Analisis Pemanfaatan Citra Landsat 7 Untuk Pemetaan Kandungan Bahan Organik Tanah Dengan Metode Pca Dan Regresi Linier Berganda Bertahap Di Kabupaten Bangkalan - Mohammad Idris; Sawitri Subiyanto; L. M. Sabri (Departement Teknik Geodesi Universitas Diponegoro, 2014)
Information on soil organic matter (SOM) is required for consideration in planning development of sustainable land as land with high SOM score could be prioritized for field potential. Bangkalan regency encounters increased shift in land use after the operation of Suramadu bridge, causing land convertion does not consider natural value of the soils. In this study, mapping of soil organic matter on july 2013 is implemented by using remote sensing techniques. The remote sensing data is Landsat 7 (bands 1, 2, 3, 4, 5, and 7) with Normalized Difference Soil Index (NDSI) as the land identification. Ground-truth data be obtained by analyzing organic matter using ASTM D 2974 combustion method (American Society for Testing and Materials on Standard Test Methods for Moisture, Ash, and Organic Matter of Peat and Other Organic Soils). Data analysis uses stepwise multiple linear regression with three types of input (pixel values of 6 bands in grayscale mode,PCA with 6 PCs, and PCA with 3 PCs). The results showed that using principal component analysis (PCA) with 6 PCs can be used to predict soil organic matter. Application of stepwise multiple linear regression (SMLR) equation by using input principal component analysis (PCA) with 6 PCs to estimate soil organic matter showed that the soil in the study area generally contain diverse organic matter (covering 61.7% of the study area). Therefore, information on texture and structure contents of soils, harvest periods, cover crops, guided use of fertilizers, socio-economic incentives are needed to improve the results of BOT in study areaKeywords: remote sensing, mapping, soil organic matter, SMLR, PCA