A system for monitoring NO 2 emissions from biomass burning by using GOME and ATSR-2 data

Bruzzone, Lorenzo; Casadio, Stefano; Cossu, R.; Sini, Francesco; Zehner, Claus

doi:10.1080/01431160210144714

Cited by 15 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total global burned area was an estimated 3.5 million km 2 in the year 2000 (Tansey et al, 2008), 2.97-3.74 million km 2 in (Giglio et al, 2006, between 3.5 million km 2 and 4.5 million km 2 during 2005 and 2007 (Tansey et al, 2008) and between 3.3 and 4.31 million km 2 in 2008 (Giglio et al, 2010). Biomass burning is known to be a major contributor to the global budgets of several trace gases and greenhouse gases, including carbon monoxide (CO) (Nam et al, 2010), CO 2 (Freitas et al, 2005;Murdiyarso, 1993;Alleaume, 2005), CH 4 (Alleaume, 2005) and nitrogen oxides (NO x = NO + NO 2 ) (Bruzzone, 2003). It also contributes aerosol particles, which are a major source of volatile organic compounds and organic halogen compounds (Crutzen and Andreae, 1990;Trentmann et al, 2001;Freitas et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Integration of remote sensing data and surface observations to estimate the impact of the Russian wildfires over Europe and Asia during August 2010

et al. 2011

View full text Add to dashboard Cite

Abstract.A series of wildfires broke out in Western Russia starting in late July of 2010. Harmful particulates and gases released into the local Russian atmosphere have been reported, as have possible negative consequences for the global atmosphere. In this study, an extremely hazy area and its transport trajectory on Russian wildfires were analysed using aerosol optical depth (AOD) images retrieved via the synergy method from Moderate Resolution Imaging Spectroradiometer (MODIS) data. In addition, we used trace gases (NO 2 and SO 2 ) and CO 2 products measured using Ozone Monitoring Instrument (OMI) data, vertical distribution of AOD data retrieved from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the mass trajectory analyses, synoptic maps from a HYSPLIT model simulation and ground-based data, including AERONET (both AOD andÅngström exponent) data and PM 2.5 . First, an Optimal Smoothing (OS) scheme was used to develop more precise and reliable AOD data based on multiple competing predictions made using several AOD retrieval models; then, integrated AOD and PM 2.5 data were related using a chemical Correspondence to: Y. Xue (yxue@irsa.ac.cn) transport model (GEOS-Chem), and the integrated AOD and visibility data were related using the 6S radiative transfer code. The results show that the PM 2.5 concentration is enhanced by a factor of 3-5 as determined from both satellite and in situ observations with peak daily mean concentrations of approximately 500 µg m 3 . Also, the visibility in many parts of Russia, for instance in Moscow, was less than 100 m; in some areas, the visibility was less than 50 m. Additionally, the possible impact on neighbouring countries due to long-transport was analysed for 31 July and 15 August 2010.

show abstract

Section: Introductionmentioning

confidence: 99%

Integration of remote sensing data and surface observations to estimate the impact of the Russian wildfires over Europe and Asia during August 2010

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Study area Time frame Topic Balis et al (2003) Northern Greece Summer 2001 Aerosols Beirle et al (2004) Central Australia 1999-2001 x Brooks and Legrand (2000) Northern hemisphere Africa 1984-1993 Aerosols Bruzzone et al (2003) Africa, Australia, Mexico, andBrazil 1996-1999 NO 2 Chandra et al (2002) Africa, Brazil, and Indonesia 1979-2000 O 3 Chin et al (2002) Global 1990, 1996Aerosols Clerbaux et al (2001 Global June 1997 CO Davison et al (2004) Indonesia Aug-Nov 1997 Aerosols Duncan et al (2003a) Global 1979-2000 CO Duncan et al (2003b) SE Asia Sep-Nov 1997 CO, O 3 Edwards et al (2003) Africa, Atlantic Ocean Jan 2001 O 3 Formenti et al (2002) South Africa 1998-1999 Aerosols Generoso et al (2003) Global 1997-2001 Aerosols Goloub and Arino (2000) Africa, America, Asia 1996-1997 Aerosols Gumbricht et al (2002) Botswana 1999-2000 Peat fire detection Hoelzemann et al 2004Global 2000 40+ chemical species, and aerosols Jenkins et al (1997) Africa, Atlantic and Indian Oceans Not provided O 3 Jenkins and Ryu (2003) Atlantic ocean, W. Africa, S. America 1979-2000 O 3 Tropical Atlantic, Africa, S. America 1998-2001 O 3 Kasischke et al (2003) Alaska, Canada, Russia 1997-2002 Fire counts Kelha et al (2003) Scandinavia, W. Russia 1999-2000 Fire counts Kim et al (2001) Pantropical 1998-1999 O 3 Ladstätter-Weißenmayer et al 2004 Wooster and Strub (2002) Indonesia 1997 Burned area Yurganov et al (2004) Northern Hemisphere 1996-2001 CO Zhao et al (2000) Northern Japan 1995-1997 HCN Zhao et al (2002) Northern Japan 1995-2000 CO, C 2 H 6 , C 2 H 2 , HCN from a burning area of 0.1 ha at 600 K to 0.01 ha at 800 K (http://dup.esrin.esa.int/ionia/wfa/algorithm.asp). Wooster and Rothery (2002) calculated a sensor saturation envelope for observations of sub-pixel sized hotspo...…”

Section: Articlementioning

confidence: 99%

Screening the ESA ATSR-2 World Fire Atlas (1997–2002)

Mota¹,

Pereira²,

Oom³

et al. 2005

Preprint

View full text Add to dashboard Cite

Abstract. The European Space Agency (ESA) World Fire Atlas (WFA), for the period 1997–2002, is built using night time data from the Along Track Scanning Radiometer (ATSR) onboard the Second European Remote-Sensing Satellite (ERS-2). The spatial resolution of the data is 1 km and the satellite revisiting period is 3 days at the equator. The WFA is the first and longest archive of global fire observations and has been used in numerous biomass burning studies. Known limitations of the WFA are the inclusion of warm surfaces, gas flares, and city lights, and an underestimation of actual global fire activity, due to the time of satellite overpass. Nevertheless, it has been considered that the WFA contains a relatively small proportion of observations that do not correspond to vegetation fires. We used ancillary land cover, night-lights and volcanic activity datasets, combined with statistical techniques to detect the occurrence of space-time clusters, to screen the algorithm 2 (308°K threshold) WFA data for the period 1997–2002. During the study period, the annual percentage of false alarms and non-vegetation fires varied from a minimum value of 20.6% in 1997 to a maximum of 27.9% in 1998. Gas flares and hot bare soils are the major sources of false alarms and non-vegetation fires.

show abstract

“…Beyond classification tasks, the processing of a complex signal can be seen as a curve-fitting problem [19] , leading to regression applications. For example, RBF networks have also been applied to Radar signals in order to derive biophysical parameters, such as snowfall, rainfall and wind speed [20] , [21] , [22] , [23] , [24] , [25] , [26] .…”

Section: Introductionmentioning

confidence: 99%

Integrating Local and Global Error Statistics for Multi-Scale RBF Network Training: An Assessment on Remote Sensing Data

Mountrakis

Zhuang

2012

PLoS ONE

View full text Add to dashboard Cite

BackgroundThis study discusses the theoretical underpinnings of a novel multi-scale radial basis function (MSRBF) neural network along with its application to classification and regression tasks in remote sensing. The novelty of the proposed MSRBF network relies on the integration of both local and global error statistics in the node selection process.Methodology and Principal FindingsThe method was tested on a binary classification task, detection of impervious surfaces using a Landsat satellite image, and a regression problem, simulation of waveform LiDAR data. In the classification scenario, results indicate that the MSRBF is superior to existing radial basis function and back propagation neural networks in terms of obtained classification accuracy and training-testing consistency, especially for smaller datasets. The latter is especially important as reference data acquisition is always an issue in remote sensing applications. In the regression case, MSRBF provided improved accuracy and consistency when contrasted with a multi kernel RBF network.Conclusion and SignificanceResults highlight the potential of a novel training methodology that is not restricted to a specific algorithmic type, therefore significantly advancing machine learning algorithms for classification and regression tasks. The MSRBF is expected to find numerous applications within and outside the remote sensing field.

show abstract

A system for monitoring NO 2 emissions from biomass burning by using GOME and ATSR-2 data

Cited by 15 publications

References 6 publications

Integration of remote sensing data and surface observations to estimate the impact of the Russian wildfires over Europe and Asia during August 2010

Integration of remote sensing data and surface observations to estimate the impact of the Russian wildfires over Europe and Asia during August 2010

Screening the ESA ATSR-2 World Fire Atlas (1997–2002)

Integrating Local and Global Error Statistics for Multi-Scale RBF Network Training: An Assessment on Remote Sensing Data

Contact Info

Product

Resources

About