This study focuses on testing the SAR coherence changes from Sentinel-1 data to detect burned areas and to compare the results with optical Sentinel-2 derived burned area product to be used as validation. Visible Infrared Imaging Radiometer Suite (VIIRS) data at 350 m resolution was used to identify active fires locations. <p> </p>We focused on a sequence of wildfires that affected the Sila mountain area during the summer of the 2017. This area of the Calabria region (southern Italy) was interested by a range of fires for the second half of July and the whole month of August (, ) due also to an extremely dry and hot summer. We used a pair of optical images acquired from Sentinel- 2 satellites on 24 July 2017 (pre-events) and 23 August 2017 (post-events). <p> </p>Firstly, we computed the Normalized Difference Vegetation Index (<i>NDVI</i>) for both images and calculated the difference between these two (<i>dNDVI</i>) at 10m resolution; the results put in evidence several areas characterized by vegetation reduction, with <i>dNDVI </i>values up to 0.3-0.4. Concerning the SAR data, we evaluated the coherence changes by exploiting two pairs of Sentinel-1 SAR data over the same area. Both pairs were acquired along descending orbit, respectively before (on July, 19th and 31st) and after (on September, 5th and 17th) the fires occurred in the Sila mountain area. The coherence was computed separately for the first (γ<sub>pre</sub>) and the second pair (γ<sub>post</sub>) and the difference γ<sub>post</sub> - γ<sub>pre</sub> was calculated. In this way, we evaluated the difference in coherence between September, i.e. post-fires, and July, i.e. pre-fires expecting a higher coherence after burning, due to the vegetation reduction. In several areas, the coherence seems to be consistent with the fire events showing increments up to 0.20-0.25. However, the increasing of coherence difference could also be due to other reasons such as the soil moisture variations in the proximity of lakes/rivers or the seasonal cultivation changes. <p> </p>Further analysis integrating more information such as the SAR amplitude signal and the cross-polarized backscattering coefficient will be conducted in order to better evaluate and discriminate any contributions.
This study describes an application of artificial neural networks for the recognition of flaming areas using hyper- spectral remote sensed data. Satellite remote sensing is considered an effective and safe way to monitor active fires for environmental and people safeguarding. Neural networks are an effective and consolidated technique for the classification of satellite images. Moreover, once well trained, they prove to be very fast in the application stage for a rapid response. At flaming temperature, thanks to its low excitation energy (about 4.34 eV), potassium (K) ionize with a unique doublet emission features. This emission features can be detected remotely providing a detection map of active fire which allows in principle to separate flaming from smouldering areas of vegetation even in presence of smoke. For this study a normalised Advanced K Band Difference (AKBD) has been applied to airborne hyper spectral sensor covering a range of 400-970 nm with resolution 2.9 nm. A back propagation neural network was used for the recognition of active fires affecting the hyperspectral image. The network was trained using all channels of sensor as inputs, and the corresponding AKBD indexes as target output. In order to evaluate its generalization capabilities, the neural network was validated on two independent data sets of hyperspectral images, not used during neural network training phase. The validation results for the independent data-sets had an overall accuracy round 100% for both image and a few commission errors (0.1%), therefore demonstrating the feasibility of estimating the presence of active fires using a neural network approach. Although the validation of the neural network classifier had a few commission errors, the producer accuracies were lower due to the presence of omission errors. Image analysis revealed that those false negatives lie in "smoky" portion fire fronts, and due to the low intensity of the signal. The proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or smoke pixels, whose presence in hyperspectral images can often undermine the performance of traditional classification algorithms. In order to improve neural network performance, future activities will include also the exploiting of hyperspectral images in the shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, which include significant emitted radiance from fire.
Hyper/Multispectral data provide information about characteristic of natural and antropic surfaces. In order to retrieve
the mineralogical species composing the Castel Porziano Beach (CPB), remote sensed data needs to be atmospherically
corrected. In this work a new tool for the atmospheric correction for spaceborne EO data, based on MODTRAN and 6S
codes, and developed on IDL/ENVI platform will be proposed and tested using NASA HYPERION and ASTER data. In
this paper the capability to identify mineral association composing the sand of the CPB emerged beach, using
hyperspectral data is shown. In order to define the mineralogical composition of the collected sample, SEM EMPA
(Scanning Electron Microscopy and Electron MicroProbe Analyser) and optical polarizing microscopy analysis have
been done. Results have been compared with 300 measurements performed directly on the CPB sand and 300
measurement acquired in the laboratory, both using an ASD-Fieldspec.
Remote sensing data acquired by satellite or airborne sensor need on ground validation measurements. As concern volcanoes monitoring, important information may be retrieved by observing these targets in the InfraRed spectral range.
A portable μFTIR (Fourier Transform Infrared Interferometer) capable of making sensitive and accurate measurements of radiance and emissivity of surface in the (600-5000 cm<sup>-1</sup>) spectral range with a spectral resolution of 2 cm<sup>-1</sup> is available at the remote sensing laboratory of INGV (Rome). These kinds of measurements are very important firstly for the validation of remote sensed data and secondly for the improvement of many gas models used in volcanology for the diagnosis of volcano inner state. On 2003 μFTIR in situ spectral emissivity measurements were made during field surveys on selected test sites on Mount Etna. This area was observed also by a Fourier interferometer (MIROR) on board on a Dornier 228 and by ASTER a satellite borne sensor. The MIROR and ASTER data have been calibrated and compared with ground measurements. The agreement suggested to organize periodic measurements on selected test sites of Italian volcanic regions e.g. Solfatara and Stromboli volcano.