Integrasi SIG (Sistem Informasi Geografis) dan Citra ASTER untuk Analisis Sebaran Deposit Nikel Laterit ( Studi Kasus pada Kabupaten Seram Bagian Barat, Provinsi Maluku)

• Main Authors: , ERFIN ELLY, , Dr.Lucas Donny Setijadji, ST, MSc
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2014
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/128507/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=68856

This research investigates the application of GIS (Geographic Information System) and ASTER image for predicting the occurrences of lateritic nickel depsosits. Research area is located in the Kabupaten Seram Bagian Barat, Maluku Province, which is known to have large amount of lateritic nickel deposits. Research methods consist of interpretation of remote sensing data especially ASTER, field geological works, spatial data management and spatial analysis using GIS. Research is conducted into four stages, starts from data collection and entry which will be used as modeling variables. The next steps consist of processing the ASTER image and digital geology and DEM data, calculation of relationship between physical variables and occurrences of lateritic nickel deposits, data integration, spatial modeling and validation using field data. GIS spatial analysis is applied to quantify spatial relationship between occurrences of nickel deposits (represented by drillhole data) and following geological factors: lithology, structures (lineaments), slope, vegetation cover, and the existence of predictor minerals hematite, goethite, and chlorite. In this case, lithology is derived from geological map, lineaments and slopes from DEM, while vegetation and minerals hematite, goethite and chlorite from ASTER. The result shows that lateritic nickel deposits are associated with seven (7) physical variable as follows: ultramafic rocks and Taunusa Complex (metamorphic rocks), low vegetation density (NDVI < 0,3), low to moderate slope class (< 36%), zone at radii > 300 m from geological lineaments, the presence of indicator minerals hematite and geothite (PCA > 6,09), and the absence of chlorite (PCA < 3,45). Then the seven physical variables are made as seven evidence or predictor maps using binary method (Suitable and Non-suitable class) which are then all combined using spatial operator AND to produce a map of potential lateritic nickel deposits. This modeling produces areas of potential lateritic nickel deposits that covers 14.05 sq. km. that equals approximately with 10% of the total of study area which is 142.01 sq. km. This result has been succesfully verified using field data that shows a good correlation between GIS model and field data. This methoid is expected can be applied to identify the potency of lateritic nickel deposits in other regions of Indonesia.