THE RELATIONSHIP BETWEEN TOTAL SUSPENDED SOLID (TSS) AND CORAL REEF GROWTH (CASE STUDY OF DERAWAN ISLAND, DELTA BERAU WATERS)

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

KAJIAN KOREKSI TERRAIN PADA CITRA LANDSAT THEMATIC MAPPER (TM)

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 T

Dinamika Perubahan Mangrove Menjadi Tambak dan Total Suspended Solid (TSS) di Sepanjang Muara Berau

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

Prospek dan Peluang Industri Penginderaan Jauh Di Indonesia

100 hlm : ill. ; 28 c

Solusi Persamaan Eigen Sebagai Metoda Umum Untuk Mencari Transformasi Linier Dalam Rangka Menurunkan Dimensi Untuk Klasifikasi

Abstract Several approaches for designing linear transformations for dimensionality reduction to be used in classification have been diccused. Existing methods based on Eigen Equations such as Karhunen Loe've expansion and Multiple Discrimi-nant Analysis have also been discussed. Modified versions of the above existing methods, by modifying the weight of the classes, have been proposed. Alio the decrement of the number of machine operations coused by the dimensionality reductions have been shown. The methods based on. the so-lutions of the Eigen Equations apparently are optimum for finding the transformation matrices. The selection of the vectors to become the column of the transformation matrix is based on the ordering of the Eigen Values or the relative Eigen Values. Experimental results show that the transformation ma-trices which are the solutions of the Eigen Equations effecti-vely reduce the dimensionality of the data These can be seen from the changing of the estimates of the classification errors with respect to dimensionality. The experimental re-sults also show that for the modified methods, the classes with large weights produce lower class conditional probabi-lity of error estimates. Ringkasan Dalam Makalah dituangkan dasar pendekatan untuk mencari transformasi tinier dalam rangka menurunkan di-mensi untuk klasiflkasi. Metoda yang biasa dipakai ber-dasarkane Persamaan Eigen, seperti metoda ekspansi Karhu-nen Loe've dan Multiple Discriminant Analysis, juga dibaha.s. Modifikasi dari berbagai metoda di atas, yaitu dengan me-ngubah bobot dari masing-masing kelas, telah pula ditawar-kan. Pula penurunan jumlah operasi karena penurunan di-mensi telah diperlihatkan. Metoda berdasarkan solusi Persamaan Eigen ini ternyataHlm. 17-3

Prospek dan Peluang Industri Penginderaan Jauh Di Indonesia

100 hlm : ill. ; 28 c

CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS

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

Antisipasi Perkembangan Teknologi Inderaja Serta Peningkatan Pemanfaatannya ; Suatu Pemikiran Pada HUT LAPAN ke 33

Hlm. 11-3

Penajaman citra dengan memanfaatkan Kanal Pankromatik

Hal.15-19: ilus.; 28 c

THE STRATEGY OF INDONESIAN SATELLITE TECHNOLOGY DEVELOPMENT

Hal. 13-18: ilus.;23,5 C