Hydrocarbon quantification using neural networks and deep learning based hyperspectral unmixing

Hydrocarbon (HC) spills are a global issue, which can seriously impact human life and the environment, therefore early identification and remedial measures taken at an early stage are important. Thus, current research efforts aim at remotely quantifying incipient quantities of HC mixed with soils. The increased spectral and spatial resolution of hyperspectral sensors has opened ground-breaking perspectives in many industries including remote inspection of large areas and the environment. The use of subpixel detection algorithms, and in particular the use of the mixture models, has been identified as a future advance that needs to be incorporated in remote sensing. However, there are some challenging tasks since the spectral signatures of the targets of interest may not be immediately available. Moreover, real time processing and analysis is required to support fast decision-making. Progressing in this direction, this thesis pioneers and researches novel methodologies for HC quantification capable of exceeding the limitations of existing systems in terms of reduced cost and processing time with improved accuracy. Therefore the goal of this research is to develop, implement and test different methods for improving HC detection and quantification using spectral unmixing and machine learning. An efficient hybrid switch method employing neural networks and hyperspectral is proposed and investigated. This robust method switches between state of the art hyperspectral unmixing linear and nonlinear models, respectively. This procedure is well suited for the quantification of small quantities of substances within a pixel with high accuracy as the most appropriate model is employed. Central to the proposed approach is a novel method for extracting parameters to characterise the non-linearity of the data. These parameters are fed into a feedforward neural network which decides in a pixel by pixel fashion which model is more suitable. The quantification process is fully automated by applying further classification techniques to the acquired hyperspectral images. A deep learning neural network model is designed for the quantification of HC quantities mixed with soils. A three-term backpropagation algorithm with dropout is proposed to avoid overfitting and reduce the computational complexity of the model. The above methods have been evaluated using classical repository datasets from the literature and a laboratory controlled dataset. For that, an experimental procedure has been designed to produce a labelled dataset. The data was obtained by mixing and homogenizing different soil types with HC substances, respectively and measuring the reflectance with a hyperspectral sensor. Findings from the research study reveal that the two proposed models have high performance, they are suitable for the detection and quantification of HC mixed with soils, and surpass existing methods. Improvements in sensitivity, accuracy, computational time are achieved. Thus, the proposed approaches can be used to detect HC spills at an early stage in order to mitigate significant pollution from the spill areas

Study on comparison of biochemistry between Trogoderma granarium Everts and Trogoderma variabile Ballion

Stored grains are paramount commodities to be preserved and stocked for future supply to the market according to the requirement. However, one of the major problems during storage is insect pests, of which insects from Trogoderma sp. especially khapra beetle (Trogoderma granarium) is considered the world most dangerous stored grain insect pests. Therefore, it has been listed as quarantine insect pests in many counties. For timely management of quarantine pest, effective and rapid diagnostic methods are required. Until now, diagnostic technology is mainly based on morphology of insects which require trained taxonomists. Recently, diagnostics based on metabolites and hyperspectral imaging coupled with machine learning is gaining importance. However, very little is known about the metabolites in Trogoderma sp. and how the host grain, gender, and geographical distribution affect the metabolomic profiling in these species is still unknown. In this thesis, volatile organic compounds (VOCs) emitted by Trogoderma variabile at different life stages were analysed as biomarkers which can help us to understand the biochemistry and metabolomic. Some compounds were identified from T. variabile different stages, which could be used as diagnostic tool for this insect. Gas chromatography coupled to mass spectrometry (GC–MS) was used as a technique to study the metabolite profile of T. variabile in different host grains. However, there are several factors that affect the volatile organic compounds including extraction time and number of insects. The results indicated that the optimal number of insects required for volatile organic compounds (VOC) extraction at each life stage was 25 and 20 for larvae and adults respectively. Sixteen hours were selected as the optimal extraction time for larvae and adults. Some of the VOCs compounds identified from this insect can be used as biomarkers such as pentanoic acid; diethoxymethyl acetate; 1-decyne; naphthalene, 2-methyl-; n-decanoic acid; dodecane, 1-iodo- and m-camphorene from larvae. While butanoic acid, 2-methyl-; pentanoic acid; heptane, 1,1'-oxybis- 2(3H)-Furanone, 5-ethyldihydro-; pentadecane, 2,6,10-trimethyl-; and 1,14-tetradecanediol VOCs, were found in male, whereas pentadecane; nonanic acid; pentadecane, 2,6,10-trimethyl-; undecanal and hexadecanal were identified from female. Additionaly, direct immersion-solid phase microextraction (DI-SPME) was employed, followed by gas chromatography mass spectrometry analysis (GC-MS) for the collection, separation, and identification of the chemical compounds from T. variabile adults fed on four different host grains. Results showed that insect host grains have a significant difference on the chemical compounds that were identified from female and male. There were 23 compounds identified from adults reared on canola and wheat. However, there were 26 and 28 compounds detected from adults reared on oats and barley respectively. Results showed that 11-methylpentacosane; 13-methylheptacosane; heptacosane; docosane, 1-iodo- and nonacosane were the most significant compounds that identified form T. variabile male reared on different host grains. However, the main compounds identified from female cultured on different host grains include docosane, 1-iodo-; 1-butanamine, N-butyl-; oleic acid; heptacosane; 13-methylheptacosane; hexacosane; nonacosane; 2-methyloctacosane; n-hexadecanoic acid and docosane. A novel diagnostic tool to identify between T. granarium and T. variabile were developed using visible near infrared hyperspectral imaging and deep learning models including Convolutional Neural Networks (CNN) and Capsule Network. Ventral orientation showed a better accuracy over dorsal orientation of the insects for both larvae and adult stages. This technology offers a new approach and possibility of an effective identification of T. granarium and T. variabile. from its body fragments and larvae skins. The results showed high accuracy to identify between T. granarium and T. variabile. The accuracy was 93.4 and 96.2% for adults and larvae respectively, and the accuracies of 91.6, 91.7 and 90.3% were achieved for larvae skin, adult fragments, larvae fragment respectively

Integrative Advances in Rice Research

This book describes some recent advances in rice research in terms of crop breeding and improvement (Section 1), crop production and protection (Section 2), and crop quality control and food processing (Section 3). It contains fourteen chapters that cover such topics as two-line rice breeding in India, the different aspects of aromatic rice, bacterial diseases of rice, quality control and breeding strategies, and much more. This volume is a useful reference for professionals and graduate students working in all areas of rice science and technology

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

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

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described