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Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna

BACKGROUND: The quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementat...

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Autores principales: Schwieder, M., Leitão, P. J., Pinto, J. R. R., Teixeira, A. M. C., Pedroni, F., Sanchez, M., Bustamante, M. M., Hostert, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5953907/
https://www.ncbi.nlm.nih.gov/pubmed/29766371
http://dx.doi.org/10.1186/s13021-018-0097-1
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author Schwieder, M.
Leitão, P. J.
Pinto, J. R. R.
Teixeira, A. M. C.
Pedroni, F.
Sanchez, M.
Bustamante, M. M.
Hostert, P.
author_facet Schwieder, M.
Leitão, P. J.
Pinto, J. R. R.
Teixeira, A. M. C.
Pedroni, F.
Sanchez, M.
Bustamante, M. M.
Hostert, P.
author_sort Schwieder, M.
collection PubMed
description BACKGROUND: The quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementation of Nationally Determined Contributions (NDCs) and the UN Sustainable Development Goals (SDGs). Especially in heterogeneous ecosystems, such as Savannas, accurate carbon quantifications are still lacking, where highly variable vegetation densities occur and a strong seasonality hinders consistent data acquisition. In order to account for these challenges we analyzed the potential of land surface phenological metrics derived from gap-filled 8-day Landsat time series for carbon mapping. We selected three areas located in different subregions in the central Brazil region, which is a prominent example of a Savanna with significant carbon stocks that has been undergoing extensive land cover conversions. Here phenological metrics from the season 2014/2015 were combined with aboveground carbon field samples of cerrado sensu stricto vegetation using Random Forest regression models to map the regional carbon distribution and to analyze the relation between phenological metrics and aboveground carbon. RESULTS: The gap filling approach enabled to accurately approximate the original Landsat ETM+ and OLI EVI values and the subsequent derivation of annual phenological metrics. Random Forest model performances varied between the three study areas with RMSE values of 1.64 t/ha (mean relative RMSE 30%), 2.35 t/ha (46%) and 2.18 t/ha (45%). Comparable relationships between remote sensing based land surface phenological metrics and aboveground carbon were observed in all study areas. Aboveground carbon distributions could be mapped and revealed comprehensible spatial patterns. CONCLUSION: Phenological metrics were derived from 8-day Landsat time series with a spatial resolution that is sufficient to capture gradual changes in carbon stocks of heterogeneous Savanna ecosystems. These metrics revealed the relationship between aboveground carbon and the phenology of the observed vegetation. Our results suggest that metrics relating to the seasonal minimum and maximum values were the most influential variables and bear potential to improve spatially explicit mapping approaches in heterogeneous ecosystems, where both spatial and temporal resolutions are critical. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13021-018-0097-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-59539072018-05-18 Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna Schwieder, M. Leitão, P. J. Pinto, J. R. R. Teixeira, A. M. C. Pedroni, F. Sanchez, M. Bustamante, M. M. Hostert, P. Carbon Balance Manag Research BACKGROUND: The quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementation of Nationally Determined Contributions (NDCs) and the UN Sustainable Development Goals (SDGs). Especially in heterogeneous ecosystems, such as Savannas, accurate carbon quantifications are still lacking, where highly variable vegetation densities occur and a strong seasonality hinders consistent data acquisition. In order to account for these challenges we analyzed the potential of land surface phenological metrics derived from gap-filled 8-day Landsat time series for carbon mapping. We selected three areas located in different subregions in the central Brazil region, which is a prominent example of a Savanna with significant carbon stocks that has been undergoing extensive land cover conversions. Here phenological metrics from the season 2014/2015 were combined with aboveground carbon field samples of cerrado sensu stricto vegetation using Random Forest regression models to map the regional carbon distribution and to analyze the relation between phenological metrics and aboveground carbon. RESULTS: The gap filling approach enabled to accurately approximate the original Landsat ETM+ and OLI EVI values and the subsequent derivation of annual phenological metrics. Random Forest model performances varied between the three study areas with RMSE values of 1.64 t/ha (mean relative RMSE 30%), 2.35 t/ha (46%) and 2.18 t/ha (45%). Comparable relationships between remote sensing based land surface phenological metrics and aboveground carbon were observed in all study areas. Aboveground carbon distributions could be mapped and revealed comprehensible spatial patterns. CONCLUSION: Phenological metrics were derived from 8-day Landsat time series with a spatial resolution that is sufficient to capture gradual changes in carbon stocks of heterogeneous Savanna ecosystems. These metrics revealed the relationship between aboveground carbon and the phenology of the observed vegetation. Our results suggest that metrics relating to the seasonal minimum and maximum values were the most influential variables and bear potential to improve spatially explicit mapping approaches in heterogeneous ecosystems, where both spatial and temporal resolutions are critical. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13021-018-0097-1) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-05-15 /pmc/articles/PMC5953907/ /pubmed/29766371 http://dx.doi.org/10.1186/s13021-018-0097-1 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Schwieder, M.
Leitão, P. J.
Pinto, J. R. R.
Teixeira, A. M. C.
Pedroni, F.
Sanchez, M.
Bustamante, M. M.
Hostert, P.
Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna
title Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna
title_full Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna
title_fullStr Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna
title_full_unstemmed Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna
title_short Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna
title_sort landsat phenological metrics and their relation to aboveground carbon in the brazilian savanna
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5953907/
https://www.ncbi.nlm.nih.gov/pubmed/29766371
http://dx.doi.org/10.1186/s13021-018-0097-1
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