Analyzing CO2 concentration changes and their influencing factors in Indonesia by OCO-2 and other multi-sensor remote-sensing data

ABSTRACT We used OCO-2 products and considered three factors that potentially affect CO2 concentration in Indonesia: sea surface temperature (SST), forest fires and vegetation. From 2014 to 2016, CO2 concentration in Indonesia showed a trend of increase, which is consistent with the global increase reported by the Greenhouse Gases Observing Satellite (GOSAT) Project. As an archipelago country, the results indicate that SST has a direct effect on the CO2 concentration in Indonesia. Their changing exhibits similar fluctuations; meanwhile, CO2 concentration and SST also presented positive correlation. In 2015, the number of fire hotspots suddenly increased to 140,699, because of occurrence of the worst forest fire. Due to special geographic conditions, forest fires did not induce CO2 concentration changes in Indonesia, but CO2 concentration in the corresponding islands showed a trend of increase. CO2 concentration increased in Kalimantan during the occurrence of forest fire in September–October 2014, and CO2 concentration increased in Kalimantan and Sumatra during the occurrence of forest fire in September–October 2015. Vegetation indices were stable and presented no correlation with CO2 concentration. This study demonstrated that OCO-2 is capable of monitoring CO2 concentration at a regional scale; additionally, an effective method for using OCO-2 Level 2 products is proposed.

[1]  Tatsuya Yokota,et al.  Retrieval algorithm for CO 2 and CH 4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite , 2010 .

[2]  Scott D. Peckham,et al.  Fire as the dominant driver of central Canadian boreal forest carbon balance , 2007, Nature.

[3]  U. Siegenthaler,et al.  Atmospheric carbon dioxide and the ocean , 1993, Nature.

[4]  Tatsuya Yokota,et al.  First observations of CO2 absorption spectra recorded in 2005 using an airship-borne FTS (GOSAT TANSO–FTS BBM) in the SWIR spectral region , 2011 .

[5]  Michael T. Rock Asia’s Clean Revolution – Industry, Growth and the Environment , 2013 .

[6]  Jiaping Wu,et al.  valuation of estimating daily maximum and minimum air temperature with ODIS data in east Africa , 2012 .

[7]  Fortunat Joos,et al.  Sensitivity of Holocene atmospheric CO 2 and the modern carbon budget to early human land use: analyses with a process-based model , 2010 .

[8]  M. G. Ryan,et al.  Effects of Climate Change on Plant Respiration. , 1991, Ecological applications : a publication of the Ecological Society of America.

[9]  David Crisp,et al.  Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data , 2015 .

[10]  Misako Kachi,et al.  Satellite‐based high‐resolution global optimum interpolation sea surface temperature data , 2006 .

[11]  P. Macdonald,et al.  Interpreting Multivariate Data , 1982 .

[12]  David Crisp,et al.  The Orbiting Carbon Observatory (OCO) mission , 2004 .

[13]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[14]  S. F. Sanchez,et al.  Spatially resolved properties of the grand-design spiral galaxy UGC 9837: a case for high-redshift 2-D observations , 2011, 1111.5205.

[15]  Hugh L. Dryden,et al.  THE NATIONAL AERONAUTICS AND SPACE ADMINISTRATION , 1958 .

[16]  P. Landschützer,et al.  Recent variability of the global ocean carbon sink , 2014 .

[17]  F. S. Nakayama,et al.  Effects of increasing atmospheric CO2 on vegetation , 2004, Vegetatio.

[18]  Gabriele Manduchi,et al.  Commonalities and differences between MDSplus and HDF5 data systems , 2010 .

[19]  Claire Smith,et al.  Encyclopedia of Global Archaeology , 2014 .

[20]  Rasmus Fensholt,et al.  Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment , 2005 .

[21]  Damien Sulla-Menashe,et al.  Enhancing MODIS land cover product with a spatial–temporal modeling algorithm , 2014 .

[22]  Hiroshi Tani,et al.  Spatial distribution of greenhouse gas concentrations in arid and semi-arid regions : A case study in East Asia , 2013 .

[23]  Yoram J. Kaufman,et al.  An Enhanced Contextual Fire Detection Algorithm for MODIS , 2003 .

[24]  Corinne Le Quéré,et al.  Trends in the sources and sinks of carbon dioxide , 2009 .

[25]  C. O. Justicea,et al.  The MODIS fire products , 2002 .

[26]  W. Ross Morrow,et al.  Belfer Center for Science and International Affairs Analysis of Policies to Reduce Oil Consumption and , 2008 .

[27]  S. Houweling,et al.  Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2 , 2013 .

[28]  Shantanu Rastogi,et al.  Study of satellite retrieved CO2 and CH4 concentration over India , 2014 .

[29]  A. D. Gordon,et al.  Interpreting multivariate data , 1982 .

[30]  Johannes W. Kaiser,et al.  Constraining CO 2 emissions from open biomass burning by satellite observations of co-emitted species: a method and its application to wildfires in Siberia , 2014 .

[31]  Corinne Le Quéré,et al.  Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks , 2007, Proceedings of the National Academy of Sciences.

[32]  David Crisp Retrieving CO₂ from Orbiting Carbon Observatory-2 (OCO-2) Spectra , 2014 .

[33]  Hiroshi Tani,et al.  Examining the relationships between land cover and greenhouse gas concentrations using remote-sensing data in East Asia , 2013 .

[34]  John S. Kimball,et al.  Response of Vegetation Growth and Productivity to Spring Climate Indicators in the Conterminous United States Derived from Satellite Remote Sensing Data Fusion , 2014 .

[35]  Pauli Heikkinen,et al.  Latitude-time variations of atmospheric column-average dry air mole fractions of CO 2 , CH 4 and N 2 O , 2012 .

[36]  A. Belward,et al.  The international geosphere biosphere programme data and information system global land cover data set (DIScover) , 1997 .

[37]  A Akbar,et al.  OS018. Maternal mortality and its mainly possible causepre-eclampsia/eclampsia in developing country (Surabaya-Indonesia as themodel). , 2012, Pregnancy hypertension.

[38]  Robert N. Stavins,et al.  Land-Use Change and Carbon Sinks: Econometric Estimation of the Carbon Sequestration Supply Function , 2005 .

[39]  Shuji Kawakami,et al.  Usability of optical spectrum analyzer in measuring atmospheric CO2 and CH4 column densities: inspection with FTS and aircraft profiles in situ , 2012 .

[40]  Richard W. Reynolds,et al.  An Improved Real-Time Global Sea Surface Temperature Analysis , 1993 .

[41]  Thomas M. Smith,et al.  Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation , 1994 .

[42]  Niklaus E. Zimmermann,et al.  Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO 2 airborne fraction , 2011 .

[43]  David Crisp,et al.  The Orbiting Carbon Observatory (OCO-2): spectrometer performance evaluation using pre-launch direct sun measurements , 2014 .