O. Nevalainen, O. Niemitalo, I. Fer, Antti Juntunen, T. Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, L. Kulmala, Åsa Stam, Otto Kuusela, Stéphanie Gérin, T. Viskari, J. Vira, J. Hyväluoma, J. Tuovinen, A. Lohila, T. Laurila, J. Heinonsalo, T. Aalto, I. Kunttu, J. Liski
Abstract. Better monitoring, reporting, and verification (MRV) of the amount, additionality, and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies, and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, modeling, and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called the Field Observatory (v1.0, https://www.fieldobservatory.org/, last access: 3 February 2022). The Field Observatory is designed as an online service for near-real-time model–data synthesis, forecasting, and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site, which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices as well as provide an MRV tool for decision support.
{"title":"Towards agricultural soil carbon monitoring, reporting, and verification through the Field Observatory Network (FiON)","authors":"O. Nevalainen, O. Niemitalo, I. Fer, Antti Juntunen, T. Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, L. Kulmala, Åsa Stam, Otto Kuusela, Stéphanie Gérin, T. Viskari, J. Vira, J. Hyväluoma, J. Tuovinen, A. Lohila, T. Laurila, J. Heinonsalo, T. Aalto, I. Kunttu, J. Liski","doi":"10.5194/gi-11-93-2022","DOIUrl":"https://doi.org/10.5194/gi-11-93-2022","url":null,"abstract":"Abstract. Better monitoring, reporting, and verification (MRV) of the amount,\u0000additionality, and persistence of the sequestered soil carbon is needed to\u0000understand the best carbon farming practices for different soils and climate\u0000conditions, as well as their actual climate benefits or cost efficiency in\u0000mitigating greenhouse gas emissions. This paper presents our Field\u0000Observatory Network (FiON) of researchers, farmers, companies, and other\u0000stakeholders developing carbon farming practices. FiON has established a\u0000unified methodology towards monitoring and forecasting agricultural carbon\u0000sequestration by combining offline and near-real-time field measurements,\u0000weather data, satellite imagery, modeling, and computing networks. FiON's\u0000first phase consists of two intensive research sites and 20 voluntary pilot\u0000farms testing carbon farming practices in Finland. To disseminate the data,\u0000FiON built a web-based dashboard called the Field Observatory (v1.0,\u0000https://www.fieldobservatory.org/, last access: 3 February 2022). The Field Observatory is designed as an online service\u0000for near-real-time model–data synthesis, forecasting, and decision support\u0000for the farmers who are able to monitor the effects of carbon farming\u0000practices. The most advanced features of the Field Observatory are visible\u0000on the Qvidja site, which acts as a prototype for the most recent\u0000implementations. Overall, FiON aims to create new knowledge on agricultural\u0000soil carbon sequestration and effects of carbon farming practices as well as\u0000provide an MRV tool for decision support.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43091207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yimin Liu, Z. Hou, Hao Zhou, G. Gao, Lun Yang, Pu Wang, Peng Wang
Abstract. The observation and estimation of the deep crustal stress state is a key and difficult problem for in situ stress measurement. Using a borehole wall strain gauge based on the overcoring stress-relieving method is one of the main methods of in situ stress measurement. In this paper, a strain-sensing array based on fiber Bragg grating (FBG) is designed by using the main structure of the classical hollow inclusion cell, and its layout scheme on the hollow inclusion is studied. According to the layout scheme, the in situ stress inversion algorithm of hole wall strain to stress is deduced. Following this, the triaxial loading and unloading experiment platform is built, and the calibration experiment for the FBG strain sensor is designed. Finally, Abaqus finite element software is used to simulate the in situ stress measurement process of the overcoring stress relief. The FBG strain values of each measurement direction before and after the overcoring process are extracted, and the stress inversion equation is used to carry out the stress inversion. The comparison of the inversion results proved that the FBG strain sensor group is feasible and reliable. The quasi-distributed FBG sensor module designed in this paper can invert the three-dimensional in situ stress by measuring the hole wall strain, which places a theoretical and experimental foundation for the development and application of an FBG hole wall strain gauge. It makes up for the deficiency of the existing hole wall strain gauge based on a resistance strain gauge, provides direct and accurate observations for hole wall strain measurement, and has important practical value for the development of in situ stress measurement technology.
{"title":"Research into using a fiber Bragg grating sensor group for three-dimensional in situ stress measurement","authors":"Yimin Liu, Z. Hou, Hao Zhou, G. Gao, Lun Yang, Pu Wang, Peng Wang","doi":"10.5194/gi-11-59-2022","DOIUrl":"https://doi.org/10.5194/gi-11-59-2022","url":null,"abstract":"Abstract. The observation and estimation of the deep crustal stress\u0000state is a key and difficult problem for in situ stress measurement. Using a\u0000borehole wall strain gauge based on the overcoring stress-relieving method\u0000is one of the main methods of in situ stress measurement. In this paper, a\u0000strain-sensing array based on fiber Bragg grating (FBG) is designed by using the main structure of\u0000the classical hollow inclusion cell, and its layout scheme on the hollow\u0000inclusion is studied. According to the layout scheme, the in situ stress\u0000inversion algorithm of hole wall strain to stress is deduced. Following this, the\u0000triaxial loading and unloading experiment platform is built, and the\u0000calibration experiment for the FBG strain sensor is designed. Finally, Abaqus\u0000finite element software is used to simulate the in situ stress measurement\u0000process of the overcoring stress relief. The FBG strain values of each\u0000measurement direction before and after the overcoring process are extracted,\u0000and the stress inversion equation is used to carry out the stress inversion.\u0000The comparison of the inversion results proved that the FBG strain sensor\u0000group is feasible and reliable. The quasi-distributed FBG sensor module\u0000designed in this paper can invert the three-dimensional in situ stress by\u0000measuring the hole wall strain, which places a theoretical and experimental\u0000foundation for the development and application of an FBG hole wall strain\u0000gauge. It makes up for the deficiency of the existing hole wall\u0000strain gauge based on a resistance strain gauge, provides direct and accurate\u0000observations for hole wall strain measurement, and has important\u0000practical value for the development of in situ stress measurement\u0000technology.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46239100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xinhua Zhou, Tian Gao, Ning Zheng, Yanlei Li, Fengyuan Yu, T. Awada, Jiaojun Zhu
Abstract. Ecosystem CO2−H2O data measured vastly from open-path eddy-covariance (OPEC) systems by infrared analyzers have numerous applications in biogeosciences. To assess the applicability, data uncertainties from measurements are needed. The uncertainties are sourced from infrared analyzers in zero drift, gain drift, cross-sensitivity, and precision variability. The sourced uncertainties are individually specified for analyzer performance, but no methodology exists to comprehend these individual uncertainties into a cumulative error for the specification of an overall accuracy, which is ultimately needed. Using the methodology for close-path eddy-covariance systems, this accuracy for OPEC systems is determined from all individual uncertainties via an accuracy model further formulated into CO2 and H2O accuracy equations. Based on atmospheric physics and the biological environment, these equations are used to evaluate CO2 accuracy (±1.21 20 mgCO2 m−3, relatively ±0.19 %) and H2O accuracy (±0.10 gH2O m−3, relatively ±0.18 % in saturated air at 35 °C and 101.325 kPa). Cross-sensitivity and precision variability are minor, although unavoidable, uncertainties. Zero drifts and gain drifts are major uncertainties but are adjustable via corresponding zero and span procedures during field maintenance. The equations provide rationales to assess and guide the procedures. In an atmospheric CO2 background, CO2 zero and span procedures can narrow CO2 accuracy by 40 %, from ±1.21 to ±0.72 mgCO2 m−3. In hot and humid weather, H2O gain drift potentially adds more to H2O measurement uncertainty, which requires more attention. If H2O zero and span procedures can be performed practically from 5 to 35 ºC, the poorest H2O accuracy can be improved by 30 %, from ±0.10 to ±0.07 gH2O m−3. Under freezing conditions, an H2O span is both impractical and unnecessary, but the zero procedure becomes imperative to minimize H2O measurement uncertainty. In cold/dry conditions, the zero procedure for H2O, along with CO2, is an operational and efficient option to ensure and improve H2O accuracy.
{"title":"Accuracies of field CO2−H2O data from open-path eddy-covariance flux systems: Assessment based on atmospheric physics and biological environment","authors":"Xinhua Zhou, Tian Gao, Ning Zheng, Yanlei Li, Fengyuan Yu, T. Awada, Jiaojun Zhu","doi":"10.5194/gi-2022-1","DOIUrl":"https://doi.org/10.5194/gi-2022-1","url":null,"abstract":"Abstract. Ecosystem CO2−H2O data measured vastly from open-path eddy-covariance (OPEC) systems by infrared analyzers have numerous applications in biogeosciences. To assess the applicability, data uncertainties from measurements are needed. The uncertainties are sourced from infrared analyzers in zero drift, gain drift, cross-sensitivity, and precision variability. The sourced uncertainties are individually specified for analyzer performance, but no methodology exists to comprehend these individual uncertainties into a cumulative error for the specification of an overall accuracy, which is ultimately needed. Using the methodology for close-path eddy-covariance systems, this accuracy for OPEC systems is determined from all individual uncertainties via an accuracy model further formulated into CO2 and H2O accuracy equations. Based on atmospheric physics and the biological environment, these equations are used to evaluate CO2 accuracy (±1.21 20 mgCO2 m−3, relatively ±0.19 %) and H2O accuracy (±0.10 gH2O m−3, relatively ±0.18 % in saturated air at 35 °C and 101.325 kPa). Cross-sensitivity and precision variability are minor, although unavoidable, uncertainties. Zero drifts and gain drifts are major uncertainties but are adjustable via corresponding zero and span procedures during field maintenance. The equations provide rationales to assess and guide the procedures. In an atmospheric CO2 background, CO2 zero and span procedures can narrow CO2 accuracy by 40 %, from ±1.21 to ±0.72 mgCO2 m−3. In hot and humid weather, H2O gain drift potentially adds more to H2O measurement uncertainty, which requires more attention. If H2O zero and span procedures can be performed practically from 5 to 35 ºC, the poorest H2O accuracy can be improved by 30 %, from ±0.10 to ±0.07 gH2O m−3. Under freezing conditions, an H2O span is both impractical and unnecessary, but the zero procedure becomes imperative to minimize H2O measurement uncertainty. In cold/dry conditions, the zero procedure for H2O, along with CO2, is an operational and efficient option to ensure and improve H2O accuracy.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46404565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hubert T. Samboko, S. Schurer, H. Savenije, H. Makurira, K. Banda, H. Winsemius
Abstract. Rapid modern technological advancements have led to significant improvements in river monitoring using unmanned aerial vehicles (UAVs), photogrammetric reconstruction software, and low-cost real-time kinematic Global Navigation Satellite System (RTK GNSS) equipment. UAVs allow for the collection of dry bathymetric data in environments that are difficult to access. Low-cost RTK GNSS equipment facilitates accurate measurement of wet bathymetry when combined with subaqueous measuring tools such as acoustic Doppler current profilers (ADCPs). Hydraulic models may be constructed from these data, which in turn can be used for various applications such as water management, forecasting, early warning and disaster preparedness by responsible water authorities, and construction of river rating curves. We hypothesise that the reconstruction of dry terrain with UAV-based photogrammetry combined with RTK GNSS equipment leads to accurate geometries particularly fit for hydraulic understanding and simulation models. This study sought to (1) compare open-source and commercial photogrammetry packages to verify if water authorities with low resource availability have the option to utilise open-source packages without significant compromise on accuracy; (2) assess the impact of variations in the number of ground control points (GCPs) and the distribution of the GCP markers on the quality of digital elevation models (DEMs), with a particular emphasis on characteristics that impact hydraulics; and (3) investigate the impact of using reconstructions based on different GCP numbers on conveyance and hydraulic slope. A novel method which makes use of a simple RTK tie line along the water edge measured using a low-cost but highly accurate GNSS is presented so as to correct the unwanted effect of lens distortion (“doming effect”) and enable the concatenation of geometric data from different sources. Furthermore, we describe how merging of the dry and wet bathymetry can be achieved through gridding based on linear interpolation. We tested our approach over a section of the Luangwa River in Zambia. Results indicate that the open-source software photogrammetry package is capable of producing results that are comparable to commercially available options. We determined that GCPs are essential for vertical accuracy, but also that an increase in the number of GCPs above a limited number of five only moderately increases the accuracy of results, provided the GCPs are well spaced in both the horizontal and vertical dimension. Furthermore, insignificant differences in hydraulic geometries among the various cross sections are observed, corroborating the fact that a limited well-spaced set of GCPs is enough to establish a hydraulically sound reconstruction. However, it appeared necessary to make an additional observation of the hydraulic slope. A slope derived merely from the UAV survey was shown to be prone to considerable errors caused by lens distortion. Combination of the
{"title":"Evaluating low-cost topographic surveys for computations of conveyance","authors":"Hubert T. Samboko, S. Schurer, H. Savenije, H. Makurira, K. Banda, H. Winsemius","doi":"10.5194/gi-11-1-2022","DOIUrl":"https://doi.org/10.5194/gi-11-1-2022","url":null,"abstract":"Abstract. Rapid modern technological advancements have led to\u0000significant improvements in river monitoring using unmanned aerial vehicles\u0000(UAVs), photogrammetric reconstruction software, and low-cost real-time\u0000kinematic Global Navigation Satellite System (RTK GNSS) equipment. UAVs\u0000allow for the collection of dry bathymetric data in environments that are\u0000difficult to access. Low-cost RTK GNSS equipment facilitates accurate\u0000measurement of wet bathymetry when combined with subaqueous measuring tools\u0000such as acoustic Doppler current profilers (ADCPs). Hydraulic models may be\u0000constructed from these data, which in turn can be used for various\u0000applications such as water management, forecasting, early warning and\u0000disaster preparedness by responsible water authorities, and construction of\u0000river rating curves. We hypothesise that the reconstruction of dry terrain\u0000with UAV-based photogrammetry combined with RTK GNSS equipment leads\u0000to accurate geometries particularly fit for hydraulic understanding and\u0000simulation models. This study sought to (1) compare open-source and\u0000commercial photogrammetry packages to verify if water authorities with low\u0000resource availability have the option to utilise open-source packages\u0000without significant compromise on accuracy; (2) assess the impact of\u0000variations in the number of ground control points (GCPs) and the\u0000distribution of the GCP markers on the quality of digital elevation models\u0000(DEMs), with a particular emphasis on characteristics that impact\u0000hydraulics; and (3) investigate the impact of using reconstructions based\u0000on different GCP numbers on conveyance and hydraulic slope. A novel method\u0000which makes use of a simple RTK tie line along the water edge measured using\u0000a low-cost but highly accurate GNSS is presented so as to correct the\u0000unwanted effect of lens distortion (“doming effect”) and enable the\u0000concatenation of geometric data from different sources. Furthermore, we\u0000describe how merging of the dry and wet bathymetry can be achieved through\u0000gridding based on linear interpolation. We tested our approach over a\u0000section of the Luangwa River in Zambia. Results indicate that the\u0000open-source software photogrammetry package is capable of producing results\u0000that are comparable to commercially available options. We determined that\u0000GCPs are essential for vertical accuracy, but also that an increase in the\u0000number of GCPs above a limited number of five only moderately increases the\u0000accuracy of results, provided the GCPs are well spaced in both the horizontal\u0000and vertical dimension. Furthermore, insignificant differences in hydraulic\u0000geometries among the various cross sections are observed, corroborating the\u0000fact that a limited well-spaced set of GCPs is enough to establish a\u0000hydraulically sound reconstruction. However, it appeared necessary to make\u0000an additional observation of the hydraulic slope. A slope derived merely\u0000from the UAV survey was shown to be prone to considerable errors caused by\u0000lens distortion. Combination of the","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49540613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Weigand, E. Zimmermann, V. Michels, J. Huisman, A. Kemna
Abstract. Spectral electrical impedance tomography (sEIT) is increasingly used to characterize the structure of subsurface systems. Additionally, petrophysical and biogeophysical processes are characterized and monitored using sEIT. The method combines multiple, spatially distributed, spectroscopic measurements with tomographic inversion algorithms to obtain images of the complex electrical resistivity distribution in the subsurface at various frequencies. Spectral data, as well as polarization measurements provide additional information about the systems under investigation, and can be used to reduce ambiguities that occur if only the in-phase resistivity values are analysed. However, spectral impedance measurements are very sensitive to details of the measurement setup, as well as external noise and error components. Despite promising technical progress in improving measurement quality, as well as progress in the static characterisation and understanding of electrical polarisation signatures of the subsurface, long-term monitoring attempts are still rare. Yet, measurement targets often show inherent non-stationarity that would require such approaches for a proper system characterisation. With the aim of improving operating foundations for similar endeavours, we here report on the design and field deployment of a permanently installed monitoring system for sEIT data. The specific aim of this monitoring installation is the characterisation of crop root evolution over a full growing season, requiring multiple measurements per day over multiple months to capture relevant system dynamics. In this contribution, we discuss the general layout and design of the monitoring system, including the core measurement system, additional on-site equipment, required corrections to improve data quality for high frequencies, data management, and remote processing facilities used to analyse the generated data. The choice and installation of electrodes, cables, and measurement configurations are discussed, as well as quality parameters used for the continuous assessment of system functioning and data quality. Exemplary analysis results of the first season of operation highlight the importance of continuous quality control. It is also found that proper cable elevation decreased capacitive leakage currents and in combination with the correction of inductive effects lead to consistent tomographic results up to 1 kHz measurement frequency. Overall, the successful operation of an sEIT monitoring system over multiple months with multiple daily tomographic measurements was achieved.
{"title":"Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT)","authors":"M. Weigand, E. Zimmermann, V. Michels, J. Huisman, A. Kemna","doi":"10.5194/gi-2021-36","DOIUrl":"https://doi.org/10.5194/gi-2021-36","url":null,"abstract":"Abstract. Spectral electrical impedance tomography (sEIT) is increasingly used to characterize the structure of subsurface systems. Additionally, petrophysical and biogeophysical processes are characterized and monitored using sEIT. The method combines multiple, spatially distributed, spectroscopic measurements with tomographic inversion algorithms to obtain images of the complex electrical resistivity distribution in the subsurface at various frequencies. Spectral data, as well as polarization measurements provide additional information about the systems under investigation, and can be used to reduce ambiguities that occur if only the in-phase resistivity values are analysed. However, spectral impedance measurements are very sensitive to details of the measurement setup, as well as external noise and error components. Despite promising technical progress in improving measurement quality, as well as progress in the static characterisation and understanding of electrical polarisation signatures of the subsurface, long-term monitoring attempts are still rare. Yet, measurement targets often show inherent non-stationarity that would require such approaches for a proper system characterisation. With the aim of improving operating foundations for similar endeavours, we here report on the design and field deployment of a permanently installed monitoring system for sEIT data. The specific aim of this monitoring installation is the characterisation of crop root evolution over a full growing season, requiring multiple measurements per day over multiple months to capture relevant system dynamics. In this contribution, we discuss the general layout and design of the monitoring system, including the core measurement system, additional on-site equipment, required corrections to improve data quality for high frequencies, data management, and remote processing facilities used to analyse the generated data. The choice and installation of electrodes, cables, and measurement configurations are discussed, as well as quality parameters used for the continuous assessment of system functioning and data quality. Exemplary analysis results of the first season of operation highlight the importance of continuous quality control. It is also found that proper cable elevation decreased capacitive leakage currents and in combination with the correction of inductive effects lead to consistent tomographic results up to 1 kHz measurement frequency. Overall, the successful operation of an sEIT monitoring system over multiple months with multiple daily tomographic measurements was achieved.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41314010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Airborne geophysical data leveling is an indispensable step to the conventional data processing. Traditional data leveling methods mainly explore the leveling error properties in the time and frequency domain. A new technique is proposed to level airborne geophysical data in view of the image space properties of leveling error, including directional distribution property and amplitude variety property. This work applied unidirectional variational model on entire survey data based on the gradient difference between the leveling errors in flight line direction and the tie-line direction. Then spatially adaptive multi-scale model is introduced to iteratively decompose the leveling errors which effectively avoid the difficulty on the parameter selection. Considering the anomaly data with large amplitude may hide the real data level, a leveling preprocessing method is given to construct a smooth field based on the gradient data. The leveling method can automatically extract the leveling errors of the entire survey area simultaneously without the participation of staff members or tie-line control. We have applied the method to the airborne electromagnetic, magnetic data, and apparent conductivity data collected by Ontario Geological Survey to confirm its validity and robustness by comparing the results with the published data.
{"title":"Leveling airborne geophysical data using a unidirectional variational model","authors":"Qiong Zhang, C. Sun, Fei Yan, Chao Lv, Yun Liu","doi":"10.5194/gi-2021-33","DOIUrl":"https://doi.org/10.5194/gi-2021-33","url":null,"abstract":"Abstract. Airborne geophysical data leveling is an indispensable step to the conventional data processing. Traditional data leveling methods mainly explore the leveling error properties in the time and frequency domain. A new technique is proposed to level airborne geophysical data in view of the image space properties of leveling error, including directional distribution property and amplitude variety property. This work applied unidirectional variational model on entire survey data based on the gradient difference between the leveling errors in flight line direction and the tie-line direction. Then spatially adaptive multi-scale model is introduced to iteratively decompose the leveling errors which effectively avoid the difficulty on the parameter selection. Considering the anomaly data with large amplitude may hide the real data level, a leveling preprocessing method is given to construct a smooth field based on the gradient data. The leveling method can automatically extract the leveling errors of the entire survey area simultaneously without the participation of staff members or tie-line control. We have applied the method to the airborne electromagnetic, magnetic data, and apparent conductivity data collected by Ontario Geological Survey to confirm its validity and robustness by comparing the results with the published data.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45843267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Soil heat flux is an important component of the Surface Energy Balance (SEB) equation. Measuring it require an indirect measurement. Every used technique may present some possible errors tied with each specific technique, soil inhomogeneities or physicals phenomenon such as latent heat conversion beneath the plates especially in a desiccation cracking soil or vertisol. The installation place may also induce imbalances. Finally, some errors resulting from the physical sensor presence, vegetation presence or soil inhomogeneities may occur and are not avoidable. For all these reasons it is important to check the validity of the measurements. One quick and easy way is to integrate results during one year. The corresponding integration should be close to zero after a necessary geothermal heat efflux subtraction which should be included into the SEB equation for long term integrations. However, below plate evaporation and vegetation absorbed water or rainfall water the infiltration may also contribute to the observed short scale or/and long scale imbalance. Another energy source is usually not included in the SEB equation: the rainfall or irrigation. Yet its importance for a short- and long-term integration is notable. As an example, the most used sensors: Soil Heat Flux Plates (SHFP), is given.
{"title":"The Soil heat flow sensor functioning checks, imbalances’ origins and forgotten energies","authors":"B. Zawilski","doi":"10.5194/gi-2021-34","DOIUrl":"https://doi.org/10.5194/gi-2021-34","url":null,"abstract":"Abstract. Soil heat flux is an important component of the Surface Energy Balance (SEB) equation. Measuring it require an indirect measurement. Every used technique may present some possible errors tied with each specific technique, soil inhomogeneities or physicals phenomenon such as latent heat conversion beneath the plates especially in a desiccation cracking soil or vertisol. The installation place may also induce imbalances. Finally, some errors resulting from the physical sensor presence, vegetation presence or soil inhomogeneities may occur and are not avoidable. For all these reasons it is important to check the validity of the measurements. One quick and easy way is to integrate results during one year. The corresponding integration should be close to zero after a necessary geothermal heat efflux subtraction which should be included into the SEB equation for long term integrations. However, below plate evaporation and vegetation absorbed water or rainfall water the infiltration may also contribute to the observed short scale or/and long scale imbalance. Another energy source is usually not included in the SEB equation: the rainfall or irrigation. Yet its importance for a short- and long-term integration is notable. As an example, the most used sensors: Soil Heat Flux Plates (SHFP), is given.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45030887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Vasić, Marina Davidović, Ivan Radosavljević, Đorđe Obradović
Abstract. Panoramic images captured using laser scanning technologies, which principally produce point clouds, are readily applicable in colorization of point cloud, detailed visual inspection, road defect detection, spatial entities extraction, diverse map creation, etc. This paper underlines the importance of images in modern surveying technologies and different GIS projects at the same time having regard to their anonymization in accordance with law. The General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for the collection and processing of personal information from individuals who live in the European Union (EU). Namely, it is a legislative requirement that faces of persons and license plates of vehicles in the collected data are blurred. The objective of this paper is to present a novel architecture of the solution for a particular object blurring. The architecture is designed as a pipeline of object detection algorithms that progressively narrows the search space until it detects the objects to be blurred. The methodology was tested on four data sets counting 5000, 10 000, 15 000 and 20 000 panoramic images. The percentage of accuracy, i.e., successfully detected and blurred objects of interest, was higher than 97 % for each data set. Additionally, our aim was to achieve efficiency and broad use.
{"title":"Architecture of solution for panoramic image blurring in GIS project application","authors":"D. Vasić, Marina Davidović, Ivan Radosavljević, Đorđe Obradović","doi":"10.5194/gi-10-287-2021","DOIUrl":"https://doi.org/10.5194/gi-10-287-2021","url":null,"abstract":"Abstract. Panoramic images captured using laser scanning technologies, which principally produce point clouds, are readily applicable in colorization of point\u0000cloud, detailed visual inspection, road defect detection, spatial entities extraction, diverse map creation, etc. This paper underlines the\u0000importance of images in modern surveying technologies and different GIS projects at the same time having regard to their anonymization in accordance\u0000with law. The General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for the collection and processing of personal\u0000information from individuals who live in the European Union (EU). Namely, it is a legislative requirement that faces of persons and license plates\u0000of vehicles in the collected data are blurred. The objective of this paper is to present a novel architecture of the solution for a particular\u0000object blurring. The architecture is designed as a pipeline of object detection algorithms that progressively narrows the search space until it\u0000detects the objects to be blurred. The methodology was tested on four data sets counting 5000, 10 000, 15 000 and 20 000 panoramic images. The percentage of accuracy, i.e., successfully detected and blurred objects of interest, was higher than 97 % for each data\u0000set. Additionally, our aim was to achieve efficiency and broad use.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43096053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knut Ola Dølven, J. Vierinen, R. Grilli, J. Triest, B. Ferré
Abstract. Accurate, high resolution measurements are essential to improve our understanding of environmental processes. Several chemical sensors relying on membrane separation extraction techniques have slow response times due to a dependence on equilibrium partitioning across the membrane separating the measured medium (i.e., a measuring chamber) and the medium of interest (i.e., a solvent). We present a new technique for deconvolving slow sensor response signals using statistical inverse theory; applying a weighted linear least squares estimator with the growth-law as measurement model. The solution is regularized using model sparsity, assuming changes in the measured quantity occurs with a certain time-step, which can be selected based on domain-specific knowledge or L-curve analysis. The advantage of this method is that it: 1) models error propagation, providing an explicit uncertainty estimate of the response time corrected signal, 2) enables evaluation of the solutions self consistency, and 3) only requires instrument accuracy, response time, and data as input parameters. Functionality of the technique is demonstrated using simulated, laboratory, and field measurements. In the field experiment, the coefficient of determination (R2) of a slow response methane sensor in comparison with an alternative, fast response sensor, significantly improved from 0.18 to 0.91 after signal deconvolution. This shows how the proposed method can open up a considerably wider set of applications for sensors and methods suffering from slow response times due to a reliance on the efficacy of diffusion processes.
{"title":"Response time correction of slow response sensor data by deconvolution of the growth-law equation","authors":"Knut Ola Dølven, J. Vierinen, R. Grilli, J. Triest, B. Ferré","doi":"10.5194/gi-2021-28","DOIUrl":"https://doi.org/10.5194/gi-2021-28","url":null,"abstract":"Abstract. Accurate, high resolution measurements are essential to improve our understanding of environmental processes. Several chemical sensors relying on membrane separation extraction techniques have slow response times due to a dependence on equilibrium partitioning across the membrane separating the measured medium (i.e., a measuring chamber) and the medium of interest (i.e., a solvent). We present a new technique for deconvolving slow sensor response signals using statistical inverse theory; applying a weighted linear least squares estimator with the growth-law as measurement model. The solution is regularized using model sparsity, assuming changes in the measured quantity occurs with a certain time-step, which can be selected based on domain-specific knowledge or L-curve analysis. The advantage of this method is that it: 1) models error propagation, providing an explicit uncertainty estimate of the response time corrected signal, 2) enables evaluation of the solutions self consistency, and 3) only requires instrument accuracy, response time, and data as input parameters. Functionality of the technique is demonstrated using simulated, laboratory, and field measurements. In the field experiment, the coefficient of determination (R2) of a slow response methane sensor in comparison with an alternative, fast response sensor, significantly improved from 0.18 to 0.91 after signal deconvolution. This shows how the proposed method can open up a considerably wider set of applications for sensors and methods suffering from slow response times due to a reliance on the efficacy of diffusion processes.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45392390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Miles, R. Dvorský, K. Greene, C. Hansen, B. Narod, M. Webb
Abstract. Fluxgate magnetometers provide sensitive and stable measurements of the static and low frequency vector magnetic field. Fluxgates form a magnetic field measurement by periodically saturating a ferromagnetic core and the intrinsic magnetic noise of this material can determine the noise floor of the instrument. We present the results of an empirical experiment to understand the physical parameters that influence the intrinsic magnetic noise of fluxgate cores. We compare two permalloy alloys – the historical standard 6 % molybdenum alloy and a new 28 % copper alloy. We examine the influence of geometry using the historical standard 1” diameter spiral wound ring-core and a new stacked washer racetrack design. We evaluate the influence of material thickness by comparing 100 µm and 50 µm foils. Finally, we investigate heat treatments in terms of temperature and ramp rate and their role in both grain size and magnetic noise. The results of these experiments suggest that thinner foils, potentially comprising the copper alloy, manufactured into continuous racetrack geometry washers may provide excellent performance in fluxgate sensors.
{"title":"Contributors to Fluxgate Magnetic Noise in Permalloy Foils Including a Potential New Copper Alloy Regime","authors":"D. Miles, R. Dvorský, K. Greene, C. Hansen, B. Narod, M. Webb","doi":"10.5194/gi-2021-30","DOIUrl":"https://doi.org/10.5194/gi-2021-30","url":null,"abstract":"Abstract. Fluxgate magnetometers provide sensitive and stable measurements of the static and low frequency vector magnetic field. Fluxgates form a magnetic field measurement by periodically saturating a ferromagnetic core and the intrinsic magnetic noise of this material can determine the noise floor of the instrument. We present the results of an empirical experiment to understand the physical parameters that influence the intrinsic magnetic noise of fluxgate cores. We compare two permalloy alloys – the historical standard 6 % molybdenum alloy and a new 28 % copper alloy. We examine the influence of geometry using the historical standard 1” diameter spiral wound ring-core and a new stacked washer racetrack design. We evaluate the influence of material thickness by comparing 100 µm and 50 µm foils. Finally, we investigate heat treatments in terms of temperature and ramp rate and their role in both grain size and magnetic noise. The results of these experiments suggest that thinner foils, potentially comprising the copper alloy, manufactured into continuous racetrack geometry washers may provide excellent performance in fluxgate sensors.\u0000","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45760627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：