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

SAR phenology across major West-African land cover types

West Africa is an important hotspot of global change facing huge environmental and societal challenges. These include climate and land use change, migration, and conflicts, all of which are a major threat to food security. Food security – and a number of other envisaged achievements of the sustainable development goals (SDG) – depends largely on wise natural resource management. For several reasons, the West African environment and its changes are still poorly understood, although a large number of scientific studies have been conducted over the past years thanks to the establishment of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) and other initiatives. However, most of the studies are focused on only few study sites, only very few aim at large-scale assessments of climate change impacts or land use. Little is known about vegetation structure, which plays a crucial role in the estimation of the greenhouse gas budget and the carbon sequestration potential of complex ecosystems such as agroforestry systems. Agroforestry systems are a mixture of different land uses, characterized by a certain tree cover and crops in between. Ideally, the trees do not only shade the fields but also provide fruits that can be used as food or to feed animals. Among the consistent datasets that provide more detailed information about vegetation properties throughout the region is the Copernicus Global Land Cover product (Buchhorn et al. 2020). It is available for multiple years (2015-2019) and provides rich information with regard to vegetation, particularly forests. The spatial resolution is 100 m. However, West African ecosystems are diverse and complex. This complexity is also true for agroforestry systems, which are important agricultural production zones and at the same time fulfill numerous ecosystem services. Unfortunately, none of the well-established nor the recent land cover and land use products such as the beforementioned Copernicus product are able to adequately resolve agroforestry systems. Even the WorldCover 2020 product (Zanaga et al. 2021) with 10 m spatial resolution is not suitable to differentiate between cropland areas, forest cover, shrubland and agroforestry systems. While our hypothesis is that the spatial resolution of the Copernicus Sentinel satellites is limiting the classification of single trees, we expect differences in the phenology within agroforestry systems that can be mapped by means of remote sensing. Phenology, the characteristic, often seasonal life cycle of plants, is an important plant species trait and hence one of the essential biodiversity variables. Many methods exist to retrieve phenology from optical remote sensing data. While the resulting information aids in differentiating plant species or plant functional types, satellite-derived products are usually different from what can be observed in the field. West Africa experiences a strong climate gradient from the hot and dry Sahara Desert region to the moist Guinean forest ecozone. In terms of optical remote sensing, capabilities to retrieve dense time series is limited by frequent cloud cover, particularly in the southern part of the region. Therefore, we propose to use Sentinel-1 Synthetic Aperture Radar (SAR) data to retrieve phenology at pixel level (10 m spatial resolution). In recent years, Sentinel-1 SAR data is increasingly used to characterize phenology of field crops. Little is known about phenology of West African vegetation, particularly non-crops. Consequently, we sampled all classes of the Copernicus Land Cover product covering the ECOWAS region in West Africa and explored Sentinel-1 time series. Our pre-processing includes radiometric terrain correction, speckle filtering and time series smoothing using a Savitzky-Golay filter. For West Africa, only data in ascending orbit is available, resulting in a reduced temporal resolution compared to other regions. From the two polarizations, VV and VH, we computed several well-established indices (e.g. VH/VV ratio, radar vegetation index). We sampled the whole region, resulting in 250 samples per land cover class. For each sampling point we extracted the time series of the backscatter as well as the indices and tested their similarity. As the Copernicus product also provides fractions of each class, we were able to explore the relationship between fractional tree cover (and others) and the SAR backscatter and indices, respectively. Our results show that some of the classes are no longer separable at high spatial resolution (e.g. open evergreen forest vs. closed evergreen forest). After adequate join of similar classes, we were able to use the backscatter information as well as the uncorrelated indices to map Copernicus land cover classes at high spatial resolution (10 m) with acceptable accuracy. From the smoothed time series, we derived phenological parameters such as start of season, end of season and length of season, greenup and senescence. While a direct link to ground phenology is challenging, we are able to map groups of similar phenological behavior, which is important for a more comprehensive characterization of vegetation