## 3 Methods  
### Land Use and Land Cover Classification and Change Assessment  
#### 3.1.1 LULC Changes in the Kilombero Floodplain  
Using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and optical satellite images from different time steps and sensors, the study area was mapped at the floodplain scale. Three time steps were considered where most of the changes were expected to occur: 1990, 2004 and 2016. Since the area is not covered by a single pass of sensors, multiple images had to be used. Table 1 lists the images analyzed in this study. For 1990 and 2004 the Landsat 5 Thematic Mapper (TM) was used. The most recent time step (2016) was covered by multiple Sentinel-2 data [43]. The Landsat TM has six optical and shortwave infrared image bands with a spatial resolution of 30 m. Sentinel-2 has thirteen optical and shortwave infrared spectral bands; four at 10 m spatial resolution, six at 20 m spatial resolution and three at 60 m spatial resolution, the latter being used mainly for atmospheric correction and cloud masking. The spectral properties of Sentinel-2 and Landsat 8 are comparable in the visible, near infrared and short-wave infrared spectral region. Due to its larger spatial coverage of 290 km--compared to the 185 km of Landsat--and its higher spatial resolution, we preferred Sentinel-2 over Landsat 8 for the 2016 time step.  
Figure 1: Sentinel-2 image of the study area. RGB: 12, 8A, 4.  
Oblique and vertical photos taken during two flight campaigns in January and June 2016 were included for training the 2016 images. The campaigns were conducted over parts of the catchment that are of importance for local authorities and the project goals. The images deliver high spatial resolution and geo-tagged photographs. Local experts assisted in their interpretation, thereby minimizing errors of misinterpretation. The photo interpretation of Landsat, Google Earth ([[https://www.google.com/intl/de/earth/](https://www.google.com/intl/de/earth/)]([https://www.google.com/intl/de/earth/](https://www.google.com/intl/de/earth/))) and Bing ([[https://www.bing.com/maps](https://www.bing.com/maps)]([https://www.bing.com/maps](https://www.bing.com/maps))) images was applied to train the 1990 and 2004 satellite images. Because one of the primary goals of the study was to quantify the expansion of agricultural areas, the area was divided into four coarse classes: open to close forest, arable land (including industrial and subsistence farming), water and wetland. This last class comprises natural non-forested areas that are seasonally flooded. The classification of built-up areas is challenging because buildings are often made of natural materials such as clay, and relatively low-density settlements blend with farmed areas. Therefore, built-up areas covered only small areas of the catchment and this class was omitted from the LULCC analysis, although the growth of the main centers such as Ifakara and the belt of settlements along the main transport infrastructure is considerable. The classification was performed using the SWOS toolbox. This software was specifically developed for the segment-based classification of wetlands. The classification process applied here includes segmentation of the satellite images and a supervised, segment-based maximum likelihood classification algorithm implemented in the SWOS toolbox. Similar methodologies have been used in other wetland monitoring projects [44], and the latest version of it is currently being applied over 25 wetlands covering a wide range of biogeographic regions. Validation was performed using 400 randomly distributed points in CollectEarth [45]. We calculated LULC area estimations for each class for each time step, following the recommendations developed by [PERSON] et al. (2014) [46]. The error matrices are expressed in area proportions rather than in unit counts, and error adjusted area estimations are calculated for each class. LULCC analysis was performed via post-classification comparison (PCC) [47] of the LULC maps.