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Zone Umide della Capitanata, located in the northeastern part of the Apulia Region, is one of the most extensive coastal wetlands of the Italian peninsula and one of the largest components of the Mediterranean wetland system. Despite its high ecological importance, this site has been undergoing a variety of pressures intensified in recent decades. This study analyzes and evaluates the changes occurred in this area between 2010 and 2020. Land cover and habitat maps were performed by photointerpretation and on-site surveys, and classified according to the FAO-LCCS and EUNIS taxonomies, respectively. To focus on local dynamics, four subset areas were analyzed separately. A set of landscape metrics was computed to analyze the landscape structure. The anthropogenic pressures affecting the study area were described through the DPSIR (Driving Forces-Pressures-State-Impact-Response) conceptual framework. Numerous changes were identified, deriving from both finest thematic redefinition and anthropogenic pressures. Both class conversions and class modifications were identified and quantified by means of transition matrices. Most of the observed conversions were borne by classes belonging to saltmarshes and to coastal dune systems. In particular, landscape configuration of coastal dune classes was well highlighted by a set of specific landscape metrics. Agriculture practices and changes in water flow pattern turned out to be the main driving forces exerting pressures on these natural systems. Significant differences were found between the four subsets under analysis, thus, indicating that different management strategies lead to different levels of conservation.

Land degradation processes have undergone towards a significant increase in recent decades which is likely to further increase if no actions are taken. The need of adopting practices to contain, mitigate and restore degraded land have been stressed by the new Soil Strategy. In order to guide actions, through a common and effective framework, a cost and time efficient approach is needed. Experiences from six pilot sites in southern European countries, selected within the NL4DL LIFE project, were examined and monitored with both available field data and satellite-acquired data, in order to be replicated, improved and transferred to similar degraded sites. The heterogeneity and complexity of degradation processes resulted from the pilot sites highlighted issues and necessities of harmonization and standardization of ecological/physical indicators, especially those derived from satellite observations, when used as proxies of land degradation. The aim of the tool is to provide a reference procedure to be applied to monitor restoration activities based on nature-based solutions on degraded lands, once the degradation processes has been identified. Variables considered in the tool include processes of degradation, physical indicators derived from remotely sensed approaches and most common practices of nature-based solutions. Connections between these variables, which form the basis of the relational map of the tool, have been initially collected through the internal knowledge of the project and will be further deepened trough expert-based questionnaires to experts of different disciplines in the field of soil and environmental sciences. The decision-making tool provide a workflow to guide the end users in the field of environmental management and planning, through the identification of land degradation processes, the selection of monitoring indicators viable and the most suitable nature based solution for that particular degraded land. The tool will be used as an input base for an action protocol at local and regional scale for environmental management and planning

The need of adopting practices to restore degraded land has been stressed by the new Soil Strategy, with regard to dry ecosystems showing greater risk of desertification. To assess the effectiveness, feasibility and replicability of restoration techniques applied to Mediterranean dry ecosystems, a set of different restoration techniques has been tested in Alta Murgia National Park (Southern Italy), one of the NewLife4Drylands project (NL4D; https://www.newlife4drylands.eu) pilot sites. This area is particularly subjected to land degradation processes as a result of recent and widespread activities of rock shattering for the conversion of calcareous pastures to croplands, which had caused an extensive loss of semi-natural vegetation and ecosystem functions. Experimental efforts were aimed at testing sustainable techniques for the restoration of protected dry grassland types that naturally occur in the study area. Within an overall surface of 9000 m2, 42 experimental plots of 20x10m areas (14 treatments with 3 replicates) were selected in either recently ploughed or unploughed surfaces. For each soil type, a set of restoration techniques was tested, including different combinations of soil processing (i.e. harrowing, topsoil inversion, sod cutting, soil compression), transfer of plant material (i.e. dry hay, seed-enriched hay, shrub seeds) and soil cover (jute bionet). Unmanaged surfaces were selected as control plots for each soil type. Restoration effects, in terms of changes in plant community and soil features, will be monitored in comparison with initial conditions. The experimented approaches, along with the assessment of its effectiveness and feasibility, will provide useful information to guide the users to the identification of the most suitable nature-based solutions for the restoration of degraded lands. Within the aims of the NL4D project, the results of this experiment will be used to validate protocols and monitoring tools for environmental management and planning at local and regional scale based on the use of remote sensing techniques.

The stable presence of humans in the Alps dates back to the Bronze Age and peaked in the mid-19th century, deeply shaping the landscape and allowing the co-evolution of numerous plant and animal species. Since the 1950s, socio-economic changes have led to the gradual depopulation of mountain areas, and the consequent abandonment of traditional agro-pastoral activities. The rupture of the long-established balance between man and nature has triggered a process of transition, further exacerbated and accelerated by climate change. The Belmont Forum project ABRESO (Abandonment and rebound: Societal views on landscape and land-use change and their impacts on water and soils) started in 2021 and aims at advancing the understanding of mitigation and adaptation strategies to environmental change, through an international partnership involving five countries (the United States, France, Italy, Japan and Taiwan). Italy contributes to the project with three case studies: Gran Paradiso National Park, Val Grande National Park and the Tesino highlands are investigated in the Italian Alps. Using an interdisciplinary approach, the project aims to study the impact of the abandonment of traditional activities on ecosystem services provisioning, such as biodiversity conservation and soil sustainability, as well as the actual perception of the ongoing environmental changes by different stakeholders and its subsequent integration into local land management practices and policies. The land use and land cover change occurring due to land abandonment can have profound implications in the critical zone (CZ), inducing changes in soil, vegetation, carbon fluxes and water resources. This project integrates the natural and social sciences approaches to study the evolution of ecosystems in response to these factors. More specifically, advanced techniques that integrate Earth Observation, biogeochemical analyses and socio-economic investigation are used in the Italian sites to understand in which extent geo-biophysical and social landscapes reciprocally interact. The environmental variables collected for ecosystem monitoring and to study and upscale the ongoing dynamics in the CZ include snow cover and phenology parameters, soil organic carbon, and land use change maps extracted from time series of satellite imagery, validated via in situ measurements. Then, the observed processes will be compared to the perception of different stakeholders (local population, policy makers, tourists, business keepers, etc.) to unveil new insights into the way land use change in the mountain areas influence and is influenced by the local land management practices and policies.

The risk of wildfires has risen significantly in recent years, not just in Europe but around the world. In Italy alone, hundreds of thousands of hectares are burned each year, resulting in deaths, the destruction of forests and loss of biodiversity, and damage to infrastructure and farms. One way to address this issue is through satellite remote sensing, which is a valuable tool for monitoring and managing fires, assessing risks, surveying and evaluating the damage caused by fires and preparing recovery actions.The objective that the Alta Murgia National Park is pursuing with this project is to gather information on fires that have occurred within the Park area, in order to quickly identify affected areas and aid in their perimeter, characterization and control, and to support the preliminary and timely design of forest restoration efforts, as well as the updating of AIB plans according to legal requirements.The project aims to develop automated satellite monitoring procedures using Landsat and Sentinel2 imagery to assess the health of forested areas and identify and characterize degradation caused by negative events such as forest fires, illegal logging, conversion of forest land to agriculture, and improper use of areas historically affected by fire. A module based on difference in the Normalized Burn Ratio (NBR) index would allow to define the effective perimeter of damage caused by a fire within the larger perimeter defined by firefighting crews. Additionally, a module using various vegetation indices, such as those related to chlorophyll and carotenoids, will be employed to compare vegetation changes across the landscape and over time.The project also involves using a time series analysis to retrospectively monitor the recovery of vegetation following a critical event, and a Bayesian approach previously developed by the group will be used to estimate expected phenological statistics with associated error. To further understand the recovery process, a single forested site burned in 2020 will be closely monitored with an eddy covariance tower and through repeated floristic surveys.Lastly, the project aims to establish a pilot low-cost ground-based monitoring and video surveillance system to supplement the existing video surveillance network. This system will focus on monitoring remote or less frequented areas of the park, where installing high-end monitoring stations would not be cost-effective. The system proposed is self-sufficient in terms of power and is capable of performing real-time image analysis over the study area. In the event of a fire or the emission of harmful gases, the system will immediately alert relevant law enforcement agencies.The current work summarizes the current state of the project and the scientific results obtained so far.

Xylella fastidiosa, subsp. pauca (Xfp), has attacked the olive trees in Southern Italy causing severe impacts on the agro-ecosystem services, such as provisioning, regulating and cultural. To reduce both the Xfp cell concentration and the symptom disease, a bio-fertilizer restoration technique has been used. Our study applied multi-resolution satellite data to evaluate the effectiveness of such restoration action at both field and tree scale. For field scale, a time series of High Resolution (HR) Sentinel-2 images, acquired in the months of July and August from 2015 to 2020, was employed. First, four spectral indices from treated and untreated fields were compared. Then, their trends were correlated to meteo-events. For tree-scale, Very High Resolution (VHR) Pléiades images were selected at the closest dates of the Sentinel-2 data to investigate the response to treatments of each different cultivar. All indices from HR and VHR images resulted higher in treated fields than in those untreated. The analysis of VHR indices revealed different cultivar response to treatments. The findings from HR data could be used to evaluate plant conditions at field level after treatments, while VHR imagery could be used to optimize treatment doses per cultivar. The findings in agreement with in-field PCR results.

This study presents the results of multi-seasonal WorldView-2 (WV-2) satellite images classification for the mapping of Ailanthus altissima (A. altissima), an invasive plant species thriving in a protected grassland area of Southern Italy. The technique used relied on a two-stage hybrid classification process: the first stage applied a knowledge-driven learning scheme to provide a land cover map (LC), including deciduous vegetation and other classes, without the need of reference training data; the second stage exploited a data-driven classification to: (i) discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map; (ii) determine the most favourable seasons for such recognition. In the second stage, when a traditional Maximum Likelihood classifier was used, the results obtained with multi-temporal July and October WV-2 images, showed an output Overall Accuracy (OA) value of ≈91%. To increase such a value, first a low-pass median filtering was used with a resulting OA of 99.2%, then, a Support Vector Machine classifier was applied obtaining the best A. altissima User’s Accuracy (UA) and OA values of 82.47% and 97.96%, respectively, without any filtering. When instead of the full multi-spectral bands set some spectral vegetation indices computed from the same months were used the UA and OA values decreased. The findings reported suggest that multi-temporal, very high resolution satellite imagery can be effective for A. altissima mapping, especially when airborne hyperspectral data are unavailable. Since training data are required only in the second stage to discriminate A. altissima from other deciduous plants, the use of the first stage LC mapping as pre-filter can render the hybrid technique proposed cost and time effective. Multi-temporal VHR data and the hybrid system suggested may offer new opportunities for invasive plant monitoring and follow up of management decision.

Snow cover plays an important role in biotic and abiotic environmental processes, as well as human activities, on both regional and global scales. Due to the difficulty of in situ data collection in vast and inaccessible areas, the use of optical satellite imagery represents a useful support for snow cover mapping. At present, several operational snow cover algorithms and products are available. Even though most of them offer an up-to-daily time scale, they do not provide sufficient spatial resolution for studies requiring high spatial detail. By contrast, the Let-It-Snow (LIS) algorithm can produce high-resolution snow cover maps, based on the use of both the normalized-difference snow index (NDSI) and a digital elevation model. The latter is introduced to define a threshold value on the altitude, below which the presence of snow is excluded. In this study, we revised the LIS algorithm by introducing a new parameter, based on a threshold in the shortwave infrared (SWIR) band, and by modifying the overall algorithm workflow, such that the cloud mask selection can be used as an input. The revised algorithm has been applied to a case study in Gran Paradiso National Park. Unlike previous studies, we also compared the performance of both the original and the modified algorithms in the presence of cloud cover, in order to evaluate their effectiveness in discriminating between snow and clouds. Ground data collected by meteorological stations equipped with both snow gauges and solarimeters were used for validation purposes. The changes introduced in the revised algorithm can improve upon the overall classification accuracy obtained by the original LIS algorithm (i.e., up to 89.17 from 80.88%). The producer’s and user’s accuracy values obtained by the modified algorithm (89.12 and 95.03%, respectively) were larger than those obtained by the original algorithm (76.68 and 93.67%, respectively), thus providing a more accurate snow cover map.

Assessing and maintaining the conservation of natural and semi-natural grassland ecosystems is one of the most important actions of the Biodiversity Strategy by the European Commission.The present study focuses on the detection of long-term changes, from 1990 to 2018, of natural grasslands ecosystem, at local scale, in the “Murgia Alta”, a National Park as well as a Natura 2000 protected area, Southern Italy. The study site represents one of the largest areas for the conservation of such ecosystem in Italy. It is under pressure and in danger of destruction due to soil graining for agricultural intensification, illegal expansion of extraction sites, fires and land abandonment with consequent biodiversity loss.Land Cover (LC) changes and class trends are one of the measures (sub-indicator) required for the implementation of the Sustainable Development Goals (SDG) 15.3.1 Indicator (“Proportion of land that is degraded over total land area”) of the Agenda 2030 by United Nations.Multisource/multiresolution free available satellite data (visible, near infrared and short wave infrared spectral bands) were considered. Historical images from Landsat (4 images per year, one per season) were analyzed to produce different LC multiclass maps for 1990, 2001, 2004, 2011 and 2018 at 30 m spatial resolution, with an automatic data-driven classifier (Support Vector Machine). For 2018 Sentinel-2 data, 10 m spatial resolution, were also considered.The mean value of the Overall Accuracies obtained for the LC maps from Landsat was 95%. Similar value was obtained in the last year from Sentinel-2.Then natural grassland layer was extracted from those maps to analyze the trend of the grasslands ecosystem over time. The findings obtained indicate a total loss in the extension of the ecosystem of about 18% from 1990 to 2018. The major decrease (26%) occurred in 1990-2001. Then a modest decrease followed up to 2004 (year of institution of the National Park). Finally a slight increase probably due to land abandonment followed to fire events was quantified after 2004.From the comparison of the different LC maps obtained, the decrease of natural grasslands resulted mainly due to transformation into agricultural areas.In addition, these results are consistent with those obtained using Corine Land Cover maps available for the same period although at a coarser scale.The SDG sub-indicator was evaluated inside the protected area and in a buffer, 10 km, area around. This sub-measure, which can be evaluated from time-series of satellite free data, can support long-term monitoring of protected area and can be used not only for the resilience evaluation of the study site to climate changes but also for the evaluation of conservation policies and as input to scenario modelling.

In Murgia Alta National Park the repeated fire perturb the stability of the environment and it s capacity to be a carbon sink. Thanks to the Landsat archive we can observed change in phenology t over the two decade (2000-2019). Unfortunately the phenological signal extracted from Landsat time series bear several uncertainties caused by missing data and error in atmospheric correction that makes difficult to reconstruct the trajectory of each pixel. Applying a Bayesian Harmonic model we can obtain not only expected values for the vegetation index time series but also confidence interval both for vegetation index and derived statistics. We took the phenological statistical framework of the Ecological Functional Attributes (EFA) to obtain annual statics and evaluate the time of recovery to obtain EFA with no statistical difference from the pre-perturbation time.The results highlighted that only of subset of burned forest recover EFA values after 10 years of critical events. In particular the values of intra year variability tend to be higher due to the different trajectory of young shoots. The burned grassland time of recovery is much shorter given that the vast majority of pixel recover pre-event EFA in less than 4 year.

Coastal wetlands are one of the most threatened ecosystems worldwide. In the Mediterranean Region, wetlands are undergoing rapid changes due to the increasing of human pressures (e.g. land reclamation, water resources exploitation) and climate changes (e.g. coastal erosion), with a resulting habitat degradation, fragmentation, and biodiversity loss.Long-term habitat mapping and change detection are essential for the management of coastal wetlands as well as for evaluating the impact of conservation policies.Earth observation (EO) data and techniques are a valuable resource for long-term habitat mapping, thanks to the large amount of available data and their high spatial and temporal resolution. In this study, we propose an approach based on the integration of time series of Sentinel-2 images and ecological expert knowledge for land cover (LC) mapping and automatic translation to habitats in coastal wetlands. In particular, the research relies on the exploitation of ecological rules based on combined information related to plant phenology, water seasonality of aquatic species, pattern zonation, and habitat geometric properties.The methodology is applied to two Natura2000 sites, “Zone umide della Capitanata” and “Paludi presso il Golfo di Manfredonia”, located in the northeastern part of the Puglia region. These two areas are the most extensive wetlands of the Italian peninsula and the largest components of the Mediterranean wetland system.Land Cover classes are labelled according to the FAO-LCCS taxonomy, which offers a framework to integrate EO data with in situ and ancillary data. Output habitat classes are labelled according to EUNIS habitat classification.

Grassland ecosystems can provide a variety of services for humans, such as carbon storage, food production, crop pollination and pest regulation. However, grasslands are today one of the most endangered ecosystems due to land use change, agricultural intensification, land abandonment as well as climate change. The present study explores the performance of a knowledge-driven GEOgraphic-Object—based Image Analysis (GEOBIA) learning scheme to classify Very High Resolution (VHR) images for natural grassland ecosystem mapping. The classification was applied to a Natura 2000 protected area in Southern Italy. The Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) hierarchical scheme was instantiated in the learning phase of the algorithm. Four multi-temporal WorldView-2 (WV-2) images were classified by combining plant phenology and agricultural practices rules with prior-image spectral knowledge. Drawing on this knowledge, spectral bands and entropy features from one single date (Post Peak of Biomass) were firstly used for multiple-scale image segmentation into Small Objects (SO) and Large Objects (LO). Thereafter, SO were labelled by considering spectral and context-sensitive features from the whole multi-seasonal data set available together with ancillary data. Lastly, the labelled SO were overlaid to LO segments and, in turn, the latter were labelled by adopting FAO-LCCS criteria about the SOs presence dominance in each LO. Ground reference samples were used only for validating the SO and LO output maps. The knowledge driven GEOBIA classifier for SO classification obtained an OA value of 97.35% with an error of 0.04. For LO classification the value was 75.09% with an error of 0.70. At SO scale, grasslands ecosystem was classified with 92.6%, 99.9% and 96.1% of User’s, Producer’s Accuracy and F1-score, respectively. The findings reported indicate that the knowledge-driven approach not only can be applied for (semi)natural grasslands ecosystem mapping in vast and not accessible areas but can also reduce the costs of ground truth data acquisition. The approach used may provide different level of details (small and large objects in the scene) but also indicates how to design and validate local conservation policies.

The present study proposes a method based on the integration of EO and census data for updating the spatial distribution map of the migrant population regularly residing in an area very exposed to migration on the Southern Mediterranean coast, i.e. Bari (Italy) and its surrounding areas. The method implemented is a vector-based variant of the dasymetric mapping approach used by the Joint Research Center (JRC) within the Data for Integration initiative to disaggregate data from census areas into a uniform grid and it draws on updated census data (1st January 2019) concerning regular migrant population, an updated human settlements (built-up) map and information about their use. The mapping of the built-up areas results from either a data-driven or knowledge-driven automatic classification techniques of multi-temporal Sentinel-2 satellite images acquired during 2018 with 10 m spatial resolution. The spatial distribution (density) map of regular migrant population obtained along with other updated ancillary data were used, at local (intra-urban) scale, to provide Sustainable Development Goal (SDG) 11 indicators. These include the ratio of land consumption rate to regular migrant population growth rate (SDG 11.3.1) and the map of regular migrant population living in inadequate housing (SDG 11.1.1) as specific categories of SDG 11 for the epochs 2011 and 2018. At the local level, the regular migrant population density map and the SDG 11 indicator values were provided for each cell of a 100 m by 100 m output grid. In recent literature, not many authors have tried to implement SDG 11 indicators at intra-urban scale. The findings obtained in the study area indicate an increase of migrant population up to 44.5% in 2018 with respect to the previous estimate provided by JRC and based on 2011 census data. In addition, the results reveal the tendency by the communities to aggregate in specific neighborhoods located in central areas of the city characterized by urban decay and abandoned buildings. The products obtained through the dasymetric method applied could be useful to support urban planners and decision makers not only in the management of increasing migration pressure but also for monitoring the progress in Agenda 2030 related to SDG 11 indicators at local level.

We would like to thank the ReCAS Computing Center of the University of Bari, and, particularly, Stefano Nicotri and Giacinto Donvito for the use of facilities; in particular their Jupiter online access to the virtual environment for computation. The manuscript was proofread by Lena Rettori. We would like to thanks the contribution of the two anonymous reviewers., Vegetation index time series from Landsat and Sentinel-2 have great potential for following the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity. Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution, producing irregularity in time series of satellite images. We propose a Bayesian approach based on a harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior distribution that integrate information across the years. From the model, the mean and standard deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak’s day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to the model’s abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO Land Cover Classification System 2., We would like to acknowledge the support of H2020 Ecopotential project with Grant Agreement No. 641762 for the discussion and the set up of a first version of the algorithm not shown in this paper, Geoessential an ERA-PLANET project, an action from ERA-NET-Cofund Grant, with Grant Agreement No. 689443 for the actual development of the algorithm and the writing of the paper.

Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and their sub-indicators. When provided at the intra-urban scale, these essential variables can facilitate the extraction of population flows, including both local and regular migrant components. This paper discusses a modification of the dasymetric method implemented in our previous work, aimed at improving the population density estimation. The novelties of our paper include the introduction of building height information and site-specific weight values for population density correction. Based on the proposed improvements, selected indicators/sub-indicators of four SDG 11 targets were updated or newly implemented. The output density map error values are provided in terms of the mean absolute error, root mean square error and mean absolute percentage indicators. The values obtained (i.e., 2.3 and 4.1 people, and 8.6%, respectively) were lower than those of the previous dasymetric method. The findings suggest that the new methodology can provide updated information about population fluxes and processes occurring over the period 2011–2020 in the study site—Bari city in southern Italy.

Summary 1.Long-term habitat mapping and change detection are essential for the management of coastal wetlands as well as for evaluating the impact of conservation policies. Earth Observation (EO) data and techniques are a valuable resource for long-term habitat mapping. Although the use of EO data is well developed for the automatic production of Land Cover (LC) maps this is not the same for habitat maps, which are highly related to biodiversity. 2.In a previous paper, we used the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) environmental attributes (e.g. water quality, lithology, soil surface aspect) for LC-to-habitat class translation. However, these environmental attributes are often not openly available, not updated or are missing. 3.This paper offers an alternative, knowledge-based solution to automatic habitat mapping. When only expert rules and EO data are used, the final overall map accuracy which is obtained by comparing reference ground-truth patches to the ones depicted in the output map, is lower (75.1%) than the accuracy obtained using environmental attributes alone (97.0%). Some ambiguities that still remain in habitat discrimination are resolved by integrating the use of LCCS environmental attributes (if available), and expert rules. 4.In this paper we use Very High Resolution (VHR) satellite data and LIDAR data. LC classes are labelled according to the LCCS taxonomy, which offers a framework to integrate EO data with in situ and ancillary data. Output habitat classes are labelled according to the European Habitats Directive (92/43 EEC Directive) Annex I habitat types and Eunis habitat classification. Two Natura 2000 coastal wetland sites in southern Italy are considered. 5.Synthesis and applications. In this paper we study the exploitation of ecological rules on vegetation pattern, plant phenology and habitat geometric properties for automatic translation of Land Cover (LC) maps to habitat maps in coastal wetlands. The methodology is useful for relatively inaccessible sites (e.g. wetlands) as it does not require in-field campaigns (generally costly) but only the elicitation of ecological expert rules. This can support site (e.g. Natura 2000) managers in long-term automatic habitat mapping. Habitat changes can be automatically detected by comparing map pairs and trends can be quantified. This is particularly useful to satisfy the commitments of the European Habitats Directive (92/43/EEC), which requires Member States to take measures to maintain as, or restore to, favourable conservation status those natural habitat types and species of community interest that are listed in the Annexes to the Directive.

A crucial challenge among remote sensing communities is the availability of open data. Several European and international programs address this aim offering a useful support to many applications. The detection of changes in ecosystems for the monitoring of biodiversity is one of the main goal for the commitments undertaken not only by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also the UN Sustainable Development Goals (SDG). This work aims to show the usefulness of free available data for the detecting of changes in (semi-) natural grasslands ecosystem for three different test site in Italy and Portugal. Due to its request for only one multispectral remote sensed image at time $\mathrm{T}_{2}$ and a land cover map at time $\mathrm{T}_{1}$ , the Cross Correlation Analysis (CCA) algorithm was preferred for the detection of changes from (semi -) natural grasslands to other land cover. A Sentinel- 2 image as $\mathrm{T}_{2}$ and the (semi-) natural grasslands Copernicus layer as $\mathrm{T}_{1}$ were considered. The accuracy assessment of the change maps was based on stratified random sampling weighting the Overall Accuracy (OA) by the proportions of the study area represented by the map classes. The reported finding for OA % of $94.21\pm 0.10$ , is encouraging and promising as a support to policy makers.

The threat, impact and management problems associated with alien plant invasions are increasingly becoming a major issue in environmental conservation. Invasive species cause significant damages, and high associated costs. Controlling them cost-effectively is an ongoing challenge, and mathematical models and optimizations are becoming increasingly popular as a tool to assist managers. The aim of this study is to develop a modelling approach for the optimal spatiotemporal control of invasive species in natural protected areas of high conservation value. Typically, control programs are either distributed uniformly across an area, or applied with a given fixed intensity, although there is no guarantee that such a strategy would be cost-effective at the conservation asset. The proposed approach, based on diffusion equations, is spatially explicit, and includes a functional response (Holling type II) which models the control rate as a function of the invasive species density. We apply a budget constraint to the control program and search for the optimal effort allocation for the minimisation of the invasive species density. Remote sensing derived input layers and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data. The approach has been applied to the Alta Murgia National Park, where the EU LIFE Alta Murgia Project is underway with the aim to eradicate Ailanthus altissima, one of the most invasive alien plant species in Europe. The Alta Murgia National Park is one of the study site of the on-going H2020 project ECOPOTENTIAL which aims at the integration of modelling tools and Earth Observations for a sustainable management of protected areas. The H2020 project 'ECOPOTENTIAL: Improving Future Ecosystem Benefits Through Earth Observations' (http://www.ecopotential-project.eu) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 641762. All ground data regarding Ailanthus altissima (Mill.) Swingle presence and distribution are from the EU LIFE Alta Murgia Project (LIFE12 BIO/IT/000213 titled "Eradication of the invasive exotic plant species Ailanthus altissima from the Alta Murgia National Park" funded by the LIFE+ financial instrument of the European Commission).

Traditionally, analyses of relationships between amphibians and habitat focused on breeding environments (i.e., pond features) more than on the features of the surrounding environment. Nevertheless, for most amphibians the terrestrial phase is longer than the aquatic phase, and consequently landscape features (i.e., habitat mosaics) may have an important role for modelling amphibian distribution. There were different aims in this analysis. Firstly, we compared the effectiveness of the information provided by land cover/use (LC/LU) classes and habitat classes defined according to a new habitat taxonomy named General Habitat Category (GHC), which is based on the concept of biological forms of dominant vegetation and class naturalness. The GHC map used was obtained from a pre-existing validated LC/LU map, by integrating spectral and spatial measurements from very high resolution Earth observation data according to ecological expert rules involving concepts related to spatial and temporal relationships among LC/LU and habitat classes. Then, we investigated the importance for amphibians of the landscape surrounding ponds within the Italian Alta Murgia National Park. The work assessed whether LC/LU classes in pond surrounds are important for the presence/absence of amphibians in this area, and identified which classes are more important for amphibians. The results obtained can provide useful indications to management strategies aiming at the conservation of amphibians within the study area. An information-theoretic approach was adopted to assess whether GHC maps allow to improve the performance of species distribution models. We used the Akaike's Information Criterion (AICc) to compare the effectiveness of GHC categories versus LC/LU categories in explaining the presence/absence of pool frogs. AICc weights suggest that GHC categories can better explain the distribution of frogs, compared to LC/LU classes.

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.

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.

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.

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth Observation (EO) data offers 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 (CLC), the Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS) and the International Geosphere-Biosphere Programme (IGBP) 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 Key words: Mapping; land cover; land use; habitat; earth observation; taxonomies; Natura 2000; classification schemes

The paper focuses on the application of the Cross-Correlation Analysis (CCA) technique for quantifying changes of semi-natural grasslands to artificial structures at different spatial resolutions (grain) on a Mediterranean Natura 2000 site as a further test case of the results reported in [1]. In that work the CCA was applied to detect specific changes associated with agricultural intensification and fires. A semi-natural grasslands layer extracted from an existing Land Cover/Land Use map (1:5000, time T1) was considered as input to the CCA jointly with a Very High Resolution (VHR) WorldView-2 satellite image (2 meters spatial resolution, time T2) and with a High Resolution Landsat 8 OLS satellite image (30 meters spatial resolution, time T2), with T2 > T1, respectively, for the fine and the coarse scale analysis. The results were compared to those obtained by applying a traditional Post Classification Comparison technique to the same reference at time T1 map and an updated at time T2 map obtained by a knowledge driven classification of four multi-seasonal Worldview-2 input images, according to [1]. Also in this case the CCA technique results encouraging offering the possibility to reduce the costs of change detection when the acquisition of multi-seasonal VHR images at time T2 is too expensive or when no archive VHR image is available in the past for comparison between at time T1 and T2 images. The areas of change detected at VHR and HR were quite similar for the specific transition analyzed with larger error values in HR change images.