ITS2.9/SSS3

After the conversion to cropland, dust emissions can lead to the degradation of agricultural soil. There are also offsite effects of dust emission due to the impact of dust on climate, human health, and global biogeochemistry. The sandy croplands in the Free State of South Africa have been identified by Eckardt et al. (2020) as one of the main dust sources in South Africa. The Free State is a semi-arid province that is dominated by grassland plains and 31% of the land is utilized for agriculture. The emission of dust from sandy Luvisols and Arenosols, which are typically used for crop farming, is mainly controlled by the cropping cycle. In general, the fields are left bare from at least July until December. When the fields have low surface roughness and stubble cover, the presence of physical soil crusts could be one of the main factors protecting the surface against wind erosion. Crusts can form before or during the growing season, before the vegetation cover is too extensive and protects the soil from raindrop impact. The aim of this study was to investigate the occurrence and strength of physical soil crusts on cropland soils in the Free State, to identify the rainfall required to form a stable crust, and to test their impact on dust emissions. Crust strength was measured using a fall cone penetrometer and a torvane, while laboratory rainfall simulations were used to form experimental crusts. Dust emissions from non-crusted and crusted soils were measured and compared with a Portable In-Situ Wind Erosion Laboratory (PI-SWERL).

Our results show that crusts with sufficient strength to limit dust emissions form on bare Arenosols and Luvisols in the field, illustrating their potential impact on dust emissions. The laboratory rainfall simulations showed that stable crusts could be formed on these soils by 15 mm of rainfall, which is a common amount for single events during the rainy season in the Free State. The PI-SWERL experiments illustrated that the PM10 emission flux of such crusted soils is between 0.14% and 0.26% of that of a non-crusted Luvisol and Arenosol, respectively. The presence of loose sand on the crust acts as an abrader and can increase the emissions up to 4% and 8 % of the non-crusted dust flux. Overall, our study shows that crusts in the field are potentially strong enough to protect the soil surfaces against wind erosion during a phase of the cropping cycle when the soil surface in not protected by plants. These conclusions are not limited to the converted grasslands in the Free State. This indicates that applying farming techniques on croplands that protect crusts or enhance crust formation could be considered as soil management approach to minimize dust emission from dryland sandy soils.

How to cite: Vos, H., Fister, W., Eckardt, F., Palmer, A., and Kuhn, N.: Crust formation on sandy savanna cropland soils and their potential to reduce dust emissions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1041, https://doi.org/10.5194/egusphere-egu21-1041, 2021.

Gully erosion is regarded as one of the worst land degradation processes in the world. Rapid identification of the location of gully features is urgently required, to aid in recognizing regions where gully erosion is prominent. Manual digitizing of gully features is both time consuming and prone to bias. Generating semi-automated or automated workflows to detect gully erosion allows quick and unbiased mapping of gully features over large extents.

In the Sandspruit catchment, South Africa, contour banks with a combined length of approximately 25000km have been constructed to mitigate soil erosion. Gullies are now mostly confined to narrow slivers in the natural vegetation, fynbos and Renosterveld, between agricultural fields. The morphological similarity and proximity of contour banks and gullies in this region provides a good test site to evaluate whether a semi-automated detection workflow could map gullies in complex, rough agricultural terrain.

Here, a Digital Surface Model (DSM) with a spatial resolution of 2m was used to test a semi-automated detection workflow in a Geographical Information System (GIS) environment. Two main building blocks were generated from the DSM: 1) a normalized DSM, created by subtracting a convolved mean DSM with a designated filter size from the original DSM, and 2) local slope generated from the normalized DSM. Subsequently, using expert knowledge, mapped gully polygons were refined and smoothed, by threshold determination, masking features not related to drainage, and pixel-based growing and shrinking. The semi-automated workflow was completed for two different spatial resolutions: 1) the native 2m-resolution and 2) a 0.5m-resolution DSM, upsampled without producing artificial values from interpolation methods. A GeoEye-1 image with a spatial resolution of 0.5m was included at the backend of the workflow as an additional step, to test whether gully mapping from using terrain attributes only, could be improved upon.     

Gully detection from terrain attributes only, achieved an overall accuracy of 0.68 (0.5m DSM) and 0.74 (2m DSM) with kappa values ranging from 0.36 (0.5m DSM) and 0.35 (2m DSM). The upsampled 0.5m DSM performed worse than the native 2m DSM due to increased noise detection. Although reasonable performance was obtained from the 2m DSM, issues encountered include: 1) vegetation that caused some inaccuracies in gully boundary delineation and discontinuities along gully channels and 2) false positive detection of contour banks. The addition of the GeoEye-1 image increased overall accuracy to 0.79 and kappa value to 0.5, mostly because of the elimination of false positives in agricultural fields.

The accuracy statistics indicate that the semi-automated detection workflow developed here shows promise as a tool to detect gully erosion on a catchment scale. Furthermore, due to the workflow being built upon the distinct morphology of gully features, it could be transferable to other regions that are dissimilar to the Sandspruit catchment. The transferability of the workflow should be tested in future, in addition to how accuracy would be affected if the DSM were substituted with a Digital Terrain Model (DTM) of similar spatial resolution.

How to cite: Olivier, G., van de Wiel, M., and de Clercq, W.: Semi-automated detection of gully slivers from a Digital Surface Model in rough agricultural terrain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5747, https://doi.org/10.5194/egusphere-egu21-5747, 2021.

Land degradation is one of the main causes of loss of productivity and ecosystem services worldwide. According to the United Nations Convention to Combat Desertification (UNCCD), sub-Saharan Africa is on a path to experiencing some of the strongest increases in pressures on land and land-based resources than any other continent. Assessing the sensitivity of sub-Saharan African countries to land degradation is, therefore, important for identifying areas of concern, setting a baseline for national land degradation neutrality targets, and for the prioritisation of mitigation measures. The widely used MEDALUS-ESA framework is employed here to assess the sensitivity of Kenya to land degradation using the year 2010 as a baseline. We modify the MEDALUS-ESA approach by adding two important variables that are closely linked with observed land degradation in Kenya: soil erosion and livestock density. Altogether, 16 indicators are estimated from existing global-to-national-scale land cover, vegetation (MCD12Q1, MOD44B), soil (ISRIC African SoilGrids), elevation (SRTM), population and livestock density data, divided into 4 main environmental quality indices (vegetation, soil, climate and management). In order to address the dynamic nature of the land degradation process, we incorporate two additional vegetation indicators: the statistically significant (p≤ 0.05) trend over the last three decades in the Normalised Difference Vegetation Index (NDVI) and the Rain Use Efficiency (RUE; estimated using the GIMMS3g dense NDVI dense time-series and precipitation from CHIRPS). Our results show that ~40% of the country is in critical and ~48% in fragile condition, with respect to environmental sensitivity. Our approach is successful in identifying areas of known long-term degradation, for example the rangelands South and East of Nairobi (e.g. Machacos and Kitengela) and the parts of the northern rangelands (e.g. Yamicha and eastern parts of Isiolo District). It is also successful in mapping the areas of least concern, including some of the major protected areas(e.g. Tsavo National Parks, Meru National Park and the Masai Mara National Reserve) and forested areas (Mt Kenya and the Aberdares). Our modification of the MEDALUS-ESA is an important tool that can be employed at the national scale using free and open-access data to assess environmental sensitivity and assist in the UNCCD efforts to successfully define land degradation neutrality targets.

How to cite: Symeonakis, E., Arnau-Rosalén, E., Wandera, A., Higginbottom, T., and Cain, B.: Incorporating vegetation trends to the MEDALUS-ESA approach for assessing environmental sensitivity at the national scale: the case of Kenya, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16468, https://doi.org/10.5194/egusphere-egu21-16468, 2021.

Semiarid rangelands have been identified as at high risk of degradation as a result of changing socio-ecological conditions. Tanzanian savannahs are typical and some areas have become degraded in recent years, while other areas maintain resilience. To track pathways to degradation, we developed a workflow to create annual maps of degradation for all of Tanzania, at a high spatial (30m) and temporal (30+ years) resolution, as a function of bare ground and invasive plant cover. Making use of the freely available Google Earth Engine (GEE) computing platform, we created annual composites of Landsat remote sensing data. Using GEE machine learning algorithms, trained with data from extensive field surveys conducted in 2016, we predicted degradation scores for all of Tanzania from the Landsat composites. Our models produced significant correlations at the pixel level between test predictions and observations, rather better for the bare ground component of degradation than the invasive plants cover (bare ground r = 0.7, invasive plant cover r = 0.44). The resulting map provides an unprecedented data source for degradation in terms of extent and spatial resolution for the region. Through a novel data analysis approach using Integrated Nested Laplace Approximations (INLA), we show that degradation correlates with rainfall, human population and livestock density, as well as different management strategies. This study showcases the potential of GEE for analysing savannah degradation over large geographical areas, whilst highlighting the usefulness of INLA for this type of analysis.

How to cite: Wiethase, J., Critchlow, R., and Beale, C.: Analyzing trends of savannah degradation in Tanzania using Google Earth Engine and INLA, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10052, https://doi.org/10.5194/egusphere-egu21-10052, 2021.

For many decades the problem of land degradation has been an issue in South Africa. This is mainly due to the high variability of the mostly semi-arid climatic conditions providing a challenging environmental setting. Strong population growth and resulting socio-economic pressure on land resources aggravate the situation. Thus, reaching a number of Sustainable Development Goals (SDGs), like achieving food security (#2), access to clean water (#6), and the sustainable use of terrestrial (#15) and marine (#14) resources represents a challenge.

In South Africa, land degradation has been linked to the terms veld degradation and soil degradation and has been addressed by numerous measures over the past decades. However, there is still uncertainty on the extent of human induced land degradation as compared to periodic climate induced land surface property changes. In cooperation with South African institutions and stakeholders the overarching goal of SALDi is to implement novel, adaptive, and sustainable tools for assessing land degradation in multi-use landscapes. Building upon the state of the art in land degradation assessments, the project aims to advance current methodologies by innovatively incorporating inter-annual and seasonal variability in a spatially explicit approach. SALDi takes advantage of the emerging availability of high spatio-temporal resolution Earth observation data (e.g. Copernicus Sentinels, DLR TanDEM-X, NASA/USGS Landsat), growing sources of in-situ data and advancements in modelling approaches.

SALDi focusses on six study sites representing a major climate gradient from the (humid) winter-rainfall region in the SW across the (semi-arid) year-round rainfall to the (very humid) summer-rainfall region in the NE. The sites cover also different geological conditions and different agricultural practices. These include commercial, rain-fed and irrigated cropland, free-range cattle and sheep farming as well as communal and subsistence farming. Protected areas within our study regions represent benchmark sites, providing a foundation for baseline trend scenarios, against which climate-driven ecosystem-service dynamics of multi-used landscapes (cropland, rangeland, forests) will be evaluated.

The aim of this presentation is to provide an overview of recent activities and advancements in the three thematic fields addressed by the project:

i) to develop an automated system for high temporal frequency (bi-weekly) and spatial resolution (10 to 30 m) change detection monitoring of ecosystem service dynamics,

ii) to develop, adapt and apply a Regional Earth System Model (RESM) to South Africa and investigate the feedbacks between land surface properties and the regional climate,

iii) to advance current soil degradation process assessment tools for soil erosion.

A number of additional SALDi team member presentations will provide detailed information on current developments.

How to cite: Baade, J., Schmullius, C., Urban, M., Kunstmann, H., Laux, P., Zhang, Z., Glotzbach, C., Gessner, U., Hirner, A., Kluter, P., Otte, I., Aucamp, I., Chirima, G., Abd Elbasit, M., Morgenthal, T., Smit, I., Strydom, T., Le Roux, J. J., von Maltitz, G., and Msibe, T.: South African Land Degradation Monitor (SALDI) – An overview of recent advancements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12808, https://doi.org/10.5194/egusphere-egu21-12808, 2021.

Global biodiversity and ecosystem services are under high pressure of human impact. Although avoiding, reducing and reversing the impacts of human activities on ecosystems should be an urgent priority, the loss of biodiversity continues. One of the main drivers of biodiversity loss is land use change and land degradation. In South Africa land degradation has a long history and is of great concern. The SPACES II project SALDi (South African Land Degradation Monitor) aims for developing new, adaptive and sustainable tools for assessing land degradation by addressing the dynamics and functioning of multi-use landscapes with respect to land use change and ecosystem services. SPACES II is a German-South African “Science Partnerships for the Adaptation to Complex Earth System Processes”. Within SALDi ready-to-use earth observation (EO) data cubes are developed. EO data cubes are useful and effective tools using earth observations to deliver decision-ready products. By accessing, storing and processing of remote sensing products and time-series in data cubes, the efficient monitoring of land degradation can therefore be enabled. The SALDi data cubes from optical and radar satellite data include all necessary pre-processing steps and are generated to monitor vegetation dynamics of five years for six focus areas. Intra- and interannual variability in both, a high spatial and temporal resolution will be accounted to monitor land degradation. Therefore, spatial high resolution earth observation data from 2016 to 2021 from Sentinel-1 (C-Band radar) and Sentinel-2 (multispectral) will be integrated in the SALDi data cube for six research areas of 100 x 100 km. Additionally, a number of vegetation indices will be implemented to account for explicit land degradation and vegetation monitoring. Spatially explicit query tools will enable users of the system to focus on specific areas, like hydrological catchments or blocks of fields.

How to cite: Otte, I., Mashiyi, N., Kluter, P., Hill, S., Hirner, A., Eberle, J., Urban, M., Mlisa, A., Kganyago, M., Schwinger, M., Gessner, U., Schmullius, C., and Baade, J.: Development of earth observation data cubes for monitoring land degradation processes in South Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14349, https://doi.org/10.5194/egusphere-egu21-14349, 2021.

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be major factors driving land degradation. During the last decades woody cover encroachment has increased over large areas in southern Africa inducing environmental, land cover as well as land use changes. 

The goal of this study is to synergistically combine SAR (Sentinel-1) and optical (Sentinel-2) earth observation information to monitor the slangbos encroachment on arable land in the Free State province, South Africa, between 2015 and 2020. Both, optical and radar satellite data are sensitive to different land surface and vegetation properties caused by sensor specific scattering or reflection mechanisms they rely on. 

This study focuses on mapping the slangbos aka bankrupt bush (Seriphium plumosum) encroachment in a selected test region in the Free State province of South Africa. Though being indigenous to South Africa, the slangbos has been documented to be the main encroacher on the grassvelds (South African grassland biomes) and thrive in poorly maintained cultivated lands. The shrub reaches a height and diameter of up to 0.6 m and the root system reaches a depth of up to 1.8 m. Slangbos has small light green leaves unpalatable to grazers due to their high oil content and is better adapted to long dry periods compared to grass communities.

We used the random forest approach to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were based on expert knowledge and field information from the Department of Agriculture, Forestry and Fisheries (DAFF). Several input variables have been tested according to their model performance, e.g. backscatter, backscatter ratio, interferometric coherence as well as optical indices (e.g. NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), EVI (Enhanced Vegetation Index), etc.). We found that the Sentinel-1 VH backscatter (vertical–horizontal/cross-polarization) and the Sentinel-2 SAVI time series information have the highest importance for the random forest classifier among all input parameters. The estimation of the model accuracy was accomplished via spatial-cross validation and resulted in an overall accuracy of above 80 % for each time step, with the slangbos class being close to or above 90 %. 

Currently we are developing a prototype application to be tested in cooperation with local stakeholders to bring this approach to the farmers level. Once field work in southern Africa is possible again, further ground truthing and interaction with farmers will be carried out.

How to cite: Urban, M., Schellenberg, K., Morgenthal, T., Dubois, C., Hirner, A., Gessner, U., Zhang, Z., Mogonong, B., Baade, J., and Schmullius, C.: Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Encroachment Mapping in the Free State Province, South Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3004, https://doi.org/10.5194/egusphere-egu21-3004, 2021.

Changing climatic conditions and unsustainable management strategies associated with biodiversity loss are perceived as major threats to Namibian savannahs. In the past, land-use in Namibia is dominated by livestock-farming as one of the major economic products. However, high grazing pressure led to degrading pastures in many regions in the country. In response, more farmers have recently shifted their land-use strategy from livestock to wildlife-based management, with so far unclear consequences for ecosystem dynamics. 
In this study, the ecohydrological, spatially explicit savanna model EcoHyD (Tietjen et al. 2009, 2010; Lohmann et al. 2012, Guo et al. 2016) was used to assess the impact of different land-use strategies on plant composition and ecosystem properties. The aim was to systematically evaluate the impact of different land-use strategies in terms of animal types and densities on the diversity of major plant groups (shrubs, perennial and annual grasses) and on several ecosystem processes. The results allow for identifying sustainable landuse strategies that avoid degradation and that lead to long-term provision of ecosystem services and economic income. 
We identified typical different functional plant types (PFTs) of the study region and parameterized the model to reflect the local environmental dynamics of the private game reserve Etosha Heights in Namibia. Afterwards, we run the model and assessed the composition and cover of our simulated PFTs, as well as water availability dependent on the land-use scenario. The results are in line with our expectations: they show that total plant cover increases with decreasing stocking rate and that cover and biodiversity are generally higher in browsing scenarios. In addition, we could explore, which PFTs of a given plant group are best adapted to grazing or browsing animals in a certain density. We could also show that perennial grasses benefitted more than shrubs from lower stocking rates. This benefit led to an improved soil water availability to plants, since less water was lost by overland flow, implying also a lower erosion risk. As the model has been applied to a variety of environmental settings regarding climatic conditions but also soil properties, we are confident that this study can serve as  blueprint to assess shifts in land-use also in other savannah systems. 

How to cite: Irob, K., Tietjen, B., Blaum, N., Strohbach, B., and Kanduvarisa, A.: Understanding the feedback of landuse practices and vegetation change in a Namibian savannah - a model assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15912, https://doi.org/10.5194/egusphere-egu21-15912, 2021.