Characteristics of free air carbon dioxide enrichment of a northern temperate mature forest.

In 2017, the Birmingham Institute of Forest Research (BIFoR) began to conduct Free Air Carbon Dioxide Enrichment (FACE) within a mature broadleaf deciduous forest situated in the United Kingdom. BIFoR FACE employs large-scale infrastructure, in the form of lattice towers, forming 'arrays' which encircle a forest plot of ~30 m diameter. BIFoR FACE consists of three treatment arrays to elevate local CO2 concentrations (e[CO2 ]) by +150 µmol/mol. In practice, acceptable operational enrichment (ambient [CO2 ] + e[CO2 ]) is ±20% of the set point 1-min average target. There are a further three arrays that replicate the infrastructure and deliver ambient air as paired controls for the treatment arrays. For the first growing season with e[CO2 ] (April to November 2017), [CO2 ] measurements in treatment and control arrays show that the target concentration was successfully delivered, that is: +147 ± 21 µmol/mol (mean ± SD) or 98 ± 14% of set point enrichment target. e[CO2 ] treatment was accomplished for 97.7% of the scheduled operation time, with the remaining time lost due to engineering faults (0.6% of the time), CO2 supply issues (0.6%) or adverse weather conditions (1.1%). CO2 demand in the facility was driven predominantly by wind speed and the formation of the deciduous canopy. Deviations greater than 10% from the ambient baseline CO2 occurred <1% of the time in control arrays. Incidences of cross-contamination >80 µmol/mol (i.e. >53% of the treatment increment) into control arrays accounted for <0.1% of the enrichment period. The median [CO2 ] values in reconstructed three-dimensional [CO2 ] fields show enrichment somewhat lower than the target but still well above ambient. The data presented here provide confidence in the facility setup and can be used to guide future next-generation forest FACE facilities built into tall and complex forest stands.

3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion.

We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed "multi-utility multi-sensor" system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation.