Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

Bharat Sharma; Jitendra Kumar; Auroop R. Ganguly; Forrest M. Hoffman

doi:10.1109/ICDMW58026.2022.00148

Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly, Forrest M. Hoffman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.

Original language	English
Title of host publication	Proceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022
Editors	K. Selcuk Candan, Thang N. Dinh, My T. Thai, Takashi Washio
Publisher	IEEE Computer Society
Pages	1136-1143
Number of pages	8
ISBN (Electronic)	9798350346091
DOIs	https://doi.org/10.1109/ICDMW58026.2022.00148
State	Published - 2022
Event	22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 - Orlando, United States Duration: Nov 28 2022 → Dec 1 2022

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2022-November
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022
Country/Territory	United States
City	Orlando
Period	11/28/22 → 12/1/22

Funding

This research was supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Science Focus Area, which is sponsored by the Regional and Global Model Analysis (RGMA) activity of the Earth & Environmental Systems Modeling (EESM) Program in the Earth and Environmental Systems Sciences Division (EESSD) of the Office of Biological and Environmental Research (BER) in the US Department of Energy Office of Science. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 for the Project m2467. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

attribution analysis
carbon cycle extremes
climate drivers
scale-free networks
spatiotemporal extremes

Access to Document

10.1109/ICDMW58026.2022.00148

Cite this

Sharma, B., Kumar, J., Ganguly, A. R., & Hoffman, F. M. (2022). Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes. In K. S. Candan, T. N. Dinh, M. T. Thai, & T. Washio (Eds.), Proceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 (pp. 1136-1143). (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2022-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW58026.2022.00148

Sharma, Bharat ; Kumar, Jitendra ; Ganguly, Auroop R. et al. / Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes. Proceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022. editor / K. Selcuk Candan ; Thang N. Dinh ; My T. Thai ; Takashi Washio. IEEE Computer Society, 2022. pp. 1136-1143 (IEEE International Conference on Data Mining Workshops, ICDMW).

@inproceedings{359265c555ae4f22a0c38bd4c0bca912,

title = "Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes",

abstract = "Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.",

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author = "Bharat Sharma and Jitendra Kumar and Ganguly, {Auroop R.} and Hoffman, {Forrest M.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 ; Conference date: 28-11-2022 Through 01-12-2022",

year = "2022",

doi = "10.1109/ICDMW58026.2022.00148",

language = "English",

series = "IEEE International Conference on Data Mining Workshops, ICDMW",

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Sharma, B , Kumar, J, Ganguly, AR & Hoffman, FM 2022, Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes. in KS Candan, TN Dinh, MT Thai & T Washio (eds), Proceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022. IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2022-November, IEEE Computer Society, pp. 1136-1143, 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022, Orlando, United States, 11/28/22. https://doi.org/10.1109/ICDMW58026.2022.00148

Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes. / Sharma, Bharat ; Kumar, Jitendra; Ganguly, Auroop R. et al.
Proceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022. ed. / K. Selcuk Candan; Thang N. Dinh; My T. Thai; Takashi Washio. IEEE Computer Society, 2022. p. 1136-1143 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2022-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2022

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N2 - Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.

AB - Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.

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Sharma B , Kumar J, Ganguly AR, Hoffman FM. Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes. In Candan KS, Dinh TN, Thai MT, Washio T, editors, Proceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022. IEEE Computer Society. 2022. p. 1136-1143. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW58026.2022.00148

Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

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