Towards an automated estimation of vegetation cover fractions on multiple scales: Examples of Eastern and Southern Africa

Vegetation cover is one of the key parameters for monitoring the state and dynamics of ecosystems. African semi-arid landscapes are especially prone to degradation due to climate change and increased anthropogenic impact on different spatial and temporal scales. In this study, a multiscale method is applied to monitor vegetation cover by deriving sub-pixel percentages of woody vegetation, herbaceous vegetation and soil. The approach is comparatively applied to two semi-arid savannas, one in Namibia and one in Kenya. The results in eastern and southern Africa demonstrate the applicability of the method to different semiarid ecosystems and to different types of remote sensing data. The presented analysis could show that continuous cover mapping is a highly suitable concept for semi-arid ecosystems, as these show gradual transitions rather than distinct borders between land cover types. Different spatial patterns of vegetation cover depending on land use practices and intensities could be revealed

Feedback of observed interannual vegetation change: a regional climate model analysis for the West African monsoon

West Africa is a hot spot region for land–atmosphere coupling where atmospheric conditions and convective rainfall can strongly depend on surface characteristics. To investigate the effect of natural interannual vegetation changes on the West African monsoon precipitation, we implement satellite-derived dynamical datasets for vegetation fraction (VF), albedo and leaf area index into the Weather Research and Forecasting model. Two sets of 4-member ensembles with dynamic and static land surface description are used to extract vegetation-related changes in the interannual difference between August–September 2009 and 2010. The observed vegetation patterns retain a significant long-term memory of preceding rainfall patterns of at least 2 months. The interannual vegetation changes exhibit the strongest effect on latent heat fluxes and associated surface temperatures. We find a decrease (increase) of rainy hours over regions with higher (lower) VF during the day and the opposite during the night. The probability that maximum precipitation is shifted to nighttime (daytime) over higher (lower) VF is 12 % higher than by chance. We attribute this behaviour to horizontal circulations driven by differential heating. Over more vegetated regions, the divergence of moist air together with lower sensible heat fluxes hinders the initiation of deep convection during the day. During the night, mature convective systems cause an increase in the number of rainy hours over these regions. We identify this feedback in both water- and energy-limited regions of West Africa. The inclusion of observed dynamical surface information improved the spatial distribution of modelled rainfall in the Sahel with respect to observations, illustrating the potential of satellite data as a boundary constraint for atmospheric models

Multi-scale time series of biophysical parameters and vegetation structure in heterogeneous landscapes of West Africa

The aim of the BMBF-funded research project CONCERT is to identify emission mitigation options for the major greenhouse gases (GHG), in parallel with improving food security in West Africa. This will be achieved – among others – through the estimation and projection of GHG emission budgets for the region using a fully-coupled regional Earth System Model (ESM), specifically adapted to the WASCAL region. Science-based information for adaptive land management requires quantification of vegetation parameters at stand-scales, and updated high-resolution land cover and vegetation maps to upscale measured GHG fluxes to country-scales. For reliable ESM predictions of future GHG budgets and crop productivity, we need to improve our understanding of the spatial pattern and temporal dynamics of land use and land cover (LULC) in West Africa. Although various LULC datasets exist at global and continental scales, they are often coarse in spatial and temporal resolution and poorly describe the thematic, temporal, and spatial patterns in the heterogeneous savanna landscapes. Here, we assess time series of the leaf area index (LAI) based on earth observation data at different spatial and temporal resolutions. Machine Learning methods allow to fill gaps in the spatial and temporal domains in order to compute dense time series and assess vegetation dynamics. Time series of Sentinel-2-based LAI allow to detect multiple growing cycles with specific magnitudes and provides structural information of vegetation as an important input of ESM

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-towall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience