Walter F. Sione

Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R² = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

Spatial distribution of wildfire risk in the Monte biome (Patagonia, Argentina)

Abstract Please click here to download the map associated with this article. In many rangeland ecosystems, the role of wildfires has been the object of detailed studies. However, in Argentina and especially in the Monte biome this knowledge is limited. This region is prone to wildfires during late spring and summer, when high winds and temperatures are coupled with low relative humidity. In addition, this region also presents cases of extreme wildfire behaviour. The aim of this study is to develop a probability-based model for estimating wildfire risk at a regional scale based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Hot Spot data. The map presented here covers an area of approximately 246,000 km at a 1:100,000 scale. It is the result of fitting the 2001–2010 hot spot time series with a logistic regression model to 15 potentially explanatory environmental and anthropogenic variables. At a regional scale, mean net primary production, spring temperature, spring and summer precipitation, elevation, slope, distance to nearest road are the most important factors explaining wildfire risk.

Análisis espacial del riesgo de establecimiento de Lymantria dispar en bosques nativos de Sudamérica

Resumen en: Lymantria dispar is a quarantine pest absent in South America. A regional phytosanitary surveillance plan was developed to control this pest due to its h...