IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES

The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands

ESTIMASI DAERAH POTENSI PENANGKAPAN IKAN BERDASARKAN PENDEKATAN KESESUAIAN AREA THERMAL FRONT DAN AREA MESOTROPIK

Research in Potential Fishing Zone (PFZ) has undergone many developments, including parameter suitability selection. The thermal front has become the primary parameter input of ZPPI (LAPAN's PFZ). The accuracy of the thermal front parameter to predict PFZ cannot be known with certainty because of the radius between ZPPI with fishing areas, so it is necessary to develop parameters to support the thermal front. The thermal front described the meeting area of two water masses with different temperature characteristics associated with high nutrients (chlorophyll-a) and indicate an upwelling's appearance. This study aims to determine ZPPI by approaching the thermal front and mesotrophic area's matching area (chlorophyll-a concentration 0.2-0.5 mg/m3). Chlorophyll-a and sea surface temperature data for thermal fronts detection are derived from Aqua MODIS satellite on Google Earth Engine (GEE). The matching area's approach between the thermal front and mesotrophic area is used in the analysis of ZPPI. The results show thermal front and mesotrophic area on WPPNRI 715 have a variation seasonally where December appears like the peak event. The two parameters are distributed evenly from coastal areas to high seas. This method generates thermal fronts that have more than 60.3% matching with the mesotrophic area where the amount is acceptable due to has more than 50% amount of moderate ZPPI. The accuracy improvement in ZPPI both on the coast and open sea can be determined through this approach.Zona potensi penangkapan ikan (ZPPI) telah mengalami banyak pengembangan salah satunya adalah pemilihan kesesuaian parameter. Thermal front telah menjadi parameter utama dalam input informasi ZPPI LAPAN. Akurasi informasi thermal front yang digunakan sebagai input ZPPI tidak dapat diketahui dengan pasti signifikansinya karena faktor jarak informasi titik ZPPI terhadap lokasi tangkapan sehingga diperlukan pengembangan parameter untuk mendukung informasi thermal front tersebut. Thermal front merupakan daerah pertemuan dua massa air yang memiliki karakteristik suhu yang berbeda dan mengindikasikan kemunculan upwelling. Penelitian ini bertujuan untuk menentukan daerah potensi penangkapan ikan dengan pendekatan kesesuaian area thermal front dengan area mesotropik (konsentrasi klorofil-a 0,2-0,5 mg/m3). Data klorofil-a untuk deteksi area mesotropik dan suhu permukaan laut untuk thermal front berasal dari satelit Aqua MODIS di Google Earth Engine (GEE). Metode kesesuaian thermal front terhadap area mesotropik digunakan dalam analisis daerah potensi penangkapan ikan. Hasil penelitian menunjukkan bahwa thermal front dan area mesotropik di WPPNRI 715 memiliki variasi terhadap musim, dengan bulan Desember sebagai puncak kejadian. Kedua parameter tersebut terdistribusi secara merata mulai dari area pesisir hingga laut lepas. Dari metode ini didapatkan thermal front yang memiliki kesesuaian area (bertampalan) terhadap area mesotropik sebesar 60,3%. Selain itu kelas ZPPI dengan tingkat kepercayaan tinggi memiliki jumlah lebih dari 50% ZPPI moderate, dimana jumlah tersebut sesuai ambang batas yang dapat diterima. Sehingga peningkatan akurasi lokasi ZPPI baik di pesisir maupun laut lepas dapat ditentukan melalui pendekatan ini

Integrasi Inderaja dalam Menentukan Batimetri Perairan Sekitar Pulau Kelapan, Kabupaten Bangka Selatan, Provinsi Kepulauan Bangka Belitung

This research conducted in the Kelapan Island of Pangkalpinang, South Bangka Regency, Bangka Belitung Archipelago Province. Kelapan Island had been overgrown by mangrove cummunity which tendtobevaried and unique. Research related to bathymetry mapping in the Kelapan Island area has never been carried out and minim information, therefore this research needs to be done to find out bathimetryof surrounding waters at the Kelapan Island by using remote sensing technology and GIS.  The research and gound check carried out on November  2016 -  January 2017, assisted by using secondary data from related agencies to test image accuracy. Image Processing and layouting bathymetry map conducted in LAPAN. Bathymetry data analysis uses satellite drivedbathymetry algoritm and linear regression. The image used in this research uses landsat 8 imagery with an image resolution of 30 meter.The result showed that there are interrealated between the depth value with satellite drived bathymetry value from image processing. The best linear regression is y=143.9x-137.4depth value with satellite drived bathymetri value, indicated the value of the coefficient of determination (R2) of 0.764 indicates that the coefficient number is positively related. The result of the layout map made using the ArcMap software show that a depth of 6 – 10 meters with using Satellite Drived Bathimetry(SDB) algorithm (range: 4 -11 m) is differentiated using a color difference for each depth

Development of Kd(490) Algorithm Using Medium Spatial Resolution Landsat 8 OLI Arround Shallow Waters In Panggang Island

The state of water quality around Panggang Island, Seribu Islands, in recent decades experienced degradation caused by human activities. The parameters of the diffuse attenuation coefficient (Kd) is an important optical property-related attenuation of light in the water column, and its brightness. Landsat 8 data has potential to map the value of Kd(490) in regional waters in Indonesia. Landsat 8 data could provide solutions to spatial data availability of Kd(490) values in addition to Ocean Color data. The purposes of this research was to developed empirical algorithm of Landsat 8 data to derive values of Kd(490) that can be use as tools for monitoring water quality optically on a regional scale which could not be done by Ocean Color data that has spatial resolution limitation. In-situ measurement of radiometric data was done by using TriOS-RAMSES hyperspectral spectroradiometer with a range of 320 – 890 nm and spectral sampling of 3.3 nm on shallow-waters around Panggang Island. The development of Kd(490) algortihm was done by simulation on ratio of Green and Near-infrared band has great determination values with Kd(490) empirically, which that empirical algorithm can be applied on Landsat 8 data to derive its values. In addition, it is noted that the shallow-waters around Panggang Island, dominant affected by absorption of chlorophyll-a rather than scattering by suspended solids

IN-SITU MEASUREMENT OF DIFFUSE ATTENUATION COEFFICIENT AND ITS RELATIONSHIP WITH WATER CONSTITUENT AND DEPTH ESTIMATION OF SHALLOW WATERS BY REMOTE SENSING TECHNIQUE

Diffuse attenuation coefficient, Kd(λ), has an empirical relationship with water depth, thus potentially to be used to estimate the depth of the water based on the light penetration in the water column. The aim of this research is to assess the relationship of diffuse attenuation coefficient with the water constituent and its relationship to estimate the depth of shallow waters of Air Island, Panggang Island and Karang Lebar lagoons and to compare the result of depth estimation from Kd model and derived from Landsat 8 imagery. The measurement of Kd(λ) was carried out using hyperspectral spectroradiometer TriOS-RAMSES with range 320 – 950 nm. The relationship between measurement Kd(λ) on study site with the water constituent was the occurrence of absorption by chlorophyll-a concentration at the blue and green spectral wavelength. Depth estimation using band ratio from Kd(λ) occurred at 442,96 nm and 654,59 nm, which had better relationship with the depth from in-situ measurement compared to the estimation based on Landsat 8 band ratio. Depth estimated based on Kd(λ) ratio and in-situ measurement are not significantly different statistically. Depth estimated based on Kd(λ) ratio and in-situ measurement are not significantly different statistically. However, depth estimation based on Kd(λ) ratio was inconsistent due to the bottom albedo reflection because the Kd(λ) measurement was carried out in shallow waters. Estimation of water depth based on Kd(λ) ratio had better results compared to the Landsat 8 band ratio

PLATFORM REEF LAGOON DETECTION FROM SENTINEL-2 IN PANGGANG ISLAND AND SEMAKDAUN ISLAND

Processing of satellite image data for the detection of platform reef lagoons is intended as one of the geo-physical parameters of the reef landform. Panggang Island and Semakdaun Island were chosen to make the detection model because they are ideal for lagoon reef landforms and tapulang court reefs. This model is only valid in the continental shelf area and the back arc and small island tectonic type. Determination of this location is done to improve the accuracy of spectral-based data processing. Platform reefs are one of four classes of reef landforms. Sentinel-2A data with a spatial resolution of 10m, blue, green, red, and near infrared bands were selected to investigate their ability to detect lagoons. Processing of data by calculating the Optimum Index Factor (OIF) to produce a composite image and drawing transect lines to produce pixel values and spectral graphics of the lagoon. The results of data processing in the form of graphs, composite images and pixel values were built to realize a digital lagoon detection model. These results are used for lagoon growth stage analysis for the classification of three reef platform landforms, visually and digitally interpretation. This digital and visual detection system design is useful for monitoring coral reef ecosystems

ANALYSIS OF WATER PRODUCTIVITY IN THE BANDA SEA BASED ON REMOTE SENSING SATELLITE DATA

Abstract. This study examines the density of potential fishing zone (PFZ) points and chlorophyll-a concentration in the Banda Sea. The data used are those on chlorophyll-a from the Aqua MODIS satellite, PFZ points from ZAP and the monthly southern oscillation index. The methods used are single image edge detection, polygon center of mass, density function and a Hovmoller diagram. The result of the analysis show that productivity of chlorophyll-a in the Banda Sea is influenced by seasonal factors (dry season and wet season) and ENSO phenomena (El Niño and La Niña). High productivity of chlorophyll-a  occurs during in the dry season with the peak in August, while low productivity occurs in the wet season and the transition period, with the lowest levels in April and December. The variability in chlorophyll-a production is influenced by the global El Niño and La Niña phenomena; production increases during El Niño and decreases during La Niña. Tuna conservation areas have as lower productivity of chlorophyll-a and PFZ point density compared to the northern and southern parts of the Banda Sea. High density PFZ point regions are associated with regions that have higher productivity of chlorophyll-a, namely the southern part of the Banda Sea, while low density PFZ point areas  are associated with regions that have a low productivity of chlorophyll-a, namely tuna conservation areas. The effect of the El Niño phenomenon in increasing chlorophyll-a concentration is stronger in the southern part of study area than in the tuna conservation area. On the other hand, the effect of La Niña phenomenon in decreasing chlorophyll-a concentration is stronger in the tuna conservation area than in the southern and northern parts of the study area.

PENGUKURAN KOEFISIEN DIFFUSE ATENUASI (Kd) DI PERAIRAN DANGKAL SEKITAR KARANG LEBAR, KEPULAUAN SERIBU, DKI JAKARTA

Nilai koefisien diffuse atenuasi Kd(λ) yang berasal dari pengukuran downwelling irradiance Ed(λ) merupakan salah satu parameter penting dalam oceanografi yang memberikan informasi mengenai ketersediaan cahaya dan tingkat penetrasi cahaya di dalam kolom air yang memberikan gambaran mengenai tingkat kecerahan, fotosintesis dan proses biologi lainnya. Informasi mengenai koefisien diffuse atenuasi memegang peranan penting dalam pengembangan algoritma Bio-Optik pada data satelit Ocean Color. Tujuan dari penelitian ini adalah untuk menjelaskan variabilitas dari koefisien diffuse atenuasi di perairan dangkal Karang Lebar, pulau Air dan Panggang dengan menggunakan sensor irradiace hyperspectral radiometer TriOS-RAMSES dengan cakupan rentang panjang gelombang 320 hingga 950 nm dengan resolusi spektral 3.3 nm. Pengukuran in situ dilakukan dengan menurunkan sensor irradiance di setiap kedalaman secara vertikal pada kolom air. Secara keseluruhan hasil pengukuran menunjukkan bahwa nilai Kd(λ) memiliki pola dimana pada region panjang gelombang 380-480 nm akan menurun dan akan meningkat kembali hingga pada region panjang gelombang merah 560-760 nm. Nilai Kd(λ) di bagian dalam gobah ditemukan lebih tinggi dibandingkan di bagian luar gobah dengan perbedaan yang signifikan terjadi di region panjang gelombang merah, perbedaan signifikan nilai Kd(λ) juga terjadi di ketiga wilayah (F = 5.581 > F critical = 5.554) dimana masing-masing area memiliki karakteristik yang berbeda-beda. Secara dominan, nilai Kd(λ) dipengaruhi oleh serapan klorofil-a dengan R2 = 0.808 dibandingkan dengan hamburan dari muatan padatan terlarut dibuktikan dengan R2 = 0.043. Nilai Kd(λ) pada rentang panjang gelombang sinar tampak (400-700 nm) dapat memberikan gambaran mengenai jangkauan penetrasi cahaya sinar tampak yang bisa dideteksi oleh satelit dengan satuan satu kedalaman optik. Hubungan nilai Kd(λ) dengan satu kedalaman optik dijelaskan secara eksponensial dengan persamaan Kd(400-700nm) = 0.375*exp(-0.095*1ζ) dengan koefisien determinasi R2 = 0.97

DETEKSI GEJALA ERUPSI STROMBOLIAN GUNUNGAPI RAUNG JAWA TIMUR MENGGUNAKAN NORMALIZED THERMAL INDEX DARI DATA MODIS (DETECTING THE PRECURSOR OF RAUNG VOLCANO STROMBOLIAN ERUPTION USING NORMALIZED THERMAL INDEX FROM MODIS)

Secara geologi, sebagian besar wilayah Indonesia berada pada jalur subduksi cincin api pasifik (pacific ring of fires) yang menyebabkan banyak bermunculan gunungapi aktif. Keberadaan gunungapi aktif tersebut membawa implikasi tersendiri akan munculnya ancaman erupsi vulkanik yang sewaktu-waktu dapat terjadi. Penelitian ini bertujuan untuk mendeteksi gejala erupsi gunungapi dengan menggunakan parameter Normalized Thermal Index (NTI) yang diturunkan dari data MODIS. Obyek gunungapi yang dipilih adalah Gunungapi Raung di Jawa Timur dimana sekitar Juni hingga Juli 2015 menunjukkan peningkatan aktivitas vulkanisme serta mengalami erupsi. Metode pengolahan data meliputi pengolahan citra Landsat-8 untuk penentuan area of interest (kaldera), pengolahan citra MODIS untuk pengukuran NTI, serta analisis pola spasial dan temporal NTI. Hasil penelitian menunjukkan bahwa gejala suatu gunungapi akan mengalami erupsi dapat diketahui dari adanya nilai NTI pada daerah kaldera yang meningkat dan relatif lebih tinggi dari daerah di sekitarnya. Parameter NTI telah teruji memiliki kemampuan yang baik dalam membedakan antara kaldera yang sedang meningkat aktivitas vulkaniknya dan obyek-obyek lainnya. Nilai NTI = 0,06 dapat diterapkan sebagai nilai ambang batas (threshold) suatu gunungapi menunjukkan gejala akan erupsi.Kata kunci: Gejala erupsi, Gunungapi Raung, Strombolian,MODIS, NT

DETECTION OF WATER-BODY BOUNDARIES FROM SENTINEL-2 IMAGERY FOR FLOODPLAIN LAKES

The impact of climate and human interaction has resulted in environmental degradation. Consistent observations of lakes in Indonesia are quite limited, especially for flood-exposure lake types. Satellite imagery data improves the ability to monitor water bodies of different scales and the efficiency of generating lake boundary information. This research aims to detect the boundaries of flood-exposure type lake water bodies from the detection model and calculate its accuracy in Semayang Melintang Lake using Sentinel-2 imagery data. The characteristics of water, soil, and vegetation objects were investigated based on the spectral values of the composite image bands from the Optimum Index Factor (OIF) calculation, to support the lake water body boundary detection model. The Object-Based Image Analysis (OBIA) method is used for water and non-water classification, by applying the machine learning algorithms random forest (RF), support vector machine (SVM), and decision tree (DT). Model validation was conducted by comparing spectral graphs and lake water body boundary model results. The accuracy test used the confusion matrix method and resulted in the highest accuracy value in the SVM algorithm with an Overall Accuracy of 95% and a kappa coefficient of 0.9. Based on the detection model, the area of Lake Semayang Melintang in 2021 is 23392.30 ha. This model can be used to estimate changes in the area of the flood-exposure lake consistently. Information on the boundaries of lake water bodies is needed to control the decline in the capacity and inundation area of flood-exposure lakes for management and monitoring plans