Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo
BACKGROUND: Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, h...
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ftpubmed:oai:pubmedcentral.nih.gov:8543914 2023-05-15T18:40:41+02:00 Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo Ayala Izurieta, Johanna Elizabeth Márquez, Carmen Omaira García, Víctor Julio Jara Santillán, Carlos Arturo Sisti, Jorge Marcelo Pasqualotto, Nieves Van Wittenberghe, Shari Delegido, Jesús 2021-10-24 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543914/ http://www.ncbi.nlm.nih.gov/pubmed/34693465 https://doi.org/10.1186/s13021-021-00195-2 en eng Springer International Publishing http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543914/ http://www.ncbi.nlm.nih.gov/pubmed/34693465 http://dx.doi.org/10.1186/s13021-021-00195-2 © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. CC0 PDM CC-BY Carbon Balance Manag Research Text 2021 ftpubmed https://doi.org/10.1186/s13021-021-00195-2 2021-10-31T00:53:34Z BACKGROUND: Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador. RESULTS: Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R(2) of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R(2) of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature. CONCLUSIONS: Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling. Text Tundra PubMed Central (PMC) Carbon Balance and Management 16 1 |
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Research Ayala Izurieta, Johanna Elizabeth Márquez, Carmen Omaira García, Víctor Julio Jara Santillán, Carlos Arturo Sisti, Jorge Marcelo Pasqualotto, Nieves Van Wittenberghe, Shari Delegido, Jesús Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo |
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Research |
description |
BACKGROUND: Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador. RESULTS: Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R(2) of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R(2) of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature. CONCLUSIONS: Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling. |
format |
Text |
author |
Ayala Izurieta, Johanna Elizabeth Márquez, Carmen Omaira García, Víctor Julio Jara Santillán, Carlos Arturo Sisti, Jorge Marcelo Pasqualotto, Nieves Van Wittenberghe, Shari Delegido, Jesús |
author_facet |
Ayala Izurieta, Johanna Elizabeth Márquez, Carmen Omaira García, Víctor Julio Jara Santillán, Carlos Arturo Sisti, Jorge Marcelo Pasqualotto, Nieves Van Wittenberghe, Shari Delegido, Jesús |
author_sort |
Ayala Izurieta, Johanna Elizabeth |
title |
Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo |
title_short |
Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo |
title_full |
Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo |
title_fullStr |
Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo |
title_full_unstemmed |
Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo |
title_sort |
multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central ecuadorian páramo |
publisher |
Springer International Publishing |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543914/ http://www.ncbi.nlm.nih.gov/pubmed/34693465 https://doi.org/10.1186/s13021-021-00195-2 |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
Carbon Balance Manag |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543914/ http://www.ncbi.nlm.nih.gov/pubmed/34693465 http://dx.doi.org/10.1186/s13021-021-00195-2 |
op_rights |
© The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
op_rightsnorm |
CC0 PDM CC-BY |
op_doi |
https://doi.org/10.1186/s13021-021-00195-2 |
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