supabase Please extract the text as it is Here is the extracted text from the image: DENOSING OF HIGH RESOLUTION REMOTE SENSING DATA USING STATIONARY WAVELET TRANSFORM Danang Surya Candra Peneliti Bidang Jianta, Pusdata, LAPAN e-mail: thedananx@yahoo. - Parsa, I Made (Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital, 2013)
Land cover change, from bare land, water and to vegetation, or vice versa can be used as the basis for paddy fields mapping using the theory of probability, that is probability a land cover can be regarded as an paddy fields if a sequence of land cover changes of water, vegetation and bare land or vice versa on multitemporal images have been detected. The data being used were Landsat multitemporal imagery, while the methods being used in this analysis is the transformation of vegetation index and converted to land covers (bare land, water and vegetation). Detection of three types of land covers (bare land-water_vegetasi or viceversa) at sample area is assumed to have a probability 1 as paddy fields, if only two of the land cover types were detected (water and bare land , or water and vegetation , or vegetation or bare land ) the land cover of that pixel is assumed to have the probability as paddy fields 0.67, whereas if only one land cover types were detected for example only of water, or bare land or vegetation only, then the probability as paddy fields is assumed to be just 0.33. The results of the study showed that multitemporal Landsat of the study area is adequate for paddy fields mapping with accuracy of 91.2%.