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[IEEE 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - HangZhou, China (2018.9.5-2018.9.7)] 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - Reconfigurable Half-Mode Substrate Integrated Waveguide Filter With Wide Out-Of-Band Rejection
摘要： A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil’s inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil’s surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil’s bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5–3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented.
关键词： rough surface,GPR,water puddles,modeling,FDTD,antipersonnel (AP) landmines,roots,dispersive,fractals,Antennas,bowtie,GprMax,grass,vegetation
更新于2025-09-19 17:13:59
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A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?
摘要： In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°–5° and 15°–20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.
关键词： understory vegetation,LiDAR,forest fuel,relative radiometric correction,software tool,processing tool
更新于2025-09-19 17:13:59
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Measuring Leaf Equivalent Water Thickness of Short-Rotation Coppice Willow Canopy Using Terrestrial Laser Scanning
摘要： Accurate measurements of leaf Equivalent Water Thickness (EWT) can help in early detection of vegetation stress. Terrestrial Laser Scanning (TLS) intensity data have the potential to provide 3D estimates of EWT, overcoming the limitations of the 2D estimates provided by remote sensing optical data. Such limitations include the sensors being solar illumination dependent and unable to provide information about the vertical variation in EWT. In this study, intensity data from the Leica P20 and P40 commercial TLS instruments were combined in a Normalized Difference Index (NDI). NDI was used to measure EWT in six short-rotation coppice willow (Salix spp.) plots from different varieties with an average error of 7.3% (R2 = 0.8, RMSE = 0.0011 g cm-2). The effects of wind and senescence of leaves on the accuracy of the EWT estimation were also investigated.
关键词： agricultural crops,ground LiDAR,biomass energy,water stress,Vegetation water content
更新于2025-09-19 17:13:59
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Buried tunnel junction current injection for InP-based nanomembrane photonic crystal surface emitting lasers on Silicon
摘要： Microwave backscatter from vegetated surfaces is influenced by vegetation structure and vegetation water content (VWC), which varies with meteorological conditions and moisture in the root zone. Radar backscatter observations are used for many vegetation and soil moisture monitoring applications under the assumption that VWC is constant on short timescales. This research aims to understand how backscatter over agricultural canopies changes in response to diurnal differences in VWC due to water stress. A standard water-cloud model and a two-layer water-cloud model for maize were used to simulate the influence of the observed variations in bulk/leaf/stalk VWC and soil moisture on the various contributions to total backscatter at a range of frequencies, polarizations, and incidence angles. The bulk VWC and leaf VWC were found to change up to 30% and 40%, respectively, on a diurnal basis during water stress and may have a significant effect on radar backscatter. Total backscatter time series are presented to illustrate the simulated diurnal difference in backscatter for different radar frequencies, polarizations, and incidence angles. Results show that backscatter is very sensitive to variations in VWC during water stress, particularly at large incidence angles and higher frequencies. The diurnal variation in total backscatter was dominated by variations in leaf water content, with simulated diurnal differences of up to 4 dB in X- through Ku-bands (8.6–35 GHz). This study highlights a potential source of error in current vegetation and soil monitoring applications and provides insights into the potential use for radar to detect variations in VWC due to water stress.
关键词： Agriculture,vegetation water content (VWC),microwaves,hydrology,water stress,diurnal differences,radar,vegetation
更新于2025-09-16 10:30:52
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A new concept for estimating the influence of vegetation on throughfall kinetic energy using aerial laser scanning
摘要： Soil loss caused by erosion has enormous economic and social impacts. Splash effects of rainfall are an important driver of erosion processes; however, effects of vegetation on splash erosion are still not fully understood. Splash erosion processes under vegetation are investigated by means of throughfall kinetic energy (TKE). Previous studies on TKE utilized a heterogeneous set of plant and canopy parameters to assess vegetation’s influence on erosion by rain splash but remained on individual plant- or plot-levels. In the present study we developed a method for the area-wide estimation of the influence of vegetation on TKE using remote sensing methods. In a literature review we identified key vegetation variables influencing splash erosion and developed a conceptual model to describe the interaction of vegetation and raindrops. Our model considers both amplifying and protecting effect of vegetation layers according to their height above the ground and aggregates them into a new indicator: the Vegetation Splash Factor (VSF). It is based on the proportional contribution of drips per layer, which can be calculated via the vegetation cover profile from airborne lidar datasets. In a case study, we calculated the VSF using a lidar dataset for La Campana National Park in central Chile. The studied catchment comprises a heterogeneous mosaic of vegetation layer combinations and types and is hence well suited to test the approach. We calculated a VSF map showing the relation between vegetation structure and its expected influence on TKE. Mean VSF was 1.42, indicating amplifying overall effect of vegetation on TKE that was present in 81 % of the area. Values below 1 indicating a protective effect were calculated for 19 % of the area. For future work, we recommend refining the weighting factor by calibration to local conditions using field-reference data and comparing the VSF with TKE field measurements.
关键词： Throughfall Kinetic Energy,Remote Sensing,Vegetation Splash Factor,Splash Erosion,LiDAR
更新于2025-09-16 10:30:52
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Evaluation of OptRx <sup>TM</sup> Active Optical Sensor to Monitor Soybean Response to Nitrogen Inputs
摘要： BACKGROUND: Active optical crop sensors have been gaining importance to determine in-season nitrogen (N) fertilization requirements for on-the-go variable rate applications. Although most of these active in-field crop sensors have been evaluated in maize (Zea mays L.) and wheat (Triticum aestivum L.emend.Thell.), these sensors have not been evaluated in soybean [Glycine max (L.)Merr.] production systems in North Dakota (ND), USA. Recent research from both South Dakota and ND, USA indicate that in-season N application in soybean can increase soybean yield under certain conditions. RESULTS: The study revealed that OptRxTM sensor reading did not show any significant differences from early to midway through the growing season. The NDRE (Normalized Difference Red Edge index) data collected towards the end of the growing season showed significantly higher values for some of the N treatments as compared to others in both years. The NDRE values were strongly correlated to grain yield for both years under tiled (r = 0.923) and non-tiled (r = 0.901) drainage conditions. Certain soybean varieties displayed significantly higher NDRE values over both years. The three varieties tested across years, under both tiled and non-tiled conditions, showed a significant linear relationship between late August NDRE values and yield (R2=0.85 for tiled and R2=0.81 for non-tiled). CONCLUSION: In this research, the study results show that the OptRxTM sensor has the potential to work for soybean as well, though later in the crop growing season. Further investigation is needed to confirm the use of OptRx? sensor for variable rate in-season N applications in soybeans.
关键词： OptRx TM sensor,Vegetation Index,Soybeans,Nitrogen
更新于2025-09-11 14:15:04
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Four Dimensional Mapping of Vegetation Moisture Content Using Dual-Wavelength Terrestrial Laser Scanning
摘要： Recently, terrestrial laser scanning (TLS) has shown potential in measuring vegetation biochemical traits in three dimensions (3D) by using reflectance derived from backscattered intensity data. The 3D estimates can provide information about the vertical heterogeneity of canopy biochemical traits which affects canopy reflectance but cannot be measured from spaceborne and airborne optical remote sensing data. Leaf equivalent water thickness (EWT), a metric widely used in vegetation health monitoring, has been successfully linked to the normalized difference index (NDI) of near and shortwave infrared wavelengths at the leaf level. However, only two previous studies have linked EWT to NDI at the canopy level in field campaigns. In this study, an NDI consisting of 808 and 1550 nm wavelengths was used to generate 3D EWT estimates at the canopy level in a broadleaf mixed-species tree plot during and after a heatwave. The relative error in EWT estimates was 6% across four different species. Temporal changes in EWT were measured, and the accuracy varied between trees, a factor of the errors in EWT estimates on both dates. Vertical profiles of EWT were generated for six trees and showed vertical heterogeneity and variation between species. The change in EWT vertical profiles during and after the heatwave differed between trees, demonstrating that trees reacted in different ways to the drought condition.
关键词： drought,leaf water content,vegetation,Lidar,water stress
更新于2025-09-11 14:15:04
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Spectral data source effect on crop state estimation by vegetation indices
摘要： Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. Even though differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from spaceborne multispectral imagery and point-based field spectroscopy in application to crop state estimation. For this purpose, irrigated chickpea field was monitored by RapidEye satellite mission and additional measurements by field spectrometer were obtained. Estimated VIs average and coefficient of variation from each observation were compared with physical crop measurements: leaf water content, LAI and chlorophyll level. The results indicate that indices calculated from spaceborne spectral images regardless of the claimed response commonly react on phenology of the irrigated chickpea. This feature makes spaceborne spectral imagery an appropriate data source for monitoring crop development, crop water needs and yield prediction. VIs calculated from field spectrometer were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability (up to 69%) of the calculated values. Consequently, the high spatial variability of field spectral measurements depreciates the estimation agricultural field state by average mean only. Nevertheless, the spatial variability might have certain behavior trend, e.g., a significant increase in the active growth or stress and can be an independent feature for field state assessment.
关键词： Vegetation indices,Spatial variability,Agriculture management,Field spectroscopy,Spaceborne spectral imagery
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Validation of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) Terrestrial Chlorophyll Index (OTCI): Synergetic Exploitation of the Sentinel-2 Missions
摘要： Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI), and to ensure its utility in a wide range of operational applications, validation efforts are required. In the past, these activities have been constrained by the need for costly airborne hyperspectral data acquisition, but the Sentinel-2 Multispectral Instrument (MSI) now offers a promising alternative. In this paper, we explore the synergetic use of Sentinel-2 MSI data for validation of the Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. High retrieval accuracy (RMSE = 0.20 g m-2) was obtained by applying machine learning techniques to Sentinel-2 MSI data, highlighting the valuable information it can provide when used in synergy with Sentinel-3 OLCI data for land product validation.
关键词： validation,Vegetation biophysical variables,Sentinel-2,Sentinel-3,canopy chlorophyll content
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Potential of Landsat-Oli for Seagrass and Algae Species Detection and Discrimination in Bahrain National Water Using Spectral Reflectance
摘要： Seagrass (Halodule uninervis and Halophila stipulacea) and algae (green and brown) species are widely distributed along the coastal zones of the Bahrain national water. In this study the potential of Landsat-OLI VNIR spectral bands was investigated for distinction and discrimination among these species using spectral reflectances. The measured spectra’s of each species considering different coverage rate (0, 10, 30, 75 and 100%) were transformed using continuum-removed (CR) approach, resampled and convolved in the solar-reflective spectral bands of OLI using a radiative transfer code, then converted to water vegetation indices (WVI). Regression analysis were performed between the transformed WVI and the coverage rates of each species individually (seagrass and algae) and mixed; as well between WVI and NIR reflectances. Spectral and CR analyses showed that the blue and the green bands perform better than the coastal and the red bands for seagrass and algae classes’ discrimination. This result was further corroborated by the WVI. Regression results between the coverage rates and WVI calculated with green and NIR bands showed that the TDAVI and WAVI discriminate significantly among the mixed species (R2 of 0.70), and between individual species (R2 of 0.80 for algae and for seagrass). Accomplished between WVI and NIR reflectances, regression correlations were more significant when all mixed samples (R2 of 0.95) have been considered, likewise when we consider individually the two seagrass (R2 of 0.95) and the two algae species (R2 of 0.82).
关键词： Bahrain,Algae,Seagrass,Spectral signature,Landsat-OLI,Water vegetation indices
更新于2025-09-11 14:15:04