Classification of Hyperspectral Remote Sensing for Production Minerals Mapping Using Geological Map and Geomatics Techniques

The classification of hyperspectral images is an interesting job since the data dimension is huge for conventional classification procedures; normally several hundreds of spectral bands are attained for each image. These spectral bands can supported very rich spectral data of each pixel to find objects material .The objective of this research is to classify hyperspectral images for detection and production of detailed minerals mapping using geological map and Environment for Visualizing Images (ENVI) software. In this research, ASTER data and geological map have been used. Some techniques on these data are used such as enhancement, matching (linking), De-correlation, Band Ratio, stacking image and classification. The results showed that comparison of the two classification results showed the classification of stack image with the aspect and the slope provide more information than classification of ASTER image alone. Also, using ENVI software to generate 3D surface views.It concluded that capability of hyperspectral and its differentiation with multispectral data to extract detailed features from ASTER image.

THE EXTENT OF AGRICULTURAL LAND DAMAGE IN VARIOUS TSUNAMI WAVE HEIGHT SCENARIOS: DISASTER MANAGEMENT AND MITIGATION

<p><strong>Abstract.</strong> The southern coastal areas of Java are highly vulnerable areas of earthquake hazard because they located 200&amp;thinsp;km from the southern Java subduction zone. This zone is an active seismicity area, resulting in many tectonic earthquakes caused by collisions and shift between the plates. This shift when it occurs under the sea surface with a large power intensity can lead to a tsunami. This research conducted to identify the extent of agricultural land (AL) damaged by the tsunami for disaster risk management and mitigation. Numerical modelling was performed to determine the run-up height of the tsunami through numerical data. This model was designed using the worst-case scenario. The tsunami inundation model analysed from the coming wave (run-up) with a height of 30&amp;thinsp;m. This model used scenarios of tsunami run-up height in a coastline, coarse coefficient and slope. The data extracted using remote sensing (RS) data was the slope obtained from the ASTER image GDEM data, the agricultural land productivity data obtained using NDVI vegetation index transformation and field data on productivity, and tsunami hazard analysis with various altitude scenarios using run-up model impact on existing AL conditions. The elevation-data was obtained from the 15&amp;thinsp;m ASTER image data (GDEM) that was reclassified into a slope class map. The risk of destruction of AL based on wave height extracted by using RS data generated rice risk loss index of AL of 190.5071&amp;thinsp;tons for a height of 1&amp;thinsp;m, 1851.522&amp;thinsp;tons for a height of 5&amp;thinsp;m, 7402.71&amp;thinsp;tons for a height of 10&amp;thinsp;m, 10776.47&amp;thinsp;tons to a height of 15&amp;thinsp;m, 11823.9&amp;thinsp;tons for height 20&amp;thinsp;m, and 11824.27&amp;thinsp;tons to a height of 30&amp;thinsp;m.</p>