Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo.

Johanna Elizabeth Ayala IzurietaCarmen Omaira MárquezVíctor Julio GarcíaCarlos Arturo Jara SantillánJorge Marcelo SistiNieves PasqualottoShari Van WittenbergheJesús Delegido

Published in: Carbon balance and management (2021)

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.

Keyphrases

high resolution
high density
climate change
deep learning
machine learning
risk assessment
mass spectrometry