Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several Co-Variables: Application to the Soils of the Beni Mellal in Morocco

Sébastien Gadal, Sébastien Gadal, Mounir Oukhattar, Catherine Keller, Ismaguil Houmma, Ismaguil Houmma

2023

Abstract

In recent decades, population growth has led to rapid urbanisation associated with a land degradation process that threatens soil organic carbon stocks (SOCS). This paper aims to model the interrelationships between SOCS and land use/land cover (LULC). The approach was based on the use of environmental covariates derived from Landsat-5 TM/8 OLI images, forty soil samples, Kriging spatial interpolation method and a Multi-layer Perceptron (MLP) model for the geo-spatialisation of SOCS. The analysis shows a high positive autocorrelations (R2>0.75) between vegetation indices and SOCS, particularly higher for SOCS derived from spatial modelling with MLP. On the other hand, the relationship between LULC and SOCS from the three approaches is very variable depending on the dynamics of LULC. The autocorrelations between SOCS and LULC units are very weak in 1985 and 2000 but significant for the year 2018. This suggests that the land use dynamics in the area are favourable to SOCS. In general, the results show that SOCS increased in the tree crop, unused land and forest areas but decreased in the cropland. The SOCS varied in the following order: forest cover>unused land>cropland>urban area>tree crops. This indicates that LULC, topography and vegetation types had an impact on SOCS distribution characteristics.

Paper Citation

in Harvard Style

Gadal S., Oukhattar M., Keller C. and Houmma I. (2023). Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several Co-Variables: Application to the Soils of the Beni Mellal in Morocco. In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-649-1, SciTePress, pages 15-26. DOI: 10.5220/0011723000003473

in Bibtex Style

@conference{gistam23,
author={Sébastien Gadal and Mounir Oukhattar and Catherine Keller and Ismaguil Houmma},
title={Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several Co-Variables: Application to the Soils of the Beni Mellal in Morocco},
booktitle={Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2023},
pages={15-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011723000003473},
isbn={978-989-758-649-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several Co-Variables: Application to the Soils of the Beni Mellal in Morocco
SN - 978-989-758-649-1
AU - Gadal S.
AU - Oukhattar M.
AU - Keller C.
AU - Houmma I.
PY - 2023
SP - 15
EP - 26
DO - 10.5220/0011723000003473
PB - SciTePress