Proxies for soil organic carbon derived from remote sensing

S.M.M. Rasel, T.A. Groen, Y.A. Hussin, I.J. Diti

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.
Original languageEnglish
Pages (from-to)157-166
JournalInternational Journal of Applied Earth Observation and Geoinformation (JAG)
Volume59
DOIs
Publication statusPublished - 2017

Fingerprint

aboveground biomass
Organic carbon
Remote sensing
organic carbon
remote sensing
Soils
Biomass
soil carbon
Carbon
soil
Akaike information criterion
carbon sequestration
tropical forest
imagery
vegetation
carbon
cost
Chemical analysis
WorldView
Costs

Keywords

  • METIS-322140
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID

Cite this

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title = "Proxies for soil organic carbon derived from remote sensing",
abstract = "The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.",
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author = "S.M.M. Rasel and T.A. Groen and Y.A. Hussin and I.J. Diti",
year = "2017",
doi = "10.1016/j.jag.2017.03.004",
language = "English",
volume = "59",
pages = "157--166",
journal = "International Journal of Applied Earth Observation and Geoinformation (JAG)",
issn = "1569-8432",
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Proxies for soil organic carbon derived from remote sensing. / Rasel, S.M.M.; Groen, T.A.; Hussin, Y.A.; Diti, I.J.

In: International Journal of Applied Earth Observation and Geoinformation (JAG), Vol. 59, 2017, p. 157-166.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

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AU - Rasel, S.M.M.

AU - Groen, T.A.

AU - Hussin, Y.A.

AU - Diti, I.J.

PY - 2017

Y1 - 2017

N2 - The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

AB - The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

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