Environmental sensitivity assessment and land degradation in southeastern Serbia: application of modified MEDALUS model

This paper aims to improve the methodology and results accuracy of MEDALUS model for assessing land degradation sensitivity through the application of different data detail levels and by introducing the application of Ellenberg indices in metrics related to vegetation drought sensitivity assessment. For that purpose, the MEDALUS model was applied at 2 levels of detail. Level I (municipality level) implied the use of available large-scale databases and level II (watershed) contains more detailed information about vegetation used in the calculation of the VQI and MQI factors (Fig. S6). The comparison was made using data based on CORINE Land Cover (2012) and forest inventory data, complemented with object-based classification. Results showed that data based on forest inventory data with the application of Ellenberg's indices and object-based classification have one class more, critical (C1 and C2) and that the percentage distribution of classes is different in both quantitative (area size of class sensitivity) and qualitative (aggregation and dispersion of sensitivity classes). The use of data from Forest Management Plans and the application of Ellenberg's indices affect the quality of the results and find its application in the model, especially if these results are used for monitoring and land area management on fine scales. Remote sensed data images (Sentinel-2B) were introduced into the methodology as a very important environmental monitoring tool and model results validation.

Automated summary

This paper aims to improve the methodology and results accuracy of the MEDALUS model for assessing land degradation sensitivity by applying different data detail levels and introducing the application of Ellenberg indices. The results show that using forest inventory data with the application of Ellenberg's indices and object-based classification can provide more detailed results. In addition, the introduction of remote sensed data images (Sentinel-2B) as an important environmental monitoring tool and model results validation.