Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe

Kiteisen kallioperän rakojen ja laatutekijöiden visualisointi ja mallintaminen 1-3 ulottuvuudessa

Building underground facilities in bedrock is increasing in the modern world. The brokenness and quality of bedrock can have a negative effect on underground construction and effect the costs. One has to be able to describe and measure the brokenness and quality to be able to take it into account in construction and excavation planning. Different mapping methods give direct information and geophysical methods indirect information about the brokenness and quality to support its modeling task. This thesis describes new methods developed to study, visualize and analyze brokenness and quality of the bedrock. The results of the development work were tested on mapping and measurement data gathered from a Palin Granit Oy owned dimension stone quarry situated in Mäntsälä, southern Finland. Observations of the rock discontinuities were gathered by several methods: by mapping and scanline measurements; by ground penetrating radar; and by stereophotogrammetry. The discontinuity data was utilized in developing methods: 1) to calculate fracture density in a rock volume; 2) to write computer scripts, which clusters fracture sets semi-automatically, calculates mean orientation and density of the fracture sets; 3) presents the connection between fracture properties and dip direction; 4) visualizes fractures and their properties in 3D and makes a stereographic projection of the fracture density and rock quality designation revealing their orientation dependency. In addition, development work in the branch of determining fracture filling material based on the polarity change of ground penetrating radar signal was made. The results indicated confidence in determining and modeling the brokenness of the bedrock increases by exploiting several study methods. Gathering abundant observation data enabled statistical analysis of the data and allowed realistic discrete fracture network models to be generated.Kalliotilojen käyttö on yleistynyt modernissa yhdyskuntarakentamisessa. Kallioperän rikkonaisuus ja laatutekijät vaikuttavat omalta osaltaan maanalaiseen rakentamiseen rakennuskivituotantoon ja voivat aiheuttaa lisäkustannuksia. Jotta kallioperän rikkonaisuus ja laatu voidaan huomioida jo maanalaisen tilan suunnitteluvaiheessa, se on pystyttävä kuvailemaan ja mittaamaan. Erilaiset kartoitusmenetelmät antavat suoraa ja geofysikaaliset menetelmät välillistä tietoa rikkonaisuudesta sen mallinnuksen tueksi. Väitöskirjatutkimuksessa kehitettiin uusia menetelmiä kallioperän rikkonaisuuden ja laadun tutkimiseen, esittämiseen ja analysointiin. Kehitystyön tuloksia testattiin kartoitus- ja mittausaineistolla, joka kerättiin Palin Granit Oy:n omistamalta rakennuskivilouhokselta, joka sijaitsee Mäntsälässä, Etelä-Suomessa. Rakohavaintoja kerättiin usealla eri menetelmällä: kartoittamalla, linjamittauksin, maatutkalla sekä stereovalokuvaamalla. Rakoaineistoa käyttäen kehitettiin menetelmiä: 1) laskea rakotiheys tutkitun kiven tilavuudessa; 2) tietokoneohjelma rakosuuntien puoliautomaattiseen ryhmittelyyn, ryhmien keskisuunnan ja rakotiheyden laskemiseen; 3) rakojen ominaisuuksien ja kaateen suuntien välisen yhteyden esittämiseen; 4) rakojen ja niiden ominaisuuksien esittämiseen avaruudessa ja rakotiheyden sekä kallion eheyden suuntariippuvuuden esittämiseen stereograafisella projektiolla. Lisäksi kehitettiin rakotäytteen määrittämistä maatutkaheijasteen perusteella perustuen heijastuvan maatutkasignaalin polariteetin muuttumiseen rakopinnoilla. Tulokset osoittivat, että kallion rikkonaisuuden tutkiminen monipuolisin menetelmin lisää rikkonaisuuden määrittelyn ja mallintamisen varmuutta. Laajan havaintoaineiston kerääminen mahdollistaa myös aineiston tilastollisen analyysin ja käyttämisen realistisessa rakoverkkomallinnuksessa

Understanding Uranium Migration in Hard Rocks

Uranium is a major radioactive constituent of spent nuclear fuel and high-level radioactive waste. However, its migration behaviour in crystalline rocks is still inadequately understood. This paper describes the results of controlled laboratory migration experiments and attempts made to simulate them using numerical models. Initial models employing generic information in ¿blind predictions¿ are progressively enhanced by data-supported interpretation. Such an approach is intended to mimic the stages of a site assessment, where conceptual and numerical models are progressively refined.JRC.F.4-Safety of future nuclear reactor

Mining in the Arctic environment – A review from ecological, socioeconomic and legal perspectives

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