Moisture content of live fuels (LFMC) is one of the main factors determining fuel flammability and, therefore, a key indicator of fire danger. In this study, we modeled the relationship between spectral indices derived from satellite imagery and field estimations of LFMC in the Chaco Serrano subregion; then, we analyzed the relationship between fire danger estimations based on LFMC calculations and fire activity. Empirical LFMC models fitted for grasslands, Chaco Serrano forests, and glossy privet forests may be considered very accurate R ² > 0.80, whereas the model corresponding to shrublands still needs to be improved (R ² = 0.57). Monthly maps of fire danger reflected the occurrence of fires consistently during years of both high and low fire activity. Most fires occurred mainly in areas with high or extreme fire danger, demonstrating a clear relationship between LFMC and fire activity in the Chaco Serrano subregion. Our LFMC models may be useful to assess the spatiotemporal distribution of fire danger in the Chaco Serrano subregion using remote sensing data. The associated fire danger maps represent a valuable tool for improving decision making processes to organize early warning and fire suppression activities.

Natural fire regimes have been modified; therefore robust post-fire monitoring tools are needed to understand the post-fire recovery process. Satellites with high temporal resolution allow us to build time series of vegetation indices for monitoring post-fire vegetation recovery. One of the techniques used is to compare the time series of a burned plot with that of an unburned control plot. However, for its implementation it is necessary to select control plots in which the vegetation has the same structure and functioning than the plot burned before the fire. Previous study defined biological criteria to detect burned and unburned control plots with identical pre-fire vegetation functioning. Moreover, a non-parametric test routine of low statistical power was proposed to test them, this was based on the analysis of the QVI (Quotient Vegetation Index), calculated between NDVI (Normalized Difference Vegetation Index) time series of the burned and control site. However, currently there are autoregressive analysis techniques with greater statistical power. Therefore the aims were to propose six new statistical routines based on autoregressive test, and compare the performance of these with the non-parametric routine. We selected 13,700 forest plots and extracted the NDVI MODIS time series between 2002 and 2005. We randomly selected 43 reference plots, and through each routine, we compared each reference time series with the other 13,657 time series. We estimated the performance of the routines measuring the euclidian distance between the time series of the reference plot and the time series of the plots accepted for each routine. We also measured the quality and the amount of the QVI time series selected by each routine. Autoregressive routines showed better performance than the non-parametric routine, since they selected control plots with NDVI time series with greatest similarity with respect to the reference plots and QVI series with highest quality.

(1) Background: Land-use change threatens biodiversity globally, making it essential to anticipate future impacts. (2) Methods: We assess future land-use change scenarios as a tool for analyzing the taxonomic and functional richness of birds in the Serrano forest. We developed two change scenarios: The “Business as usual” scenario assumes that the trend of land-use change observed between 2004 and 2019 will continue without modifications by 2035 and 2050. The “Sustainable” scenario seeks to achieve a sustainable relationship between anthropogenic land-use activities and ecosystem conservation. We created distribution models and derived the potential distribution of the taxonomic and functional richness of forest and understory specialist birds in the change scenarios. (3) Results: The taxonomic and functional richness of both bird groups was strongly affected in the “Business as usual” change scenario, which presented extreme deforestation events, while the “Sustainable” change scenario tended to maintain bird richness over time. We detected areas with a reduction in richness greater than 20% and areas where richness increased due to being distant from urbanization and exotic forests.