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Sensing Biodiversity Across Scales


Scientific background

It is generally established that through the intensification of fertilization and mowing practices in grasslands, the provision of “feed production” as ecosystem service increases, but at the cost of biodiversity and other ecosystem functions. Remote sensing is useful to map certain plant traits and classify the vegetation into functional groups at coarse scales. However, due to the spatial discrepancies between ecological processes and management units in coupled social-ecological systems, we still have a limited understanding of the effects of land use on the relationship between biodiversity – ecosystem functions – and ecosystem services. For instance, while field ecologists measure traits at the species scale, satellites measure traits at pixel scale. Thus, pixels represent inter-pixel variance instead of inter-specific variance. Also, biodiversity and ecosystem functions can vary in space and time with a large number of drivers (e.g. succession stages, climatic gradients and variability, soil properties, land use, etc.) making extrapolations challenging.



The SeBAS (Sensing Biodiversity Across Scales) project is part of the Biodivesity Exploratories, a German Science Foundation funded research project (DFG Priority Programme 1374) with the objective of understanding of the relationship between biodiversity of different taxa and levels, the role of land use and management for biodiversity, and the role of biodiversity in ecosystem processes. The study is developed in three sites across Germany, covering different land use intensity and environmental gradients: the Biosphere Reserve, Schorfheide-Chorin in the State of Brandenburg, the National Park Hainich and its surroundings in the State of Thuringia, and the  Biosphere Reserve Schwäbische Alb in the State of Baden-Württemberg. The three exploratories serve as open research platform for multiple biodiversity and ecosystem research groups, and have been operating since 2006.




In SeBAS, we aim to improve the mechanistic understanding of the effects of land use on the interplay between biodiversity – ecosystem functions – and ecosystem services. For that, we are analyzing the relationships between functional and structural diversity and the ecosystem service of forage production, and their temporal variation for three spatial scales (plot, farm and landscape). We will achieve this by combining plot-based ecological and remote sensing research on land use intensity and 5 Essential Biodiversity Variables (EBVs): Above Ground Biomass (AGB), Above Net Primary Productivity (ANPP), Leaf Area Index (LAI), plant phenology and functional diversity.



The data collected from the 5 EBVs during a full annual cycle is allowing us to quantify forage production along with functional composition and diversity across the season at sub-plot level. At the same time and in collaboration with Core-3, we are retrieving multispectral and microwave data from UAVs and multiple satellites (Sentinel-1 & 2, Landsat-8, MODIS and PlanetScope). Following a multi-scale sampling and upscaling strategy we are using machine learning and artificial intelligence algorithms to scale up the field information to the satellite pixels, and to a landscape scale.


Figure 1: Different steps in our fieldwork. In cooperation with Core-3, multispectral data is collected with cameras on board of fixed-wing (a) and quadcopter (b) UAVs. A field spectrometer is used to collect spectral information in a wider range (c). Afterward, plant trait and biomass data are collected to be analyzed later in the laboratory (d).



We hypothesize that (i) the five EBVs can be derived on multiple spatial scales using multimodal satellite image time series data; and that (ii) the effects of land use on the relationship of biodiversity to ecosystem functions and services vary across spatial scales. Here, the functional and structural diversity is likely to play a key role in the level and temporal stability of feed production.


Figure 2: Upscaling from plant functional traits (a), to cover estimations (b), UAVs with  multispectral sensor (c), and satellite multispectral imagery from Planet Cubesat (d).