GC8-Hydro-126, updated on 08 May 2023
https://doi.org/10.5194/egusphere-gc8-hydro-126
A European vision for hydrological observations and experimentation
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Clustering Rangelands Based on NDVI Annual Patterns with different aridity grades

Ernesto Sanz1,2, Juan José Martín Sotoca1,2, Antonio Saa-Requejo1,3, Carlos H. Díaz-Ambrona1,4, Margarita Ruiz-Ramos1,4, Alfredo Rodríguez1,5, Andres Almeida1,2, Rubén Moratiel1,4, and Ana M. Tarquis1,2
Ernesto Sanz et al.
  • 1CEIGRAM, Universidad Politécnica de Madrid, 28040 Madrid, Spain
  • 2Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
  • 3Evaluación de Recursos Naturales, ETSI Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain
  • 4AgSystems, ETSI Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain
  • 5Departamento de Análisis Económico y Finanzas, Universidad de Castilla-La Mancha, 45071 Toledo, Spain

Soil-vegetation-atmosphere transfer (SVAT) schemes explicitly consider the role of vegetation in affecting water and energy balance by considering its physiological properties. However, most current SVAT schemes and hydrological models do not consider vegetation a dynamic component. The seasonal and monthly evolution of the physiological parameters is kept constant year after year. This fact is likely crucial in transient climate simulations for hydrological models used to study climate change impact. Therefore, the analysis of vegetation dynamics became crucial to study these scenarios.

Vegetation dynamics, especially over large scales, can be monitored using remote sensing. The Normalised Difference Vegetation Index (NDVI) is still the most well-known and frequently used spectral indices derived from remote sensing, identifying vegetated areas and their condition. NDVI is based on plants' differential reflectance for different parts of the solar radiation spectrum.

In this work, we present a classification of rangelands in Spain based on the NDVI time series using them, like the result of SVAT and defining metrics and the Hurst Exponent from detrended fluctuation analysis. These areas are located in different precipitation and temperature regimen but with a Mediterranean climate with different aridity grades: Huescar, Castuera and Lozoya. K-means and unsupervised random forest were used to cluster the pixels using time series metrics and Hurst exponents. The clustering results will be discussed by comparing them to climate and topographical data.

References

Sanz E, Sotoca JJM, Saa-Requejo A, Díaz-Ambrona CH, Ruiz-Ramos M, Rodríguez A, Tarquis AM. Clustering Arid Rangelands Based on NDVI Annual Patterns and Their Persistence. Remote Sensing. 2022; 14(19):4949. https://doi.org/10.3390/rs14194949

Acknowledgements

Financial support from the project "CLASIFICACIÓN DE PASTIZALES MEDIANTE MÉTODOS SUPERVISADOS - SANTO" code RP220220C024, by Universidad Politécnica de Madrid, is highly appreciated.

How to cite: Sanz, E., Martín Sotoca, J. J., Saa-Requejo, A., Díaz-Ambrona, C. H., Ruiz-Ramos, M., Rodríguez, A., Almeida, A., Moratiel, R., and Tarquis, A. M.: Clustering Rangelands Based on NDVI Annual Patterns with different aridity grades, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-126, https://doi.org/10.5194/egusphere-gc8-hydro-126, 2023.