A New Variant of RESET for Distributed Lag Models

We propose a new variant of RESET that is appropriate for distributed lag models. Monte Carlo evidence on size and power strongly supports the use of the new variant instead of the traditional RESET.

Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R^2>0.93) and good accuracies (RMSE<0.83, rRMSE_m<23.6% and rRMSE_r<16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring

A high-resolution, integrated system for rice yield forecasting at district level

To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements

Methodologies for developing fuzzy classification systems using evolutionary algorithms: application to highly-dimensional classification tasks

This doctoral dissertation deals with the general problem of classification using fuzzy logic techniquesand evolutionary algorithms. The main objective is to develop methodologies that efficientlyconstruct interpretable classification models for solving problems characterized by highdimensionality of the feature space, while minimizing the structural complexity of the derivedclassifiers. To this end, five new methodologies for developing genetic fuzzy rule-based classifiersare proposed, which address effectively a wide range of real-world classification problems. Thesemethodologies are complementary. Each displays comparative advantages in certain classes ofproblems, depending on the input space’s dimensionality and the desired balance between classificationaccuracy and structural complexity. A particular aribute of the proposed systems is thecreation of simple and understandable classification models, which are made up from rules thatresemble the simple deductive reasoning of humans. The minimization of the classifiers’ structuralcomplexity is accomplished through the progressive decomposition of complex optimizationtasks into separate subproblems, the sequential solution of which is ultimately easier than solvingthe original problems. Furthermore, the training algorithm evaluates deterministic information regardingeach feature’s suitability throughout the derivation of the final classification model, thusenabling the effective treatment of highly dimensional classification problems. The proposed classifiersare primarily applied to land cover classification tasks, using both multispectral and hyperspectralsatellite imagery. Within this framework, higher-order features are extracted throughthe wavelet transform, which increase the class separability in remote sensing classification tasksusing hyperspectral data. Finally, the proposed systems are also applied to modern biomedicalclassification problems, which comprise up to tens of thousands of available features.Η παρούσα διδακτορική διατριβή πραγματεύεται το γενικότερο πρόβλημα της ταξινόμησης,αξιοποιώντας τεχνικές της ασαφούς λογικής και των εξελικτικών αλγορίθμων. Κύριος στό-χος της είναι η ανάπτυξη μεθοδολογιών εξαγωγής ερμηνεύσιμων μοντέλων ταξινόμησης γιατην αντιμετώπιση προβλημάτων που χαρακτηρίζονται από υψηλή διαστατικότητα του χώρουτων χαρακτηριστικών, με ταυτόχρονη ελαχιστοποίηση της δομικής πολυπλοκότητας των πα-ραγόμενων ταξινομητών. Για το σκοπό αυτό προτείνονται πέντε νέες μεθοδολογίες ανάπτυ-ξης γενετικών ασαφών ταξινομητών, που αντιμετωπίζουν αποτελεσματικά ένα ευρύ φάσμαπροβλημάτων ταξινόμησης του πραγματικού κόσμου. Οι μεθοδολογίες αυτές είναι μεταξύτους συμπληρωματικές. Καθεμία εμφανίζει συγκριτικά πλεονεκτήματα σε συγκεκριμένεςκατηγορίες προβλημάτων, ανάλογα με τη διαστατικότητα του χώρου εισόδου, καθώς και τηνεπιθυμητή ισορροπία μεταξύ ακρίβειας ταξινόμησης και δομικής πολυπλοκότητας. Ιδιαίτεροχαρακτηριστικό των προτεινόμενων συστημάτων είναι η δημιουργία απλών και εύληπτωνμοντέλων ταξινόμησης, βασισμένα σε κανόνες που προσιδιάζουν στις απλές συμπερασμα-τικές προτάσεις της ανθρώπινης σκέψης. Η ελαχιστοποίηση της δομικής πολυπλοκότηταςτων ταξινομητών επιτυγχάνεται μέσω της προοδευτικής αποσύνθεσης συνολικότερων προ-βλημάτων βελτιστοποίησης σε επιμέρους υποπροβλήματα, η διαδοχική επίλυση των οποίωνκαταλήγει εντέλει να είναι συνδυαστικά πιο εύκολη από την επίλυση των αρχικών προβλη-μάτων. Επιπλέον προτείνεται η αξιολόγηση αιτιοκρατικής πληροφορίας ως προς την καταλ-ληλότητα κάθε χαρακτηριστικού καθ’ όλη τη διάρκεια εξαγωγής του τελικού μοντέλου ταξι-νόμησης, επιτρέποντας έτσι την αντιμετώπιση προβλημάτων πολύ υψηλής διαστατικότητας.Οι προτεινόμενοι ταξινομητές εφαρμόζονται κατά κύριο λόγο σε προβλήματα ταξινόμησηςκάλυψης γης, με τη χρήση τόσο πολυφασματικών όσο και υπερφασματικών δορυφορικώνεικόνων. Στο πλαίσιο αυτό εξετάζεται και η χρήση του μετασχηματισμού κυματίου για τηνεξαγωγή χαρακτηριστικών ανώτερης τάξης σε προβλήματα ταξινόμησης με τη χρήση υπερ-φασματικών δεδομένων. Τέλος, αντιμετωπίζονται και σύγχρονα προβλήματα ταξινόμησηςτης βιοϊατρικής, που περιλαμβάνουν ως και δεκάδες χιλιάδες διαθέσιμα χαρακτηριστικά

A Region-Based GeneSIS Segmentation Algorithm for the Classification of Remotely Sensed Images

This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms

Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery

The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image analysis techniques: (a) an efficient feature selection method based on the Fuzzy Complementary Criterion (FuzCoC) and (b) the Support Vector Machine (SVM) classifier. In both cases (pixel and object-based), a number of higher-order spectral and spatial features were produced from the original image. The proposed methodology was tested in areas of Greece recently affected by severe forest fires, namely, Parnitha and Rhodes. The extensive comparative analysis indicates that the SVM object-based scheme exhibits higher classification accuracy than the respective pixel-based one. Additionally, the accuracy increased with the addition of derivative features and subsequent implementation of the FuzCoC feature selection (FS) method. Apart from the positive effect in the classification accuracy, the application of the FuzCoC FS method significantly reduces the computational requirements and facilitates the manipulation of the large data volume. In both cases (pixel and objet) the results confirmed that the use of an efficient feature selection method is a prerequisite step when extra information through higher-order features is added to the classification process of VHR imagery for burned area mapping

Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers

Wildfires constitute a significant environmental pressure in Europe, particularly in the Mediterranean countries. The prediction of fire danger is essential for sustainable forest fire management since it provides critical information for designing effective prevention measures and for facilitating response planning to potential fire events. This study presents a new midterm fire danger index (MFDI) using satellite and auxiliary geographic data. The proposed methodology is based on estimations of a dry fuel connectivity measure calculated from the Moderate Imaging Spectrometer (MODIS) time-series data, which are combined with biophysical and topological variables to obtain accurate fire ignition danger predictions for the following eight days. The index&rsquo;s accuracy was assessed using historical fire data from four large wildfires in Greece. The results showcase that the index predicted high fire danger (&ge;3 on a scale within [ 1 , 4 ] ) within the identified fire ignition areas, proving its strong potential for deriving reliable estimations of fire danger, despite the fact that no meteorological measurements or forecasts are used for its calculation

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

Monitoring post-fire vegetation response using remotely-sensed images is a top priority for post-fire management. This study investigated the potential of very-high-resolution (VHR) GeoEye images on detecting the field-measured burn severity of a forest fire that occurred in Evros (Greece) during summer 2011. To do so, we analysed the role of topographic conditions and burn severity, as measured in the field immediately after the fire (2011) and one year after (2012) using the Composite Burn Index (CBI) for explaining the post-fire vegetation response, which is measured using VHR satellite imagery. To determine this relationship, we applied redundancy analysis (RDA), which allowed us to identify which satellite variables among VHR spectral bands and Normalized Difference Vegetation Index (NDVI) can better express the post-fire vegetation response. Results demonstrated that in the first year after the fire event, variations in the post-fire vegetation dynamics can be properly detected using the GeoEye VHR data. Furthermore, results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time. Our analysis showed that, in this specific case, burn severity variations are mildly affected by the topography, while the NDVI index, as inferred from VHR data, can be successfully used to monitor the short-term post-fire dynamics of the vegetation recovery