rice fields. The analysis results show that many areas included in the green zoning in West Jakarta have turned the function of land into built-up land, making it difficult for the local government to acquire land. This research found alternative lands with existing non-built land use conditions and clear land status and potential spatial pattern zoning targeted as green open space land acquisition targets. The analysis results show that from 4071 plots of land, there are 784 plots of land that are very potential with 179 ha. For potential land, there are 3234 plots of land with an area of 301 Ha and 53 plots of land classified as standard with an area of 2.4 Ha. Land with great potential can be used as a procurement priority for the relevant local government in the procurement of green open space in terms of the ease of procurement aspect.Keywords: Green Open Space, GIS, Green Open Space Potential
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