Maria Adamo

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.

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.

Detection and tracking of oil slicks on sun-glittered visible and near infrared satellite imagery

The use of the visible and near infrared (VNIR) bands of MODIS and MERIS imaging sensors acquired in sunglint conditions to reveal smoothed regions such as those affected by oil pollution is investigated. The underlying physical mechanism that enables oil slick detection is based on the modification of the surface slope distribution composing the wind-roughened sea due to the action of mineral oils. The role of sunglint as the chief mechanism that allows the imaging of oil slick features with VNIR wavelengths is assessed for selected case studies in the Mediterranean Sea. The high rate of acquisition and the frequent occurrence of MODIS and MERIS imagery affected by sunglint, especially in low-latitude seas, can thus significantly contribute to increase the actual oil slick detection capability offered by synthetic aperture radar (SAR) systems. We also show how the combined observations from any of the microwave and optical sensors permit the slick to be followed during its movement. Finally, a simulation study specific to the Mediterranean Sea was carried out in order to demonstrate the feasibility of such an approach supporting SAR observations.

On the Estimation of Thickness of Marine Oil Slicks From Sun-Glittered, Near-Infrared MERIS and MODIS Imagery: The Lebanon Oil Spill Case Study

The detection of marine oil slicks using satellite sun-glittered optical imagery has been recently assessed. As the nature of the imaging mechanism involves the altered features of the wind-roughened oil-covered sea surface, it is expected that the radiation reflected from the oil-water system carries information about the physical properties of the floating oil layer. In this paper, we report an investigation on the capability to retrieve the average thickness of thin marine oil slicks by using the sun-glittered component of the solar radiation in the near-infrared (NIR) bands of MEdium Resolution Imaging Spectrometer Instrument (MERIS) and MODerate Resolution Imaging Spectroradiometer (MODIS) images. The developed procedure exploits the Cox and Munk model to compute sun glint reflectance at the sea surface level for both clean and oil polluted sea surface as well. It is assumed that the Fresnel reflection coefficient of the oil-water system carries the relevant optical dependence on oil layer thickness and oil type. The expected oil-water system reflectance is computed by taking into account the non-uniform spatial distribution of the oil volume. This is achieved by considering a pdf of oil thicknesses that matches the observations on controlled oil slicks already reported in the scientific literature. MERIS and MODIS images gathered during the Lebanon oil spill occurred on July and August 2006 were selected as case study. When available, co-located SAR imagery was also considered to corroborate NIR-detected oil slicks.

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.

Assessing water quality in the northern Adriatic Sea from HICO™ data

This letter focuses on water-quality estimation in the northern Adriatic Sea using physically-based methods applied to image obtained with the Hyperspectral Imager for the Coastal Ocean (HICO™). Optical properties of atmosphere and water were synchronously measured to parameterise such methods. HICO™-derived maps of chlorophyll-a (chl-a) and suspended particulate matter (SPM) indicated low values, in the range of 0–3 mg m−3 and 0–4 g m−3, respectively, correlating significantly with field data (R2 = 0.71 for chl-a and R2 = 0.85 for SPM). The results, on analysis, identify clear waters in the open sea and moderately turbid waters near the coast due to river sediment discharge and organic matter from coastal lagoons. These findings support the use of HICO™ data to assess water-quality parameters in coastal zones and suggest the feasibility of integrating them with future-generation space-borne hyperspectral images.

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.

Detection of changes in semi-natural grasslands by cross correlation analysis with WorldView-2 images and new Landsat 8 data

Focusing on a Mediterranean Natura 2000 site in Italy, the effectiveness of the cross correlation analysis (CCA) technique for quantifying change in the area of semi-natural grasslands at different spatial resolutions (grain) was evaluated. In a fine scale analysis (2 m), inputs to the CCA were a) a semi-natural grasslands layer extracted from an existing validated land cover/land use (LC/LU) map (1:5000, time T1) and b) a more recent single date very high resolution (VHR) WorldView-2 image (time T2), with T2 > T1. The changes identified through the CCA were compared against those detected by applying a traditional post-classification comparison (PCC) technique to the same reference T1 map and an updated T2 map obtained by a knowledge driven classification of four multi-seasonal Worldview-2 input images. Specific changes observed were those associated with agricultural intensification and fires. The study concluded that prior knowledge (spectral class signatures, awareness of local agricultural practices and pressures) was needed for the selection of the most appropriate image (in terms of seasonality) to be acquired at T2. CCA was also applied to the comparison of the existing T1 map with recent high resolution (HR) Landsat 8 OLS images. The areas of change detected at VHR and HR were broadly similar with larger error values in HR change images.

Quantitative characterization of marine oil slick by satellite near-infrared imagery and oil drift modelling: the Fun Shai Hai case study

Near-infrared (NIR) satellite images of the oil spill event caused by the Fu Shan Hai wreck on 31 May 2003 in the waters between Sweden and Denmark were compared with numerical simulations provided by the MIKE 21 oil drift model. Assuming a skewed probability density function (pdf) of oil parcel thicknesses, a model of the NIR image oil–water contrast reflectance was developed to characterize the expected oil slick distribution in terms of average and maximum oil slick thickness. Since MIKE 21 Spill Analysis (SA) also allows non-uniform distribution of oil volume within the oil slick, both distributions were thus compared by coincidence of the Moderate Resolution Imaging Spectroradiometer (MODIS/Aqua) acquisition, which imaged the oil slick 3 days after the oil spill started. Results showed an excellent agreement in the numerical values of both the expected average and the maximum thickness. In addition, repartition of the oil volume within the slick in the usual thin (sheen) and thick (brown) parts resulted, consistent with the empirical rule of 20% and 80% of the total oil volume, respectively.

Combined use of SAR and Modis imagery to detect marine oil spills

SAR spaceborne capability to detect marine oil spills through damping of wind-generated short gravity-capillary waves has been extensively demonstrated during past years. In contrast, it has not yet been found the optimal use of optical/NIR imaging sensors for detection and monitoring of polluted areas. We propose the use of Modis images acquired in sun glint conditions to reveal smoothed regions such as those affected by oil pollution. The underlying physical mechanism is based on the modification of the surface slopes distribution composing the roughened sea due to the action of mineral oils. The methodology is demonstrated for selected case studies in the Mediterranean Sea and North Atlantic where spills were detected by ERS SAR imaging. The corresponding Modis images acquired within a few hours were under sun glint conditions according to satellite imaging geometry and wind field distribution over the selected areas. Results of a detailed study about the effective applicability of the method is discussed. The importance of these results are based on the possible extensive exploitation of combined Modis and SAR data in view of the high repetitive coverage (about two times a day).