From ocean sensors to traceable knowledge by harmonizing ocean observing systems

Society is requesting more than ever being better informed on the state and effects of Earth’s changing oceans. This has direct implications on ocean observing systems, including scientific planning and technology. For instance better knowledge implies that data on health, climate and overall dynamics of our oceans have a known level of quality, be up-to-date, be easily discoverable, be easily searchable both in time and space, and be human- and machine-readable in order to generate faster decisions when and where needed. Requirements with respect to spatial regions and scales (seas and ocean basins, from millimeters to hundreds of kilometers), time scope and scales (past, present, future, from microseconds to decades) indeed have direct implications on observing systems’ spatio-temporal sampling capabilities. Possibly high spatial and temporal resolution also means unprecedented amounts of data, communication bandwidth and processing power needs. Technological implications are thus quite substantial and, in this short article, we will try to provide a review of some initiatives of global and local focus that are aiming to respond to at least some of these needs, starting with the application of the Global Earth Observation System of Systems (GEOSS) guidelines to ocean observatories. Then we will address real scenarios in real ocean observing facilities, first with the European Seas Observatory Network and the European Multidisciplinary Seafloor Observation (ESONET-EMSO), then two recently associated Spanish initiatives, the Oceanic Platform of the Canary Islands (PLOCAN) infrastructure and deep sea observatory in the Canary Islands, and the Expandable Seafloor Observatory (OBSEA) shallow water Western-Mediterranean observatory of the Technical University of Catalonia, one of the first real-time ocean observatories implemented with state-of- the-art interoperable concepts, down to the sensor interface.Postprint (published version

Vision Goes Symbolic Without Loss of Information Within the Preattentive Vision Phase: The Need to Shift the Learning Paradigm from Machine-Learning (from Examples) to Machine-Teaching (by Rules) at the First Stage of a Two-Stage Hybrid Remote... Part I

Life sciences: general issue

Status of the Global Observing System for Climate

Status of the Global Observing System for Climat

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.Taitavuoret sijaitsevat Kaakkois-Keniassa ja muodostavat pohjoisimman osan Itäisistä Kaarivuorista. Conservation International -järjestön mukaan Itäisten Kaarivuorten alue kuuluu luonnon monimuotoisuuden (biodiversiteetin) kannalta kymmenen tärkeimmän joukkoon maailmassa. Taitavuorilla, kuten monilla muilla kehittyvien maiden alueilla, viime vuosikymmenten aikana väestönkasvu on johtanut merkittäviin maankäytön muutoksiin kuten esimerkiksi kiihtyvään maan heikkenemiseen, erityisesti maaperäeroosion muodossa. Moniaikaiset optisen alueen SPOT-satelliittikuvat tarjoavat arvokasta tietoa ympäristön tilan seurantaan ja ympäristömallinnukseen paikallisella ja alueellisella tasolla. SPOT-satelliittikuva-aineiston hyödyntäminen kvantitatiivisessa kaukokartoituksessa vaatii kuitenkin ilmakehän vaikutuksen poistamista sekä maanpinnan heijastussuhteen määrittämistä. Lisäksi alueilla, joilla maasto on epätasaista, kuten Taitavuorilla, satelliittikuvalle on tehtävä topografinen korjaus, jotta maanpinnan heijastusarvot olisivat vertailukelpoisia koko satelliittikuvan alueella. Maankäytön muutosten monitorointi ja maaperän huononemisen sekä väestön levinneisyyden ja runsauden mallintaminen ovat ratkaisevan tärkeitä kestävälle kehitykselle, luonnonvarojen hallinnalle, biologisen monimuotoisuuden suojelulle ja ilmastonmuutoksen hillitsemiselle ja sen vaikutusten vähentämiselle. Tämän tutkimuksen tarkoituksena oli kehittää ja arvioida tehostettuja prosessointimenetelmiä SPOT-satelliittikuville. Tutkimuksessa kehitettyjä menetelmiä voidaan hyödyntää ympäristön tilan seurannassa ja mallintamisessa kehittyvissä maissa alueilla, joilla täydentävä tutkimusaineisto on puutteellista. Tässä tutkimuksessa Taitavuoret oli varsinainen tutkimusalue, jossa sovellukset kehitettiin ja Helsinki toimi kontrollialueena validoinnissa ja ilmakehäkorjausten hyvyyden arvioinnissa. Tutkimuksessa esitetty ilmakehäkorjaus menetelmä, ns. historical empirical line method (HELM), osoittautui ainoaksi menetelmäksi, jolla maanpinnan heijastussuhteen arvion keskivirhe (RMSE) oli < 0.02 ja suhteellinen tarkkuus < 10%. Yllä mainittu tarkkuustaso on yleisesti hyväksytty vertailuarvo osoittamaan ilmakehäkorjauksen onnistumisen. Monitasoista kuvasegmentointia ja objekti-orientoitunutta mallintamista (MSS/ORM) hyödynnettiin Taitavuorten maankäytön kartoittamisessa SPOT-satelliittikuvalta. Objekti-orientoitunut menetelmä onnistui parantamaan huomattavasti yksi-tasoista maximum-likelihood -luokitusta. Kuvasegmentoinnilla tuotettua Taitavuorten maankäyttöaineistoa käytettiin maaperän huonontumisen mallintamisessa yhdessä alhaisen kustannuksen geospatiaalisten karttatasojen kanssa, jotka kuvaavat mm. Taitavuorten topografiaa, sadantaa ja maaperää. Mallintamisessa arvioitiin potentiaalista maa-aineksen häviämistä ns. USLE-eroosiomallin avulla. Lisäksi Taitavuorten väestön leviämistä ja väestön määrää mallinnettiin SPOT-satelliittikuvalta ja paikkatieto-aineistoista saaduilla geospatiaalisilla muuttujilla. Ennustemallit kalibroitiin käyttäen epälineaarista regressiota. Mallinnuksessa pyrkimyksenä oli sen toistettavuus myös kehittyvissä maissa. Täten mallinnuksessa pyrittiin hyödyntämään alhaisen kustannuksen tai vapaasti saatavilla olevia aineistoja ja ohjelmistoja

Oceanography

How inappropriate to call this planet Earth when it is quite clearly Ocean (Arthur C. Clarke). Life has been originated in the oceans, human health and activities depend from the oceans and the world life is modulated by marine and oceanic processes. From the micro-scale, like coastal processes, to macro-scale, the oceans, the seas and the marine life, play the main role to maintain the earth equilibrium, both from a physical and a chemical point of view. Since ancient times, the world's oceans discovery has brought to humanity development and wealth of knowledge, the metaphors of Ulysses and Jason, represent the cultural growth gained through the explorations and discoveries. The modern oceanographic research represents one of the last frontier of the knowledge of our planet, it depends on the oceans exploration and so it is strictly connected to the development of new technologies. Furthermore, other scientific and social disciplines can provide many fundamental inputs to complete the description of the entire ocean ecosystem. Such multidisciplinary approach will lead us to understand the better way to preserve our "Blue Planet": the Earth