Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

Smart Sentinel: Monitoring and Prevention System in the Smart Cities

Today, faced with the constant rise of the Smart cities around the world, there is an exponential increase of the use and deployment of information technologies in the cities. The intensive use of Information Technology (IT) in these ecosystems facilitates and improves the quality of life of citizens, but in these digital communities coexist individuals whose health is affected developing or increasing diseases such as electromagnetic hypersensitivity. In this paper we present a monitoring, detection and prevention system to help this group, through which it is reported the rates of electromagnetic radiation in certain areas, based on the information that the own Smart City gives us. This work provides a perfect platform for the generation of predictive models for detection of future states of risk for humans.

Presence of Siloxanes in the Biogas of a Wastewater Treatment Plant Separation in Condensates and Influence of the Dose of Iron Chloride on its Elimination

The siloxanes present in the biogas produced during anaerobic digestion damage the mechanism of cogeneration equipment and, consequently, negatively affect the energy valorization process. For this reason, the detection and elimination of these silicon-derived chemical compounds are a priority in the management of cogeneration facilities. In this regard, the objectives of this paper are, firstly, to characterize the siloxanes in the biogas and, secondly, to qualitatively evaluate the influence of the dose of iron chloride on its elimination. The research was performed at the Rincón de León Wastewater Treatment Plant (Alicante, Spain). The outflow biogas of the digesters and of the pressurized gasometers was sampled and analyzed. The results obtained made it possible to demonstrate, firstly, the absence of linear siloxanes and that, of the cyclic siloxanes, the predominant type was decamethylcyclopentasiloxane, and, secondly, that the addition of iron chloride in the digesters significantly reduces the siloxane content in the biogas. Additionally, it was demonstrated that the process of compression of the biogas, with the elimination of condensates, also produces significant reductions in the concentration of siloxanes in the biogas.