General Habitat Categories Research Articles

A survey of habitats on agricultural land in Estonia: I Construction and validation of the database using the botanical field data

The project was initiated to model the species change in agricultural land under different scenarios in Estonia. This paper describes the first stages of the work.Thirty-five 1-km squares were selected for a survey using a dispersed random procedure within the eight classes of the Environmental Stratification of Estonia, which were weighted according to the number of 1-km squares.In each 1-km square, up to 11 vegetation plots were recorded using predetermined rules in three habitats where succession was taking place, four habitats that were potential sources of seed, and four habitats with linear features. Linear features had between 141 and 178 species present and were a major contribution to the biodiversity resources on agricultural land.Full lists of vascular plants were recorded, and the details of the methods that were used to locate the plots are provided. All the habitats over 400 m2 were also mapped using the General Habitat Categories, which are consistent throughout Europe.The field survey was performed from 2015 to 2017 in 35 1-km squares. Overall, 433 species were recorded, which covers 42% of the Estonian flora, as presented in the new Atlas.Interpretation of the frequency and cover of the different types of plots provides details of the structure and the composition of the current Estonian land and shows that there are still significant biodiversity resources in agricultural land − for both species and vegetation. Neglected and abandoned land were in 21 and 23 of the 35 squares, respectively, but the former land had 5% cover in only one stratum. Abandoned land was over 14% in the Southern Uplands, but only 10% in the Northern Lowlands. Crops varied from 2% to 53% cover, whereas forest cover was always above 25% and reached 55% in one stratum.

Exploring the causes of high biodiversity of Iberian dehesas: the importance of wood pastures and marginal habitats

In extensive low input farming and in agroforestry systems, the importance for biodiversity of managed productive fields with respect to unmanaged marginal habitats that occupy a low proportion of farm surface, is still poorly understood, contrasting with the well-known key importance of marginal habitats in intensive systems. We analyzed the importance of open and wood pastures and marginal habitats for species richness of Iberian dehesas in Central-Western Spain. We sampled 155 plots classified into 9 general habitat categories: wood pastures (n = 41 plots); open pastures dominated by annual plants (n = 11), by perennial plants (n = 15) and co-dominated by annuals and perennial plants (n = 16); shrublands (n = 19); agricultural crops (n = 12); herbaceous strips (n = 10); woody strips (n = 11); and water bodies (n = 10). In each plot we measured the abundance and species richness of four taxonomic groups: vascular plants, bees, spiders, and earthworms. We detected 431 plant species (37 ± 2.5 CI95 in 100 m2 on average), 60 bee species (3.1 ± 1.1 in 600 m2), 128 spider species (7.4 ± 1.2 in 1.5 m2) and 18 earthworm species (2.5 ± 1.0 in 0.27 m2) in 145 sampling plots. Wood pastures supported fewer species of spiders and earthworms at the plot level, but more plants and earthworm species at the landscape level than open pastures. The low proportion of shared species among habitats and among plots within each habitat type, and the high proportion of species found in unique plots or habitats indicated that every habitat contributes to farm biodiversity. Overall, our extensive survey confirms the hypothesis that the high diversity of dehesas depends on the coexistence within farms of a wide mosaic of habitats, including marginal habitats, which seemed to harbor a disproportionately high number of species as compared to their small extent. Results support policy measures for the maintenance of farm keystone structures such as linear features, small wood/shrub patches and ponds, and reveal that these measures should not be exclusively applied to more intensive farming systems.

Extrapolation of in situ data from 1-km squares to adjacent squares using remote sensed imagery and airborne lidar data for the assessment of habitat diversity and extent.

Habitat surveillance and subsequent monitoring at a national level is usually carried out by recording data from in situ sample sites located according to predefined strata. This paper describes the application of remote sensing to the extension of such field data recorded in 1-km squares to adjacent squares, in order to increase sample number without further field visits. Habitats were mapped in eight central squares in northeast Estonia in 2010 using a standardized recording procedure. Around one of the squares, a special study site was established which consisted of the central square and eight surrounding squares. A Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image was used for correlation with in situ data. An airborne light detection and ranging (lidar) vegetation height map was also included in the classification. A series of tests were carried out by including the lidar data and contrasting analytical techniques, which are described in detail in the paper. Training accuracy in the central square varied from 75 to 100%. In the extrapolation procedure to the surrounding squares, accuracy varied from 53.1 to 63.1%, which improved by 10% with the inclusion of lidar data. The reasons for this relatively low classification accuracy were mainly inherent variability in the spectral signatures of habitats but also differences between the dates of imagery acquisition and field sampling. Improvements could therefore be made by better synchronization of the field survey and image acquisition as well as by dividing general habitat categories (GHCs) into units which are more likely to have similar spectral signatures. However, the increase in the number of sample kilometre squares compensates for the loss of accuracy in the measurements of individual squares. The methodology can be applied in other studies as the procedures used are readily available.

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.

Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC)

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/semi-automatic translation framework of LC/LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed.

Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system

Monitoring land cover and habitat change is a key issue for conservation managers because of its potential negative impact on biodiversity. The Land Cover Classification System (LCCS) and the General Habitat Categories (GHC) System have been proposed by the remote sensing and ecological research community, respectively, for the classification of land covers and habitats across various scales. Linking the two systems can be a major step forward towards biodiversity monitoring using remote sensing. The translation between the two systems has proved to be challenging, largely because of differences in definitions and related difficulties in creating one-to-one relationships between the two systems. This paper proposes a system of rules for linking the two systems and additionally identifies requirements for site-specific contextual and environmental information to enable the translation. As an illustration, the LCCS classification of the Le Cesine protected area in Italy is used to show rules for translating the LCCS classes to GHCs. This study demonstrates the benefits of a translation system for biodiversity monitoring using remote sensing data but also shows that a successful translation is often depending on the degree of ecological knowledge of the habitats and its relationship with land cover and contextual information.

Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories

The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77–82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to mixed phenology signals; (ii) the GHC scheme classification design, which allows for parcels of heterogeneous covers, and (iii) the low number of the training samples available from field surveys. A mapping strategy integrating EO-based phenology with vegetation height information is expected to be more effective than a purely phenology-based approach.

The turtle is in the details: microhabitat choice by Testudo hermanni is based on microscale plant distribution

Abstract Although research on habitat use and habitat selection is essential for understanding population ecology and behavior, most such zoological studies have used only general habitat categories describing main habitat features instead of using modern plant ecological approaches. Here, we analyze Testudo hermanni microhabitat use at a coastal Mediterranean site in central Italy by modeling tortoise presence/absence at three spatial scales, using a logistic regression design and quantitative vegetation and plant community analysis to reveal correlates of tortoise habitat use on a fine scale. Our analyses showed that only a few plant species among the many present, and these on a very small spatial scale, are important determinants of tortoise presence and site selection. We also find that tortoises chose a paradoxical combination of high levels of bare soil and high total vegetation cover. This suggests that these tortoises are selecting small patches of habitat in a matrix of less desirable habitat. Our findings also have important implications for habitat management, in that increasing the number of habitat patches containing the few significant plants is likely to be desirable, whereas increasing the size of such patches is probably less relevant.

A standardized procedure for surveillance and monitoring European habitats and provision of spatial data

Both science and policy require a practical, transmissible, and reproducible procedure for surveillance and monitoring of European habitats, which can produce statistics integrated at the landscape level. Over the last 30 years, landscape ecology has developed rapidly, and many studies now require spatial data on habitats. Without rigorous rules, changes from baseline records cannot be separated reliably from background noise. A procedure is described that satisfies these requirements and can provide consistent data for Europe, to support a range of policy initiatives and scientific projects. The methodology is based on classical plant life forms, used in biogeography since the nineteenth century, and on their statistical correlation with the primary environmental gradient. Further categories can therefore be identified for other continents to assist large scale comparisons and modelling. The model has been validated statistically and the recording procedure tested in the field throughout Europe. A total of 130 General Habitat Categories (GHCs) is defined. These are enhanced by recording environmental, site and management qualifiers to enable flexible database interrogation. The same categories are applied to areal, linear and point features to assist recording and subsequent interpretation at the landscape level. The distribution and change of landscape ecological parameters, such as connectivity and fragmentation, can then be derived and their significance interpreted.

European framework for surveillance and monitoring of habitats: a methodological approach for Spain

There is a well defined policy requirement for a practical and reproducible procedure for surveillance and monitoring of habitats in Spain that can subsequently be fitted into a European framework. Any such procedure also needs to incorporate records of the Spanish habitat classification. A procedure is described that will satisfy those requirements and has been field tested both in Spain and in Europe. Rigorous rules and training are required; otherwise changes from baseline records cannot reliably be separated from background noise. The procedure uses the classical plant life forms long used in biogeography and is based on their statistical relationship with the environment. This relationship has been validated statistically and the procedure has also been tested in the field in all European environmental zones and widely in Spain. 130 General Habitat Categories are defined and these are enhanced in the field by recording environmental, site and management qualities to produce a flexible database that can then be interrogated. The rules for mapping the habitats mean that they can be used for descriptive purposes or for monitoring. In conjunction with stratification and subsequent sampling, national estimates of stock and change can then be produced. Finally, based on the previous experience of SISPARES, a methodological scheme is proposed for adapting the approach to Spain. The proposal will allow links to be made between European scale surveillance and monitoring to those already obtained for Spain.