Floodplain vegetation has important implications for flood safety, food supply, biodiversity and recreation. Because of river restauration projects, floodplain vegetation is becoming more heterogeneous in space and time. To evaluate and document the development of floodplain vegetation, frequent monitoring is necessary. For monitoring, the detection of changes in vegetation type is a major objective, because of the potential consequences on hydraulic roughness or habitat availability. Manual photo interpretation is a common method to document these changes, but it is time-consuming and expensive. Moreover, interpretation errors can influence the accuracy. Hence, it is preferable to detect change without classification of the RS data. Change detection based on trend deviation or threshold exceedance (multitemporal spectral analysis) is complicated for floodplain vegetation and requires the use of a single type of sensor data, which limits the frequency of available data. Furthermore, the atmospheric corrections will influence the accuracy of the change detection. The aim of this study is to present a method that quickly detects changes (i.e. where did changes occur?) and allows focussed analysis of change identification (i.e. how did it change?). Rather than classification or multitemporal spectral analysis, we compare object attribute values within specific classes at a single moment and detect outlier objects. If objects are identified as outlier multiple times, we consider them as changed. The basic assumptions for this approach are that 1) objects belonging to the same class have similar attribute values; 2) classes differ from each other for (some) attribute values; 3) the majority of objects does not change class within the monitoring period. The method is tested in the floodplains along a 100-km river section along the River Waal in the Netherlands. Frequent and open remote-sensing data is available on the Google Earth Engine, such as Landsat, Sentinel, airborne imagery and surface models. A classified vegetation polygon map based on high resolution airborne imagery from 2008 is used to extract the zonal statistics from the multisensor and multitemporal data. These object statistics are used to detect the outliers at each time step and to monitor them. Changed objects are validated with yearly high resolution airborne imagery. In short, this study evaluates whether sequential deviation of a vegetation object from the average (e.g. spectral) value of its class can be used for change monitoring and whether our basic assumptions are correct. The advantages of this approach are 1) the high frequency of remote-sensing data, because any available spectral/LiDAR/RADAR dataset can be used, allows close monitoring of changes; 2) the independence of classification or multitemporal spectral analysis, which are problematic due to the small (e.g. spectral) differences in vegetation and (3) it allows for fast processing.