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This article addresses the simulation of urban air temperatures with a focus on evaluating the Urban Weather Generator (UWG) model over Toulouse, France. As urban temperatures, influenced by factors like urbanization, anthropogenic heat release, and complex urban geometry, exhibit an urban heat island (UHI) effect, understanding and mitigating UHI become crucial. With increasing global warming and urban populations, aiding urban planners necessitates accurate simulations requiring data at the canyon level. The paper evaluates UWG's performance in simulating air temperatures under realistic conditions, emphasizing an operational context and a non-specialist user's perspective. The evaluation includes selecting the most suitable meteorological station, assessing the impact of the rural station choice, and conducting a sensitivity analysis of input parameters. The validation demonstrates good agreement, with a mean bias error (MBE) of 0.02 °C and a root mean square error (RMSE) of 1.73 °C. However, we highlight the fact that UWG performs better in a densely urbanized area, and exhibits limitations in sensitivity to urban surface parameter variations, particularly in less urbanized areas. [ABSTRACT FROM AUTHOR]

Pluvial floods caused by extreme overland flow inland account for half of all flood damage claims each year along with fluvial floods. In order to increase confidence in pluvial flood susceptibility mapping, overland flow models need to be intensively evaluated using observations from past events. However, most remote-sensing-based flood detection techniques only focus on the identification of degradations and/or water pixels in the close vicinity of overflowing streams after heavy rainfall. Many occurrences of pluvial-flood-induced damages such as soil erosion, gullies, landslides and mudflows located further away from the stream are thus often unrevealed. To fill this gap, a transferable remote sensing fusion method called FuSVIPR, for Fusion of Sentinel-2 & Very high resolution Imagery for Pluvial Runoff, is developed to produce damage-detection maps. Based on very high spatial resolution optical imagery (from Pléiades satellites or airborne sensors) combined with 10 m change images from Sentinel-2 satellites, the Random Forest and U-net machine/deep learning techniques are separately trained and compared to locate pluvial flood footprints on the ground at 0.5 m spatial resolution following heavy weather events. In this work, three flash flood events in the Aude and Alpes-Maritimes departments in the South of France are investigated, covering over more than 160 km2 of rural and periurban areas between 2018 and 2020. Pluvial-flood-detection accuracies hover around 75% (with a minimum area detection ratio for annotated ground truths of 25%), and false-positive rates mostly below 2% are achieved on all three distinct events using a cross-site validation framework. FuSVIPR is then further evaluated on the latest devastating flash floods of April 2022 in the Durban area (South Africa), without additional training. Very good agreement with the impact maps produced in the context of the International Charter "Space and Major Disasters" are reached with similar performance figures. These results emphasize the high generalization capability of this method to locate pluvial floods at any time of the year and over diverse regions worldwide using a very high spatial resolution visible product and two Sentinel-2 images. The resulting impact maps have high potential for helping thorough evaluation and improvement of surface water inundation models and boosting extreme precipitation downscaling at a very high spatial resolution. [ABSTRACT FROM AUTHOR]

Wide area surveillance has become of critical importance, particularly for border control between countries where vast forested land border areas are to be monitored. In this paper, we address the problem of the automatic detection of activity in forbidden areas, namely forested land border areas. In order to avoid false detections, often triggered in dense vegetation with single sensors such as radar, we present a multi sensor fusion and tracking system using passive infrared detectors in combination with automatic person detection from thermal and visual video camera images. The approach combines weighted maps with a rule engine that associates data from multiple weighted maps. The proposed approach is tested on real data collected by the EU FOLDOUT project in a location representative of a range of forested EU borders. The results show that the proposed approach can eliminate single sensor false detections and enhance accuracy by up to 50%. [ABSTRACT FROM AUTHOR]

Considering large and complex areas like the Tibetan Plateau, an analysis of the spatial distribution of the solar radiative budget over time not only requires the use of satellite remote sensing data, but also of an algorithm that accounts for strong variations of topography. Therefore, this research aims at developing a method to produce time series of solar radiative fluxes at high temporal and spatial resolution based on observed surface and atmosphere properties and topography. The objective is to account for the heterogeneity of the land surface using multiple land surface and atmospheric MODIS data products combined with a digital elevation model to produce estimations daily at the kilometric level. The developed approach led to the production of a three-year time series (2008-2010) of daily solar radiation budget at one kilometer spatial resolution across the Tibetan Plateau. The validation showed that the main improvement from the proposed method is a higher spatial and temporal resolution as compared to existing products. However, even if the solar radiation estimates are satisfying on clear sky conditions, the algorithm is less reliable under cloudy sky condition and the albedo product used here has a too coarse temporal resolution and is not accurate enough over rugged terrain. [ABSTRACT FROM AUTHOR]

Several methods currently exist to efficiently correct topographic effects on the radiance measured by satellites. Most of those methods use topographic information and satellite data at the same spatial resolution. In this study, the 30 m spatial resolution data of the Digital Elevation Model (DEM) from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) are used to account for those topographic effects when retrieving land surface reflectance from satellite data at lower spatial resolution (e.g., 1 km). The methodology integrates the effects of sub-pixel topography on the estimation of the total irradiance received at the surface considering direct, diffuse and terrain irradiance. The corrected total irradiance is then used to compute the topographically corrected surface reflectance. The proposed method has been developed to be applied on various kilometric pixel size satellite data. In this study, it was tested and validated with synthetic Landsat data aggregated at 1 km. The results obtained after a sub-pixel topographic correction are compared with the ones obtained after a pixel level topographic correction and show that in rough terrain, the sub-pixel topography correction method provides better results even if it tends to slightly overestimate the retrieved land surface reflectance in some cases. [ABSTRACT FROM AUTHOR]

Along with fluvial floods (FFs), surface water floods (SWFs) caused by extreme overland flow are one of the main flood hazards occurring after heavy rainfall. Using physics-based distributed hydrological models, surface runoff can be simulated from precipitation inputs to investigate regions prone to soil erosion, mudflows or landslides. Geomatics approaches have also been developed to map susceptibility towards intense surface runoff without explicit hydrological modeling or event-based rainfall forcing. However, in order for these methods to be applicable for prevention purposes, they need to be comprehensively evaluated using proxy data of runoff-related impacts following a given event. Here, the IRIP geomatics mapping model, or "Indicator of Intense Pluvial Runoff", is faced with rainfall radar measurements and damage maps derived from satellite imagery and supervised classification algorithms. Six watersheds in the Aude and Alpes-Maritimes departments in the South of France are investigated over more than 2000 km2 of rural areas during two flash-flood events. The results of this study show that the greater the IRIP susceptibility scores, the more SWFs are detected by the remote sensing-based detection algorithm. Proportions of damaged plots become even larger when considering areas which experienced heavier precipitations. A negative relationship between the mean IRIP accumulation scores and the intensity of rainfall is found among damaged plots, confirming that SWFs preferably occur over potentially riskier areas where rainfall is lower. Land use and soil hydraulic conductivity are identified as the most relevant indicators for IRIP to define production areas responsible for downslope deteriorations. Multivariate logistic regression is also used to determine the relative weights of upstream and local topography, uphill production areas and rainfall intensity for explaining SWF occurrence. This work overall confirms the relevance of IRIP methodology while suggesting improvements to its core framework to implement better prevention strategies against SWF-related hazards. [ABSTRACT FROM AUTHOR]

Most of the methods used to retrieve land surface temperature (LST) from thermal infrared (TIR) satellite data in urban areas do not take into account the complexity of the surface. Cities are characterized by high surface roughness and one of the main constraints to estimate LST over those areas is the difficulty to define an effective emissivity for a given pixel at a given scale. When working with mixed pixels, the emissivity used to estimate the LST is an effective emissivity composed of the emissivities of each basic element constituting the pixel. In urban areas, the surface geometry has a strong impact on this effective emissivity. Its estimation from TIR satellite data must be carried out considering multiple surface reflections and diffusions within the urban canopy in order to retrieve accurate LST values. The objective of this study is then to evaluate the impact of the surface geometry within the pixel on effective emissivity estimation and to propose a method to derive an effective emissivity corrected for those effects. Emissivity can be derived at 90 m of spatial resolution from the TIR data acquired by ASTER. To evaluate the impact of the geometry at the scale of an ASTER pixel, several urban canyon configurations are designed to develop and test the correction method. The basic principle behind the method is to accurately estimate the downwelling TIR radiation received by a pixel integrating contributions from both the atmosphere and the scene inside this pixel and then derive the corrected effective emissivity from ASTER data using the TES (temperature emissivity separation) algorithm. First, the total downwelling TIR radiation is estimated from the geometric characteristics of the scene, using morphological indicators and integrating the non-isothermal behavior of the pixel thanks to 3D thermo-radiative model simulations. The validation of those estimations for each canyon configuration provides a maximum RMSE (Root Mean Square Error) value of 2.2 W·m−2. The validation performed over a district extracted from the 3D numerical model of Strasbourg (France) shows a RMSE of 2.5 W·m−2. Once the method to estimate the total downwelling TIR radiation is validated, LSE and LST maps are retrieved from an ASTER image over three districts of Strasbourg, showing that accounting for the surface geometry highlights thermal behavior differences inside districts, and that the impact of the geometry seems more influenced by building height than street width or building density. [ABSTRACT FROM AUTHOR]