The cover management factor (C-factor) calculation requires the assessment of the intra-annual spatiotemporal variability of biomass cover, owed to the natural growth cycle of vegetation and the impact of agriculture on land cover. However, this is frequently omitted, and the vegetation conditions are approximated by assigning constant values to static classified Land Use/Land Cover (LULC) maps, such as the Coordination of Information on the Environment (CORINE) Land Cover (CLC). Using as test site the Sperchios River catchment, Central Greece, this study introduces a new approach to estimate C-factor in a spatiotemporally exhaustive manner. The goal is to increase estimation accuracy in erosion modelling applications. The C-factor computations are performed on monthly scale, based on LULC maps that portray the basin's agricultural areas in unprecedented detail. The methodology involves the use of a biophysical index, namely Fraction of Vegetation Cover (Fcover) and empirical literature data on crop types. Fcover was developed from Sentinel-2 (S2) imagery in 10-m analysis. Such analysis (compared to the 300-m one provided by the EU) is a major improvement towards a more precise estimation of C-factor. The study identified the monthly C-factor fluctuation at basin scale, and the most susceptible months seasons at localities in terms of land cover/soil loss potential. The higher C-factor values were acquired in October and the lower in May. Mean annual (numerical) C-factor complies with the value of July. All monthly values are significantly higher – almost double – than the mean annual stationary one. The revealed patterns would not have been detected in a lower temporal (e.g., annual) resolution without the incorporation of vegetation density seasonality. The study shows high reproducibility and upscaling potential, as the utilized datasets are available in all European Union (EU) Member States, having similar structure, thus they can be harmonized towards a unified continental approach.
Efthimiou, N., Psomiadis, E., Papanikolaou, I., Soulis, K.X., Borrelli, P., Panagos, P. (2022). A new high resolution object-oriented approach to define the spatiotemporal dynamics of the cover-management factor in soil erosion modelling. CATENA, 213, 106149 [10.1016/j.catena.2022.106149].
A new high resolution object-oriented approach to define the spatiotemporal dynamics of the cover-management factor in soil erosion modelling
Borrelli P.Membro del Collaboration Group
;
2022-01-01
Abstract
The cover management factor (C-factor) calculation requires the assessment of the intra-annual spatiotemporal variability of biomass cover, owed to the natural growth cycle of vegetation and the impact of agriculture on land cover. However, this is frequently omitted, and the vegetation conditions are approximated by assigning constant values to static classified Land Use/Land Cover (LULC) maps, such as the Coordination of Information on the Environment (CORINE) Land Cover (CLC). Using as test site the Sperchios River catchment, Central Greece, this study introduces a new approach to estimate C-factor in a spatiotemporally exhaustive manner. The goal is to increase estimation accuracy in erosion modelling applications. The C-factor computations are performed on monthly scale, based on LULC maps that portray the basin's agricultural areas in unprecedented detail. The methodology involves the use of a biophysical index, namely Fraction of Vegetation Cover (Fcover) and empirical literature data on crop types. Fcover was developed from Sentinel-2 (S2) imagery in 10-m analysis. Such analysis (compared to the 300-m one provided by the EU) is a major improvement towards a more precise estimation of C-factor. The study identified the monthly C-factor fluctuation at basin scale, and the most susceptible months seasons at localities in terms of land cover/soil loss potential. The higher C-factor values were acquired in October and the lower in May. Mean annual (numerical) C-factor complies with the value of July. All monthly values are significantly higher – almost double – than the mean annual stationary one. The revealed patterns would not have been detected in a lower temporal (e.g., annual) resolution without the incorporation of vegetation density seasonality. The study shows high reproducibility and upscaling potential, as the utilized datasets are available in all European Union (EU) Member States, having similar structure, thus they can be harmonized towards a unified continental approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
