Valeria Tomaselli

Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: a Mediterranean assessment

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of land cover (LC) and/or land use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies—CORINE Land Cover, the Food and Agricultural Organisation (FAO) land cover classification system (LCCS) and the International Geosphere-Biosphere Programme to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring—a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result.

A rule-based classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and fuzzy logic☆

Abstract Habitat mapping is a core element in numerous tasks related to sustainability management, conservation planning and biodiversity monitoring. Land cover classifications, extracted in a timely and area-extensive manner through remote sensing data, can be employed to derive habitat maps, through the use of domain expert knowledge and ancillary information. However, complete information to fully discriminate habitat classes is rarely available, while expert knowledge may suffer from uncertainty and inaccuracies. In this study, a rule-based classification methodology for habitat mapping through the use of a pre-existing land cover map and remote sensing data is proposed to deal with uncertainty, missing information, noise afflicted data and inaccurate rule thresholds. The use of the Dempster–Shafer theory of evidence is introduced in land cover to habitat mapping, in combination with fuzzy logic. The framework is able to handle lack of information, by considering composite classes, when necessary data for the discrimination of the constituting single classes is missing, and deal with uncertainty expressed in domain expert knowledge. In addition, a number of fuzzification schemes are proposed to be incorporated in the methodology in order to increase its performance and robustness towards noise afflicted data or inaccurate rule thresholds. Comparison with reference data reveals the improved performance of the methodology and the efficient handling of uncertainty in expert rules. The further scope is to provide a robust methodology readily transferable and applicable to similar sites in different geographic regions and environments. Although developed for habitat mapping, the proposed rule-based methodology is flexible and generic and may be well extended and applied in various classification tasks, aiming at handling uncertainty, missing information and inaccuracies in data or expert rules.

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.

Definition and application of expert knowledge on vegetation pattern, phenology, and seasonality for habitat mapping, as exemplified in a Mediterranean coastal site

Abstract Habitats are effective indicators of biodiversity. Remote sensing data and techniques are of great utility for their long-term monitoring. Habitat maps can be derived from land cover (LC) maps through rules obtained from expert knowledge and integrated with in situ data. Spatial (vegetation pattern) and temporal (phenology and water seasonality) relationships were explored and documented to infer reliable rules for LC (according to the Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) taxonomy) to habitat (Annex I to the 92/43 EEC Directive and EUNIS) class translation. A coastal site in southern Italy was considered as study site for the definition and validation of such rules. Phenological data of the plant communities were collected on the basis of vegetation plots randomly distributed within the study site. Water seasonality was extracted from periodical observation of the water surface. Vegetation pattern was analyzed by means of vegetation survey along transects. The potentiality of rules, based on this specific expert knowledge, was tested in an experimental setting for habitat mapping. The overall accuracy of the habitat map was 75.1%. Such a result supports the usefulness of prior expert knowledge for habitat mapping from LCCS classes and disambiguation on one-to-many relations between LC/LU and habitat types.

Land cover to habitat map translation: Disambiguation rules based on Earth Observation data

Earth Observation (EO) images have been extensively used to provide a synoptic view of land cover/use (LC/LU) patterns and land cover/use changes. Land covers are not as clearly relatable to biodiversity in comparison to habitat classifications which can provide more scope for biodiversity monitoring. The main purpose of the paper is to provide an automatic general framework for translating LC maps (in LCCS taxonomy) into habitat maps (in GHC taxonomy) by means of VHR remote sensing data.

8-Band Image Data Processing of the Worldview-2 Satellite in a Wide Area of Applications

Recent years have seen advances in remote sensing in many fields with applications at a spatial scale which range from global to local. As a consequence, the need to observe the Earth with more specialized and sophisticated sensors and data analysis techniques to obtain more accurate information has increased. On the 8th October 2009 a new second nextgeneration Worldview-2 satellite was launched by DigitalGlobe: it represents the latest innovation among sensors for the acquisition of remote sensed imagery. It has an advanced agility due to control moment gyros (like Worldview-1) and combines an average revisiting time of 1.1 days around the globe with a large scale collection capacity. Moreover, it is also the first commercial satellite able to provide panchromatic imagery at 46 cm of spatial resolution and 8-band multispectral imagery at 1.84 m spatial resolution. In addition to the standard panchromatic and multispectral BLUE, GREEN, RED and NEAR INFRARED (NIR1) bands the Worldview-2 sensor has:

Combined use of expert knowledge and earth observation data for the land cover mapping of an Italian grassland area: An EODHaM system application

The aim of this paper is the development of an algorithm, based on expert knowledge, for the Land cover classification of an Italian Grassland Area. To accomplish this task, a dataset composed by 4 Worldiew-2 (WV-2) images, at 2 m of spatial resolution, has been considered. Despite their poor spectral resolution, Very High spatial Resolution (VHR) data allow the identification of individual objects by means of the information in the relationship between adjacent pixels, including texture and shape. For this reason a Geographic Object-Based Image Analysis (GEOBIA) approach consisting in a rule set based on the elicitation of expert rules concerning phenology, spatial features and agricultural practices in conjugation with prior spectral knowledge, has been used. The study area, of almost 500 kmq, is located in Southern Italy (Puglia Region) within the Natura 2000 “Alta Murgia” site (SCI/SPA IT9120007, according to Habitat Directive 92/43 and Bird Directive 147/2009), partly designated as a National Park as from 2004. Semi-natural dry grasslands cover almost 24% of the total area of the site which represent one of the most important areas for the conservation of this kind of ecosystems in Europe.

Multi-modal knowledge base generation from very high resolution satellite imagery for habitat mapping

Abstract Monitoring of ecosystems entails the evaluation of contributing factors by the expert ecologist. The aim of this study is to examine to what extent the quantitative variables, calculated solely by the spectral and textural information of the space-borne image, may reproduce verified habitat maps. 555 spectral and texture attributes are extracted and calculated from the image. Results reached an overall accuracy of 65% per object, 76% per pixel, and 77% in reproducing the original objects with segmentation. Taking into consideration the large number of different habitats queried and the lack of any ancillary information the results suggest the discriminatory power of the finally selected attributes. Potential and limitations are discussed.