Espectrofenología con datos Sentinel 2definición de curvas de referencia para la caracterización de ecosistemas forestales

  1. David López Trullén
  2. Jose Manuel Álvarez-Martínez 1
  3. Jesús David Sánchez Labrador
  4. Borja Jiménez-Alfaro 2
  5. Ignacio Pérez-Silos 1
  6. Gonzalo Hernández-Romero 1
  7. José Barquín 1
  1. 1 IHCantabria - Instituto de Hidráulica Ambiental de la Universidad de Cantabria
  2. 2 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Ecosistemas: Revista científica y técnica de ecología y medio ambiente

ISSN: 1697-2473

Ano de publicación: 2022

Título do exemplar: Seguimiento de la biodiversidad en la era del Big Data

Volume: 31

Número: 3

Tipo: Artigo

Outras publicacións en: Ecosistemas: Revista científica y técnica de ecología y medio ambiente

Resumo

Monitoring the spectral response of habitat types obtained by medium resolution satellite imagery can provide relevant information about the distribution and dynamics of vegetation types through environmental gradients and geographic scales. The combination of the orbital cycle and the bandwidth of Sentinel 2 sensor provides data every 5 days in mid-latitudes, which allows high-resolution temporal monitoring related to ecosystem phenology cycles as well as alterations of their compositional, structural and functional properties. In this context, the objective of this study was to obtain average spectrophenological curves for main tree formations present in Cantabria, as well as the analysis and characterization of phenological metrics that allow characterizing distributions and determining particular vegetation functions across landscape continuum. For the definition of the curves, we have processed all the historical data available from the MSI sensor of Sentinel 2 A and B satellites in order to generate a high temporal resolution time series of the NDVI index. Results have been temporarily averaged for a monthly representation after applying a manual filter of clouds and shadows that maximizes the quality of the data at the pixel level and a temporal smoothing of the series that filters out anomalous and missing values. NDVI values have been extracted at the pixel level for a sample of 230 points located with centimetric GPS precision and whose floristic composition has been characterized by botanists in the field. Seasonal phenological curves for the years from 2017 to 2020 and annual average curves have been obtained, determining their variability and the degree of representativeness for each habitat type of interest. Finally, the equations of the functions adjusted to the average phenological curves by plant formation have been obtained by applying Fourier analyses. Results show particular features for each of the habitat type due to the existence of specific functional traits linked to intra- and inter-annual phenological variations. These results highlight the interest of using spectrophenological time series of satellite data for defining reference curves of habitat types. These curves allow, on the one hand, identifying the spatial distribution as well as structural and function variations of different ecosystems, and on the other hand, having at hand an early detection system of anomalous phenological situations that can be related to natural or anthropic disturbances in a context of Global Change.