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Dinamika Perubahan Mangrove Menjadi Tambak dan Total Suspended Solid (TSS) di Sepanjang Muara Berau Parwati, Ety; Soewardi, Kadarwan; Kusumastanto, Tridoyo; Kartasasmita, Mahdi; Nurjaya, I Wayan
Jurnal Biologi Edukasi Vol 3, No 1 (2011): Jurnal Biologi Edukasi
Publisher : Jurnal Biologi Edukasi

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Abstract

The mangrove conversion become fish pond, bareland or others has an impact in water quality. One of water quality parameter is Total Suspended Solid (TSS), increasing TSS means the rising in pollution.  Landsat remote sensing data with multi channels used in studying the dynamic of mangrove – fishpond change and TSS along the Berau waters. Several regions with its variation are used in that dynamic studying.  The TSS algorithm for Berau waters is TSS (mg/l) = 3.3238 * exp (34.099*Red Band) , Red band=the atmospheric reflectance band 2 validated with field data. The result study is the conversion of mangrove become fish pond has the strong indication in the rising TSS .
DAMPAK PERUBAHAN KAWASAN HUTAN MENJADI AREAL INDUSTRI BATUBARA TERHADAP KUALITAS AIR DI SEPANJANG DAS BERAU–KALIMANTAN TIMUR Parwati, Ety; Soewardi, Kadarwan; Kusumastanto, Tridoyo; Kartasasmita, Mahdi; Nurjaya, I Wayan
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol 8 (2011)
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Abstract

The study of landused change: forest area become coal industrial area and its impact in Total Suspended Solid is done by remote sensing data. The different combination channel of remote sensing data are taken to extract landuse and Total Suspended Solid (TSS) spatial information. The supervised classification is used for land used spatial extraction and otherwise for TSS, there is a specifict algorithm; TSS = 3.8926 * exp (31.417*Red Band). The result showed that there was the relationship between landuse change from forest into coal industrial, shrub, paddy field, bareland and settlement area and the dynamic change of TSS along Berau watershed Key word: Total Suspended Solid (TSS), Remote sensing
KAJIAN KOREKSI TERRAIN PADA CITRA LANDSAT THEMATIC MAPPER (TM) Trisakti, Bambang; Kartasasmita, Mahdi; Kustiyo, -; Kartika, Tatik
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol 6, (2009)
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Abstract

Terrain correction is used to minimize the shadow effect due to variation of earth`s topography. So, the process is very useful to correct the distortion of the pixel value at the mountainous area in the satellite image. The aim of this paper is to study the terrain correction process and its implementation for Landsat TM. The algoritm of the terrain correction was built by determining the pixel normal angle which is defined as an angle between the sun and surface normal directions. The calculation of the terrain correction needs the information of sun zenith angle, sun elevation angle (obtained from header data), pixel slope, and pixel aspect derived from digital elevation model (DEM). The C coefficient from each band was determined by calculating the gradient and the intercept of the correlation between the Cos pixel normal angle and the pixel reflectance in each band. Then, the Landsat TM image was corrected by the algorithm using the pixel normal angle and C coefficient. C Coefficients used in this research were obtained from our calculation and from Indonesia National Carbon Accounting System (INCAS). The result shows that without the C coefficient, pixels value increases very high when the pixel normal angle approximates 900. The C coefficient prevents that condition, so the implementation of the C coefficient obtained from INCAS in the algorithm can produce the image which has the same topography appearance. Further, each band of the corrected image has a good correlation with the corrected band from the INCAS result. The implementation of the C coefficient from our calculation still needs some evaluation, especially for the method to determine the training sample for calculating the C coefficient. Keywords: Terrain correction, Pixel normal angle, C coefficient, Landsat TM
THE RELATIONSHIP BETWEEN TOTAL SUSPENDED SOLID (TSS) AND CORAL REEF GROWTH (CASE STUDY OF DERAWAN ISLAND, DELTA BERAU WATERS) Parwati, Ety; Kartasasmita, Mahdi; Soewardi, Kadarwan; Kusumastanto, Tridoyo; Nurjaya, I Wayan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 2 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

Total suspended solid (TSS) is one of the water quality parameters and limiting factor affecting coral reef growth. In this study, we used the algorithm of TSS= 3.3238*e(34.099* Green band) (where green band is reflectance band 2) to extract TSS from Landsat satellite data. The algorithm was validated with field data. Water column correction method developed by Lyzenga was used to map coral reef. The result showed that the coral reef area in Berau waters decreased significantly (about 12,805 ha or around 36 % ) from the year of 1979 to 2002. The most coral reef reduced area was detected around Derawan Island (about 5,685 ha). Further, some areas changed into sand dune. TSS concentration around Delta Berau and Derawan Island increased aproximately twice from 15- 35 mg/l in 1979 to 20-65 mg/l in 2002. The increase of TSS concentration was followed by the decrease of coral reef area.
CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS Ari Sambodo, Katmoko; Murni, Aniati; Kartasasmita, Mahdi; Kartasasmita, Mahdi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 4,(2007)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

This paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Keywords: Polarimetric-SAR, scattering model, freeman decomposition, Cloude decomposition, texture analysis, feature extraction, classification, neural networks.
CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS Ari Sambodo, Katmoko; Murni, Aniati; Kartasasmita, Mahdi; Kartasasmita, Mahdi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 4,(2007)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

This paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Keywords: Polarimetric-SAR, scattering model, freeman decomposition, Cloude decomposition, texture analysis, feature extraction, classification, neural networks.
POLARIMETRIC-SAR CLASSIFICATION USING FUZZY MAXIMUM LIKEHOOD ESTIMATION CLUSTERING WITH CONSIDERATION OF COMPLEMENTARY INFORMATION BASED ON PHYSICAL POLARIMETRIC PARAMETERS, TARGET SCATTERING CHARACTERISTIK, AND SPATIAL CONTEXT Ari Sambodo, Katmoko; Murni, Aniati; Dewanti, Ratih; Kartasasmita, Mahdi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 5,(2008)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

This paper shows a study on an alternative method for unsupervised classification of polarimetric-Syenthetic Aperture Radar (SAR) data. The first step was to extract several main physical polarimetric parameters (polarization power, coherence, and phase difference) from polarimetric covariance matrix (or coherency matrix) and physical scattering characteristics of land use/cover based on polarimetric decomposition (Cloude decomposition model). In this paper, we found that these features have complementary information which can be integrated in order to improve the discrimination of different land use or cover types. Classification stage was performed using Fuzzy Maximum Likelihood Estimation (FMLE) clustering algorithm. FMLE algorithm allows for ellipsoidal clusters of arbitrary extent and is consequently more flexible than standard Fuzzy K-Means clustering algorithm. Hoever, basic FMLE algorithm makes use exclusively the spectral (or intensity) properties of the individual pixel vectors and spatial-contextual information of the image was not taken into account. Hence, poor(noisy) classification result is ussualy obtained from SAR data due to speckle noise. In this paper, we propose a modified FMLE which integrate basic FMLE clustering with spatial-contextual information by statistical analysis of local neightbourhoods. The effectiveness of the proposed method was demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Result showed classified images improving land-cover discrimination performance. Exhibiting homogeneous region, and preserving edge and other fine structures. Keywords: Cloudes polarimetric decomposition, FMLE clustering, polarimetric coherence, Polarimetric-SAR, unsupervised classification.