Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

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Land Degradation:"the many human-caused processes that drive the decline or loss in biodiversity, ecosystem functions or ecosystem services in any terrestrial […] ecosystems" (IPBES 2018).

<p>The project ‘South African Land Degradation Monitor (SALDi)’ contributes to the German-South African Science Program SPACES by addressing the dynamics and functioning of multi-use landscapes with respect to land use, land cover change, water fluxes, and implications for habitats and ecosystem services. Particularly, SALDi aims: i) to develop an automated system for high temporal (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 as a limiting factor for ecosystem services. Protected areas (SANParks and other) within our six study regions represent benchmark sites, providing a foundation for baseline trend scenarios, against which climate-driven ecosystem service dynamics of multi-used landscape (cropland, rangeland, forests) are evaluated. Our study regions follow a climatic SW-NE transect: 1-Overberg, 2-Kai !Garib/Augrabies Falls, 3-Sol Plaatje/Kimberley, 4-Mantsopa/Ladybrand, 5-Bojanala Platinum/Pilanesberg, 6-Ehlanzeni /Mpumalanga.</p><p>We are utilizing Sentinel-1A/B C-Band VV/VH-SAR time series with a 10 m resolution. The revisit time is 12 days on average for South Africa. Pre-processing is done using pyroSAR, a Python framework for large-scale SAR-processing providing processing utilities in ESA’s Sentinel Application Platform (SNAP) as well as GAMMA Remote Sensing software. The first two analytical approaches for the evaluation of the Sentinel-1 time series to detect surface changes, are based on the recognition of irregularities in the radar backscatter or coherence dynamics. Sentinel-2A/B data were pre-processed to L2A and used to calculate a wide range of vegetation indices (e.g. NDVI, EVI, SAVI, REIP) using DLR’s Sen2Cor-processor. The time frame starts with the first Sentinel-1 and -2 acquisitions and continues. The analysis-ready data, that is, harmonized, standardized, interoperable, radiometrically and geometrically consistent data, is being ingested in the SALDi Data Cube. Algorithms and models for developing products such as land degradation indicators are being developed using Jypiter notebooks. SANSA in collaboration with SARAO (South African Radio Astronomy Observatory), is developing the open data cube Digital Earth South Africa (DESA) based on SPOT data. Other datasets from different sensors will be ingested at a later stage. SALDi’s Data Cube will be open access to make it available to the wider scientific community, and also for teaching and training purposes. The application/use of the individual development stages should be possible on the fly for the partners in South Africa. The SASSCAL platform shall be used for distribution of the finalised SALDi Data Cube.</p><p>This presentation demonstrates results from hyper-temporal Sentinel-1 and -2 timeseries concerning woody cover mapping and breakpoint analyses of the complex savanna systems, invasive slangbos (Seriphium plumosum) bush encroachment in grassland areas and regional soil moisture retrievals. Validation has been performed by cross-comparisons with VHR airborne DMC surface products, field trips and permanently installed soil moisture networks and interaction with local South African stakeholders.</p><p> </p>

<p>Land degradation is a human-induced process deteriorating ecosystem functioning and services including soil fertility or biological productivity and, usually, it is accompanied by a loss of biodiversity. Land degradation causes on-site and off-site damages like a profound change or removal of vegetation cover and soil erosion on one hand as well as flooding of receiving streams and siltation of reservoirs one the other hand. Thus, land degradation poses a threat to a number of Sustainable Development Goals (SDG) including foremost sustainable life on land and under water, the provision of clean water and eventually the eradication of poverty and hunger on Earth.</p><p>Often, land cover change is a valid indicator of land degradation providing the opportunity to take advantage of the increasing geometrically and temporally high-resolution remote sensing capabilities to identify and monitor land degradation. However, especially in semi-arid regions like savanna environments, globally driven inter-annual and decadal climate variations cause as well profound land cover dynamics which might be mistaken for land degradation.</p><p>Assessing and combating land degradation has already a long scientific, socio-economic and political history. Based on this, the aim of this session is to explore the wide range of methodological approaches to assess land degradation, its dynamics over all spatial and temporal scales as well as the implications for society and the interaction with the different spheres of the Earth including the anthroposphere, atmosphere, biosphere, hydrosphere and pedosphere. Contributions to this session can be based on field work, remote sensing approaches or modelling exercises, they can also focus on specific physical and socio-economic aspects of land degradation like land management, land cover change or soil erosion or discuss land degradation in a broader societal context. The aim of this contribution is to provide a concise overview of the thematic framework, current activities, research questions and advancements.</p>