## 3 Methods
### Land Use and Land Cover Classification and Change Assessment
#### 3.1.2 LULC Changes at the Catchment Scale  
As contributing catchments have an essential impact on the hydrological functioning of a wetland as well as on its water and energy balance, it is crucial to monitor long-term LULCC at catchment scale. For wetlands in tropical and subtropical Africa, frequent cloud cover often hampers the performance of more conventional compositing and classification approaches for optical satellite imagery, particularly when high spatial resolution is required [48]. Synthetic Aperture Radar (SAR) systems are therefore often preferred over optical systems, in particular when seasonal dynamics are to be considered [49; 50; 51]. Their availability, however, is limited since consistent operational image acquisitions have not been taken until recently. With Sentinel-1, operating as a two satellite constellation, data are continuously available and free of charge [52; 53]. Historic data records are restricted to specific observation periods and regions. Therefore, optical data from different periods have been used to assess long-term LULCC. In order to obtain complete spatial coverage of the Kilombero catchment and to account for intra-annual water and land use dynamics, all available Landsat scenes from a three-year epoch around 1994, 2004, and 2014 were subjected to the calculation of temporal metrics [45]. This method follows the assumption that images of dry periods with low water tables as well as rainy season images with high water tables are included. \begin{table}
\begin{tabular}{c c c} \hline
**1990 (Landsat 5)** & **2004 (Landsat 5)** & **2016 (Sentinel 2)** \\ \hline
1 June 1991 & 7 May 2004 & 6 December 2015 \\
11 July 1990 & 17 July 2004 & 26 December 2015 \\  & & 24 May 2016 \\  & & 13 June 2016 \\ \hline \end{tabular}
\end{table} 
Table 1: Images used for the LULC maps of the Kilombero floodplain. The use of temporal metrics became popular during the past years due to data availability and its efficiency in LULC assessment [54]. Temporal metrics are capable of suppressing outliers due to remaining artifacts originating from inaccurate cloud masking. Accurate cloud masking, however, is desired. Landsat surface reflectance higher level data products [55; 56] were acquired from the USGS downloading platform Earth Explorer ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))). Cloud masking was performed using the pixel quality file appended to each dataset. It contains information about cloud cover and is generated based on the Fmask algorithm [57; 58]. The catchment area delineation was based on the SRTM digital elevation model (DEM) applying the DEM processing tool of the Soil and Water Assessment Tool (SWAT, [59]). From multi-temporal statistical metrics the Normalized Difference Vegetation Index (NDVI, [60]), the Normalized Difference Water Index (NDWI [61]), and Tasseled Cap components describing brightness, greenness and wetness [62; 63; 64; 65] were calculated. Combined with the SRTM digital elevation model (DEM) and derivatives (slope, surface roughness, topographic wetness index [66]), an input dataset for a Random Forest (RF) classification was composed. RF is a supervised ensemble classifier developed by [PERSON] [67; 68], reported to be stable with limited or imbalanced input data [69], not to overfit [68; 70] and to generally yield high accuracies in land use and land cover classification (e.g., [71; 72; 73]). Field observations, flight campaigns and Google Earth were used as reference for model training and validation. We made use of a hierarchical classification scheme containing two levels, with Level 1 comprising only four main classes (forest vegetation, non-forest vegetation, non-vegetated, water), and Level 2 including ten sub-classes (montane forest, closed woodland, open woodland, teak plantation, cropland, grassland, bare soil, swamp, built-up and water).