KAJIAN PENGOLAHAN CITRA ALOS AVNIR-2 UNTUK ESTIMASI KANDUNGAN AIR KANOPI VEGETASI (CANOPY WATER CONTENT) DI SEBAGIAN WILAYAH KABUPATEN GUNUNGKIDUL - , PENDI TRI SUTRISNO, Nur Mohammad Farda, S.Si., M.Cs. ([Yogyakarta] : Universitas Gadjah Mada, 2014)
Remote sensing can be used in the identification and estimation of vegetation canopy water content, but in general has not been done in Indonesia. This study examined data bertjuan for the lot and is relatively easy to obtain multispectral image data, especially image ALOS AVNIR-2 separately estimate surface vegetation canopy water content (CWC) in most areas Gunungkidul forest, knowing the ability and accuracy of some of the methods used is the NDVI, NDWI, and arithmetic channels / bands, and mapping the CWC in most of the forest area Gunungkidul. The results of image processing using simple linear regression model between the value of the index NDVI image processing results, NDWI, and arithmetic channel (band ratio) NIR / R, R / NIR, NIR / G, and G / NIR measurement of moisture content in the vegetation samples were successfully taken in the field indicate that the image data of ALOS AVNIR-2 can be used to estimate canopy water content. The method is considered capable and analyzed further is the regression equation resulted in values of r and R2 values exceed the accepted hypothesis of tables used product moment r. The method exceeds hypothesis is received and can be used with sufficient accuracy the method of NDVI, arithmetic channel R / NIR and B / NIR analysis of both the mixed vegetation types as well as on the type of vegetation Kayuputih. The best method used in canopy water content mapping is the method are produce the best accuracy, that is the arithmetic channel R / NIR. The regression equations of arithmetic index channel R / NIR imagery of ALOS AVNIR-2 resulted in values of r 0.470 and R2 0.221 with 59.05% accuracy in mixed vegetation types, as well as on the type of vegetation Kayuputih yield resulted value of r 0.604 and R2 0.365 with an accuracy of 63, 96%.