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Solifluction is the slow downslope movement of soil mass due to freeze-thaw processes. It is widespread on hillslopes in Polar and Alpine regions and contributes substantially to sediment transport. As solifluction lobe movement is in the order of millimeters to centimeters per year, it is difficult to measure with high spatial and temporal resolution and accuracy. In this study we developed a semi-automated approach to monitor movement using unmanned aerial vehicles, image co-alignment, and COSI-Corr (Co-registration of Optically Sensed Images and Correlation) to track slope movement from orthophotos. The method was applied on yearly images acquired between 2017 and 2021 of three solifluction lobes with different degrees of vegetation cover along an elevational gradient in Turtmann Valley, Swiss Alps. We found movement patterns across all three lobes with highest movement rates at the solifluction lobes center and lowest rates at lobe fronts. Overall, at the highest elevations (2560 m) lobe movement rates were highest with up to 14.0 cm yr−1 and intermediate elevations (2417 m) had the lowest values up to 2.9 cm yr−1. The lobe at the lowest elevation (2170 m) showed intermediate movement rates with up to 4.9 cm yr−1 for single years. Our monitoring approach provides yearly, spatially extensive movement estimates across the complete spatial extent of a lobe for each 1 cm2 of its surface, strongly increasing measurement resolution in comparison to traditional solifluction monitoring approaches using point measurements. In comparison to previous close-range remote sensing approaches, the use of a co-alignment procedure for the acquired drone data enabled a time-saving field setup without Ground Control Points (GCPs). The resulting high co-registration accuracy enabled us to detect solifluction movement if it exceeds 5 mm with sparse vegetation cover. Dense vegetation cover limited feature-tracking but detected movement ra

Tidal flats are among the ecologically richest areas of the world where sediment composition (e.g. median grain size and silt content) and the macrozoobenthic presence play an important role in the health of the ecosystem. Regular monitoring of environmental and ecological variables is essential for sustainable management of the area. While monitoring based on field sampling is very time-consuming, the predictive performance of these variables using satellite images is low due to the spectral homogeneity over these regions. We tested a novel approach that uses features from a variational autoencoder (VAE) model to enhance the predictive performance of remote sensing images for environmental and ecological variables of tidal flats. The model was trained using the Sentinel-2 spectral bands to reproduce the input images, and during this process, the VAE model represents important information on the tidal flats within its layer structure. The information in the layers of the trained model was extracted to form features with identical spatial coverage to the spectral bands. The features and the spectral bands together form the input to random forest models to predict field observations of the sediment characteristics such as median grain size and silt content, as well as the macrozoobenthic biomass and species richness. The maximum prediction accuracy of feature-based maps was close to 62% for the sediment characteristics and 37% for benthic fauna indices. The encoded features improved the prediction accuracy of the random forest regressor model by 15% points on average in comparison to using just the spectral bands. Our method enhances the predictive performance of remote sensing, in particular the spatiotemporal dynamics in median grain size and silt content of the sediment thereby contributing to better-informed management of coastal ecosystems.

Globally, 40% of the population lives in coastal areas, m, often using satellite data. However, little efforts have been undertaken to study detailed observations of channel morphology on the Amazon coast, and its interplay with erosion and accretion is therefore not well understood. Unmanned aerial vehicles (UAV’s) combined with photogrammetric procedures have proven to be a valuable method for observing small-scale (changes in) coastal morphology, as it provides high resolution imagery and elevation data. However, research into the application of this technique in observing coastal elevation is scarce. This thesis focusses specifically on the application of UAV- photogrammetry in analysis of changes and spatial patterns in coastal erosion and accretion, and channel geometry. This research comprises a case study on coastal change and spatial variations in morphological features at Weg naar Zee, a coastal area in Paramaribo, Suriname, through the use of UAV imagery. The objectives were as follows. The potential of using UAV imagery for the assessment of coastal morphology is examined (1). Then, spatial patterns (2) and temporal changes (3) in channel geometry, vegetation cover and elevation were analysed. Orthophotos and DEM’s were created from the UAV imagery for two locations that were 2 km apart, and for one of these locations a second dataset was made for 8 months later in time. Morphological units were mapped manually based on the orthophoto and DEM. For each site, channels width, depth, W:D ratio, area and channel bed elevation and slope were derived from transects with the DEM elevation data. These geometries were compared for alongshore and cross-shore patterns and differences. Lastly, elevation differences between the 8-month period were derived from the DEM’s. Significant differences were visible despite the small spatial scale and short time period. The site closest to the river is bounded by dikes on the coastal side and a cross-shore levee. It showed si

Met de huidige generatie (Sentinel-)satellietbeelden is het goed mogelijk om in landbouwgebieden onbedekte percelen, kale plekken en plasvorming te karteren. Als onderdeel van het Kennisimpuls Waterkwaliteit-project NutriëntenMaatregelen onderzochten Deltares en Universiteit Utrecht hoe deze risicofactoren voor nutriëntenverliezen te volgen zijn vanuit de ruimte.

Debris flows can grow greatly in size and hazardous potential by eroding bed and bank material, but effective hazard assessment and mitigation is currently hampered by limited understanding of erosion and deposition dynamics. We have collected high-resolution pre- and post-flow topography for 6 debris flows over a 3 km long unconsolidated reach of the Illgraben channel in the Swiss Alps with drone-based photogrammetry. We show that the spatio-temporal patterns of erosion and deposition in debris-flow torrents are highly variable and dynamic. Check dams strongly control the spatial patterns of erosion and deposition. We identify a memory effect where erosion is strong at locations of strong deposition during previous flows and vice versa. Large sediment inputs from subcatchments initially result in new channel erosion through the subcatchment deposits and simultaneous upstream deposition, likely as a result of backwater effects. It is generally believed that erosion increases with debris-flow magnitude, but we show that there is a limit to debris-flow bulking set by channel geometry. These findings provide key guidelines for flow volume forecasting, emphasizing the importance of memory effects and the need to resolve both erosion and deposition in predictive models.

Spaceborne remote sensing performs a significant role today in geological mapping, interpretation and analysis of large areas in a rapid and cost-efficient manner. Spaceborne satellite imagery have been successfully utilized in lithological mapping of arid areas, however, similar approaches for lithological mapping in semi-arid regions with high degree of structural complexity, vegetation and topographic effects are yet to be developed. The primary objective of this study is to combine field-based spectroscopic research with geological remote sensing in obtaining representative lithological maps of the Buëch area in South-East France. To achieve this aim Support Vector Machine (SVM) classifier and Spectral Angle Mapper (SAM) were used on Sentinel-2 and ASTER imagery. Spectroscopic examination of field samples enabled the creation of spectral endmembers representative for the area and detection of local weathering mechanisms. The results derived from SVC classifier showed great pattern-recognition ability and more accurate classification of different lithological groups. SAM derived thematic mapper demonstrated the ability to correctly classify a great portion of blue marl outcrops, by recognising the lack of spectral response displayed by blue marls as being the distinctive indicator of the class. Using and evaluating different satellite imagery shows that Sentinel-2 allows better mapping in this semi-arid area, indicating that the higher spatial resolution of Sentinel-2 has an edge over the higher spectral resolution in the SWIR range of ASTER for this specific setting and methodology. The study has shown that field-based spectroscopic research complements geological remote sensing well. The limiting factor in achieving better classification is the spectral and spatial properties of the satellite imagery. Therefore, future research should consider the integration of hyperspectral data and assimilation of geomorphic products to increase the accuracy of lithological

Anthropogenic landscape change (i.e., disturbance) is recognized as an important factor in the decline and extirpation of wildlife populations. Understanding and monitoring the relationship between wildlife distribution and disturbance is necessary for effective conservation planning. Many studies consider disturbance as a covariate explaining wildlife behavior. However, we propose that there are several advantages to considering the spatial relationship between disturbance and wildlife directly using utilization distributions (UDs), including objective assessment of the spatially explicit overlap between wildlife and disturbance, and the ability to track trends in this relationship over time. Here, we examined how central mountain woodland caribou (Rangifer tarandus caribou) distribution changed ov

Water availability is an important constraint on tree and shrub development in Mediterranean ecosystems. During prolonged periods of summer drought, water stored in the soil column is the only available water source. Some Mediterranean tree species are known to have extensive root system penetrating deeply into fractured bedrock. Accurate characterisation of the soil and the ability of trees to subtract water from the soil profile are crucial for the understanding of Mediterranean ecosystems and the modelling of primary production. However, with shallow soils on rocky substrate, it is hard to obtain soil moisture data at depths below 30-50 cm. We explored the use of Electrical Resistivity Tomography (ERT) to detect vegetation water use trough the whole soil column. ERT provides spatial information on soil conditions and moisture content down to 5 m and deeper in a 2D cross-section. It uses a multi-electrode array that is connected to the soil through steel pins that are inserted in the ground. This method of installation allows measurements on rocky substrate. We used ERT to detect spatial and temporal patterns of soil moisture in variable shallow soils and weathered bedrock of the Peyne area in Mediterranean southern France. The Peyne area has a sub-humid climate and is covered mainly in sclerophyllous trees and shrubs. ERT measurements were made for 13 sites in the study area which were all visited in June, at the onset of the dry season, and again in September, near the end of a three-month dry period. Our measurements show root penetration and water use by trees 5m and deeper, despite the shallow soils in the study area. We also show large short-scale spatial variability and the importance of the geological substrate in moisture processes. ERT is a useful new technique for the exploration of soil and ecosystem functioning, even with rocky soils, providing information on rooting depth and water use otherwise unavailable.

Het anti-schurftbeleid komt tot stand door samenwerking tussen het Proefstation, de Gezondheidsdienst voor Dieren en de Landbouwuniversiteit