Abstract: Senegalese cities have experienced rapid urbanisation, leading to profound landscape changes. Dakar, one of the cities with the fastest urban expansion, has seen major negative impacts on the availability and quality of its green spaces. This study examines the spatio-temporal evolution of Dakar’s green spaces over recent decades. Using satellite imagery, this study produces land use maps to quantify green space coverage. The results show a gradual decline in green spaces in Dakar between 1990 and 2022. 1990 green spaces covered an estimated 13.36 % of Dakar’s area, which decreased significantly to 9.54 % by 2022. Meanwhile, other land uses, such as built-up areas, increased significantly during this period, from 19.23 % in 1990 to 39.34 % in 2022. This pattern highlights the growing challenge of green space availability as built-up areas expand rapidly, particularly when growth is unplanned. This study underscores the importance of sustainable urban planning that integrates the protection and conservation of Dakar’s vegetation to preserve vital ecosystem services.

Abstract: LEO satellites offer reduced signal loss, fast movement, multi-beam, typically providing single coverage. This paper introduces a novel multi-beam power positioning method for low-orbit single-satellite, addressing the slow convergence and low accuracy of Doppler positioning. It establishes a power observation equation system, initializes with the nearest neighbor algorithm, and refines with the least squares method. Monte Carlo simulations indicate that with good initial values, the method converges in under 10 iterations, achieving 88.06% availability at 20° elevation with errors of 5331m (vertical) and 8798m (horizontal), and a timing error of 205μs. At 70° elevation, all users converge with errors of 1614m and 1088m, and a timing error of 31.3μs, demonstrating high power positioning availability. The statistical results show that power positioning users can obtain the positioning accuracy of kilometers and the timing accuracy of microseconds, which meets initial timing needs under strong confrontation, enhancing the medium and high orbit satellite navigation.

Abstract: Hyperspectral technology has become increasingly important in monitoring soil heavy metal pollution, yet hyperspectral data often contain substantial band redundancy, and band selection methods are typically limited to single algorithms or simple combinations. Multi-algorithm combinations for band selection remain underutilized. To address this gap, this study, conducted in Gejiu, Yunnan Province, China, proposes a multi-algorithm band selection method to enable rapid prediction of lead (Pb) contamination levels in soil. To construct a preliminary Pb content prediction model, initial selection of spectral bands utilized methods including CARS (Competitive Adaptive Reweighted Sampling), GA (Genetic Algorithm), MI (Mutual Information), SPA (Successive Projections Algorithm), and WOA (Whale Optimization Algorithm). Results indicated that WOA achieved the highest modeling accuracy. Building on this, a combined WOA-based band selection method was developed, including combinations such as WOA-CARS, WOA-GA, WOA-MI, and WOA-SPA, with multi-level band optimization further refined by MI (e.g., WOA-GA-MI, WOA-CARS-MI, WOA-SPA-MI). Results showed that the WOA-GA-MI model exhibited optimal performance, achieving an average R² of 0.75, with improvements of 0.32, 0.11, and 0.02 over the full-spectrum model, the WOA-selected spectral model, and the WOA-GA model, respectively. Additionally, spectral response analysis identified 22 common bands essential for Pb content inversion. The proposed multi-level combined model not only significantly enhances prediction accuracy but also provides new insights into optimizing hyperspectral band selection, serving as a valuable scientific foundation for assessing soil heavy metal contamination.

Abstract: Small object detection has always been a challenging task in the field of remote sensing image detection. Due to the small proportion and limited pixel size of small objects in images, especially when using Convolutional Neural Networks (CNNs), the downsampling operation in traditional algorithms often leads to the loss of detailed information of small objects, causing missed detection issues. To address this problem, this paper proposes an improved YOLOv8 algorithm. We designed an adaptive feature extraction and multi-scale fusion module, which enhances the expressive ability of features and effectively extracts the detailed information of small objects. We incorporated the AFGCAttention attention mechanism to strengthen the network's focus on key regions, suppress irrelevant background information, and improve the model's ability to recognize small objects. To overcome the resolution loss problem in small object detection, we adopted the CARAFE (Content-Aware ReAssembly of FEatures) upsampling operator. By reorganizing feature maps with content-awareness, it avoids the blurriness and information loss commonly found in traditional upsampling methods, especially showing significant advantages in the reconstruction of small object details, making their boundaries clearer and more accurate. Meanwhile, to improve the accuracy of bounding box regression, we combined the GIoU loss function to optimize the geometric shape matching between the target and predicted boxes, solving the issue of inaccurate bounding box localization in small object detection and improving localization precision. Experimental results show that the proposed algorithm achieves significant accuracy improvement in small object detection tasks, maintaining high detection robustness in complex background scenes. The improved model reaches a mean average precision (mAP) of 83.0% and an accuracy of 85.0%, which is an increase of 2.0% and 5.0%, respectively, compared to the baseline model. Compared with existing methods, this approach has significant advantages in detection accuracy, localization precision, and model computational efficiency, especially demonstrating outstanding performance in small object detection.

Abstract:

Electromagnetic vortex radar, with its characteristics of carrying orbital angular momentum and spiral phase wavefront, provides a new method for achieving super-resolution radar imaging. This paper combines the characteristics of vortex electromagnetic waves with the downward-looking electromagnetic vortex SAR imaging model to conduct in-depth research and analysis of SAR imaging technology based on vortex electromagnetic waves. We design corresponding imaging models, derive the imaging echo formula, and propose a novel three-dimensional ω K imaging algorithm based on fractional orbital angular momentum (OAM), specifically targeting multiple scattering targets. The three-dimensional Omega-K imaging algorithm compresses the distance by exploiting the relationship between azimuth terms in the slow time domain to obtain the azimuth information of the target; then, by combining the two-dimensional azimuth-range imaging information, the two-dimensional azimuth-range-elevation imaging information, and the elevation information of the target, the height information of the target is determined; finally, the three-dimensional imaging of the target is completed based on the Cartesian coordinate relationship. Through experimental simulation, this paper verifies the effectiveness of the proposed imaging algorithm and successfully achieves three-dimensional imaging of point targets.

Abstract: The Santa Eulália Plutonic Complex is a large granitoid ring-complex exhibiting several granite facies, gabbro-diorite rocks and roof pendants from the country rocks. This lithologic variability crossed with the spatial distribution of each lithotype within the complex enables the grouping of the different magmatic rocks in: G0 group (“pink-reddish” granites in an external belt), G1 group ("greyish” granites in the inner part”) and M group (gabbro and diorite as enclaves in the eastern regions of the G0 group). All these groups host facies of interest for dimension stones due to their aesthetic value and, therefore, a systematic thematic mapping has been conducted and refined throughout the years. In this work we verify the applicability of Sentinel-2 multispectral images combined with Random Forest as a classification methodology to distinguish the different gran-itoid groups and support large-scale lithological mapping. The results show overall accurate outline of the facies, with areas corresponding to the G0 group displaying good predictions, areas representing the G1 group moderately good predictions, and areas established for the M group poor predictions, obtaining Producer’s accuracies of ~82%, ~60%, and ~30%, respectively.

Abstract: Climate change has increased the risk of snow drought, which is associated with the deficit in snowfall and snowpack. The objectives of this research are to improve drought identification in warming climate by developing a new snow-based hydroclimatic aggregate drought index (SHADI) and to assess the impacts of snowpack and snowmelt in drought analyses. To derive the SHADI, an R-mode principal component analysis is performed on precipitation, snowpack, surface runoff, and soil water storage. Then, a joint probability distribution function of drought frequencies and drought classes, conditional expectation, and k-means clustering are used to categorize droughts. The SHADI was applied to the Red River of the North Basin (RRB), a typical cold climate region, to characterize droughts in a mostly dry period from 2003 to 2007. The SHADI was compared with the hydroclimatic aggregate drought index (HADI) and the U.S. drought monitor (USDM) data. Cluster analysis was also utilized as a benchmark to compare the results of HADI and SHADI. SHADI showed better alignment with cluster analysis results than HADI, closely matching the identified dry/wet conditions in the RRB. The major differences between SHADI and HADI were observed in cold seasons and in transition periods (dry-to-wet or wet-to-dry). The derived variable threshold levels for different categories of drought based on the SHADI were close to, but different from those of HADI. SHADI can be used for short-term lead prediction of droughts in cold climate regions and, in particular, can provide an early warning for drought in the warming climate.

Abstract: The time period around the Noachian-Hesperian boundary, 3.7 million years ago, was an epoch when great geodynamical and environmental changes occurred on Mars. Currently available remote sensing data are crucial for understanding the Martian heat loss pattern and global thermal state in this transitional period. We here derive surface heat flows in specific locations based on estimations of the depth of five large thrust faults, in order to constrain both surface and mantle heat flows. Then, we use heat-producing elements (HPEs) abundances mapped from orbital measurements by the Gamma-Ray Spectrometer (GRS) onboard Mars Odyssey 2001 spacecraft, and geographical crustal thickness variations, to perform a global model for the surface heat flow. The heat loss contribution of large mantle plume beneath the Tharsis and Elysium magmatic provinces is also considered for our final model. We thus obtain a map of the heat flow variation across the Martian surface at the Noachian/Hesperian boundary. Our model also predicts an average heat flow between 32 and 50 mW m−2, which implies that the heat loss of Mars at that time was lower than the total radioactive heat production of the planet, which has profound implications for the thermal history of Mars.

Abstract: This study investigates the reactivity of municipal solid waste incineration residues (MSWIr) to aqueous carbonation, focusing on CO2 absorption rates, uptakes, and heavy metal (HM) leachability. Various combinations of boiler, electrofilter, and bag filter residues were assessed under typical incineration conditions. Bagh filter residues from lime-sorbent plants exhibited the highest CO2 uptake (244.5 gCO2/kg), while bottom ashes (BA) fine fraction, boiler/electrofilter fly ashes (FA), and other mixed air pollution control residues (APCr) demonstrated uptakes of 101, 0, 93, and 167 gCO2/kg, respectively. Carbonation kinetics revealed that high calcium content FA and APCr, followed similar CO2 absorption trends. Notably, BA carbonation was predominantly driven by Ca-aluminates rather than lime. Carbonation reduces leaching of Al, As, Cd, Co, Cu, Ni, Pb and Zn compared to water washing, though significant concerns arise with anions such as Sb and Cr. In BA, critical behaviours of Cr, Mn, and Fe were observed, with Cr leaching likely controlled by Fe-Mn-Cr oxide particle dissolution. These findings highlight the potential of integrating EMR through density or magnetic separation in BA prior to carbonation to reduce HM leaching and recycle critical metals (Ag, Cu, Cr, Ni, Mn, etc).

Abstract: The concentration of endocrine disruptor compounds (EDC) in wastewater is increasing, posing significant risks to living organisms. This study concerns the simultaneous degradation of a variety of EDCs from wastewater, including methylparaben (MeP), propylparaben (PrP), butylparaben (BuP), benzophenone (BP), bisphenol A (BPA) and estrone (E), in the presence of the microalgae Scenedesmus sp. or Chlorella vulgaris. The potential for abiotic removal of these EDCs and the underlying degradation mechanisms were also studied. The presence of microalgae significantly enhanced the degradation of parabens, achieving complete removal within 7 days, primarily through the mechanism of biodegradation. BPA removal was also improved by microalgae, reaching 82% and 90% within 7 days with Scenedesmus sp. and C. vulgaris, respectively. BP degradation was predominantly abiotic, accomplishing 95% removal in 7 days. E degradation was mainly abiotic, achieving approximately 40% within 7 days, with a notable contribution from a biodegradation mechanism in the later stages, accounting for 27% and 40% of the final total removal in the presence of Scenedesmus sp. and C. vulgaris, respectively. This study provides insights into the mechanism of EDC degradation by microalgae, highlighting the potential of Scenedesmus sp. and C. vulgaris for removing a mixture of EDCs from wastewater.