Javier Litago

Modeling and forecasting MODIS-based Fire Potential Index on a pixel basis using time series models

Abstract The aim of this research was to model and forecast MODIS-based Fire Potential Index (FPI), implemented with Normalized Difference Water Index (NDWI), as a proxy of forest fire risk, in Navarre (Spain) on a pixel basis using time series models with a forecasting horizon of one year. We forecast FPI NDWI for 2009 based on time series from 2001 to 2008. In the modeling process, the Box and Jenkins methodology was applied in two consecutive stages. First, several generic models based on average FPI NDWI time series from different “fuel type-ecoregion” combinations were developed. In a second stage, the generic models were implemented at the pixel level for the entire study region. The usefulness of the proposed autoregressive (AR) model, using the original data and introducing significant seasonal AR parameters, was demonstrated. Results show that 93.18% of the estimated models (EMs) are highly accurate and present good forecasting ability, precisely reproducing the original FPI NDWI dynamics. Best results were found in the Mediterranean areas dominated by grasslands; slightly lower accuracies were found in the temperate and alpine regions, and especially in the transition areas between them and the Mediterranean region.

Assessment of the gross primary production dynamics of a Mediterranean holm oak forest by remote sensing time series analysis

Agroforestry ecosystems have a significant social, economic and environmental impact on the development of many regions in the world. On the Iberian Peninsula the Mediterranean agroforestry oak forest known as the dehesa or montado (usually formed by species of the genus Quercus) is considered to be the extreme case of transformation of a Mediterranean forest by human management to provide a wide range of natural resources. The great variability of the Mediterranean climate and the different extensive management practices carried out by humans on the dehesa produces a high spatial and temporal variability in the dynamics of the ecosystem. This leads to a complex pattern of CO2 exchange between the atmosphere and the ecosystem that can act as a sink or as a source of CO2 over the years, depending on the various factors interacting with them. It is thus essential to assess the carbon cycle on the dehesa in order to obtain the maximum economic benefits and ensure environmental sustainability. The availability of high-frequency remote sensing time series allows the evolution of an ecosystem to be assessed at different temporal and spatial scales. In this study our overall objective is to assess the gross primary production (GPP) dynamics of a dehesa ecosystem in Central Spain by analysing the time series (2004–2008) of two models: (1) GPP provided by remote sensing images from the MODIS sensor (MOD17A2 product); and (2) GPP estimated by the implementation of a site-specific light-use efficiency model taking into account local ecological and meteorological parameters. Both models were compared to the production provided by an eddy covariance flux tower located in our study area. Dynamic relationships between models of GPP and precipitation and soil water content were investigated by means of cross-correlations and Granger causality tests. Our results indicate that both models of GPP show a typical dehesa dynamic where there are primarily two layers, the arboreal and the herbaceous strata. However, MODIS underestimates the production of the dehesa in a Mediterranean climate, while our site-specific model produced more similar values and dynamics to those of the eddy covariance tower. The analysis of the dynamic relationships corroborated the strong dynamic link between GPP and available water for plant growth. In conclusion, we succeeded in avoiding the main source of underestimation of the MODIS model by the implementation of a site-specific model. It therefore appears that the different ecological and meteorological parameters used in the MODIS model are primarily responsible for this underestimation. Finally, the Granger causality tests indicate that GPP prediction can be improved by including precipitation or soil water in the models.