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Through the EU FP7 Horizon 2020 ECOPOTENTIAL project, a novel and innovative approach to classification has been developed, which is termed the Earth Observation Data for Ecosystem Monitoring (EODESM), and has been built on concepts behind an implementation of the Earth Observation Data for Habitat Monitoring (EODHaM) system generated as part of the EU FP7 BIOSOS project, applied to Very High Resolution (VHR) W orldview data. The EODESM system facilitates routine classification of land covers according to the Food and Agricultural Organisations Land Cover Classification System (FAO-LCCS), translates these to other taxonomies (including General Habitat Classifications; GHCs) and facilitates routine detection of change and the generation of maps indicating the causes and consequences of such change.

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 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.

This paper focuses on the application of a semi-automatic unsupervised change detection algorithm called Cross Correlation Analysis (CCA) to the detection of (semi-) natural grasslands changes at Very High Resolution (VHR). A reference validated Land Cover/Land Use map at time T1 and only one satellite image at time T2, with T2>T1, are required to detect changes occurred at T2 in the selected target class. This approach offers the possibility to reduce the costs of change detection when the acquisition of multi-seasonal VHR images at time T2 for supervised change detection is too expensive or when no archive VHR image is available in the past for unsupervised comparison between T1 and T2 images. A summer Worldview-2 image for a Natura 2000 test site was considered and the results appear encouraging.

The derivation of habitat maps is enhanced if land cover maps are used as basis for the mapping procedure. In this study, a supervised learning framework is proposed to perform object-based classification to General Habitat Categories. A Land Cover Classification System map is used as basis, and an approach to generate numerical features from the object land cover class names and attributes is introduced. An additional number of spectral, morphological, and topological features are extracted from very high resolution satellite imagery and classification accuracies up to 80.4% for 14 classes are reached. Inclusion of LiDAR (Light Detection And Ranging) data or proposed texture analysis features, improve accuracies to 86% and around 83%, respectively, with the latter proving as promising surrogates of LiDAR data features. The method outperformed rule-based approaches, indicating its potential in accurate and labor- and time-efficient habitat classification.

This paper presents a method for hedgerow extraction from very high resolution optical images using image segmentation. The method is based on a connected component region growing approach followed by an object based image analysis filtering allowing to introduce high level knowledge about the characteristics of the desired hedgerows. The proposed approach is used as a part of the EODHaM image processing system developed in the framework of the BIO SOS FP7 project.