Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Savanna aliens

Numerous alien plant species are invading African savannas causing loss of biodiversity and altering ecosystem functioning. The ecological factors and underlying mechanisms causing these invasions are poorly understood. This hinders invasive species management and biodiversity conservation. In this thesis, a range of approaches (i.e., field measurements, a greenhouse experiment, field experiments, a long-term burning experiment, remote sensing, and Geographical Information System (GIS) techniques) was used to understand how the availability of two key resources limiting primary productivity in African savannas (water and nutrients) and how major disturbances (i.e., fire, grazing) determine the invasion of these systems by alien plant species

Detection of the vegetation change cover using landsat TM5 in the Burabay State National Natural Park, Kazakhstan

The SNNP «Burabay» is the famous touristic place in Kazakhstan, where the frequent climate change and anthropogenic activities has a significant impact in land cover change. Especially, there is significant impact on vegetation lost and these changes have a detrimental impact on biodiversity, as SNPP is defined as a home for 305 animal species and 800 flora species (https: // kazakhstan.orexca.com/ national park burabay.shtml). The tourism industry has become the dominant contributor to Burabay»s development, so many vegetated areas, green spaces are reducing in order to build hotels, restaurants and different types of entertainment centres. Thus, the SNNP « Burabay» is under the control of the International Union for Conservation of Nature (1UCN) in the category 2 which aims: «to protect biodiversity along with its underlying ecological structure and supporting environmental processes, and to promote education and recreation)) (1UCN, 2017 https://www.iucn.org/)

Biodiversity mapping via Natura 2000 conservation status and ebv assessment using airborne laser scanning in alkali grasslands

L

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Application of remote sensing to state and regional problems

There are no author-identified significant results in this report

Using spectral diversity and heterogeneity measures to map habitat mosaics: An example from the Classical Karst

Questions Can we map complex habitat mosaics from remote-sensing data? In doing this, are measures of spectral heterogeneity useful to improve image classification performance? Which measures are the most important? How can multitemporal data be integrated in a robust framework? Location Classical Karst (NE Italy). Methods First, a habitat map was produced from field surveys. Then, a collection of 12 monthly Sentinel-2 images was retrieved. Vegetation and spectral heterogeneity (SH) indices were computed and aggregated in four combinations: (1) monthly layers of vegetation and SH indices; (2) seasonal layers of vegetation and SH indices; (3) yearly layers of SH indices computed across the months; and (4) yearly layers of SH indices computed across the seasons. For each combination, a Random Forest classification was performed, first with the complete set of input layers and then with a subset obtained by recursive feature elimination. Training and validation points were independently extracted from field data. Results The maximum overall accuracy (0.72) was achieved by using seasonally aggregated vegetation and SH indices, after the number of vegetation types was reduced by aggregation from 26 to 11. The use of SH measures significantly increased the overall accuracy of the classification. The spectral β-diversity was the most important variable in most cases, while the spectral α-diversity and Rao's Q had a low relative importance, possibly because some habitat patches were small compared to the window used to compute the indices. Conclusions The results are promising and suggest that image classification frameworks could benefit from the inclusion of SH measures, rarely included before. Habitat mapping in complex landscapes can thus be improved in a cost- and time-effective way, suitable for monitoring applications

Managing hybrid methods for integration and combination of data

This chapter looks at how monitoring can combine data from multiple sources, including basic observations coupled with auxiliary information and the use of reference data for classification and modelling. A vital component of monitoring research is to be able to combine and synthesize data in a systematic, transparent way that can integrate social and environmental factors and show how these reflect, overlap with, correlate to, and influence each other. Data types and relevant analytical methods are briefly discussed, as well as aspects of classification and semantics, showing best practice in analysis and some suitable methods for describing data properties such as data quality. Typical problems of incompleteness, lack of fit to semantic classes, thematic and geometric inaccuracy, and data redundancy are discussed, with a range of examples showing how these challenges can be met by identifying and filling gaps in datasets