Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

Remote Extraction of Latent Fingerprints (RELF)

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordLatent fingerprints are the kind left on objects after direct contact with a person’s finger, often unwittingly at crime scenes. Most current techniques for extracting these types of fingerprint are invasive and involve contaminating the fingerprint with chemicals which often renders the fingerprint unusable for further forensic testing. We propose a novel and robust method for extracting latent fingerprints from surfaces without the addition of contaminants or chemicals to the evidence. We show our technique works on notoriously difficult to image surfaces, using off-the-shelf cameras and statistical analysis. In particular, we extract images of latent fingerprints from surfaces which are transparent, curved and specular such as glass lightbulbs and jars, which are challenging due to the curvature of the surface. Our method produces results comparable to more invasive methods and leaves the fingerprint sample unaffected for further forensic analysis. Our technique uses machine learning to identify partial fingerprints between successive images and mosaics them

Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin

The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping

One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data

An investigation in the use of advanced remote sensing and geographic information system techniques for post-fire impact assessment on vegetation.

2006/2007Gli incendi boschivi rappresentano uno dei maggiori problemi ambientali nella regione Mediterranea con vaste superfici colpite ogni estate. Una stima dell’impatto ambientale degli incendi (a breve e a lungo termine) richiede la raccolta di informazioni accurate post-incendio relative al tipo di incendio, all’intensità, alla rigenerazione forestale ed al ripristino della vegetazione. L’utilizzo di tecniche avanzate di telerilevamento può fornire un valido strumento per lo studio di questi fenomeni. L’importanza di queste ricerche è stata più volte sottolineata dalla Commissione Europea che si è concentrata sullo studio degli incendi boschivi ed il loro effetto sulla vegetazione attraverso lo sviluppo di adeguati metodi di stima dell’impatto e di mitigazione. Scopo di questo lavoro è la stima dell’impatto post-incendio sulla vegetazione in ambiente Mediterraneo per mezzo di immagini satellitari ad alta risoluzione, di rilievi a terra e mediante tecniche avanzate di analisi dei dati. Il lavoro ha riguardato lo sviluppo di un sistema per l’integrazione di dati telerilevati ad altissima risoluzione spaziale e spettrale. Per la stima dell’impatto a breve termine, un modello di classificazione ad oggetti è stato sviluppato utilizzando immagini Ikonos ad altissima risoluzione spaziale per cartografare il tipo di incendio, differenziando l’incendio radente dall’incendio di chioma. I risultati mostrano che la classificazione ad oggetti potrebbe essere utilizzata per distinguere con elevata accuratezza (87% di accuratezza complessiva) le due tipologie di incendio, in particolare nei boschi Mediterranei aperti. È stata inoltre valutata la capacità della classificazione ad oggetti di distinguere e cartografare tre livelli di intensità del fuoco utilizzando le immagini Ikonos e l’accuratezza del risultato è stimata all’ 83%. Per la stima dell’impatto a lungo termine, la mappatura della rigenerazione post-incendio (pino) e la ripresa della vegetazione arbustiva sono state valutate mediante tre approcci: 1) la classificazione ad oggetti di immagini ad altissima risoluzione QuickBird che ha permesso di mappare la ripresa della vegetazione e l’impatto sulla copertura a seguito dell’incendio distinguendo due livelli di intensità dell’incendio (accuratezza della classificazione 86%). 2) l’analisi statistica di dati iperspettrali rilevati in campo che ha permesso una riduzione del 97% del volume di dati e la selezione delle migliori 14 bande per discriminare l’età e le specie di pino e le 18 migliori bande per la caratterizzazione delle specie arbustive. Successivamente, i dati iperspettrali Hyperion sono stati utlizzati per mappare la rigenerazione forestale e la ripresa della vegetazione. L’accuratezza complessiva della classificazione è stata del 75.1% considerando due diverse specie di pino ed altre specie vegetali. 3) una classificazione ad oggetti che ha combinato l’analisi dei dati QuickBird ed Hyperion. Si è registrato un aumento dell’accuratezza della classificazione pari all’8.06% rispetto all’utilizzo dei soli dati Hyperion. Complessivamente, si osserva che strumenti avanzati di telerilevamento consentono di raccogliere le informazioni relative alle aree incendiate, la rigenerazione forestale e la ripresa della vegetazione in modo accurato e vantaggioso in termini di costi e tempi.Forest fires are a major environmental problem in the Mediterranean region, where large areas are affected each summer. An assessment of the environmental impact of forest fires (in the short-term and in the long-term) requires the collection of accurate and detailed post-fire information related to fire type, fire severity, forest regeneration and vegetation recovery. Advanced tools in remote sensing provide a powerful tool for the study of this phenomenon. The importance of this work was often emphasized by the European Commission, which focused on the studying of forest fires and their effect on vegetation through the development of appropriate impact assessment and mitigation methods. The aim of this study was to assess the post-fire impact on vegetation in a Mediterranean environment by employing high quality satellite and field data and by using advanced data processing techniques. The work entailed the development of a whole system integrating very high spatial and spectral resolution remotely sensed data. For short-term impact assessment, an object-oriented model was developed using very high spatial resolution Ikonos imagery to map the type of fire, namely, canopy fire and surface fire. The results showed that object-oriented classification could be used to accurately distinguish and map areas of surface and crown fire spread (overall accuracy of 87%), especially that occurring in open Mediterranean forests. Also, the performance of object-based classification in mapping three levels of fire severity by employing high spatial resolution Ikonos imagery was evaluated, and accuracy of the obtained results was estimated to be 83%. As for long-term impact assessment, the mapping of post-fire forest regeneration (pine) and vegetation recovery (shrub) was performed by following three different approaches. First, the developed object-based classification of QuickBird (very high spatial resolution) allowed post-fire vegetation recovery and survival mapping of canopy within two different fire severity levels (86% of classification accuracy). The main effect of fire has been to create a more homogeneous landscape. Second, statistical analysis of field hyperspectral data allowed a 97% reduction in data volume and recommended 14 best narrowbands to discriminate among pine trees (age and species) and 18 bands that best characterize the different shrub species. Then, hyperspectral Hyperion was employed for mapping post-fire forest regeneration and vegetation recovery. The overall classification accuracy was found to be 75.81% when mapping two different regenerated pine species and other species of vegetation recovery. Third, an object-oriented combined analysis of QuickBird and Hyperion was investigated for the same objective. An improvement in classification accuracy of 8.06% was recorded when combining both Hyperion and QuickBird imageries than by using only the Hyperion image. Overall, it was observed that advanced tools in remote sensing provided the necessary means for gathering information about the burned areas, the regenerated forests and the recovered vegetations in a successful and a timely/cost effective manner.XX Ciclo197

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

Study of land degradation and desertification dynamics in North Africa areas using remote sensing techniques

In fragile-ecosystem arid and semi-arid land, climatic variations, water scarcity and human pressure accelerate ongoing degradation of natural resources. In order to implement sustainable management, the ecological state of the land must be known and diachronic studies to monitor and assess desertification processes are indispensable in this respect. The present study is developed in the frame of WADIS-MAR (www.wadismar.eu). This is one of the five Demonstration Projects implemented within the Regional Programme “Sustainable Water Integrated Management (SWIM)” (www.swim-sm.eu ), funded by the European Commission and which aims to contribute to the effective implementation and extensive dissemination of sustainable water management policies and practices in the Southern Mediterranean Region. The WADIS-MAR Project concerns the realization of an integrated water harvesting and artificial aquifer recharge techniques in two watersheds in Maghreb Region: Oued Biskra in Algeria and wadi Oum Zessar in Tunisia. The WADIS MAR Project is coordinated by the Desertification Research Center of the University of Sassari in partnership with the University of Barcelona (Spain), Institut des Régions Arides (Tunisia) and Agence Nationale des Ressources Hydrauliques (Algeria) and the international organization Observatorie du Sahara et du Sahel. The project is coordinated by Prof. Giorgio Ghiglieri. The project aims at the promotion of an integrated, sustainable water harvesting and agriculture management in two watersheds in Tunisia and Algeria. As agriculture and animal husbandry are the two main economic activities in these areas, demand and pressure on natural resources increase in order to cope with increasing population’s needs. In arid and semiarid study areas of Algeria and Tunisia, sustainable development of agriculture and resources management require the understanding of these dynamics as it withstands monitoring of desertification processes. Vegetation is the first indicator of decay in the ecosystem functions as it is sensitive to any disturbance, as well as soil characteristics and dynamics as it is edaphically related to the former. Satellite remote sensing of land affected by sand encroachment and salinity is a useful tool for decision support through detection and evaluation of desertification indicating features. Land cover, land use, soil salinization and sand encroachment are examples of such indicators that if integrated in a diachronic assessment, can provide quantitative and qualitative information on the ecological state of the land, particularly degradation tendencies. In recent literature, detecting and mapping features in saline and sandy environments with remotely sensed imagery has been reported successful through the use of both multispectral and hyperspectral imagery, yet the limitations to both image types maintain “no agreed-on best approach to this technology for monitoring and mapping soil salinity and sand encroachment”. Problems regarding the image classification of features in these particular areas have been reported by several researchers, either with statistical or neural/connectionist algorithms for both fuzzy and hard classifications methods. In this research, salt and sand features were assessed through both visual interpretation and automated classification approaches, employing historical and present Landsat imagery (from 1984 to 2015). The decision tree analysis was chosen because of its high flexibility of input data range and type, the easiness of class extraction through non-parametric, multi-stage classification. It makes no a priori assumption on class distribution, unlike traditional statistical classifiers. The visual interpretation mapping of land cover and land use was undergone according to acknowledged standard nomenclature and methodology, such as CORINE land cover or AFRICOVER 2000, Global Land Cove 2000 etc. The automated one implies a decision tree (DT) classifier and an unsupervised classification applied to the principal components (PC) extracted from Knepper ratios composite in order to assess their validity for the change detection analysis. In the Tunisian study area, it was possible to conduct a thorough ground truth survey resulting in a record of 400 ground truth points containing several information layers (ground survey sheet information on various land components, photographs, reports in various file formats) stored within the a shareable standalone geodatabase. Spectral data were also acquired in situ using the handheld ASD FieldSpec 3 Jr. Full Range (350 – 2500 nm) spectroradiometer and samples were taken for X-ray diffraction analysis. The sampling sites were chosen on the basis of a geomorphological analysis, ancillary data and the previously interpreted land cover/land use map, specifically generated for this study employing Landsat 7 and 8 imagery. The spectral campaign has enabled the acquisition of spectral reflectance measurements of 34 points, of which 14 points for saline surfaces (9 samples); 10 points for sand encroachment areas (10 samples); 3 points for typical vegetation (halophyte and psammophyte) and 7 points for mixed surfaces. Five of the eleven indices employed in the Decision Tree construction were constructed throughout the current study, among which we propose also a salinity index (SMI) for the extraction of highly saline areas. Their application have resulted in an accuracy of more than 80%. For the error estimation phase, the interpreted land cover/use map (both areas) and ground truth data (Oum Zessar area only) supported the results of the 1984 to 2014 salt – affected areas diachronic analysis obtained through both automatic methods. Although IsoDATA classification maps applied to Knepper ratios Principal Component Analysis has proven its good potential as an approach of fast automated, user-independent classifier, accuracy assessment has shown that decision tree outstood it and was proven to have a substantial advantage over the former. The employment of the Decision Tree classifier has proven to be more flexible and adequate for the extraction of highly and moderately saline areas and major land cover types, as it allows multi-source information and higher user control, with an accuracy of more than 80%. Integrating results with ancillary spatial data, we could argue driving forces, anthropic vs natural, as well as source areas, and understand and estimate the metrics of desertification processes. In the Biskra area (Algeria), results indicate that the expansion of irrigated farmland in the past three decades contributes to an ongoing secondary salinization of soils, with an increase of over 75%. In the Oum Zessar area (Tunisia), there was substantial change in several landscape components in the last decades, related to increased anthropic pressure and settlement, agricultural policies and national development strategies. One of the most concerning aspects is the expansion of sand encroached areas over the last three decades of around 27%