Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 157-166 |
| Journal | International Journal of Applied Earth Observation and Geoinformation (JAG) |
| Volume | 59 |
| DOIs | |
| Publication status | Published - 2017 |
Fingerprint
Keywords
- ITC-ISI-JOURNAL-ARTICLE
- ITC-HYBRID
Cite this
}
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 journal › Article › Academic › peer-review
TY - JOUR
T1 - Proxies for soil organic carbon derived from remote sensing
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.
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2017/isi/groen_pro.pdf
UR - http://www.scopus.com/inward/record.url?scp=85032273132&partnerID=8YFLogxK
U2 - 10.1016/j.jag.2017.03.004
DO - 10.1016/j.jag.2017.03.004
M3 - Article
VL - 59
SP - 157
EP - 166
JO - International Journal of Applied Earth Observation and Geoinformation (JAG)
JF - International Journal of Applied Earth Observation and Geoinformation (JAG)
SN - 1569-8432
ER -