Soil and water conservation structures are vital for environmental resilience but present maintenance challenges due to their wide distribution and remote locations. To tackle these issues, a method using unmanned aerial vehicles (UAVs) combined with 360 degree photography was developed. UAVs captured images that were processed into panoramic and 3D models, enabling precise inspections of structural damage. These models were integrated into the disaster environment review and update (DER&U) rating system, enhanced by a fuzzy inference classification mechanism for improved damage estimation. Additionally, a management platform was created to boost overall efficiency and provide decision-making support for relevant authorities. The UAV-assisted inspection method demonstrated promising results, though certain limitations were also noted.

Erosive processes occur naturally and are essential for soil formation. However, they have been accelerated by anthropogenic actions, contributing to social, environmental, and economic damages. The aim of this study was to develop a methodology for the identification and quantification of soil loss using digital elevation models obtained through imagery from unmanned aerial vehicles (UAVs). In the three selected study areas in Te & oacute;filo Otoni - MG, the generated models were compared before and after precipitation events. The annual erosivity factor can be classified as very low, indicating regional characteristics of low erosive potential. This work proposed different equations for the use of Digital Elevation Models as a data source for the identification and quantification of soil loss through water erosion. The results obtained indicate that flights conducted up to 70 meters contribute to mapping quality and highlight the need for further studies to calibrate the methodology for quantifying soil loss and making it replicable in different situations.

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

Seasonal subsidence induced by ground ice melt can be measured by interferometric synthetic aperture radar (InSAR) techniques to infer active layer thickness (ALT) in permafrost regions. The magnitude of subsidence depends on both how deep the soil thawed and how much ice/water content existed in the active layer soil. To provide the later, P-band polarimetric synthetic aperture radar (PolSAR) backscatter is used due to its sensitivity to subsurface soil moisture and freeze/thaw conditions. In this study, which is the second in a two-part series of Permafrost Dynamics Observatory (PDO), we exploit L-band InSAR subsidence and P-band PolSAR backscatter in a joint retrieval scheme to simultaneously estimate ALT and soil moisture profile of permafrost active layer. Both subsidence and backscatter are explicitly characterized by physics-based models and share a common set of soil parameters including porosity and water saturation profiles. The PDO joint retrieval has been applied to the L- and P-band SAR data acquired by National Aeronautics and Space Administration/Jet Propulsion Laboratory 's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and western Canada during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign. This high-resolution (30 m) regional estimates of ALT and soil moisture profile spanning over the ABoVE study domain can help link the ground-based field surveys with satellite observations to further understand the permafrost and active layer soil process dynamics to disturbances and climate change occurring across the northern circumpolar region.