Proyectos vigentes

Emerging trends in adaptive management of tree-grass agroecosystems: exploring the links between spectral and functional diversity to monitor ecosystem functioning

Equipo

Laboratorio de Espectro-radiometría y teledetección ambiental (SpecLab)

Acrónimo

DiverSpec-TGA

IP

M. PIlar Martín Isabel

Resumen

Monitoring tree-grass agroforestry systems requires compilation of data on the biochemical and structural characteristics of the (one/two) vegetation layers composing these ecosystems in order to derive reliable information about relevant management indicators such as biomass, quality, phenological stage, productivity, species composition, etc. In situ methods can be extremely informative at a local scale but they are labor intensive and not feasible for large-scale coverage. Alternatively, remote sensing offers a unique opportunity to address this issue by monitoring ecosystems at synoptic temporal and spatial scales. However, a full integration of space-sensed spectral information with ground observations, and the generation of accurate predictive models has not been yet successfully achieved; in particular in drought-prone ecosystems with complex vegetation structure, large fractions of senescent material and high biodiversity, such as most of Mediterranean TGA. Some methodological questions still require attention and further investigation as for example: 1) how field measurements should be properly used to calibrate and validate models based on optical data, and 2) how to properly use remote sensing sources at different scales (from local to regional and global) to insure accuracy and further applicability of the models. We aim to contribute to the development of remote sensing products for the estimation of relevant vegetation parameters in the context of pasture management by understanding the relationships between those parameters and the spectral behavior of grass species at different scales.

Actividades del proyecto

Field campaigns, proximal and remote sensing data adquisition and processing, empirical and RTM modeling

Fecha inicio

01/06/2020

Fecha finalización

31/05/2023

Organismo financiador

Ministerio de Ciencia e Innovación

Cuantía