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ESTIMASI BATIMETRI DARI DATA SPOT 7 STUDI KASUS PERAIRAN GILI MATRA NUSA TENGGARA BARAT Setiawan, Kuncoro Teguh; Manessa, Masita Dwi Mandini; Winarso, Gathot; Anggraini, Nanin; Girrastowo, Gigih; Astriningrum, Wikanti; Herianto, Herianto; Rosid, Syamsu; Supardjo, A. Harsono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 2 Desember 2018
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.473 KB) | DOI: 10.30536/j.pjpdcd.2018.v15.a3008

Abstract

Indonesia merupakan negara kepulauan dengan ribuan pulau besar dan kecil yang memliki perairan laut dangkal. Salah satu informasi yang dibutuhkan dari pulau-pulau tersebut adalah peta batimetri khususnya diperairan laut dangkal. Informasi tersebut masih sangat terbatas pada skala yang besar untuk skala yang lebih detil masih sangat terbatas. Untuk menyelesaikan permasalahan tersebut dibutuhkan teknogi penginderaan jauh. Salah satu pemanfaatan teknologi penginderaan jauh adalah untuk menghasilkan informasi batimetri. Banyak metode yang dapat digunakan untuk menghasilkan informasi batimetri dengan teknologi tersebut. Metode yang digunakan dalam penelitian ini adalah metode regresi linier berganda (MLR) yang dikembangkan oleh Lyzenga, 2006. Data yang akan di gunakan adalah citra satelit SPOT 7 di Perairan Laut Dangkal Gili Trawangan, Gili Meno dan Gili Air Pulau Lombok Provinsi Nusa Tenggara Barat. Metode penentuan batimetri tersebut dilakukan pada data kedalaman insitu dengan melakukan dua modifikasi yaitu yang pertama dengan tidak memperhatikan jenis objek habitat dasar dan yang kedua memperhatikan objek habitat dasar karang, lamun, makroalga dan substrat.Hasil dari penelitian ini memberikan korelasi R2 yang meningkat dari 0,721 menjadi 0,786 serta penuruanan nilai kesalahan RMSE dari 3,3 meter menjadi 2,9 meter.
Isu Penyelarasan Flight Information Region di atas Wilayah Natuna Supriyadi, Asep Adang; Manessa, Masita Dwi Mandini; Gultom, Rudy Agus Gemilang
JURNAL MANAJEMEN TRANSPORTASI & LOGISTIK Vol 5, No 3 (2018): NOVEMBER
Publisher : Sekolah Tinggi Manajemen Transportasi (STMT) Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25292/j.mtl.v5i3.273

Abstract

Citizen sentiment is essential to evaluate the support toward government program. In 2015, Indonesian government proposed an acceleration program on re-alignment on Flight Information Region above Natuna area. Since then, primary of discussion is often be held as a formal or informal event. The data collected from 210 respondent, which consist of pilots, military staff, ATC staff, and academician. Furthermore, this study uses TF-IDW weighting technique to cluster the argument as positive, neutral, and negative sentiment. The result shows that most of Indonesia aviation community (75%) argue that FIR management should base on sovereignty and safety. Moreover, FIR issue under economic, national security and management shows significant positive respond (>90%) while FIR management under Singapore shows a negative response (100%). The result indicates that the aviation community supports the national program Natuna FIR re-alignment.
DETERMINATION OF THE BEST METHODOLOGY FOR BATHYMETRY MAPPING USING SPOT 6 IMAGERY: A STUDY OF 12 EMPIRICAL ALGORITHMS Manessa, Masita Dwi Mandini; Haidar, Muhammad; Hartuti, Maryani; Kresnawati, Diah Kirana
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1003.316 KB) | DOI: 10.30536/j.ijreses.2017.v14.a2827

Abstract

For the past four decades, many researchers have published a novel empirical methodology for bathymetry extraction using remote sensing data. However, a comparative analysis of each method has not yet been done. Which is important to determine the best method that gives a good accuracy prediction. This study focuses on empirical bathymetry extraction methodology for multispectral data with three visible band, specifically SPOT 6 Image. Twelve algorithms have been chosen intentionally, namely, 1) Ratio transform (RT); 2) Multiple linear regression (MLR); 3) Multiple nonlinear regression (RF); 4) Second-order polynomial of ratio transform (SPR); 5) Principle component (PC); 6) Multiple linear regression using relaxing uniformity assumption on water and atmosphere (KNW); 7) Semiparametric regression using depth-independent variables (SMP); 8) Semiparametric regression using spatial coordinates (STR); 9) Semiparametric regression using depth-independent variables and spatial coordinates (TNP), 10) bagging fitting ensemble (BAG); 11) least squares boosting fitting ensemble (LSB); and 12) support vector regression (SVR). This study assesses the performance of 12 empirical models for bathymetry calculations in two different areas: Gili Mantra Islands, West Nusa Tenggara and Menjangan Island, Bali. The estimated depth from each method was compared with echosounder data; RF, STR, and TNP results demonstrate higher accuracy ranges from 0.02 to 0.63 m more than other nine methods. The TNP algorithm, producing the most accurate results (Gili Mantra Island RMSE = 1.01 m and R2=0.82, Menjangan Island RMSE = 1.09 m and R2=0.45), proved to be the preferred algorithm for bathymetry mapping.
SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY Manessa, Masita Dwi Mandini; Kanno, Ariyo; Sekine, Masahiko; Haidar, Muhammad; Yamamoto, Koichi; Imai, Tsuyoshi; Higuchi, Takaya
Geoplanning: Journal of Geomatics and Planning Vol 3, No 2 (2016): (October 2016)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1241.06 KB) | DOI: 10.14710/geoplanning.3.2.117-126

Abstract

In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy. 
BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS Setiawan, Kuncoro Teguh; Suwargana, Nana; Br. Ginting, Devica Natalia; Manessa, Masita Dwi Mandini; Anggraini, Nanin; Adawiah, Syifa Wismayati; Julzarika, Atriyon; Surahman, Surahman; Rosid, Syamsu; Supardjo, Agustinus Harsono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.189 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3085

Abstract

The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery.
HOTSPOT DISTRIBUTION ANALYSIS IN EAST KALIMANTAN PROVINCE 2017-2019 TO SUPPORT FOREST AND LAND FIRES MITIGATION Sari, Nurwita Mustika; Rachmita, Nurina; Manessa, Masita Dwi Mandini
Indonesian Journal of Environmental Management and Sustainability Vol. 4 No. 1 (2020): March
Publisher : Graduate Program Faculty of Mathematic and Natural Sciences, Sriwijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2048.609 KB) | DOI: 10.26554/ijems.2020.4.1.28-33

Abstract

Forest and land fires that have occurred in the territory of East Kalimantan Province have caused immediate disaster to the area from year to year and become a global concern in recent years. Hotspots that potentially cause forest and land fires can be detected using satellites such as NOAA-20. The purposes of this study are to analyze the distribution pattern of hotspots in East Kalimantan Province during 2017-2019, identify areas with the highest risk of fires caused by the high intensity of hotspot. The method used in this study is the Nearest Neighbor Analysis and Kernel Density Estimation analysis. The results showed that the distribution pattern of hotspots in East Kalimantan Province during 2017-2019 was clustered with the highest intensity of hotspots were in Berau, East Kutai and Kutai Kartanegara Districts. And from the result of the analysis, the highest number of days has a peak hotspots on September each year. Keywords: forest and land fires, hotspots, Nearest Neighbor, Kernel Density Estimation
SPATIAL DISTRIBUTION PATTERNS ANALYSIS OF HOTSPOT IN CENTRAL KALIMANTAN USING FIMRS MODIS DATA Pratamasari, Adisty; Permatasari, Ni Ketut Feny; Pramudiyasari, Tia; Manessa, Masita Dwi Mandini; Supriatna, Supriatna
Jurnal Geografi Lingkungan Tropik Vol 4, No 1 (2020): February
Publisher : Open Journal System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.511 KB) | DOI: 10.7454/jglitrop.v4i1.74

Abstract

One of the ways to observe the hotspot created by forest fires in Indonesia is through Remote sensing imagery, such as MODIS, NOAA AVHRR, etc. Central Kalimantan is one of the areas in Indonesia with the highest hotspot data. In this research, MODIS FIRMS hotspot data in Central Kalimantan collected from 2017 ? 2019, covering 13 districts: South Barito, East Barito, North Barito, Mount Mas, Kapuas, Katingan, Palangkaraya City, West Kotawaringin, East Kotawaringin, Lamandau, Murung Raya, Pulang Pisau, Seruyan, and Sukamara. That is four aspects that this research evaluated: 1) evaluating the spatial pattern using the Nearest Neighbor Analysis (NNA); 2) evaluate the hotspot density appearance using Kernel Density; and 3) correlation analysis between rainfall data and MODIS FIRMS. As a result, the hotspot in Central Kalimantan shows a clustered pattern. While the natural breaks KDE algorithm shows the most relevant result to represent the hotspot distribution. Finally, the hotspot is low correlated with rainfall; however, is see that most of the hotspot (~90%) appeared in low rainfall month (less than 3000 mm/month).Keywords: Forest fire, Hotspot, NNA, Kernel density, Central Kalimantan