event 22 Mar 2022

Report // Assessment and Monitoring of Soil Erosion Parameters in theTransboundary Lake Kivu and Ruzizi River Basin

Lake kivu final report erosion

Executive Summary

In the EO4Lake Kivu project, earth observation data is used to assess the ability to analyse erosion risk from a satellite perspective. With this, the EO4Lake Kivu project supplements the analysis performed in the course of the Baseline Study for the Basin of Lake Kivu and Ruizizi River (Sher Consult, 2020) including the herein performed RUSLE simulations. The scientific work of the EO4Lake Kivu project is divided into four work packages (WP). WP1 and WP2 analyze the parameters vegetation dynamics and extreme precipitation events. These two parameters are combined into an erosion risk index. The results show that there are regions with seasonally repeating low vegetation cover reflected by a low NDVI. This especially applies to agricultural fields and grasslands as identified by the comparison of the NDVI with ESA’s WorldCover map. Particularly low vegetation cover is regularly found in the Ruzizi plain and the southern slopes of the vulcanoes in the north of the study area, where the main agricultural fields are found. In the precipitation analysis not a single hot-spot month or region could be determined.

However, an increased in the number of extreme events is detected for the months April or May, August and November or December in most years. By bringing the vegetation dynamics and extreme precipitation events together, the erosion risk for each season of the years 2016-2020 is identified. Erosion risk hot-spots differ from year to year and from season to season. Nevertheless, some high risk areas could be identified: The Ruzizi plain, the area between the city of Goma and the northern volcanic region, the city agglomerations of Goma and Bukavu and the grasslands east of lake Kivu. A comparison of the satellite based erosion risk analysis and the results of the RUSLE simulation show that both approaches come to similar results (although some hot-spots do not completely align in their location and extend). This suggests that both approaches can be used complementary.

In WP 4, turbidity data of lake Kivu is used to determine, if high erosion risk leads to an increased lake turbidity due to increased sediment input. The quantitative comparison of erosion risk and turbidity does not show a clear connection between the two parameters. This is likely due to the short temporal and small spatial extend of a turbidity increase subsequent to soil erosion events. The turbidity increase may only be visible for the river deltas and may not affect the lake water further away from the coast. A closer look at turbidity information near the coastline and in a higher temporal resolution may lead to more supportive results.

WP 5 analyses another erosion risk parameter: the population growth. By using the World Settlement Footprint, the growth of the two city agglomerations of Goma and Bukavu is analyzed. Especially Goma shows a strong growth in city area from 1995 to 2005. For Bukavu, only minor growth is shown in the last decades. This is probably due to the rugged terrain around the city which does not allow for intense growth outside the city outlines. The growth between 1995 and 2015 depicted by the World Settlement Footprint is used as one input parameter for the SLEUTH model, with which the growth of these 3 EO4Lake Kivu two agglomerations until 2050 is simulated. The predicted urban growth shows only a small increase in city area for Bukavu. Still, this is likely to be a result of the steep slopes around the city. For Goma, a stronger growth of the city area is predicted, especially along the road network on the Rwandan side of the agglomeration. All in all one can expect a growth of the cities in the study area, leading to an increase in population, an increase of sealed surfaces leading to stronger surface runoff of precipitation and a higher demand for agricultural products. These factors are likely to further increase the pressure on the soils in the study area and can provoke more intense soil erosion and soil degradation.

The EO4Lake Kivu project shows, how satellite data can be used to identify erosion hotspots over a large area. The results suggest that soils are endangered in the area. Threats are unsuitable land use /agriculture practices leading to temporally low vegetation cover. An increase in population can further increase the pressure on the available land surfaces. However, the analysis also shows how efficient nature conservation measures can protect lands from soil erosion. The large protected areas in the region show very low erosion risk throughout the year due to year-round dense vegetation cover - a result that was found by both, the remote sensing analysis and the RUSLE simulation.


March 2022


German Aerospace Center - German Remote Sensing Data Center(DLR-DFD)


Download the full report here.

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