Search Results (341)

Leaf Area Index (LAI) is a critical biophysical parameter for characterizing vegetation canopy structure and function. However, fine-scale LAI estimation remains challenging due to limitations in spatial resolution and structural detail in traditional remote sensing data and the insufficiency of single-index models like the LiDAR Penetration Index (LPI) in capturing canopy complexity. This study proposes a multi-scale LAI estimation approach integrating high-density UAV-based LiDAR data with LPI and point cloud texture features. A total of 40 field-sampled plots were used to develop and validate the model. LPI was computed at three spatial scales (5 m, 10 m, and 15 m) and corrected using a scale-specific adjustment coefficient (μ). Texture features including roughness and curvature were extracted and combined with LPI in a multiple linear regression model. Results showed that μ = 15 provided the optimal LPI correction, with the 10 m scale yielding the best model performance (R² = 0.40, RMSE = 0.35). Incorporating texture features moderately improved estimation accuracy (R² = 0.49, RMSE = 0.32). The findings confirm that integrating structural metrics enhances LAI prediction and that spatial scale selection is crucial, with 10 m identified as optimal for this study area. This method offers a practical and scalable solution for improving LAI retrieval using UAV-based LiDAR in heterogeneous forest environments. Full article

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value. Full article

Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order vegetation model that considers vegetation density and volume scattering. Utilizing multi-angle GNOS-R observations, the algorithm derives surface reflectivity, which is combined with ancillary data on opacity, vegetation water content, and soil moisture from SMAP (Soil Moisture Active Passive) to optimize the retrieval process. The algorithm has been specifically tailored for different surface conditions, including bare soil, areas with low vegetation, and densely vegetated regions. The algorithm directly incorporates the angle-dependence of observations, leading to enhanced retrieval accuracy. Additionally, a new approach parameterizes surface roughness as a function of angle, allowing for refined corrections in reflectivity measurements. For vegetated areas, the algorithm effectively isolates the soil surface signal by eliminating volume scattering and vegetation effects, enabling the accurate estimation of soil moisture. By leveraging multi-angle data, the algorithm achieves significantly improved retrieval accuracy, with root mean square errors of 0.0235, 0.0264, and 0.0191 (g/cm³) for bare, low-vegetation, and dense-vegetation areas, respectively. This innovative methodology offers robust global soil moisture estimation capabilities using the GNOS-R instrument, surpassing the accuracy of previous techniques. Full article

Urban green spaces (UGS) supply a wide range of ecosystem services (ESs), which are key to mitigation and adaptation to climate changes. In this study, we focus on two ESs, i.e., greenhouse gas sequestration by terrestrial ecosystems and mitigating the heat island effect through vegetation, as defined by the Common International Classification of Ecosystem Services. The purpose is to support municipalities with characteristics similar to those of the municipality investigated in this study with a rough assessment of ESs through freely available data. The ES delivery capacity assessment relies on the adoption of two indicators: (i) increased carbon storage in forests and (ii) the Heat Island Mitigation Index (HIMI). We applied the method to the UGS of the municipality of Sassari (Italy) and found that the potential amount of carbon storage is 42,052.7 t, while the value of HIMI provided by the green spaces in the homogeneous territorial areas is 67.73%. The methodological approach adopted in this study is potentially applicable in Italian as well as Mediterranean small to medium municipalities to integrate the quantitative assessment of ESs in local planning tools. The novelty of this study lies in the applied practical approach, which is implementable by public bodies lacking data and resources, to assessing prima facie the need for operational climate adaptation and mitigation strategies. Full article

High-resolution remotely sensed data, which are characterised by their advanced spectral and spatial capabilities, provide unprecedented opportunities to monitor and analyse the dynamic structures of urban environments. Platforms like Google Earth Engine (GEE) enhance these capabilities, as they provide access to vast datasets and tools for analysing key urban parameters, including land use, vegetation cover, and surface roughness–all critical components in urban sustainability studies. This study presents a knowledge-based framework for processing high-resolution satellite imagery tailored to address the demands of sustainable urban planning in the Municipality of Kalamaria in Thessaloniki, Greece. The framework emphasises the extraction of essential urban parameters, such as the spatial distribution of built-up and green spaces, alongside the analysis of surface roughness attributes, including displacement height and roughness length. Unlike conventional methods, our framework enables a detailed intra-urban analysis as these surface roughness attributes are calculated within 200 m × 200 m sub-units. Surface roughness indicators offer essential insights into aerodynamic drag and turbulent air mixing, both of which are directly influenced by the structural characteristics of the urban landscape. Using this approach, ‘wake interference flow’ type was identified as the dominant airflow pattern in the study area. This type was observed in 105 out of 150 sub-units, suggesting that these areas likely suffer from poor air circulation and are prone to higher concentrations of air pollutants. The integration of Google Earth Engine offered a scalable and replicable solution for large-scale urban analysis making it easily adaptable to other urban areas, especially where detailed morphological datasets are unavailable. By providing a robust, scalable, and data-driven tool for assessing urban form and airflow characteristics, our study offers a significant advancement in sustainable urban planning and climate resilience strategies, with clear potential for adaptation in other cities facing similar data limitations. Full article

Vegetal sources are a continuous research field and different types of extracts have been obtained over time. The most challenging part is compounding them in a pharmaceutical product. This study aimed to integrate a mixture (EX) of four extracts (SE-Sophorae flos, GE-Ginkgo bilobae folium, ME-Meliloti herba, CE-Calendulae flos) in formulations with polymers (polyhydroxybutyrate, polylactic-co-glycolic acid) and their physicochemical profiling. The resulting samples consist of particle suspensions, which were subjected to Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy analysis. When compared to single-extract formulations spectra, they revealed band changes, depending on the complex interactions. Using X-ray Diffractometry, the partially crystalline phase was highlighted for EX-PLGA, while the others were amorphous. Moreover, Atomic Force Microscopy pointed out the nanoscale particles and the topography of the samples, and the outstanding roughness belonging to EX-PHB-PLGA. A 30 min period of immersion was enough for the formulations to spread on the surface of the compression stockings material (CS) and after drying, it became a polymeric film. TGA analysis was performed, which evaluated the impregnated content: 5.9% CS-EX-PHB, 6.4% CS-EX-PLGA, and 7.5% CS-EX-PHB-PLGA. In conclusion, the extract’s phytochemicals and the interactions established with the polymers or with the other extracts from the mixture have a significant impact on the physicochemical properties of the obtained formulations, which are particularly important in pharmaceutical product development. Full article

Salt marsh vegetation significantly changes water motion and sediment transport in coastal wetlands, which further influences the geomorphological evolution of coastal wetlands. Accurate determination of the vegetation drag coefficient (Manning’s roughness coefficient) is critical to vegetation flow resistance research. Previous studies on the vegetation roughness coefficient mainly conducted flume experiments under the one-dimensional steady flow condition, which could not reflect the two-dimensional unsteady flow condition in salt marsh vegetated zones. Through theoretical formula analysis and synchronized field observations in a salt marsh vegetated zone, we propose a novel method for calculating the roughness coefficient of salt marsh vegetation especially under the two-dimensional unsteady flow condition. The results indicate that the vegetation roughness coefficient under the two-dimensional unsteady flow condition can be obtained by integrating the flow resistance equation with the discretized momentum conservation equation. Then, in combination with field observation data, the temporal variations in the vegetation roughness coefficient can be derived. The salt marsh vegetated zone in the Jiuduansha Wetland is dominated by flooding currents, and ebbing currents are of secondary importance. The flow resistance of vegetation on flooding and ebbing currents is remarkable. Moreover, the roughness coefficient shows an inverse power-law relationship with the product of flow velocity and water depth (i.e., U_fh_f) at the control volume center. Under the same U_fh_f scenario, due to the increase in the water-facing area of vegetation, the roughness coefficient during the submerged period is generally greater than that during the non-submerged period. The calculated roughness coefficients and their relationships with U_fh_f are consistent with those shown in previous flume experiments, indicating that our proposed method is reasonable. This new method could help determine vegetation flow resistance accurately (particularly under the two-dimensional unsteady flow condition), and it may provide implications for eco-geomorphological simulations of coastal wetlands. Full article

The presence of vegetation in agricultural fields affects the accuracy of soil moisture retrieval using synthetic aperture radar (SAR) data. As a result, the estimation of soil moisture using the existing Oh model produces high error values. The magnitude of this error primarily depends upon the nature of crops, crop coverage, and the roughness of the field. Hence, in this study, along with the Oh model, we proposed a novel approach using model-based decomposition to reduce the volume contribution of the vegetation. This proposed method is employed on fallow as well as different crop fields in the summer of 2023 in the Kharagpur region of India using the Sentinel-1 dual polarimetric SAR data. The Root Mean Square Error (RMSE) of the proposed method is ≈25% to 52% lower over different crop types as compared to the existing Oh model. Moreover, the proposed method is also compared with the Chang model, designed to estimate soil moisture in vegetative fields. The proposed method exhibits RMSE that is around ≈10% to 17% lower across various crop kinds, in comparison to the Chang model. Thus, the proposed novel approach, with the advantage of not requiring in situ plant descriptors, will simplify the application of dual polarimetric SAR data for soil moisture estimation in a variety of land-use scenarios. Full article

Riparian vegetation reduces the conveyance capacity and increases the likelihood of floods. Studies that consider vegetation in flow modeling rely on unmanned aerial vehicle (UAV) data, which restrict the covered area. In contrast, this study explores advances in remote sensing and machine learning techniques to obtain vegetation data for an entire river by relying solely on satellite data, superior to UAVs in terms of spatial coverage, temporal frequency, and cost effectiveness. This study proposes a machine learning method to obtain key vegetation parameters at a resolution of 10 m. The goal was to evaluate the applicability of remotely sensed vegetation data using the proposed method on a dynamic roughness distributed runoff model in the Abukuma River to assess the effect of vegetation on the typhoon Hagibis flood (12 October 2019). Two machine learning models were trained to obtain vegetation height and density using different satellite sources, and the parameters were mapped in the river floodplains with 10 m resolution based on Sentinel-2 imagery. The vegetation parameters were successfully estimated, with the vegetation height overestimated in the urban areas, particularly in the downstream part of the river, then integrated into a dynamic roughness calculation routine and patched into the RRI model. The simulations with and without vegetation were also compared. The machine learning models for density and height obtained fair results, with an R² of 0.62 and 0.55, respectively, and a slight overestimation of height. The results showed a considerable increase in water depth (up to 17.7% at the Fushiguro station) and a decrease in discharge (28.1% at the Tateyama station) when vegetation was considered. Full article

The accurate estimation of Manning’s roughness coefficient (n) is critical for hydraulic modeling in open channels. In fish passes designed as close-to-nature structures, this coefficient has a strong influence on the overall design and operation. This study evaluates n for the Veľké Kozmálovce fish pass using high-resolution drone imagery and image analysis techniques to determine riverbed surface characteristics and extract a grain size distribution curve. Various empirical equations based on Strickler’s formula were applied to specific grain sizes, yielding average n values of 0.036 and 0.037. Cowan’s method, which considers surface material, irregularities, vegetation, obstructions, and meandering, provided an upper-bound estimate of 0.040. However, this method is known to overestimate roughness in some cases. The Step-by-Step method, applied with hydraulic field measurements, resulted in a narrower range of n from 0.027 to 0.037. Overall, estimated values across all methods ranged between 0.023 and 0.040, reflecting the structural complexity of the fish pass, which includes boulders embedded in concrete and coarse gravel infill. These findings highlight the limitations of using generalized tabulated values for artificial channels and demonstrate that drone-based photogrammetry combined with empirical and analytical approaches can effectively capture spatial variability in hydraulic roughness. Full article

Ground-based ground-penetrating radar (GPR) has been applied successfully for decades in archaeological geophysics. However, there are sometimes severe problems arising in cases of rough terrain, permission to enter a site, or due to vegetation. Other issues may also make it impossible to use conventional ground-based GPR. Therefore, mounting the GPR antenna below a drone could be a potential alternative. Successful applications of drone-based GPR have already been reported, e.g., in the fields of geological mapping, glaciology, and UXO-detection. However, it is not clear whether faint archaeological remains can also be mapped using this approach. In the survey discussed below, we tested such a drone-based GPR setup at an archaeological site in Bavaria, where well-preserved Roman foundations at a shallow depth are known from previous geophysical surveys with magnetics and ground-based GPR. The aim was to evaluate the possibilities and problems arising with this new approach through a comparison with the afore-mentioned data, obtained in previous ground-based surveys of this site. The results show that under certain circumstances, the archaeological remains can be resolved while using a drone. However, the remains are much harder to detect with a lower degree of resolution and survey setup and acquisition time play a crucial role for a successful survey. Especially relevant are two factors: First, the correct choice of profile orientation, as there are strong reflections caused by near-surface features (like field boundaries) due to decoupling the antenna from the ground. Second, a very dry soil is mandatory, as otherwise too much signal is lost at the air-ground-interface. Considering these factors, drone-based GPR represents a valuable tool for modern archaeological geophysics. Full article

The design of functional paper coatings with excellent barrier properties, including water repellence, anti-microbial properties, and recyclability, is highly demanded in view of the sustainable use of paper as flexible substrates for various industrial applications such as packaging. The enhanced coating functionalities should be incorporated through a combination of selected bio-based materials and the creation of appropriate surface textures enhancing coating performance. The bio-inspired approaches through the replication of hierarchical surface structures with multi-scale dimensional features in combination with selection of appropriate bio-based functional groups offer new concepts for coating design. In this short perspective paper, concepts in the field are illustrated with a focus on the combination of hydrophobic and anti-microbial properties. Based on long-term work with the available toolbox of bio-based building blocks and nanoscale architectures, they can be processed into applicable aqueous suspensions for sprayable paper coatings. The macroscopic roughness profile of paper substrates can be complemented through the decoration of nanoscale bio-based polymer particles of polyhydroxybutyrate or vegetable oil capsules with dimensions in the range of 20–50 nm or 100–500 nm depending on the synthesis conditions. The anti-microbial properties can be provided by the surface modification of nanocellulose with biologically active molecules sourced from nature. Besides the more fundamental issues in design and synthesis, the industrial application of the bio-inspired coatings through spray-coating becomes relevant. Full article

Shielings are seasonal settlements found in upland pastures across Scandinavia and the North Atlantic. New investigations in the county of Møre and Romsdal, Norway, demonstrate the existence of this transhumant system by the Viking Age and Early Middle Ages. Sub-terranean features in these pastoral mountain landscapes have been identified by remote sensing technologies, but non-invasive methods still face challenges in terms of practical applicability and in confirming the presence of archaeological sites. Generally, aerial surveys, such as LiDAR and image-based modelling, excel in documenting visual landscapes and may enhance detection of low-visibility features. Thermography may also detect shallow subsurface features but is limited by solar conditions and vegetation. Magnetic methods face challenges due to the heterogeneous moraine geology. Ground-penetrating radar has yielded better results but is highly impractical and inefficient in these remote and rough landscapes. Systematic soil coring or test-pitting remain the most reliable options for detecting these faint sites, yet non-invasive methods may offer a better understanding of the archaeological contexts—between the initial survey and the final excavation. Altogether, the study highlights the dependency on landscape, soil, and vegetation, emphasising the need to consider each method’s possibilities and limitations based on site environments and conditions. Full article

The safe operation of power transmission lines is critical for ensuring the stability of the power supply, especially given the increasing frequency of extreme weather events and the risks posed by tree growth. This study proposes a novel method for detecting and predicting the tree barrier risks on transmission lines using Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) technology. The method employs point cloud classification to effectively separate ground, conductor, tower, and vegetation points, followed by 3D reconstruction of the power lines using the catenary equation. Tree growth models are integrated with measured data to predict future tree barrier risks. The experimental results demonstrate that the point-cloud-based method detects 31 tree barriers, with an RMSE of 0.08 m, while the 3D-reconstruction-based method detects 32 tree barriers, with an RMSE of 0.04 m, indicating its higher accuracy. The Cloth Simulation Filter (CSF) ground point classification method achieved the lowest roughness (1.5%), mean error (0.147 m), and RMSE (0.174 m), proving its effectiveness for flat terrain. Additionally, the assisted seed point individual tree segmentation method extracted tree height with high accuracy (R² = 0.84, RMSE = 1.01 m). This study predicts an average tree growth rate of 0.248 m/year over the next five years, identifying a new tree barrier at the coordinates 30°15′16.64″ N, 119°43′16.01″ E. This method enhances the efficiency and accuracy of transmission line inspections, supporting both power line safety and sustainable forest management. Its findings provide a robust technical approach to improving power line operations and forest resource utilization. Full article

Plant colonization patterns on deglaciated terrain give insight into the factors influencing alpine ecosystem development. Our objectives were to use a chronosequence, extending from the Little Ice Age (~1850) terminal moraine to the present glacier terminus, and biophysical predictors to characterize vegetation across Sperry Glacier’s foreland—a mid-latitude cirque glacier in Glacier National Park, Montana, USA. We measured diversity metrics (i.e., richness, evenness, and Shannon’s diversity index), percent cover, and community composition in 61 plots. Field observations characterized drainage, concavity, landform features, rock fragments, and geomorphic process domains in each plot. GIS-derived variables contextualized the plots’ aspect, terrain roughness, topographic position, solar radiation, and curvature. Overall, vegetation cover and species richness increased with terrain age, but with colonization gaps compared to other forelands, likely due to extensive bedrock and slow soil development, potentially putting this community at risk of being outpaced by climate change. Generalized linear models revealed the importance of local site factors (e.g., drainage, concavity, and process domain) in explaining species richness and Shannon’s diversity patterns. The relevance of field-measured variables over GIS-derived variables demonstrated the importance of fieldwork in understanding alpine successional patterns and the need for higher-resolution remote sensing analyses to expand these landscape-scale studies. Full article