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Map projection is required for modeling the earth from sphere into flat form. Out of so many types of projection systems, Universal Transverse Mercator (UTM) is the most common map projection system used in Indonesia. The concept of Mercator projection system is to maintain the angle but cause distortion on the area and distance. This will become problem if it is used for calculating an area that require minimum distortion, especially on a large scale. This study aims to find out the effect of the projection system for any scale to the area, and find out the best projections system in Indonesia. In this paper, the area was calculated using 72 projection systems with various scale using MATLAB software. The reference area that considered true was the polygon area of an ellipsoid so the difference between area of ellipsoid and projection can be known. The projection systems that give minimum distortion are the most optimum result. Based on the study, the most suitable projection system for calculating the area with minimal distortion is Collignon for 1: 250,000 scale and 1: 50,000, Eckert II projection system for 1: 25,000 scale and Equal-area Conic Albers Standard projection system for scale 1: 5,000.

Timely and accurate bathymetry information is needed to support an effective policy on utilization and management of coastal natural resources. Satellite derived bathymetry (SDB) has been widely considered as an advanced and low-cost method for shallow water depth estimation. This is due to the availability of multi-temporal and multi-resolution satellite data. This study focuses on evaluating the accuracy of satellite derived bathymetry derived from multispectral images recorded by various sensors with various spatial resolution. The study area is located in a small island nearby Morotai Island, Indonesia. Four SDB models were compared. The implementation of the SDB model was carried out by combining echo-sounding measurements and the reflectance of blue, green, red, and near infrared bands of three satellite images (World View 2, Sentinel 2A and Landsat 8). Our findings reveal that all three satellite images performed well in assessing SDB at various spatial and spectral resolution, however, the use of high-resolution imagery did not always improve accuracy, for example when using SVM (Support Vector Machine). When using RF (Random Forest), Sentinel 2A produced the best accuracy and when using GAM (Generalized Additive Model), the most feasible result was generated only by using WorldView 2 image. In all cases, RF performed well and provided the most accurate SDB prediction.

AbstrakPembuatan Peta RBI skala 1:5.000 membutuhkan waktu yang lama, khususnya untuk pembuatan layer kontur. Layer kontur bisa didapatkan dari data hasil ekstraksi foto udara dan data Light Detection and Ranging (LiDAR). Sekarang ini, teknologi LiDAR lebih diandalkan untuk membuat Data Surface Model (DSM). Dari DSM dilakukan proses ekstrasi data untuk mendapatkan data Digital Terrain Model (DTM) atau Digital Elevation Model (DEM) yang prosesnya lebih cepat dan membutuhkan biaya yang relatif rendah. Metode filtering yang digunakan adalah metode Simple Morphological Filtering (SMRF) dengan memasukkan nilai parameter cell size, slope, windows, elevation threshold dan scalling factor. Hasil Cohen's kappa rata-rata menunjukkan indikator DTM dalam kondisi baik, yang artinya dengan menggunakan metode SMRF, nilai kappa berada diantara 0,4-0,7. Smoothing filter dilakukan untuk menghilangkan sel kosong/ sel tanpa data. DTM yang dihasilkan dibandingkan dengan data validasi lapangan. Root Mean Square Error (RMSE) yang diperoleh berkisar antara 0,621-0,930 dan nilai Linear Error 90% (LE90) yang diperoleh berkisar antara 1,025-1,605. Hasil penelitian ini menunjukkan nilai RMSE dan LE90 tersebut memenuhi ketelitian vertikal peta skala 1: 5.000 dan masuk dalam kelas 2 dan 3 sesuai Peraturan BIG No.6 Tahun 2018 mengenai perubahan atas Perka BIG No.15 Tahun 2014 tentang Pedoman Teknis Ketelitian Peta Dasar. Abstract [Title: Digital Terrain Model (DTM) Generation From Light Detection and Ranging (LiDAR) Data By Using Simple Morphological]The production of Indonesian Topographic Map (RBI) in the scale of 1:5000 takes a long time, especially in the making of contour layer. Contour layer can be extracted from both Aerial Photogrammetry and LiDAR data. Nowadays, LiDAR technology is getting more reliable for DSM. From DSM can be exctracted to get DTM/DEM. DTM/ DEM generation because of its shorter processing time and relatively low cost. The filtering method used in this research is Simple Morphological Filtering (SMRF) which input parameters are: cell size, slope, windows, elevation threshold and scaling factor. Average value of Cohen's kappa is in the range of 0.4-07 which means that the generated DTM is good. Because of the existence of null values in the generated DTM, the smoothing filters have been applied. The extracted DTM then be compared to in situ data. The RMSE ranged from 0,621 to 0,930 m and LE90 about 1,025-1,605. Those RMSE and LE90 values satisfied the vertical accuracy of the 1: 5000 topographic map and graded as the second and third class in accordance to BIG Regulation No.6 of 2018 as revision of Perka BIG No.15 of 2014 focusing on Technical guidelines for Basic Map Accuracy.