Search Results (6)

Based on marine environmental factors of different sea areas, a high-performance sea clutter time series modeling algorithm for the real sea surface is developed to study the amplitude mean and Doppler spectrum characteristics of sea clutter. The European Centre for Medium-Range Weather Forecasts (ECMWF) data set (ERA-Interim) and ESA’s soil moisture and ocean salinity (SMOS) data set are utilized to establish databases of different marine environmental factors. Combined with the mixed spectrum model, the geometric fine structure of wind-driven sea surface with swell superposition is established by using the double-superposition method (DSM) and comprehensively considering small-scale capillary ripples, large-scale gravity waves and swell. A triangle facet-based sea clutter series modeling algorithm is developed, in which the quasi-specular scattering based on a triangle and the scattering based on gravity wave modulation capillary spectrum are calculated, respectively, and compared with the measured results. For high-resolution radar, dynamic sea surface modeling and sea clutter calculation are very time consuming. In this paper, the Tesla K80 GPU manufactured by NVIDIA in Santa Clara, Computed Unified Device architecture (CUDA) high-performance parallel technology and some optimization strategies are adopted to improve the efficiency of sea clutter modeling. The results can be used to analyze the distribution characteristics of marine factors, the average amplitude and Doppler characteristics of sea clutter in different sea areas. Full article

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture. Full article

Ultrasound non-destructive testing (NDT) is a common technique used for defect detection in different materials, from aluminium to carbon-fiber-reinforced polymers (CFRPs). In most cases, a liquid coupling medium/immersion of the inspected component is required to maximize impedance matching, limiting the size of the structure and materials. Air-coupled inspection methods have recently been developed for noncontact inspections to reduce contact issues in standard ultrasonic inspections. However, transmission of ultrasound in air is very inefficient because of the enormous impedance mismatch between solids and air, thus requiring a signal amplification system of high-sensitivity transducers. Hence, the captured signal amplitude may not be high enough to reveal any wave distortion due to defects or damage. This work presents a design of a holey-structured metamaterial lens with a feature size of λ/14 aiming at improvement of acousto-ultrasonic imaging using air-coupled transducers. The required effect is obtained by matching geometrical parameters of the proposed holey-structured metamaterials and the Fabry–Perot resonance modes of the structure. Transmission tests have been conducted on different fabricated metamaterial-based structures, to assess the frequency component filtering of the proposed method in both acoustic (f = 5 kHz, 20 kHz) and ultrasonic range (f = 30 kHz, 40 kHz). Results showed an improved sensitivity of damage imaging, with an increase in amplitude of the design frequencies of the lens by 11 dB. Air-coupled inspections were conducted on a stress-corrosion cracked aluminum plate and impacted CFRP plate using the holey-structured lens. Results showed an improvement in the damage-imaging resolution due to a wave-amplitude increase across the defective features, thus demonstrating its potential as an efficient and sensitive inspection tool for damage-detection improvement in geometrically complex components of different materials. Full article

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R² = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations. Full article

Leaf pigment contents, such as chlorophylls a and b content (C ${}_{ab}$ ) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI < 1 m ${}^2$ /m ${}^2$ ), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C ${}_{ab}$ and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C ${}_{ab}$ , and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m ${}^2$ /m ${}^2$ , $R^2$ = 0.81); the band ratio $\frac{{\rho }_{0.750\mathsf{\mu }\mathrm{m}}}{{\rho }_{0.550\mathsf{\mu }\mathrm{m}}}$ retrieved C ${}_{ab}$ more accurately than other chlorophylls indices (RMSE = 5.21 $\mathsf{\mu }$ g/cm ${}^2$ , $R^2$ = 0.73); RMSE over the 0.5–0.55 $\mathsf{\mu }$ m interval showed encouraging results for Car estimations. Full article

In Europe, heating of houses and commercial areas is one of the major contributors to greenhouse gas emissions. When considering the drastic impact of an increasing emission of greenhouse gases as well as the finiteness of fossil resources, the usage of efficient and renewable energy generation technologies has to be increased. In this context, small-scale heating networks are an important technical component, which enable the efficient and sustainable usage of various heat generation technologies. This paper investigates how the potential of district heating for different settlement structures can be assessed. In particular, we analyze in which way remote sensing and GIS data can assist the planning of optimized heat allocation systems. In order to identify the best suited locations, a spatial model is defined to assess the potential for small district heating networks. Within the spatial model, the local heat demand and the economic costs of the necessary heat allocation infrastructure are compared. Therefore, a first and major step is the detailed characterization of the settlement structure by means of remote sensing data. The method is developed on the basis of a test area in the town of Oberhaching in the South of Germany. The results are validated through detailed in situ data sets and demonstrate that the model facilitates both the calculation of the required input parameters and an accurate assessment of the district heating potential. The described method can be transferred to other investigation areas with a larger spatial extent. The study underlines the range of applications for remote sensing-based analyses with respect to energy-related planning issues. Full article