Silvia Merino-de-Miguel

Integration of AWiFS and MODIS active fire data for burn mapping at regional level using the Burned Area Synergic Algorithm (BASA).

The present paper presents an algorithm that synergistically combines data from four different parts of the spectrum (near-, shortwave, middle- and thermal infrared) to produce a reliable burned-area map. It is based on the use of a modified version of the BAIM (MODIS – Moderate Resolution Imaging Spectrometer – Burned Area Index) together with active fire information. The following study focusses in particular on an image from the AWiFS (Advanced Wide Field Sensor) sensor dated 21 August 2006 and MODIS active fires detected during the first 20 days of August as well as ancillary maps and information. The methodology was tested in Galicia (north-west Spain) where hundreds of forest fires occurred during the first 20 days of August 2006. Burned area data collected from the present work was compared with official fire statistics from both the Spanish Ministry of the Environment and the Galician Forestry Service. The speed, accuracy and cost-effectiveness of this method suggest that it would be of great interest for use at both regional and national levels.

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.

Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km2. AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.

MODIS Reflectance and Active Fire Data for Burn Mapping in Colombia

Abstract Satellite-based strategies for burned area mapping may rely on two types of remotely sensed data: postfire reflectance images and active fire detection. This study uses both methods in a synergistic way. In particular, burned area mapping is carried out using MCD43B4 [Moderate Resolution Imaging Spectrometer (MODIS); Terra + Aqua nadir bidirectional reflectance distribution function (BRDF); adjusted reflectance 16-day L3 global 1-km sinusoidal grid V005 (SIN)] postfire datasets and MODIS active fire products. The developed methodology was tested in Colombia, an area not covered by any known MODIS ground antenna, using data from 2004. The resulting burned area map was validated using a high-spatial-resolution Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image and compared to two global burned area products: L3JRC (terrestrial ecosystem monitoring global burnt area product) and MCD45A1 (MODIS Terra + Aqua burned area monthly global 500-m SIN grid V005). The results showed that this method would b...

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.