During 2008, under a region-wide drought, there were a large number of simultaneous fires in the Paraná River Delta region: the most affected vegetation was in marshes dominated by Schoenoplectus californicus (C.A.Mey.) Soják or Cyperus giganteus Vahl. The objective of this paper was to study fire severity in terms of fire effect on vegetation cover and soil properties, and the recovery of those properties after one growing season, using optical remote sensing techniques and fieldwork data. To this aim, we performed unsupervised classification of Landsat TM imagery and conducted vegetation censuses and soil sampling in November 2008 and May 2009. Our results show that we could identify three fire severity classes: low severity, medium severity, and high severity. These classes are characterized by a remnant vegetation cover of approximately 75 %, 25 %, and 5 %, respectively, and a diminution of soil organic carbon and nitrogen of 66 % and 59 % in the case of medium severity and high severity. Fire had almost no effect over pH and a slight effect on electrical conductivity. After one growing season, vegetation recovery is dependent on fire severity and hydrological condition, while soil properties did not show signs of recovery. This is one of the first studies of fire effects and recovery on fluvial herbaceous wetlands.

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Abstract:The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented.

In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by the Argentinian National Meteorological and Hydrological Services are based on field precipitation data, such as the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). In this research we explored the performance of the satellite-based Soil Moisture Agricultural Drought Index (SMADI) for agricultural drought detection in Argentina during 2010-2015, and compared it with the one from the Standardized Soil Moisture Anomalies (SSMA), SPI and SPEI (at 1-month and 3-month temporal scales), using the Agricultural Ministry's drought emergency database as a benchmark. The performances were analyzed in terms of the suitability of each index to be included in an early warning system for agricultural droughts, including true positive rate, and both false positive and false negative rates. In our experiments, SMADI showed the best overall performance, with the highest true positive rate and F1-score, and the second best false positive rate, positive predictive value, and overall accuracy. SMADI also showed the largest difference between true positive rate and false positive rate. SSMA showed the lowest false positive rate, but also the lowest true positive rate, making it not useful for an alert system. Furthermore, field precipitation-based indices, yet simple and widely used, showed not to be suitable indicators for detection of agricultural drought for Argentina, neither in the 1-month nor in the 3-month scale.