scholarly journals Intercomparing CO<sub>2</sub> amounts from dispersion modeling, 1.6 μm differential absorption lidar and open path FTIR at a natural CO<sub>2</sub> release at Caldara di Manziana, Italy

2015 ◽  
Vol 8 (4) ◽  
pp. 4325-4345 ◽  
Author(s):  
M. Queißer ◽  
D. Granieri ◽  
M. Burton ◽  
A. La Spina ◽  
G. Salerno ◽  
...  

Abstract. We intercompare results of three independent approaches to quantify a vented CO2 release at a strongly non-uniform CO2 Earth degassing at Caldara di Manziana, central Italy. An integrated path differential absorption lidar prototype and a commercial open path FTIR system were measuring column averaged CO2 concentrations in parallel at two different paths. An Eulerian gas dispersion model simulated 3-D CO2 concentration maps in the same area, using in situ CO2 flux input data acquired at 152 different points. Local processes the model does not account for, such as small-scale and short-lived wind eddies, govern CO2 concentrations in the instrument measurement paths. The model, on the other hand, also considers atmospheric effects that are out of the field of view of the instruments. Despite this we find satisfactory agreement between modeled and measured CO2 concentrations under certain meteorological conditions. Under these conditions the results suggest that an Eulerian dispersion model and optical remote sensing can be used as an integrated, complementary monitoring approach for CO2 hazard or leakage assessment. Furthermore, the modeling may assist in evaluating CO2 sensing surveys in the future. CO2 column amounts from differential absorption lidar are in line with those from FTIR for both paths with a mean residual of the time series of 44 and 34 ppm, respectively. This experiment is a fundamental step forward in the deployment of the differential absorption lidar prototype as a highly portable active remote sensing instrument probing vented CO2 emissions, including volcanoes.

2021 ◽  
Vol 13 (15) ◽  
pp. 3000
Author(s):  
Georg Zitzlsberger ◽  
Michal Podhorányi ◽  
Václav Svatoň ◽  
Milan Lazecký ◽  
Jan Martinovič

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.


1999 ◽  
Vol 38 (Part 1, No. 1A) ◽  
pp. 110-114 ◽  
Author(s):  
Kouki Ikuta ◽  
Noboru Yoshikane ◽  
Nilesh Vasa ◽  
Yuji Oki ◽  
Mitsuo Maeda ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2211
Author(s):  
Yu Gong ◽  
Lingbing Bu ◽  
Bin Yang ◽  
Farhan Mustafa

Developments in mid-infrared Differential Absorption Lidar (DIAL), for gas remote sensing, have received a significant amount of research in recent years. In this paper, a high repetition rate tunable mid-infrared DIAL, mounted on a mobile platform, has been built for long range remote detection of gas plumes. The lidar uses a solid-state tunable optical parametric oscillator laser, which can emit laser pulse with repetition rate of 500 Hz and between the band from 2.5 μm to 4 μm. A monitoring channel has been used to record the laser energy in real-time and correct signals. Convolution correction technology has also been incorporated to choose the laser wavelengths. Taking NO2 and SO2 as examples, lidar system calibration experiment and open field observation experiment have been carried out. The observation results show that the minimum detection sensitivity of NO2 and SO2 can reach 0.07 mg/m3, and 0.31 mg/m3, respectively. The effective temporal resolution can reach second level for the high repetition rate of the laser, which demonstrates that the system can be used for the real-time remote sensing of atmospheric pollution gas.


2021 ◽  
Author(s):  
Oleg A. Romanovskii ◽  
Sergey A. Sadovnikov ◽  
Semyon V. Yakovlev ◽  
Dmitry A. Tuzhilkin ◽  
Ol'ga V. Kharchenko ◽  
...  

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