Biodiversity / Terrestrial Ecosystems

The City Biodiversity Index (CBI), also known as the Singapore Index on Cities' Biodiversity, has been specifically developed for monitoring and evaluating biodiversity in cities. In times of an ever increasing population in urban areas - today more than half of the world's population live in cities - this trend poses a range of challenges for cities and their surroundings, not only in terms of resource availability and the quality of urban environments, but also in terms of impacts on biodiversity in cities.

Since the 2010 target to halt biodiversity loss has been collectively missed, the new Aichi biodiversity targets aim to improve the status of biodiversity and to reduce the pressures on biodiversity by 2020. Capturing the status and trends of biodiversity, ecosystems and their services has therefore become a central aspect at local, regional and global scales.

The CBI has been adopted during COP-10 of the CBD in 2010 and is conceived as a self-assessment tool to evaluate the state of biodiversity in cities and to provide insights for improving conservation efforts. This includes an initial baseline measurement, the identification of policy priorities based on these measurements, and a monitoring at periodic intervals.

The CBI includes 23 indicators from three different categories, i.e., (i) native biodiversity in the city, (ii) ecosystem services provided by biodiversity, and (iii) governance and management of biodiversity. During the early implementation of the CBI, it turned out that many cities are missing relevant data, personnel and (GIS) skills to assess some of the 23 indicators.

The current DUE Innovators III project addresses this gap and will develop an approach to provide the required information for four indicators which are often difficult for cities to implement:

Indicator 1: Proportion of natural areas in the city

Indicator 2: Connectivity measures or ecological networks to counter fragmentation

Indicator 11: Regulation of quantity of water

Indicator 12: Climate regulation: carbon storage and cooling effect of vegetation

While the innovation for indicators 1, 11 and 12 is related to the application of harmonised satellite derived information, indicator 2 will be produced by means of an improved method for measuring connectivity based on the effective mesh size metric. This new method replaces the method previously implemented in the CBI. It has already been tested outside of the current project, and the findings show that it much better accounts for intra-patch connectivity and barriers to connectivity than the previous method.

The project team will use satellite-based data and combine them with appropriate in-situ and ancillary data to produce the four indicators. The sensors used (SPOT-5, SPOT-5 Take5, RapidEye) are all in the same range of geometric resolution (around 10 m) to prepare for the use of Sentinel-2 data as soon as they become available. The indicator products will be designed in a way to be directly usable by cities to assess their performance regarding the biodiversity targets.

During the demonstration phase the indicators will be developed for the cities of Barcelona, Edmonton and Tallinn. In phase 2, the approach will be rolled out to additional European and non-European cities, such as Lisbon, Addis Ababa and Durban. 

Movement is an essential variable in animal ecology. It provides crucial information on individual and collective behavior and is the basis for efficient conservation planning of e.g. migratory species. To fully understand movement it is essential to consider the underlying environmental conditions. Animal movements are strongly influenced by the presence and distribution of resources. In this context, remote sensing has gained particular attention. Earth Observation (EO) data supports the monitoring of surface conditions providing a larger perspective on regional processes on multiple spatial and temporal scales. While remote sensing has been intensively used in the scope of movement ecology its application can be improved to take full advantage of the information acquired through EO. Existing applications focus on pixel based information aiming for the spatial and temporal matching of satellite and tracking data. However, this fails to provide an accurate description of the individual’s perception of space and of the interactive learning process that guides resource selection. Additionally, the direct integration of remote sensing data disregards the dynamic nature of the landscape. Spatial dissimilarities are often hidden when considering temporally invariant information.

Selecting adequate spatial, temporal and thematic scales becomes thus essential. Following this premise this research will discuss the impact of scale on the analysis of animal space use and in the consequent understanding of resource selection. It will provide an assessment of the explanatory power of space use comparing the selection of different spatial, temporal and thematic resolutions. Movement data for a population of White Storks (cicogna cicogna) collected in the scope of the LifeTrack program is used as a reference for this analysis. Relevant remote sensing variables are derived from medium spatial resolution and high temporal resolution optical data as well as existing land cover information. Additionally, the integration of Synthetic Aperture Radar (SAR) based information is also tested in the scope of this research.

The alarming rates of global biodiversity loss and degree of ecosystem transformation require new strategies to monitor the coupled ecological-societal system. Existing space data archives and novel data streams from the ESA Sentinels offer unprecedented opportunities to provide rapid, high quality indicators for monitoring these changes. An integrated view of system transitions is also a prerequisite for a well-informed management of key ecosystem services. Yet, by today it remains largely unclear how space and ground-based observations can be optimally integrated to generate data products required by end user communities. “BACI: Detecting changes in essential ecosystem and biodiversity properties – towards a Biosphere Atmosphere Change Index” (http://baci-h2020.eu) is a new H2020 funded project fusing extensive expertise on optical and radar remote sensing, ground data on ecosystem states and functioning, including “big data” scientists and practitioners. The three key aims of BACI are, firstly, to translate space data to new variables (not directly observable from space) that encode ecosystem functional properties and status metrics; Secondly, to prototype an early-warning system the so-called “Biosphere-Atmosphere Change Index” for tracking fundamental changes in land ecosystems; and thirdly, to develop an attribution scheme allowing to disentangle climate-induced ecosystem changes from socioeconomic/ecological transformation processes. The overarching objective of societal relevance is empowering the concept of “essential biodiversity variables” and exploring its practical potential. In this contribution we present an overview of the project as well as the latest advances in integrating in-situ and remote sensing data as well as our prototype of the “Biosphere-Atmosphere Change Index”.

Biodiversity analysis and conservation decision relies on adequate and meaningful data that are available on a long-term and global basis.  Such environmental information need adequate spatial and temporal resolution and remote sensing data does provide a wide range of potentially suitable data sets. The CEOS Biodiversity and the GEO Observation Network (GEO BON) aim to improve the availability, acquisition, coordination and delivery of biodiversity relevant information. This is especially important since the use of remote sensing has increased widely over the past decades, and therefore a better interdisciplinary collaboration will foster the utilization of remote sensing in conservation. One approach to improve the understanding of needed parameters for biodiversity monitoring is through Essential Biodiversity variables (EBVs), which provide guidance to observation systems as to what and how to measure key aspects of biodiversity. The EBV concept was further developed to suit remote sensing specific needs and Skidmore and colleagues developed a first set of remote sensing EBVs (RS- EBVs). The global initiatives aiming to improve the use of remote sensing within biodiversity research and conservation, such as Conservation Remote Sensing network, CEOS biodiversity and GEO BON will be introduced and the past and ongoing activities outlined. An improved understanding of ongoing activities will help both the remote sensing community and space agencies to better address the needs of the biodiversity and conservation community.

The extent of species-rich, semi-natural grasslands has declined drastically in Europe over the last 100 years, primarily due to intensive agriculture and afforestation. Consequently, conservation efforts have been directed to maintaining existing areas of these habitats and several projects seeking to restore these semi-natural grasslands (for instance by converting agricultural land) have been installed.

Restoration activities typically imply a combination of different management practices like mowing, grazing and sodding, mainly intended to gradually lower the amount of available nutrients in the soil. For planning, follow-up and monitoring the effect of those activities, maps depicting the spatially-explicit distribution of floristic composition, biodiversity and ecosystem conditions can be very valuable. However, as it may be difficult to collect this information over wide areas solely from field-based inventories, remote sensing technologies in general, and airborne imaging spectroscopy in particular, might be very useful. A first question which we ask in this study is therefore:

Can we assess the impact of different management regimes (in a grassland system) on the floristic composition, patterns in (beta-) diversity and ecosystem conditions (as expressed in Ellenberg N and R values) from imaging spectroscopy?

Moreover, as in many conservation efforts, budget restrictions often limit the use of imaging spectroscopy, free of charge multispectral sensors like Landsat-8 OLI or Sentinel-2 MSI provide an attractive alternative. Compared to imaging spectroscopy, this multispectral data of course come with a limited spectral information and therefore we also ask the following questions:

Can we also use multispectral sensors for deriving spatially-explicit information on floristic composition, patterns in biodiversity and ecosystem conditions and how do these results compare to maps derived from imaging spectroscopy?

How do spectral and spatial characteristics of the sensor influence the results?

To answer these questions we compared the performance of i) airborne spectroscopy data (APEX), ii) simulated multispectral data (Sentinel-2 MSI) obtained through spectrally and spatially resampling the APEX data and iii) “real” multispectral data sets from Sentinel-2 MSI and Landsat-8 OLI, for mapping the floristic composition, beta-diversity and ecosystem condition in “Landschap de Liereman”, a Natura 2000 site and part of a LIFE project, situated in the north of Belgium. Based on those three remote sensing data sets, maps were generated using a combination of field data, ordination and regression techniques.

Results showed that, although outperformed by APEX, Sentinel-2 provided reasonable accuracies when mapping floristic composition and ecosystem conditions. Compared to Landsat-8, accuracies were about 10% higher. We discuss these observations considering the difference in spectral bands and spatial resolution of the different sensors. Furthermore we also link these results back to the applied management and we discuss the potential of Sentinel-2 for biodiversity research.