ORCID Profile
0000-0001-5351-7618
Current Organisations
Aarhus University
,
Aarhus Universitet
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Copernicus GmbH
Date: 26-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-791
Abstract: & & Peatlands play a major role in the global carbon cycle as they constitute around 20% of the soil carbon (C) stock and act simultaneously as C sinks (for CO& sub& & /sub& absorption) and sources (of CH& sub& & /sub& emission). Additionally, they support bio ersity preservation and provide important ecosystem services like climate regulation. Draining the peat for agriculture purposes results in its consolidation, enhanced decomposition, and subsequent subsidence. This accompanied by global warming promotes the emission of greenhouse gases making peatlands a C source ecosystem. Globally, as pent-up demand, different initiatives are put forward to protect, properly manage, and restore peatlands mainly to reduce these emissions and slow down climate change. For ex le, from 2021 onwards, under the EU 2030 climate and energy framework, all the member states are supposed to report on the emissions and removals of greenhouse gases from wetland areas. Denmark has its own national goal of reducing CO& sub& & /sub& emissions by 70% by 2030. However, the extent and status of peatlands are still poorly determined. Comprehensive mapping is required to enforce measures to prevent their further degradation, estimate the C stock and forecast the future emissions from peatlands. The conventional mapping approach using peat probes is time-consuming, tedious, and provides only localized and discrete measurements. Though these measurements are somewhat reliable, it is still challenging because occasionally the probes are obstructed by stones or human artefacts. On contrary to the latter, sometimes they might also easily penetrate the soil underlying the actual peat. While remote sensing based on satellite and aerial imagery makes delineation of the spatial extent possible, electromagnetic methods that have a deeper penetration into the soil are required to provide knowledge on peat volume estimates and groundwater depth. As a part of the ReDoCO2 (viz. Reducing and Documenting CO2 emissions from Peatlands) project, we employ state-of-the-art geophysical sensors, precisely, working on electromagnetic induction, ground-penetrating radar, and gamma-ray radiometric principles to accurately characterize three peatland areas in Denmark. The sensors are being tested in both proximal and remote configurations and efforts are underway to develop a novel drone-based transient electromagnetic induction sensor. Later, we plan to fuse the multisource datasets using machine learning to improve the prediction accuracy and advanced modelling techniques to study the effects of different management scenarios on greenhouse gas emissions. We envision developing a framework for detailed three-dimensional mapping of peatlands and a tool to estimate the reduction in greenhouse gas emissions to support decision-makers in choosing an appropriate management strategy. The project outcomes will have a significant economic, societal, and environmental impact strengthening Denmark& #8217 s position as a green frontrunner.& &
Publisher: Unpublished
Date: 2021
Publisher: MDPI AG
Date: 15-04-2021
DOI: 10.3390/S21082800
Abstract: Agricultural subsurface drainage systems are commonly installed on farmland to remove the excess water from poorly drained soils. Conventional methods for drainage mapping such as tile probes and trenching equipment are laborious, cause pipe damage, and are often inefficient to apply at large spatial scales. Knowledge of locations of an existing drainage network is crucial to understand the increased leaching and offsite release of drainage discharge and to retrofit the new drain lines within the existing drainage system. Recent technological developments in non-destructive techniques might provide a potential alternative solution. The objective of this study was to determine the suitability of unmanned aerial vehicle (UAV) imagery collected using three different cameras (visible-color, multispectral, and thermal infrared) and ground penetrating radar (GPR) for subsurface drainage mapping. Both the techniques are complementary in terms of their usage, applicability, and the properties they measure and were applied at four different sites in the Midwest USA. At Site-1, both the UAV imagery and GPR were equally successful across the entire field, while at Site-2, the UAV imagery was successful in one section of the field, and GPR proved to be useful in the other section where the UAV imagery failed to capture the drainage pipes’ location. At Site-3, less to no success was observed in finding the drain lines using UAV imagery captured on bare ground conditions, whereas good success was achieved using GPR. Conversely, at Site-4, the UAV imagery was successful and GPR failed to capture the drainage pipes’ location. Although UAV imagery seems to be an attractive solution for mapping agricultural subsurface drainage systems as it is cost-effective and can cover large field areas, the results suggest the usefulness of GPR to complement the former as both a mapping and validation technique. Hence, this case study compares and contrasts the suitability of both the methods, provides guidance on the optimal survey timing, and recommends their combined usage given both the technologies are available to deploy for drainage mapping purposes.
Publisher: MDPI AG
Date: 15-02-2022
DOI: 10.3390/S22041508
Abstract: Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by utilizing electrical conductivity (EC) values derived by the inversion of apparent electrical conductivity data measured by an electromagnetic induction instrument. The NRH areas were derived from the subzones generated from clustering the EC values via two methods, unsupervised ISODATA clustering and the Optimized Hot Spot Analysis, that highly complement each other. The clustering of EC values generated three classes, wherein the classes with high EC values correspond to NRH areas as indicated by their low redox potential values and nitrate (NO3−) concentrations. Nitrate concentrations in the NRH were equal to 13 to 17% of the concentrations in non-NRH areas and occupied 26% of the total area of the drainage catchments in the study. It is likely that, with the identification of NRH areas, the degree of nitrogen reduction in the vadose zone may be higher than initially estimated at the subcatchment scale.
Publisher: Burleigh Dodds Science Publishing
Date: 21-02-2023
Abstract: Due to economic and environmental considerations, there exists a need for effective, efficient, and nondestructive methods for locating buried agricultural drainage pipes. Ground penetrating radar (GPR), a proximal soil sensing method, can potentially provide a means for drain line detection. This chapter details the evolution of research, through a series of studies conducted over the past twenty years, which focused on farm field mapping of subsurface drainage systems using GPR. The chapter first describes the evaluation of GPR against other proximal soil sensing methods. It then considers the factors potentially impacting GPR drainage pipe detection, goes on to examine GPR assessment of agricultural drainage pipe conditions and associated functionality implications, the effects of GPR antenna orientation relative to drain line directional trends and the integration of GPR with Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) technology. A section on drainage mapping with a multichannel, stepped-frequency, continuous wave 3D-GPR system is also provided which is then followed by a review of complementary employment of GPR and unmanned aerial vehicle (UAV) imagery for drainage system characterization. The chapter concludes with a summary and recommendations for future research.
Publisher: Elsevier BV
Date: 2022
Publisher: MDPI AG
Date: 10-06-2020
DOI: 10.20944/PREPRINTS202006.0127.V1
Abstract: Subsurface drainage systems remove excess water from the soil profile thereby improving crop yields in poorly drained farmland. Knowledge of the position of the buried drain lines is important: 1) to improve understanding of leaching and offsite release of nutrients and pesticides, and 2) for the installation of a new set of drain lines between the old ones for enhanced soil water removal efficiency. Traditional methods of drainage mapping involve the use of tile probes and trenching equipment. While these can be effective, they are also time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has focused on the use of time-domain ground penetrating radar (GPR), with variable success depending on local soil and hydrological conditions and the central frequency of the specific equipment employed. The objectives of this study were 1) to test the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) for subsurface drainage mapping, and 2) to evaluate the performance of a 3D-GPR with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor (DUALEM) in-combination. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth. The EMI sensor simultaneously provides information about the apparent electrical conductivity (ECa) for different soil volumes, corresponding to different depths. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3-D GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to limited penetration depth (PD) of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of ECa data measured by the DUALEM sensor could be a useful proxy to explain the success achieved by the 3D-GPR in finding the drain lines. The high attenuation of electromagnetic waves in highly conductive media limiting the PD of the 3D-GPR can explain the findings obtained in this research.
Publisher: Elsevier BV
Date: 2017
DOI: 10.1016/J.SCITOTENV.2016.10.224
Abstract: In order to understand the drivers of topsoil salinization, the distribution and movement of salt in accordance with groundwater need mapping. In this study, we described a method to map the distribution of soil salinity, as measured by the electrical conductivity of a saturated soil-paste extract (EC
Publisher: Elsevier BV
Date: 10-2021
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-1887
Abstract: & & Artificial subsurface drainage systems are installed in agricultural areas to remove excess water and convert poorly naturally drained soils into productive cropland. Some of the most productive agricultural regions in the world are a result of subsurface drainage practices. Drain lines provide a shortened pathway for the release of nutrients and pesticides into the environment, which presents a potentially increased risk for eutrophication and contamination of surface water bodies. Knowledge of drain line locations is often lacking. This complicates the understanding of the local hydrology and solute dynamics and the consequent planning of mitigation strategies such as constructed wetlands, saturated buffers, bioreactors, and nitrate and phosphate filters. In addition, accurate knowledge of the existing subsurface drainage system is required in designing the installation of a new set of drain lines to enhance soil water removal efficiency. The traditional methods of drainage mapping involve the use of tile probes and trenching equipment which are time-consuming, tiresome, and invasive, thereby carrying an inherent risk of damaging the drain pipes. Non-invasive geophysical sensors provide a potential alternative solution to the problem. Previous research has focused on the use of time-domain ground penetrating radar (GPR) with variable success depending on local soil and hydrological conditions and the center frequency of the specific equipment used. For ex le, 250 MHz antennas proved to be more suitable for drain line mapping. Recent technological advancements enabled the collection of high-resolution spatially exhaustive data. In this study, we present the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) mounted in a motorized survey configuration with real-time georeferencing for subsurface drainage mapping. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth (60-3000 MHz). In addition, the wide array swathe of the antenna array (1.5 m covered by 20 measurement channels) enables effective coverage of three-dimensional (3D) space. The surveys were performed on twelve different study sites with various soil types with textures ranging from sand to clay till. While we achieved good success in finding the drainage pipes at five sites with sandy, sandy loam, loamy sand and organic topsoils, the results at the other seven sites with more clay-rich soils were less successful. The high attenuation of electromagnetic waves in highly conductive clay-rich soils, which limits the penetration depth of the 3D-GPR system, can explain our findings obtained in this research.& &
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 03-2021
Publisher: Wiley
Date: 16-05-2018
DOI: 10.1002/LDR.2973
Publisher: MDPI AG
Date: 31-07-2020
DOI: 10.3390/RS12152458
Abstract: Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw ECa measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity (σ) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of ECa data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw ECa data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R2) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of ECa data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-2227
Abstract: & & Pristine peatlands are unique ecosystems for bio ersity conservation and climate regulation. They have the capacity to regulate local hydrology and balance carbon (C) fluxes between land and the atmosphere. Despite their importance, most peatlands can no longer be considered pristine mainly due to anthropogenic alterations. Although existing peatlands still support ecosystem services, they do so at a reduced capacity. Peatlands are the largest natural terrestrial C reserve with a global C stock estimated at about 20%. & Naturally, peatlands act as C sinks. However, excessive drainage for agricultural use and rising global temperatures may tip them into C sources and risk an increase in the emission of greenhouse gases (GHGs). Therefore, it is important to assess the magnitude of the coupled impacts of climate and anthropogenic changes on peatland status and coverage. A major limitation in achieving this lies in the lack of coherent detailed records documenting the spatiotemporal changes in the peat properties such as its thickness and spatial extent. Increasingly, there is a global interest in sustainable, and restorative peatland research as both a mitigation and adaptation strategy to climate change. The challenge still holds where without sufficient understanding of the status, extent, and controls on the changes in peat, there could be a mismatch between targeted management strategies for conservation. This study will focus on characterizing a peatland area in Store Vildmose, Denmark. This is a & the largest raised bog in Denmark and selected due to its age, various land uses over time and historical significance. There is compelling evidence for peat subsidence in this area due to anthropogenic influence. This can be jointly attributed to both the State and in idual activities over the years. For ex le, the conversion of part of the bog to grazing lands by the State in 1920 (which required drainage by digging ditches and laying an extensive pipe network) and construction of cattle farms considerably degraded the peat. Additionally, the consumption of peat as an energy source favoured its extraction over conservation historically. In spite of the physical evidence, there is no accurate estimate of the changes in peat volume through time. This information is crucial to estimate the depletion and the current status of C stocks. Therefore, we propose to assess the changes in the peatland extent and volume by the use of historical cadastral maps (starting from 1880 onwards and yet to be digitized) and recent digital maps generated by the digital soil mapping approach. We will further perform scenario analysis and predictive modelling of the peat coverage with machine learning algorithms using additional covariates to more accurately quantify the C stocks and GHG emissions. The findings from the study will support stakeholder decision making for reducing the peatlands& #8217 CO& sub& & /sub& emissions.& &
Publisher: MDPI AG
Date: 14-07-2019
DOI: 10.3390/S20143922
Abstract: Subsurface drainage systems are commonly used to remove surplus water from the soil profile of a poorly drained farmland. Traditional methods for drainage mapping involve the use of tile probes and trenching equipment that are time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Effective and efficient methods are needed in order to map the buried drain lines: (1) to comprehend the processes of leaching and offsite release of nutrients and pesticides and (2) for the installation of a new set of drain lines between the old ones to enhance the soil water removal. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has mainly showcased the use of time-domain ground penetrating radar, with variable success, depending on local soil and hydrological conditions and the central frequency of the specific equipment used. The objectives of this study were: (1) to test the use of a stepped-frequency continuous wave three-dimensional ground penetrating radar (3D-GPR) with a wide antenna array for subsurface drainage mapping and (2) to evaluate its performance with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor in-combination. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3D-GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to the limited penetration depth of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of apparent electrical conductivity data measured by the EMI sensor could be a useful proxy for explaining the success achieved by the 3D-GPR in finding the drain lines.
Publisher: Springer Science and Business Media LLC
Date: 27-02-2017
Publisher: Copernicus GmbH
Date: 26-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-795
Abstract: & & Agricultural subsurface drainage systems are installed in naturally poorly drained soils and areas with a rising water table to drain the excess water, eradicate soil salinization issues and increase crop yields. Globally, some of the most productive regions are a result of these artificial drainage practices. The installation of drainage systems provides many agronomic, economic, and environmental benefits. However, inevitably, they act as shortened pathways for the transport of undesired substances (nutrients, pesticides, and pathogens) through the soil profile promoting their increased leaching and offsite release to the surface water bodies. This drainage water cause potential eutrophication risk to the aquatic ecosystem. For ex le, the hypoxic zone in the Gulf of Mexico and harmful algal blooms in Lake Erie can be linked to the nitrogen and phosphorus losses from the Midwest USA agricultural areas. Hence, the knowledge of the location of these installations is essential for hydrological modelling and to plan effective edge-of-field mitigation strategies such as constructed wetlands, saturated buffer zones, denitrifying bioreactors, and phosphate filters. Moreover, their location is also important either in order to initiate repairs or retrofit a new drainage system to the existing one. Nevertheless, subsurface drainage installations are often poorly documented and this information is inaccurate or unavailable, inducing the need for extensive mapping c aigns. The conventional methods for drainage mapping involve tile probing and trenching equipment. While the use of tile probes provide only localized and discrete measurements, employing trenching with heavy machinery can be exceedingly invasive and carry a risk of severing the drainage pipes necessitating costly repairs. Non-destructive soil and crop sensors might provide a rapid and effective alternative solution. Previous studies show ground penetrating radar (GPR) to be especially successful owing to its superior resolution over other near-surface geophysical methods. In this study, we tested the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) at study sites in Denmark and a time-domain GPR (Noggin 250 MHz SmartCart) at study sites in the Midwest USA to map the buried drainage pipes. The 3D-GPR mounted in a motorized survey configuration and mobilized behind an all-terrain vehicle proved certainly advantageous to get full coverage of the farm field area and provided the flexibility of adjusting the frequency bandwidth depending on the desired resolution and penetration depth (PD). Two different approaches were tested to estimate the PD and comparisons were made with electrical conductivity data measured using an electromagnetic induction instrument. With the impulse GPR, data collected along limited parallel transects spaced a few meters apart, spiral and serpentine segments incorporated into random survey lines proved sufficient when used adjacently with unmanned aerial vehicle imagery. In general, a better success can be expected when the average soil electrical conductivity is less than 20 mS m& sup& -1& /sup& and it is a recommendation to perform the GPR surveys preferably in a direction perpendicular to the expected drain line orientation when the water table is at/below the drainage pipes& #8217 depth.& &
Publisher: Elsevier BV
Date: 12-2017
DOI: 10.1016/J.SCITOTENV.2017.05.074
Abstract: The cation exchange capacity (CEC) is one of the most important soil properties as it influences soil's ability to hold essential nutrients. It also acts as an index of structural resilience. In this study, we demonstrate a method for 3-dimensional mapping of CEC across a study field in south-west Spain. We do this by establishing a linear regression (LR) between the calculated true electrical conductivity (σ - mS/m) and measured CEC (cmol(+)/kg) at various depths. We estimate σ by inverting Veris-3100 data (EC
Publisher: Elsevier BV
Date: 12-2020
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-2251
Abstract: Pristine peatlands are precious for their Carbon (C) storage ability and the vast range of ecosystem services they provide. Globally, peatlands were heavily altered over the years especially by draining the water table for meeting energy and agricultural needs. Draining the peat results in its enhanced microbial decomposition, increased dissolved C leaching and increased susceptibility to peat fires, thus turning peatlands into C-source ecosystems. Currently, the carbon dioxide (CO2) released from degraded peatlands amounts to approximately 5% of global anthropogenic emissions. Climate change concerns have sparked an interest to reduce these emissions and different initiatives are put forward for the protection, proper management, and restoration of the peatlands. Denmark has its own national goal of reducing CO2 emissions by 70% by 2030 of which agriculture is expected to be a significant contributor. Comprehensive characterization of peat inventory providing status on the C stocks, water table depths and emissions is required for improved land use planning as almost 4.8 million tonnes of CO2 per annum is released from cultivated organic lands (~ 170,000 ha in total). To achieve this, measurements of peat depth (PD) for volume characterization are invaluable. The conventional mapping approach of PD using peat probes is laborious, time-consuming, and provides only localized and discrete measurements. In addition, these manual probing measurements are also prone to errors as occasionally the probes are obstructed by stones, wood and human artefacts causing underestimation and other times they might easily penetrate the soil underlying the actual peat causing overestimation. In Denmark, we are comparing and contrasting the suitability of different electromagnetic sensors, precisely, working on electromagnetic induction (EMI), ground penetrating radar (GPR), and gamma-ray radiometric (GR) principles to accurately characterize the Danish peatlands. We are testing the sensors on both ground-based and air-borne configurations to improve the feasibility, increase accessibility and save costs. A novel drone-based transient EMI sensor is being designed in this direction. So far the results suggest that the EMI and GR techniques are promising to demarcate the peatland boundaries and estimate the PDs up to a certain extent depending on the gradient in transition between the mineral and organic soils. Ground penetrating radar provided unequivocal results in high-resistive ombrotrophic peat while failing in low-resistive minerotrophic peat due to low signal penetration. In the drone-borne configuration, GR proved superior due to its ease of use and less to no success was achieved using a GPR. Moving forward, we plan on fusing the multisensor datasets using machine learning to improve the prediction accuracy of PDs, find a means for mapping water table depths and perform advanced modelling for comprehending the effects of different management scenarios on CO2 emissions.
Publisher: Elsevier BV
Date: 11-2023
No related grants have been discovered for TRIVEN KOGANTI.