The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.

<p>In recent decades, due to the increasing weight of agricultural machineries, undesirable soil compaction has become a severe factor for soil degradation. It does not only have negative ecological, but also economic effects. Successfully detecting, monitoring and predicting depths of ruts that were used by heavy vehicles can potentially reduce soil compaction. A crucial task is to generate real-world data at site-specific locations. UAV-based approaches have the advantage of providing sufficient spatial coverage and resolution to assess vital information, which can be used to directly evaluate the compaction resulting from the utilization of these heavy machineries. This information could be used for agricultural predictions, optimized routing and best time for treatments, soil regeneration purposes and many more. Therefore, the aim of this research was to spatially detect the depth of ruts, caused by heavy farm machineries on agricultural fields with consumer-grade Unmanned Aerial Vehicles (UAVs).</p><p>We therefore created a semi-automatic processing pipeline for UAV based data. The georeferenced RGB orthomosaic was used to spatially predict lanes, in the early stages of the crop cycle, by employing a Machine Learning approach. This prediction was subsequently used to extract the height information of the rut and the surrounding area from the SfM (Structure from Motion) Digital Elevation Model. As a reference method for the absolute height information, we compared this DEM (DJI Phantom 4) to the UAV - LiDAR derived DEM (RIEGL miniVUX-1UAV). For both systems, no substantial difference in the quality of the evaluated compaction depth was observed. This allows the use of low-cost UAV RGB systems to contribute to the ongoing research on soil compaction.</p>