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[Developments in Earth Surface Processes] Remote Sensing of Geomorphology Volume 23 || Terrestrial laser scanner applied to fluvial geomorphology
摘要： Measuring river geometry and its evolution through time has always been a cornerstone of fluvial geomorphology. While experimental and numerical modeling of fluvial dynamics has been central in understanding long-term dynamics and testing ideas, they remain simplified versions of complex natural systems and cannot necessarily include all relevant processes. Field measurements are thus central to our understanding of elementary processes such as sediment entrainment and deposition, bank erosion, bedrock incision as well as the macroscopic dynamics of river reaches such as channel bed accretion/erosion, bedforms mobility, and river meandering. It is therefore not surprising that fluvial geomorphologists have quickly embraced the use of terrestrial laser scanner (TLS) to study rivers (e.g., Heritage and Hetherington, 2007; Hodge et al., 2009a). TLS allows 3D digitization of fluvial environment in a dense (sub-cm), accurate (mm precision), and nearly exhaustive way (Fig. 1). The very large range of spatial scales covered is particularly impressive, from individual pebbles to km long river reaches (e.g., Brasington et al., 2012). Sub-cm accuracy also offers the possibility of detecting very subtle changes (Lague et al., 2013), a key attribute to measure slow processes such as bedrock abrasion (Beer et al., 2017). Given the recent emphasis on the role of riparian processes on fluvial processes, the ability to digitize vegetation in 3D in relation to channel morphology offers a unique perspective in biogeomorphology. However, many of the promises of TLS have not really been fulfilled, and the scientific potential of the TLS dataset remains often untapped. This is largely due to the challenging aspects surrounding the processing of TLS data which, to a large extent, also apply to structure from motion (SfM) surveys (Passalacqua et al., 2015). Three challenges, akin to typical Big Data issues can be identified as follows: 1. Data Complexity: TLS data are 3D data and nearly exhaustive. This makes for very rich data but also extremely complex to process as the relevant information (e.g., ground, grains, riverbanks, vegetation) must be detected prior to scientific analysis (Fig. 1). TLS data is also natively non-regularly sampled, with strong spatial variations in point density and requires processing methods that are more complex than for 2D raster-based data such as satellite imagery. 2. Data Volume: the latest generation of TLS instruments generates billions of points in a day. Manual processing cannot realistically be applied, and automatic processing methods are paramount. This requires good programing skills as well as a culture of machine learning and computer vision approaches that are not necessarily part of the training of geomorphologists and requires bridging the gap with computer sciences. 3. Data Incompleteness: despite the very large field of view of TLS sensors, the resulting 3D data do not sample the entire surface (Fig. 1). The ground-based viewpoint imparts missing data behind obstacles (grains of any size and vegetation) and the laser is generally fully absorbed by water resulting in the lack of bathymetric data, a strong limitation in river environments. Processing methods must account for this lack of information.
关键词： Terrestrial laser scanner,sediment transport,vegetation classification,bank erosion,3D digitization,point cloud processing,bedrock incision,fluvial geomorphology
更新于2025-09-23 15:19:57
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Compact Fully Metallic Millimeter-Wave Waveguide-Fed Periodic Leaky-Wave Antenna Based on Corrugated Parallel-Plate Waveguides
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2025-09-23 15:19:57
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IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities
摘要： Climate change may alter the spatial distribution, composition, structure and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate the solar radiation absorbed by individual plants in order to understand and predict their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming that crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the results of random distribution of plants. The tests show that the model results are very close to the averages of the random distributions. This model is ef?cient in computation, and can be included in vegetation models to simulate long-term transient responses of plant communities to climate change. The code and a user’s manual are provided as Supplement of the paper.
关键词： climate change,individual plant radiation model,solar radiation,ecotones,vegetation models,IPR
更新于2025-09-23 15:19:57
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Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal
摘要： The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in S?o Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.
关键词： vegetation index,satellite images,yield monitoring,hyperspectral sensor,crop residue management,remote sensing
更新于2025-09-19 17:15:36
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Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data
摘要： Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.
关键词： hyperspectral,multispectral,vegetation indices,Sentinel-2,machine learning regression algorithms,PROSAIL,field-spectroradiometer,LUT inversion,leaf area index
更新于2025-09-19 17:15:36
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Soil Salinity Mapping and Hydrological Drought Indices Assessment in Arid Environments Based on Remote Sensing Techniques
摘要： Vegetation indices are mostly described as crop water derivatives. Normalized Difference Vegetation Index (NDVI) is one of the oldest remote sensing applications that widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives are exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. Water Supply Vegetation Index (WSVI), Soil Adjusted Vegetation Index (SAVI), Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity level on crop water stress in arid environments. In arid environments; such as Saudi Arabia, water resources are under pressure especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater; which exceed crop water requirements in most of the cases are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat OLI-8 data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi Ad-Waser. Principal Component Analysis and Artificial Neural Network Analysis are complementary tools to understand the regression pattern of the hydrological drought indices in the designated study area.
关键词： Soil Salinity Mapping,Arid Environment,Vegetation Indices,Remote Sensing techniques
更新于2025-09-19 17:15:36
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The effect of laser and ultrasound synchronization in photo-mediated ultrasound therapy
摘要： Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing ?eld measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which ?tted ?eld-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This ?nding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.
关键词： fractional vegetation cover,Coarse resolution,product validation,SPOT/VEGETATION
更新于2025-09-19 17:13:59
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Carrier-envelope offset frequency stabilization of a thin-disk laser oscillator via depletion modulation
摘要： Unreliability involved in the extraction of shaded vegetation-covered surfaces (VS) is a common problem in urban vegetation mapping. Serving as a solution to it, a novel method named Nonlinear Fitting-based Seeded Region Growing (NFSRG) is explored. With NFSRG, a series of classi?ed results are organized by a seeded-region-growing process. In order to adapt to the variable separability between VS and background, the growing is limited in several weighted buffers de?ned by some nonlinear ?tting relationships. When searching new VS members (member means both pixel and patch) within such a buffer, a gradually reduced weight makes the buffer width continually narrowed as the separability worsens. To avoid unexpected entrances of water and smooth shaded background members, a during-growing constraint, named expansion rate, is proposed. Accuracy assessments reveal that more than 96% of VS members can be accurately extracted by the proposed method.
关键词： shadow,Classi?cation,urban,vegetation,seeded region growing
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Slow Light using Photorefractive Beam Fanning
摘要： Unreliability involved in the extraction of shaded vegetation-covered surfaces (VS) is a common problem in urban vegetation mapping. Serving as a solution to it, a novel method named Nonlinear Fitting-based Seeded Region Growing (NFSRG) is explored. With NFSRG, a series of classi?ed results are organized by a seeded-region-growing process. In order to adapt to the variable separability between VS and background, the growing is limited in several weighted buffers de?ned by some nonlinear ?tting relationships. When searching new VS members (member means both pixel and patch) within such a buffer, a gradually reduced weight makes the buffer width continually narrowed as the separability worsens. To avoid unexpected entrances of water and smooth shaded background members, a during-growing constraint, named expansion rate, is proposed. Accuracy assessments reveal that more than 96% of VS members can be accurately extracted by the proposed method.
关键词： shadow,Classi?cation,urban,vegetation,seeded region growing
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Modulational Instability of a Plane Wave in the Presence of Localized Perturbations: Experiments in Nonlinear Fiber Optics
摘要： A multispectral full-waveform light detection and ranging (LiDAR) instrument prototype with four wavelengths and a supercontinuum laser as a light source was designed to monitor the fine structure and the biochemical parameters of vegetation. Components of the instrument included a 2-D scanning platform, a supercontinuum laser source, a receiving optical system, and a multichannel full-waveform measurement module. The LiDAR instrument can simultaneously measure multichannel-returned full-waveform laser signals. Position information in the recorded waveform allowed us to compute the distance from the target, whereas the intensity of the signal provided the spectral reflectance. Performance for the measuring distance and the spectrum was evaluated. Experiments indicated that the instrument has high measurement accuracy and has the ability to detect the biochemical characteristics of vegetation via construction of the normalized difference vegetation index and the photochemical reflectance index. The experiment also indicated that the instrument has the potential to generate spectral 3-D point clouds. Therefore, the instrument could play a significant role in detecting the vertical distribution of structural and biochemical characteristics of vegetation.
关键词： light detection and ranging (LiDAR),Biochemical,multispectral,full waveform,vegetation
更新于2025-09-19 17:13:59