Many communities coexist with wildfires that lead to loss of lives, property, and ecosystem services. Remote sensing tools can aid disaster response and post-event assessment, offering fire agencies opportunities for additional surveillance with radar, an all-weather instrument that can image day or night. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas are imaged with NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar L-band instrument. We test whether polarimetric radar can detect fire scars, burn severity, and different fuel types through its sensitivity to different scattering mechanisms. Polarimetric SAR products are moved into geographic information system-friendly formats, and in lieu of available field measurements are analyzed alongside agency data showing fire perimeters, burn progression outlines, and soil burn severity. We find that the HV polarization returns and the primary scattering mechanism, quantified through the Cloude-Pottier decomposition, are the most sensitive parameters. Higher HV values pre-fire correspond well to areas of moderate and high soil burn severity, and the pattern of fire progression follows higher HV to some extent. Using an HV difference threshold of 1.5 dB, the Bobcat burn scar is identified at 0.70 accuracy when compared with the published fire perimeter. Alpha 1 Angle can also demonstrate sensitivity to soil burn severity pre- and post-fire, showing vegetation types with increased surface scattering post-fire, which can be used to map burn scars and track recovery by vegetation type. Wildfires around the world lead to loss of lives, property, and environmental benefits. The increasing usage of satellite imagery to aid disaster response and monitoring offers fire agencies an opportunity for additional surveillance. Radar instruments can see through smoke, haze, and clouds during the day or night, which is especially relevant when cloud cover or weather conditions block traditional visual surveys of damage. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas were imaged with NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar, an airborne sensor with high quality measurements and detailed resolution. For these neighboring fires, we investigate the usage of radar remote sensing to detect fire scars, burn severity, and different fuel (vegetation) types. These fire characteristics are observed using a variety of polarimetric radar products. These products are analyzed alongside agency data such as burned area outlines, burn progression outlines, and burn severity. We demonstrate the advantages of using radar data sets to understand the vegetation which contributed to the fires and to monitor post-fire recovery. Polarimetric radar products can offer supplementary information on available fuels, past fire scars, and vegetation recovery Alpha angle from eigenvectors is able to separate burn severity classes, while HV polarization better identifies burned from unburned area Long-term monitoring with a similar L-band instrument can be achieved once the upcoming NASA-ISRO Synthetic Aperture Radar sensor is fully operational

There have been several investigation about water-ice depositions on the lunar polar regions. Earlier, studies were based on criterion circular polarization ratio (CPR). However, It is quite challenging to classify waterice deposits on the basis of criterion CPR>1, because it occurs in water-ice and rough surface region both. Thus, it is essential to examine the CPR>1, laterally with other significant parameters fractal dimension and conformity coefficient (mu) for better classification. First fractal dimension (D) based method has been used to differentiate between the rough and smooth surface. Further, conformity coefficient (mu) is used to identify possible water-ice region associated with volume scattering. This dominant volume scattering pixels points were extracted from the degree of polarization (DOP) for better classification. Finally, obtained results have been compared with existing methods. The entire study indicates that the classification of water-ice deposits using conformity coefficient (mu) gives good results.

There have been many investigations regarding water-ice depositions on the lunar surface and it is always been challenging. The previous studies were based on the circular polarization ratio (CPR). However, the CPR has proved to be inefficient in making distinctive classification of smooth (water-ice) and rough surface. Therefore, instead of using single polarimetric parameter CPR, it is required to analyze the CPR>1, along with other significant physical and electrical properties for better textural classification. In this paper, we have established the relationship between icy region and rough region based on physical property that is surface roughness measured with the help of fractal dimension method ('D') and electrical properties like real part of dielectric constant (epsilon'), imaginary part of dielectric constant (epsilon''), real (n) and imaginary (k) part of refractive index, skin depth (d) and reflectivity (R). The whole investigation indicates that the textural classification of the lunar surface with the help of physical and electrical properties gives superior results as compared to the single polarimetric parameter CPR.

Investigating the feasibility of water-ice deposits on lunar surface has been a very challenging task, which requires meticulous effort. Conceptualization of MiniSAR was a break-through because it had capability to image the shadowed regions that may have higher possibility of water-ice. Earlier studies have found that circular polarization ratio (CPR) is greater than unity in regions having volume scattering due to dielectric mixing (or water-ice deposits). However, later experiments revealed that CPR > 1 might also occur due to surface roughness. Thus, instead of using single polarimetric parameter CPR, it is required to use textural (or roughness) behavior of lunar surface along with scattering mechanisms, for obtaining the regions having higher possibility of dielectric mixing. For this purpose, information of two different approaches namely, polarimetric approach (i. e., m-delta decomposition and m-chi decomposition) and fractal approach have been fused together. The polarimetric approaches, i. e., m-delta decomposition and m-chi decomposition, help in identifying scattering mechanisms associated with lunar surface, whereas fractal-based approach helps in characterizing lunar surface on the basis of surface roughness using a measure called fractal dimension D. Finally, a decision tree algorithm has been proposed, in which decision criteria are decided on the basis of CPR, m-delta decomposition, m-chi decomposition, and fractal dimension D. The proposed approach seems to resolve the vagueness caused by CPR > 1 assumption, and to segregate areas representing volume scattering in relatively smooth surfaces inside anomalous craters, where possibility of dielectric mixing (or water-ice) may be high.

Classification of water ice region on lunar surface with Mini-SAR data is quite challenging. Therefore, a probability density function (pdf) based pattern analysis approach has been applied to classify lunar surface. This paper represents the pattern analysis approach to fit data points to a distribution function for understanding the distribution behaviour of Mini-SAR data which helps in developing a method based on density functions to differentiate two types of craters namely icy (type-I) and non-icy (type-II) craters. Circular polarization ratio (CPR) is a very important parameter in study of lunar surface. More specifically, the criterion CPR>1 is used to determine possible presence of water-ice deposits on lunar surface So, it's important to study distribution behaviour of CPR pixels and to determine best fitted distribution function representing this behaviour. Therefore, in this paper, pattern analysis techniques have been applied to differentiate two crater types based on the distribution behaviour of CPR. The best fitted function for CPR has been obtained as Generalized Extreme Value function which clearly differentiate type-I and type-II craters.

The present paper deals with the task of identifying lunar craters having possible existence of water-ice deposits on their surface. For this purpose, a decision tree algorithm has been proposed, in which decision criterion are decided on the basis of CPR, m-delta decomposition, fractal dimension 'D' and conditions proposed by Thompson et al., The proposed algorithm is successfully applied on Chandrayaan-1's MiniSAR data.