MODEL SIMULASI BANJIR MENGGUNAKAN DATA PENGINDERAAN JAUH, STUDI KASUS KABUPATEN SAMPANG DENGAN MENGGUNAKAN METODE GRIDDED SURFACE SUBSURFACE HYDROLOGIC ANALYSIS

The problem of flood that yearly occurred in Sampang district was due to the very large amount of runoff flow to the Sampang Cit, very high sedimentation in the river that crosses the city, as well as the lack of good drainage system especially in urban residential areas. Some of that problems eventually can lead to flooding in the City of Sampang. The method used for flood simulation model was GSSHA (Gridded Surface Subsurface Hydrologic Analysis), which is able to produce a good hydrological components. The data used data in this research among others are: Qmorph, DEM-SRTM (Digital Elevation Model-Shuttle Radar Topography Mission), SPOT-5 of 2010, land map, river cross sections and field data. This flood simulation model research resulting flood discharge, which is described in the hydrograph and flood depth calculations. The peak discharge resulted in several catchment areas (CA): Klampis CA is 5.40 m³/s, Jelgung CA is 364788.9 m³/s, Kamoning CA is 32.40 M³/s, and 3 CAs which are associated with the above CAs is 174059 m³/s

MODEL BAHAYA BANJIR MENGGUNAKAN DATA PENGINDERAAN JAUH DI KABUPATEN SAMPANG

Flood is the first biggest disaster in Indonesia, as stated by the National Disaster Management Agency (BNPB) in the BNPB’s natural disaster data of year 2000 to 2009. Considering the flood has the significant impact of causing the casualties and material losses, it is necessary to study on it. One of useful data for studying the flood is remote sensing data. The advantage of good historical data makes it possible to see the changes of cover/land use from year to year in a region. The extensive area coverage of remote sensing data allows it to view and analyze in a comprehensive manner. The method of the study of flood hazard models is using multiple variables, where each variable has a class of criteria. Determination of the weight of each flood variable by using the Composite Mapping Analysis. The results of this study shows the main cause of flooding in the District of Sampang is that most of the land system in the cities are the combined estuary and swamp plain, forming a low land and is triggered by the torrential rain. The model of flood hazard maps produced by variable weighting floods with a multi criteria analysis method which is function of rainfall, landuse, slope, land system and elevation

EVALUASI PRODUK MODIS GROSS PRIMARY PRODUCTION PADA HUTAN RAWA GAMBUT TROPIS INDONESIA

Gross Primary Production (GPP) estimation method was developed as one approach for calculating the amount of carbon stored in vegetation. One of the GPP products which can be operationally downloaded free of charge from Terra/Aqua MODIS (NASA satellite) is MODIS GPP product (MOD17). The examination of this product needs to be performed in several ecosystem types due to its global properties. Recently, a new version of the product has been launched, however its examination on tropical forests particularly over Indonesia has not been implemented yet. In this study, new version of MODIS GPP (MOD17A2-51) was evaluated in tropical peat swamp forest, in Central Kalimantan Province using time series and statistical analysis of field data (GPP EC). The study results show that the time series of 8-daily MODIS GPP provide a similar pattern although it has low correlation. In general, MODIS GPP tend to be underestimate either on rainy or dry season. However, an overestimate result was found during the ENSO-caused long dry season in 2002. Nevertheless, the accumulated value of GPP with seasonal consideration (dry and rainy) shows good relationship (r=0.94, RMS= 17.47, and Efficiency score= 0.68). The 2nd dry season period (August-October) shows better distribution than other periods. This study concludes that the MODIS GPP product version 51 can be used for biomass seasonal monitoring of tropical peat swamp forests in Indonesia

KLASIFIKASI SPASIAL PENUTUP LAHAN DENGAN DATA SAR DUAL-POLARISASI MENGGUNAKAN NORMALIZED DIFFERENCE POLARIZATION INDEX DAN FITUR KERUANGAN DARI MATRIK KOOKURENSI

In this study, the land cover classification method using the spatial information features of co-occurrence matrix and Normalized Difference Polarization Index (NDPI) data from dual polarization SAR Data was proposed. The spatial information features are used as input of supervised classification, and to get the performance of the proposed method, land cover classification was conducted with SAR C-band and L-band satellite data of Envisat ASAR and ALOS PALSAR. The results of the study are, the size of window on the SAR image to get the spatial information features of co-occurrence matrix and the use of additional NDPI data are giving effect to the accuracy of classification results. At the test area in Siak Riau Province which have 7 classes of land use, the optimum window size for co-occurrence matrix is 7 pixel x 7 pixel for ASAR data which has 75m spatial resolution, and more than 9 pixel x 9 pixel for PALSAR data which has 10m spatial resolution. The addition of the co-occurrence matrix information of NDPI data can improve the classification of accuracy up to 2%

PERBANDINGAN KARAKTERISTIK SPEKTRAL (SPECTRAL SIGNATURE) PARAMETER KUALITAS PERAIRAN PADA KANAL LANDSAT ETM+ DAN ENVISAT MERIS

This study analyzed 3 optically active water constituents (Total Suspended Matter (TSM), Chlorophyll a (Chl a) and Color Dissolved Organic Matter (CDOM)) measured by in situ spectroradiometer (350-950 nm, interval 3.3 nm) and laboratory analysis for respective concentration measurements. Remote sensing reflectances ($R_s$) were derived from radiance and irradiance measurements and estimated using bio-optical model approach. The spectral signatures of derived $R_s$ were converted to ETM+ and MERIS bands using respective spectral response sensitivity. The converted $R_s$ were used to estimate the concentrations of 3 optically active water constituents using optimization method. The derived concentrations were validated with measured concentrations from laboratory analysis. ETM+ sensor with 4 bands provided better estimation for TSM ($R^2=0.70$) and CDOM ($R^2=0.64$) while the coefficient determination ($R^2$) for Chl a is only 0.46. However, MERIS sensor with 10 bands provided better estimation than ETM+, with coefficient determinations were higher than 0.70 for all water constituents. RMSE values for MERIS sensor showed smaller error estimation (16.84 gm-3 (TSM), 2.66 mg m-3 (Chl a) and 0.26 m-1 (CDOM)) compare with ETM+ sensor (19.89 gm-3 (TSM), 4.96 mg m-3 (Chl a) and 0.29 m-1 (CDOM))

KAJIAN PERUBAHAN GARIS PANTAI MENGGUNAKAN DATA SATELIT LANDSAT DI KABUPATEN KENDAL

Indonesia has the second longest of coastal line in the world. The change of coastal line is generated by sediment transport from the upland and the sea or generated by coming energies of sea current and wave. Coastal line change have been analyzed by multi-temporal analysis using Landsat Series Satellite (MSS, TM and ETM+). Visual interpretation of RGB 542 was done to identify coastal line, and using other combination if necessary. Based on analysis of Landsat data the coastal lines length on 1972, 1991, 2001 and years 2008 was 43.172 m, 52.646 m, 50.171 m, 53.827 m, respectively, and the change of coastal lines occurred dominant along the capes and the bays and the other places was not significantly changed. Based on Landsat satellite data analysis, in 1972 to 1991 period the abrasion and accretion occurred on 765,14 ha and 356,00 ha area, in 1991 to 2001 period were 90,64 ha and 261,89 ha, in period 2001 to 2008 were 111,67 ha and 80,37 ha

RANCANG BANGUN SISTEM PENAMPILAN DINAMIKA TITIK PANAS DI INDONESIA BERBASIS KEYHOLE MARKUP LANGUAGE (KML) DINAMIS

Hot spot dynamics display system based on Dynamic Keyhole Markup Language (KML) have been designed and built in Indonesia as a complement of web-based Indonesian fire watch system that have been developed by Indofire. Indofire’s system uses web browsers to display it’s output data, while the built system uses earth/geo browser to display data. The results show that the built system has feature that more user friendly, data access speed up to 5.22 times faster, and reducing data volume storage need up to 80.87 % rather than system that has been developed by Indofire

KAJIAN DAMPAK PERUBAHAN IKLIM TERHADAP KEBAKARAN HUTAN DAN DEFORESTASI DI PROVINSI KALIMANTAN BARAT

Increasing or decreasing of rainfall intensity, due to the climate change, affects the environment condition in many Indonesia areas. For instance: low rainfall intensity causes high number of forest fire occurrence in Kalimantan Island. The impact of climate change is studied by analyzing the correlation among rainfall intensity, number of forest fire occurrence and forest area change in West Kalimantan Province. The rainfall is extracted using Tropical Rainfall Measurement Mission (TRMM) data for 2001-2008. The number of forest fire occurrence is identified by the number of hotspot extracted from thermal sensor of satellite data MODIS for 2001 - 2008. The forest area is calculated from MODIS data for 2003, 2005, 2007 and 2009. Pixel which has Normalized Difference Vegetation Index (NDVI) value more than 0,7 along a year round is assumed as forest pixel. The NDVI value is obtained by doing training sample in forest area. The result shows that the rainfall has slightly upward trend in Kalimantan. The rainfall has negative correlation with the number of hotspot. When the rainfall was the lowest and the number of hotspot was the highest in 2004, the forest area between 2003 and 2005 decreased (deforestation) significantly. On the other hand, when the rainfall was high and the hotspot was low in 2008, no decreasing in forest area otherwise we found the increasing of forest area. It is probably due to reforestation and expansion of plantation area (such as oil palm)

PENGEMBANGAN METODE PENENTUAN INDEKS LUAS DAUN PADA PENUTUP LAHAN HUTAN DARI DATA SATELIT PENGINDERAAN JAUH SPOT-2

It is necessary to develop the methods of Leaf Area Index (LAI) estimation based on satellite remote sensing data as first step to study the carbon storage and carbon emission which affect to global climate change. Direct measurements of Leaf Area Index in the field are expensive, take a long time, and so inefficient. The application of remote sensing data may gives an appropriate solution for Leaf Area Index estimation by more efficient and effective. Objective of the research is to develop the method of Leaf Area Index estimation by using remote sensing data. The method of Leaf Area Index estimation will be developed by using the reference method taken from back up algorithm of the Algorithm Theoretical Basis Document (ATBD) MOD15. The research will try to develop the model and applicate it for another remote sensing data, especially those of acquired or distributed by Indonesian National Institute of Aeronautics and Space (LAPAN) such as SPOT-2. Results of the research show that the LAI based on MOD 15 has low correlation with the measured LAI, but the measured LAI has good correlation with NDVI from SPOT-2 for forest area