Wetness monitoring is very important issue especially on wetlands ecosystems, because they are very vulnerable to changes, particularly those made by human. The upper Narew valley with eminence was analyzed. Described area is in north-eastern Poland and covers the valley from Tykocin to Łazy. This area is unique wetland habitat in Europe. In natural part is an anastomosing river system, whereas second part is covered by agricultural areas (wetlands which were drained in ‘70 of XX century). The aim of this paper is to demonstrate quantitative multitemporal analyses of changes in this environment by using various wetness indices and comparing them. To investigate the amount of changes the images from Landsat were used: from TM and ETM+ scanner (available from http://glovis.usgs.gov/). They were from two time series: the end of XX century (1989, 1992, 1993 and 1994) and the beginning of XXI century (2006 and 2007). All of the images were from the beginning or the middle of the vegetation season. In addition, meteorological data were used (from www. tutiempo.es), to detect the precipitation influence on analyzed indices. NDVI was calculated using image from the 2006, then the mask was created to remove all apart from the vegetation (everything under 0,4). After that the Tasseled Cap transformation was made to obtain Wetness band (TCW). Values under -37 on image from 1993 were masked to eliminate cloudy areas. In next step two wetness indices were calculated: Normalized Difference Infrared Index (NDII) and Moisture Stress Index (MSI). TCW is based on visual, near-infrared and-middle infrared electromagnetic radiation, because of that it could depend on atmospheric conditions. NDII and MSI are calculated only from 4th and 5th Landsat bands. Scattering from aerosols in that part of wavelength is weaker and doesn’t have big impact on indices values. Three describing indices are used when atmospheric correction isn’t possible or needed. Values of the three parameters were mapped by dividing into four classes: higher, medium, lower and the lowest wetness. Maps were averaged in the two time series (end of XX and beginning of XXI century). They were reclassified into tree difference maps to show the differences in wetness conditions and between various indices. Three maps showing changes in wetness were classified into five categories: much more wet, more wet, no changes, drier and much drier. These set of data could be compared. The results show that about 55% of analyzing area is stable. Table 3 present that about 2% of all changes were big. About 30% of total amount of transformation are connected with drainage areas. Areas which were more wet cover about 10%. Drained areas are getting extremely wet based on TCW, but opposite tendency can be noted on MSI and NDII maps. Big discrepancy between the maps of changes was discovered. TCW showed that the natural valley is getting drier and eminences are getting wet, but the results are different for the other two analyzed indices. Apart from that, some of the results are different for the parameters. In further research this kind of analysis should be compared with land cover and field measurements.