Mean Backscatter Research Articles

Bambara groundnut starch-soluble dietary fibre nanocomposite stabilised emulsions: Optimisation of emulsion stability and studies on time-dependent rheological properties

Due to their inherent thermodynamic instability, emulsions require stabilisers to maintain their integrity and prevent destabilisation. The effect of emulsion components (Bambara groundnut starch-soluble dietary fibre nanocomposite (STASOL), orange oil and water) on the rheological and stability properties of STASOL-stabilised emulsions was optimised. A randomised D-optimal exchange mixture design was used to determine the ratio of STASOL, orange oil and water required for a stable emulsion. The stability of these emulsions was evaluated using the initial mean backscattering index (BSAVO), Turbiscan stability index (TSI) and hysteresis loop area (HLA). The BSAVO (%), TSI and HLA of the emulsions ranged from 50.73% (14:30:56 STASOL:oil:water) to 70.47% (20:30:50 STASOL:oil:water), 0.0005 (20:30:50 STASOL:oil:water) to 0.1000 (8:42:50 STASOL:oil:water) and 0.37 Pas-1 (10:32:58 STASOL:oil:water) to 5.69 Pas-1 (20:30:50 STASOL:oil:water), respectively. Emulsions exhibited a direct relationship between STASOL concentration and stability metrics, with the highest stability achieved at 20% STASOL concentration, 30% orange oil and 50% water. An increase in STASOL concentration coupled with a decrease in oil concentration results in a stable emulsion. Numerical optimisation and experimental validation confirmed the effectiveness of the selected formulation, demonstrating STASOL's potential as a natural stabiliser in beverage emulsions and its ability to enhance stability compared to synthetic alternatives.

Characterizing prey fields in humpback whale foraging areas of southern British Columbia

Humpback whales ( Megaptera novaeangliae) use southern British Columbia waters to feed, but the type and quantity of prey in many areas used for feeding is unknown. We conducted active acoustic prey mapping in 55 small grid-surveys in two regions off Vancouver Island. We quantitatively compared fish and zooplankton-dominated biomass in known feeding areas with and without foraging humpback whales, and qualitatively described the prey characteristics of the foraged areas. Surveys of the water column suggest that, on average, humpback whale foraging was associated more with increased zooplankton than fish biomass. Prey characteristics varied between the two regions (∼500 km apart), but there was no significant difference in mean backscatter strength in the actively foraged areas between the two regions. Frequency differencing discriminated between the dominant taxa in the water column, but potential epipelagic prey (<10 m) would have been omitted from analysis. However, average depth at the maximum acoustic prey detections was significantly deeper when whales were present (84 m) versus absent (60 m), suggesting predominantly subsurface foraging opportunities suitable to prey mapping.

ANALYSIS OF NOVASAR-1 S-BAND DATA IN DEVELOPING AN ALTERNATIVE LAND COVER MAPPING

Abstract. The Advanced Science and Technology Institute of the Department of Science and Technology (DOST-ASTI), through its Synthetic Aperture Radar and Automatic Identification System (SARwAIS) Project, has gained access to S-Band SAR images acquired by the NovaSAR-1 satellite of UK’s Surrey Satellite Technology Ltd. (SSTL) To help maximize the utility of these images especially in the aspect of terrain-related applications, their viability as potential alternatives to datasets like optical satellite images and other SAR images in characterizing land cover types was evaluated. Statistical analyses on the backscatter values from the tri-polarization ScanSAR datasets using Multivariate Analysis of Variance (MANOVA) and its corresponding post-hoc tests showed that there is a significant difference on the mean backscatter values at 0.05 level of significance. Moreover, Tukey’s honestly significant difference (Tukey’s HSD) test determined which pairs contribute to the significant difference. Using the Random Forest algorithm resulted in an accuracy of 66.93% without further optimization and/or reduction in the number of land covers being classified. Despite the relatively unremarkable accuracy score, it still showed potential for data augmentation with optical satellites for land cover mapping and other terrain-related applications.

Tesserae: Surface differences across Venus’s “continents”

The heavily deformed upland tesserae are some of the most ancient geologic units on Venus and, as such, record the longest history of surface evolution. Our geologic understanding of these landforms is based largely on radar images from the Magellan mission, in which gross morphology and small-scale properties can be difficult to deconvolve. Here we use Magellan radar backscatter data for ridge slope surfaces in 22 highland areas to understand whether the tesserae can be subdivided in ways that differentiate surface property variations. Significant variations occur in the mean backscatter of ridge slopes, and we divide the tesserae into two groups with echoes lower (n = 15) or higher (n = 7) than an average tessera radar scattering behavior. While both few-kilometers-scale slopes and centimeter-scale roughness can modulate the radar returns, at least seven out of 15 tesserae with lower echoes are correlated with fine-grained impact crater ejecta deposits that smooth the surface. We propose that distal ejecta deposition plays a major role in creating the observed range of tessera radar properties and obscuring aspects of their original formation and in situ weathering. Our twofold classification system provides a new way of assessing the physical characteristics of tesserae from the Magellan data. Upcoming missions must consider both their original morphology and post-emplacement processes if we are to unlock the geologic record preserved in tesserae.

Relating side scan sonar backscatter data to geotechnical properties for the investigation of surficial seabed sediments

Side scan sonar is a common tool for seafloor imaging and surveying due its efficiency and high resolution. The backscatter information from side scan sonar enables to identify sediment types and seabed roughness, often used to study sediment dynamics. Theory suggests that side scan sonar backscatter can be correlated to the geotechnical properties of the seabed surface. This could enhance the prediction of erodibility and efficiency of seabed sediment characterization, considering that side scan sonar can offer large spatial coverage in a short time. In this study, high-frequency (1000 kHz) side scan sonar backscatter data, sediment samples, and in-situ seabed strength profiles were collected of the seabed surface at ten locations. Statistical analysis of the backscatter data compared with geotechnical data showed trends between mean backscatter, soil strength, and textural sediment properties. Generally, mean backscatter increased when sediment strength and mean grain size increased and when water content and fines content decreased. However, roughness from bedforms, the presence of oysters, shell hash as well as variations in water content (i.e., porosity) of the seafloor heavily influenced the backscatter and sometimes masked any relationships with the strength properties directly.

Synthetic Aperture Radar Remote Sensing for Crop Classification

This Study proposes the approach for crop classification using the Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes the SAR Images acquired by Sentinel 1A SAR Data and extract textural features using GLCM. In this study, we investigate the potential of Grey Level Co-occurrence Matrix (GLCM)-based texture information for horticulture crop classification with SAR images in Kharif and cloud weather condition. A study on Synthetic Aperture Radar (SAR) satellite imagery was conducted in Chhattisgarh with the objective to evaluate the potential of different texture parameters among crop. The SAR data were pre-processed for textural analysis having entire angle and equal distance quantization. The results were categorized among different parameters showing significant variation for horticulture crops for Contrast, Dissimilarity, Homogeneity, ASM, Energy, Entropy and GLCM Mean. The statistical analysis was done for fruit crop along with major kharif crop of study area. The results shows that mean backscatter value was lowest for banana (99.12 dB) and highest for Mango (198.26 dB) regarding contrast textural property in VH Channel whereas mean backscatter value in VH Channel w.r.t to energy was maximum for banana (0.60 dB) followed by papaya (0.49 dB) and guava (0.45 dB) and least for mango (0.44 dB). The mean backscatter value for GLCM mean textural property in VH channel was shown maximum by banana (51.24 dB) followed by papaya (41.96 dB) and mango (32.98 dB). These results indicate the usefulness of texture information for classification of SAR images, particularly when acquisition of optical images is difficult in Kharif and cloud weather condition for crop classification. Thus GLCM feature of SAR Data proven to be significant for the classification of horticulture crops.

Influence of flow structures and combustion on cross-scale turbulent kinetic energy transfer in premixed swirl flames.

This paper experimentally analyzes the simultaneous influence of combustion and large-scale vortex structures on the transfer of kinetic energy across scales around the laminar flame thickness δL0 in a turbulent premixed swirl flame and a non-reacting swirl flow. High-resolution tomographic particle image velocimetry and formaldehyde planar laser induced fluorescence measurements are used to obtain 3D velocity fields and estimates of the progress variable and density fields. The kinetic energy transfer across a filter scale of Δ=1.5δL0 was then quantified using physical space analysis. Coherent flow structures were identified using the swirling strength and proper orthogonal decomposition was used to identify the dominant periodic flow structure. While non-reacting regions of the flow show mean down-scale energy transfer (forward-scatter), mean back-scatter is observed internal to the flame. Importantly, the back-scatter magnitude in the flame increases in regions undergoing flame/vortex interaction. That is, the mean back-scatter magnitude at locations simultaneously inside the flame and a large-scale coherent vortex is higher than regions in the flame and not in a vortex, with the mean back-scatter magnitude increasing with the swirling strength. This increased back-scatter may be due to locally higher heat release rates in locations of flame/vortex interaction. Overall, the results demonstrate the importance of kinetic energy back-scatter at scales around the flame thickness and the complicated relationship between back-scatter and local flame/flow structure; these results should be considered when modeling turbulence in flames.

Analysis of the Temporal Evolution of Ice Aprons in the Mont-Blanc Massif Using X and C-Band SAR Images

This paper investigates the backscatter evolution and surface changes of ice aprons (IAs) by exploiting time series of X- and C-band SAR images from PAZ and Sentinel-1 satellites. IAs are extremely small ice bodies of irregular shape present on steep slopes and complex topographies in all the major high-Alpine environments of the world. Due to their small size and locations in complex topographies, they have been very poorly studied, and very limited information is known about their evolution and responses to climate change. SAR datasets can provide handy information about the seasonal behaviour of IAs since physical changes of IA surfaces modify the backscattering of RaDAR waves. The analysis of the temporal variations of the backscatter coefficient illustrates the effects of increasing temperatures on the surface of the IAs. All IAs considered in the analysis show a strong decrease in backscatter coefficient values in the summer months. The backscattering patterns are also supported by the annual evolution of the coefficient of variation, which is an appropriate indicator to evaluate the heterogeneity of the surface. Higher mean backscatter values in the X-band than in the C-band indicate surface scattering phenomena dominate the IAs. These features could provide key information for classifying IAs using SAR images in future research.

Decline and recovery of pelagic acoustic backscatter following El Niño events in the Gulf of California, Mexico

Climatic variability exerts enormous pressures on the structure and function of open ocean ecosystems. Although the responses of primary producers and top predators to these pressures are being increasingly well-documented, little is known about how midtrophic communities respond to oceanographic and climatic variability. We address this knowledge gap through a study of the effects of El Niño Southern Oscillation (ENSO) and local environmental conditions on acoustic proxies of the midtrophic community in the Gulf of California, Mexico. We quantified the intensity and distribution of nighttime acoustic backscatter (120 kHz) in the upper 200 m of the water column during 10 oceanographic cruises (2007–2017) and described its response to environmental variability using generalized additive models. ENSO conditions were the strongest drivers of variability in backscatter after accounting for seasonal increases in backscatter with sea surface temperature and chlorophyll-a concentration. Acoustic backscatter in the central Gulf of California decreased significantly during the positive phase of ENSO. Following El Niño events in 2009–10 and 2015–16, mean backscatter declined by an order of magnitude and remained depressed for more than two years before recovering to pre-El Niño levels. Scattering layer density increased with total backscatter, likely an influential factor determining prey availability for pelagic predators. Our findings demonstrate large and sustained impacts of El Niño on the midtrophic community in the Gulf of California and further highlight the need to better understand the responses of midtrophic communities to environmental variability.

Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer

Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form of firn aquifers (FAs) in the interior of the GrIS. Monitoring the changes in FAs over the GrIS is of great importance to evaluate the stability and mass balance of the ice sheet. This is challenging because FAs are not visible on the surface and the direct measurements are lacking. A new method is proposed to map FAs during the 2010–2020 period by using the C-band Advanced Scatterometer (ASCAT) data based on the Random Forests classification algorithm with the aid of measurements from the NASA Operation IceBridge (OIB) program. Melt days (MD), melt intensity (MI), and winter mean backscatter (WM) parameters derived from the ASCAT data are used as the input vectors for the Random Forests classification algorithm. The accuracy of the classification model is assessed by ten-fold cross-validation, and the overall accuracy and Kappa coefficient are 97.49% and 0.72 respectively. The results show that FAs reached the maximum in 2015, and the accumulative area of FAs from 2010 to 2020 is 56,477 km2, which is 3.3% of the GrIS area. This study provides a way to investigate the long-term dynamics in FAs which have great significance for understanding the state of subsurface firn and subglacial hydrological systems.

Addressing critical influences on L-band radar backscatter for improved estimates of basal area and change

L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two “observational epochs”: ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha−1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW.

Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

ABSTRACT Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective in the rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV- and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, and the difference between maximum and minimum values – MMD) were computed from 30 Sentinel-1 images, from 2019, used as input parameters of the NN classifier. The samples were manually selected, including forested and deforested areas. After deforestation, mean backscatter signals decreased on the average of 2 dB for VV and 2.3 dB for VH from May to September–October. A Multi-Layer Perceptron (MLP) network was used for detecting near-real time forest disturbances larger than 2 ha. Case studies were performed for both polarizations considered the following input sets to the MLP: mean; mean and standard deviation; mean and MMD; and mean, standard deviation, and MMD. For the 2019 dataset, the latter showed the best performance of the NN algorithm with accuracy and F1 score of 99%. Automatic extraction using 2018 Sentinel-1 images reached accuracy and F1 score of 89% with the MapBiomas reference data and accuracy of 81% and F1 score of 79% with the PRODES reference data.

Parametric Modeling of Sea Clutter Doppler Spectra

For radar modeling and simulation of sea clutter, there are a number of key characteristics used to describe the observed features. These include the mean backscatter power, amplitude statistics, spatial and long-time temporal correlation, and the Doppler spectrum. Regarding the latter, a common modeling approach is to use the mean Doppler characteristics (width and center point) to relate to the sea conditions. However, this does not capture the time- and range-varying fluctuations of the Doppler spectra, which are important for assessing the behavior of coherent detection schemes. The evolving Doppler spectrum model offers a way to model these variations and can be described with a moderate number of parameters. Relating these to the observed geometry, environment and radar characteristics are important to make the models accessible. Previous work has characterized X-band radar sea clutter over a range of sea conditions and azimuth and grazing angles. In this article, new models are presented that capture variations with both the radar resolved area and integration time. This enables the evolved Doppler spectra model to be used over a much wider range of applications.

Evaluation of corneal backscatter using Scheimpflug imaging in Fuchs' endothelial corneal dystrophy

AbstractPurposeTo compare corneal densitometry obtained using Scheimpflug imaging in the eyes of patients with Fuchs' Endothelial Corneal Dystrophy (FECD) versus the eyes of controls without corneal pathology.MethodsAn observational cross‐sectional case‐control study was carried out on 26 eyes of 14 patients with FECD, and 20 eyes of 11 controls in which corneal pathology had previously been ruled out. All patients underwent a Scheimpflug corneal tomography with Pentacam HR. An analysis of the corneal densitometry results is performed by corneal layers divided into anterior (120 µm), central and posterior (60 µm) and in different areas of concentric circumferences: 0–2 mm; 2–6 mm; 6–10 mm; 10–12 mm.ResultsIn the FECD group, the mean backscatter of light was higher than the control group in the circumferences of 0–2 and 2‐6 mm in the anterior, central and posterior layers (p < 0.05). No statistically significant differences were found in the 6–10 and 10–12 mm circumferences. In the posterior layer, the total backscatter average was higher in the FECD group (p < 0.05).ConclusionsPatients with FECD present higher corneal densitometry values than subjects without corneal pathology in the posterior layer of the cornea and in the central circumferences of the anterior, central and posterior layers.

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest areas can lead to false detections. We combined a GEDI Forest Height product and Sentinel-1 radar data to improve FNF masks in wetland areas in Gabon using a Random Forest model. The GEDI Forest Height, together with texture metrics derived from Sentinel-1 mean backscatter values, were the most important contributors to the classification. Quantitatively, our mask outperformed existing global FNF masks by increasing the Producer’s Accuracy for the non-forest class by 14%. The GEDI Forest Height product by itself also showed high accuracies but contained Landsat artifacts. Qualitatively, our model was best able to cleanly uncover non-forest areas and mitigate the impact of Landsat artifacts in the GEDI Forest Height product. An advantage of the methodology presented here is that it can be adapted for different application needs by varying the probability threshold of the Random Forest output. This study stresses that, in any application of the suggested methodology, it is important to consider the UA/PA trade-off and the effect it has on the classification. The targeted improvements for wetland forest mapping presented in this paper can help raise the accuracy of tropical deforestation monitoring.

Proposed Methodology to Quantify the Amount of Methane Seepage by Understanding the Correlation Between Methane Plumes and Originating Seeps

In recent years, discoveries of methane plumes (also called methane flares) have been reported in various sea areas around the world. Clusters of naturally seeping methane bubbles rising from the seafloor are visualized as methane plumes on the echograms of quantitative echo sounders and multibeam sonars. In order to determine if seeping methane can be used as energy resources and its environmental impact, it is necessary to estimate the amount of naturally seeping methane. From April, 2020, a 3-year project is being conducted in Japan to evaluate the amount of methane seepage from methane plumes. The authors propose the following steps to quantify the amount of methane seepage accurately. First of all, methane plumes in the Exclusive Economic Zone (EEZ) of Japan are mapped out using acoustic devices such as quantitative echo sounders and multibeam sonars. Secondly, methane bubbles of a few millimeters in diameter from methane seeps at seafloor are collected and sampled using a cone-shaped collector with 20 cm in diameter, operated by Remotely Operated Vehicle (ROV). If we can identify the number of seep mouths that form into one single plume, we will be able to quantify the methane seepage from one plume. Based on this result, calibration of the mean backscattering strength and the amount of seeping methane from methane plumes becomes possible and will be applied to the mapped plumes in order to estimate the methane seepage in the EEZ of Japan. Once this calibration is established, it can be applied to the methane plumes observed worldwide, and methane seepage can be quantified simply by acoustic observations of methane plumes. In this study, a method to verify the correlation between methane plumes and methane seeps is introduced, as well as a method to locate methane seeps effectively using the Target Position function of a quantitative echo sounder. The authors intend to use this as the basic data for establishing a method to estimate the amount of methane released from a methane plume by observing the methane plume acoustically.

Meteor radar observations of polar mesospheric summer echoes over Svalbard

Abstract. A 31 MHz meteor radar located in Svalbard was used to observe polar mesospheric echoes (PMSEs) during summer 2020. Data from 19 July were selected for detailed analysis, with a focus on extracting additional information to characterize the atmosphere in the PMSE region. The use of an all-sky meteor radar adds an additional use to data collected for meteor observations and enables the detection of PMSE layers across a wide field of view. Comparison with data from a 53.5 MHz narrow-beam mesosphere–stratosphere–troposphere (MST) radar shows good agreement in the morphology of the layer as detected between the two systems. Doppler spectra of PMSE layers reveal fine structure, including regions of enhanced return that move across the radar's field of view. Examination of the relationship between range and Doppler shift of off-zenith portions of the layer enables the estimation of wind speeds with high temporal resolution during PMSE conditions. Trials demonstrate good agreement between wind speeds obtained from PMSE Doppler spectra and those calculated from specular meteor trail radial velocities. Combined with the antenna polar diagram of the radar, this same relationship was used to infer the aspect sensitivity of observed PMSE backscatter, yielding a mean backscatter angular width of 6.8±3.3∘. A comparison of underdense meteor radar echo decay times during and outside of PMSE conditions did not demonstrate a strong correlation between the presence of PMSEs and shortened underdense meteor radar echo durations.

Examining the Links between Multi-Frequency Multibeam Backscatter Data and Sediment Grain Size

Acoustic methods are routinely used to provide broad scale information on the geographical distribution of benthic marine habitats and sedimentary environments. Although single-frequency multibeam echosounder surveys have dominated seabed characterisation for decades, multifrequency approaches are now gaining favour in order to capture different frequency responses from the same seabed type. The aim of this study is to develop a robust modelling framework for testing the potential application and value of multifrequency (30, 95, and 300 kHz) multibeam backscatter responses to characterize sediments’ grain size in an area with strong geomorphological gradients and benthic ecological variability. We fit a generalized linear model on a multibeam backscatter and its derivatives to examine the explanatory power of single-frequency and multifrequency models with respect to the mean sediment grain size obtained from the grab samples. A strong and statistically significant (p < 0.05) correlation between the mean backscatter and the absolute values of the mean sediment grain size for the data was noted. The root mean squared error (RMSE) values identified the 30 kHz model as the best performing model responsible for explaining the most variation (84.3%) of the mean grain size at a statistically significant output (p < 0.05) with an adjusted r2 = 0.82. Overall, the single low-frequency sources showed a marginal gain on the multifrequency model, with the 30 kHz model driving the significance of this multifrequency model, and the inclusion of the higher frequencies diminished the level of agreement. We recommend further detailed and sufficient ground-truth data to better predict sediment properties and to discriminate benthic habitats to enhance the reliability of multifrequency backscatter data for the monitoring and management of marine protected areas.

Estimation of Surface Canopy Water in Pacific Northwest Forests by Fusing Radar, Lidar, and Meteorological Data

Surface Canopy Water (SCW) is the intercepted rain water that resides within the tree canopy and plays a significant role in the hydrological cycle. Challenges arise in measuring SCW in remote areas using traditional ground-based techniques. Remote sensing in the radio spectrum has the potential to overcome the challenges where traditional modelling approaches face difficulties. In this study, we aim at estimating the SCW by fusing information extracted from the radar imagery acquired with the Sentinel-1 constellation, aerial laser scanning, and meteorological data. To describe the change of radar backscatter with moisture, we focused on six forest stands in the H.J. Andrews experimental forest in central Oregon, as well as four clear cut areas and one golf course, over the summers of 2015–2017. We found significant relationships when we executed the analysis on radar images in which individual tree crowns were delineated from lidar, as opposed to SCW estimated from individual pixel backscatter. Significant differences occur in the mean backscatter between radar images taken during rain vs. dry periods (no rain for >1 h), but these effects only last for roughly 30 min after the end of a rain event. We developed a predictive model for SCW using the radar images acquired at dawn, and proved the capability of space-based radar systems to provide information for estimation of the canopy moisture under conditions of fresh rainfall during the dry season.

A statistical approach for analyzing and modeling multibeam echosounder backscatter, including the influence of high-amplitude scatterers.

The statistical analysis of acoustic backscatter samples recorded by multibeam echosounders can be a valuable tool for remote seafloor characterization and interpretation. The present paper aims at analyzing the statistics of backscatter data values, both in "raw" status and after various averaging operations, using field data. It is shown that the statistics of the data can be adequately described by a Weibull distribution parametrized by the incidence angle and the level of applied processing: the distribution of the averaged backscatter amplitude, processed according to various schemes, varies from a Rayleigh law for raw data to lognormal and finally to Gaussian distribution after successive averaging operations. Based on these results, some recommendations for the calculation of the mean backscatter strength are presented. Finally, the influence of high-amplitude scatterers in the backscatter probability density function is addressed; a scheme is suggested to separate the contributions of the substrate from the contributions of the scatterers on the statistical distribution of sonar data samples.