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All Journal The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Mahdi Kartasasmita
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Earth & Planetary Sciences

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THE RELATIONSHIP BETWEEN TOTAL SUSPENDED SOLID (TSS) AND CORAL REEF GROWTH (CASE STUDY OF DERAWAN ISLAND, DELTA BERAU WATERS) Ety Parwati; Mahdi Kartasasmita; Kadarwan Soewardi; Tridoyo Kusumastanto; I Wayan Nurjaya
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 2 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1849

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.
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 KATMOKO ARI SAMBODO; ANIATI MURNl; RATIH DEWANTI; MAHDI KARTASASMITA
International Journal of Remote Sensing and Earth Sciences Vol. 5 (2008)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2008.v5.a1225

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.
CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS Katmoko Ari Sambodo; Aniati Murni; Mahdi Kartasasmita
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1212

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.
Co-Authors Aniati Murni ANIATI MURNl Ety Parwati I Wayan Nurjaya Kadarwan Soewardi Katmoko Ari Sambodo Ratih Dewanti Tridoyo Kusumastanto