Sarip Hidayat

Coral reefs are one of ecosystems that have high biodiversity and productivity. The condition of coral reefs in Seribu Islands is currently being threatened by various marine industry development activities. This study aims to determine coral reef health in Seribu Islands, DKI Jakarta. The study was conducted at eight observation stations in Kepulauan Seribu Marine National Park area on 7-10 March 2022 (First Monitoring) and 31 May until 3 June 2022 (Second Monitoring). Coral reef data collecting were using Underwater Photo Transect (UPT) method. Reef fish data collecting were using Underwater Visual Census (UVC) method. While for macrobenthos data collecting were using the Belt Transect method. The coral reef health index was determined based on two main components, there are benthic component and reef fish component. Overall, coral reef health from eight observation stations during two monitoring sessions had high recovery potential, although some stations had low coral cover and low target fish biomass. In addition, several species of macrobenthos are bioindicators that can contribute to purifying waters.

The Trans-Sulawesi railway construction runs for 144 km from South to North of the South Sulawesi's east coast. The railway line mostly was built on an embankment over agricultural flat, while on the hill, the railway line built in an incised slope. These actions essentially alter the surrounding landform. The changing landform could have its effect on how the surface runoff flow during the rainy season. In this study, we explore the potential of satellite-derived digital elevation models (DEMs) on detecting the changed landform over the Takkalasi Watershed. We employed the SPOT-7's tri-stereo acquisition mode, acquired on 17th April 2020. In these preliminary results, we compare the DEM results from each pair combination as well as the combination of all three pairs. The use of different tie-points (TP) and ground control points (GCP) were also investigated. Fast Fourier Phase Transformation algorithm were employed for the TP extraction, while the semi-global matching algorithm were employed for the epipolar DEM extractions. The produced DEMs then validated using the reference data taken from dGPS, UAV DEM, as well as the ICESat-2 ATL08 data. Our initial results shows that the best DEM generated is produced by the forward and backward stereo pair using only nine (9) GCP with the average vertical Root Mean Square Error (RMSE) of 4.4 meter. At 3 m ground sampling distance, the incised hill is easier to detect on SPOT-7 DEM compared to the embankment.

Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they require near-real-time geospatial information on palm stands. We developed a semi-automated classification scheme for mapping sago palm using machine learning within an object-based image analysis framework with Pleiades-1A imagery. In addition to spectral information, arithmetic, geometric, and textural features were employed to enhance the classification accuracy. Recursive feature elimination was applied to samples to rank the importance of 26 input features. A support vector machine (SVM) was used to perform classifications and resulted in the highest overall accuracy of 85.00% after inclusion of the eight most important features, including three spectral features, three arithmetic features, and two textural features. The SVM classifier showed normal fitting up to the eighth most important feature. According to the McNemar test results, using the top seven to 14 features provided a better classification accuracy. The significance of this research is the revelation of the most important features in recognizing sago palm among other similar tree species.

Ekstraksi informasi penutup/penggunaan lahan area luas seperti di Pulau Kalimantan umumnya terkendala oleh variasi nilai spektral di beberapa area yang berbeda, serta sulitnya mendapatkan hasil perekaman yang bebas dari awan. Klasifikasi visual, meski memberikan hasil yang baik, merupakan pekerjaan yang membutuhkan waktu dan tenaga yang relatif banyak, belum lagi potensi pengaruh subjektifitas interpreter. OBIA yang sudah mulai diterima dan banyak digunakan dalam klasifikasi digital bisa menjadi alternatif tambahan selain interpretasi visual maupun analisis digital berbasis piksel konvensional. Penelitian ini menggunakan data Landsat 8 OLI orthorectified yang telah melalui proses mosaicking dan cloud masking untuk mendapatkan citra satu Pulau Kalimantan yang bebas awan. Layer NDVI, MNDWI, NDBI, BSI, SAVI, dan Built-up Index kemudian diturunkan dari data Citra Landsat untuk dimasukkan ke dalam tahap segmentasi dan klasifikasi. Segmentasi dilakukan dengan menggunakan algoritma Multiresolution Segmentation dan Spectral Difference Segmentation. Klasifikasi dilakukan dengan menggunakan serangkaian multilevel threshold yang disusun dalam bentuk decision tree. Empat belas kelas penutup/penggunaan lahan kemudian berhasil diekstrak, dengan nilai overall accuracy 77,65%. Metode yang digunakan juga menunjukkan akurasi yang tinggi untuk kelas hutan lahan kering, perkebunan, kebun campur dan semak belukar dengan nilai akurasi di atas 80%. Hasil ini menunjukkan bahwa metode ini bisa dijadikan sebagai alternatif dalam mengidentifikasi dan mengekstrak informasi tutupan vegetasi untuk kegiatan pemetaan area luas. Kata kunci: OBIA, area luas, perubahan penutupan/penggunaan lahan, citra landsat, decision tree ABSTRACT Large area landuse/landcover extraction such as on the island of Borneo using Landsat 8 data are generally constrained by the great variations in the spectral values, due to the vast use of different scenes with different acquisition time, as well as the fact that it almost impossible to get a completely cloud-free image of the whole island. Visual classification, despite the good results, is a labour-intensif job that requires a huge time and effort, not to mention the potential influence of interpreter’s subjectivity. While the pixel based digital classification suffer from“salt pepper” effect as well as almost exclusively relied on spectral information, OBIA has been accepted and widely used in digital classification as an alternative for the visual interpretation and conventional pixel-based classification, with its ability to use additional contextual information. This study aimed to used OBIA method on Landsat 8 OLI cloudfree mosaic dataset for the whole Borneo region to create a landuse/landcover map using both spectral and contextual information, as well as ancilarry DEM data. Additional layers of NDVI, MNDWI, NDBI, BSI, SAVI, and Built-up Index were then derived from Landsat data to be used in the segmentation and classification process. Multiresolution Segmentation algorithm and Spectral Difference Segmentation were then conducted respectively. The classification wasdone by using a series of multilevel crisp classification using thresholds in the form of a decision tree. Fourteen of landuse/landcover classes were then successfully extracted, with a value of 77.65% on overall accuracy. The proposed method showed reasonable high accuracy for the forest, plantation, mixed garden and shrub classes with the accuracy all above 80%. These results indicate that the proposed method can be used as an alternative to identify and extract information related to vegetation cover for large areamapping activities.Keywords: OBIA, large area, land use cover change (LULC), landsat image, decision tree

Orthorectification in the study conducted on 14 scene multispectral imagery SPOT4 SPOT2 and West Java. The composition of the scene to form a wide block with no gaps, making it possible to do block triangulation. This triangulation is used to determine the relationship between the satellite image and the earth. The information generated from the triangulation will be used as input during orthorectification. Ground control points (GCP) were taken from the data vector Indonesian Topographic Map 1:25,000 scale and elevation data extracted from the DEM derived from contour maps RBI data is the same. Triangulation blocks performed with various configurations GCP and tie points (TP), the minimal configuration, just along perimeter blocks, regularly spaced regularly spaced half scene and a third scene. Each configuration is lowered into several configurations based on the use of TP. Triangulation accuracy result was tested with 244 check points (CP) that spread in the block configuration can produce a minimum GCP accuracy RMSE (Root Mean Square Error) 1.25 pixels if equipped with a TP meeting. GCP configuration on the perimeter of the block is not recommended for use if it is not equipped with TP. Highest accuracy resulting from configuration GCP/TP per a third scene with RMSE 0.98 pixels. Accuracy will be determined by the distribution and the number of GCP and the use of TP. Best accuracy of this research is suitable for medium-scale map updates 1:65.000.