Availability of pasture and water are essential factors determine time and direction of pastorals movement. In Ethiopian pastorals mobility is a common coping strategy for dealing drought and its induced risks. Helping pastoralists more informed on decisions to manage their resource reduces response costs and livelihood losses. Matching scientiﬁc systems with traditional knowledge can lead to the successful resource managements. The aim of this study is improving the Integrated Pastoral Resource Management of SAPARM Information System using Landsat NDVI Value for Mobility Decision Making. The study was conducted on pastorals and agro-pastorals livelihood zones where livestock production is common. Data was collected in Lege-Hidha district using FGD, key informant interviews, community mapping and spatial satellite images from USGS. The spatial data was analysed using GIS/RS spatial data analysis tools. The data analysis on improving the integrated pastoral resource management (SAPARM and traditional system) using Landsat NDVI value is derived from USGS; Landsat at Path 166/167 and Row 54/55 (study area location found between 166/54, 166/55 and 167/54, 167/55) that verified and visualized using ArcGIS 10.3 and Google earth in order to compare Landsat8 NDVI values of 30m2 resolutions with SAPARM information from Meteosat NDVI at 10km2. Landsat8 analysis confirmed that areas where mobility is conducted have better and detailed vegetation (greenness) of enhanced reflectance than SAPARM. This was due to Landsat resolution capacity provides visible and detailed images of the invisible reflectance area on SAPARM, which improves pastoral mobility and decisions based on distances, direction, greenness, classification and allow knowing immediately specific places. Integration systems in this study attempted to apply traditional resource management with satellite assisted information using images of better resolution capacity enables to clarify and detailed reflectance. The improvement using NDVI values of Landsat ensure images with intensive areas of vegetation cover than Meteosat images of SAPARM.