scholarly journals Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 422 ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann ◽  
Christopher S. Lapszynski ◽  
Anna Christina Tyler ◽  
Sarah Goldsmith
Keyword(s):  
Moisture Content ◽  
Bulk Density ◽  
Filling Factor ◽  
Imaging System ◽  
Hyperspectral Imagery ◽  
Ground Truth ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ground Truth Data ◽  
The Common

This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne.

scholarly journals Retrieval of Salt Marsh Above-Ground Biomass from High-Spatial Resolution Hyperspectral Imagery Using PROSAIL

2019 ◽  
Vol 11 (11) ◽  
pp. 1385 ◽  
Author(s):  
Rehman S. Eon ◽  
Sarah Goldsmith ◽  
Charles M. Bachmann ◽  
Anna Christina Tyler ◽  
Christopher S. Lapszynski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Salt Marsh ◽  
Imaging System ◽  
Hyperspectral Imagery ◽  
Spatial Scales ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Above Ground Biomass ◽  
Ground Truth Data ◽  
Ground Biomass

Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, VA, where the morphology and biomass of the dominant plant species, Spartina alterniflora, varies widely. The high-resolution hyperspectral imagery allowed the detailed delineation of variations in above-ground biomass, which we retrieved from the imagery using the PROSAIL radiative transfer model. The retrieved biomass estimates correlated well with contemporaneously collected in situ biomass ground truth data ( R 2 = 0.73 ). In this study, we also rescaled our hyperspectral imagery and retrieved PROSAIL salt marsh biomass to determine the applicability of the method across spatial scales. Histograms of retrieved biomass changed considerably in characteristic marsh regions as the spatial scale of the imagery was progressively degraded. This rescaling revealed a loss of spatial detail and a shift in the mean retrieved biomass. This shift is indicative of the loss of accuracy that may occur when scaling up through a simple averaging approach that does not account for the detail found in the landscape at the natural scale of variation of the salt marsh system. This illustrated the importance of developing methodologies to appropriately scale results from very fine scale resolution up to the more coarse-scale resolutions commonly obtained in airborne and satellite remote sensing.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

scholarly journals Airborne Hyperspectral Imaging for Submerged Archaeological Mapping in Shallow Water Environments

2019 ◽  
Vol 11 (19) ◽  
pp. 2237 ◽  
Author(s):  
Alexandre Guyot ◽  
Marc Lennon ◽  
Nicolas Thomas ◽  
Simon Gueguen ◽  
Tristan Petit ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Water Column ◽  
Sea Level ◽  
Hyperspectral Imagery ◽  
Airborne Lidar ◽  
Radiative Transfer Model ◽  
Invasive Technique ◽  
Transfer Model ◽  
Water Bottom

Nearshore areas around the world contain a wide variety of archeological structures, including prehistoric remains submerged by sea level rise during the Holocene glacial retreat. While natural processes, such as erosion, rising sea level, and exceptional climatic events have always threatened the integrity of this submerged cultural heritage, the importance of protecting them is becoming increasingly critical with the expanding effects of global climate change and human activities. Aerial archaeology, as a non-invasive technique, contributes greatly to documentation of archaeological remains. In an underwater context, the difficulty of crossing the water column to reach the bottom and its potential archaeological information usually requires active remote-sensing technologies such as airborne LiDAR bathymetry or ship-borne acoustic soundings. More recently, airborne hyperspectral passive sensors have shown potential for accessing water-bottom information in shallow water environments. While hyperspectral imagery has been assessed in terrestrial continental archaeological contexts, this study brings new perspectives for documenting submerged archaeological structures using airborne hyperspectral remote sensing. Airborne hyperspectral data were recorded in the Visible Near Infra-Red (VNIR) spectral range (400–1000 nm) over the submerged megalithic site of Er Lannic (Morbihan, France). The method used to process these data included (i) visualization of submerged anomalous features using a minimum noise fraction transform, (ii) automatic detection of these features using Isolation Forest and the Reed–Xiaoli detector and (iii) morphological and spectral analysis of archaeological structures from water-depth and water-bottom reflectance derived from the inversion of a radiative transfer model of the water column. The results, compared to archaeological reference data collected from in-situ archaeological surveys, showed for the first time the potential of airborne hyperspectral imagery for archaeological mapping in complex shallow water environments.

scholarly journals Retrieval of subvisual cirrus cloud optical thickness from limb-scatter measurements

2013 ◽  
Vol 6 (1) ◽  
pp. 105-119 ◽  
Author(s):  
J. T. Wiensz ◽  
D. A. Degenstein ◽  
N. D. Lloyd ◽  
A. E. Bourassa
Keyword(s):  
Particle Size ◽  
Optical Thickness ◽  
Imaging System ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Reconstruction Technique ◽  
Cirrus Cloud ◽  
Multiplicative Algebraic Reconstruction Technique ◽  
Cloud Particle

Abstract. We present a technique for estimating the optical thickness of subvisual cirrus clouds detected by OSIRIS (Optical Spectrograph and Infrared Imaging System), a limb-viewing satellite instrument that measures scattered radiances from the UV to the near-IR. The measurement set is composed of a ratio of limb radiance profiles at two wavelengths that indicates the presence of cloud-scattering regions. Cross-sections and phase functions from an in situ database are used to simulate scattering by cloud-particles. With appropriate configurations discussed in this paper, the SASKTRAN successive-orders of scatter radiative transfer model is able to simulate accurately the in-cloud radiances from OSIRIS. Configured in this way, the model is used with a multiplicative algebraic reconstruction technique (MART) to retrieve the cloud extinction profile for an assumed effective cloud particle size. The sensitivity of these retrievals to key auxiliary model parameters is shown, and it is shown that the retrieved extinction profile, for an assumed effective cloud particle size, models well the measured in-cloud radiances from OSIRIS. The greatest sensitivity of the retrieved optical thickness is to the effective cloud particle size. Since OSIRIS has an 11-yr record of subvisual cirrus cloud detections, the work described in this manuscript provides a very useful method for providing a long-term global record of the properties of these clouds.

scholarly journals A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

2019 ◽  
Vol 1 (3) ◽  
pp. 904-927 ◽  
Author(s):  
Usman A. Zahidi ◽  
Ayan Chatterjee ◽  
Peter W. T. Yuen
Keyword(s):  
Radiative Transfer ◽  
Hyperspectral Image ◽  
Mathematical Formulation ◽  
False Negative ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Regression Learning

The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under specialized constraint by means of in-scene white and black calibration panels. The reflectance of material is invariant to illumination. Exploiting this property, we articulated a mathematical formulation based on the RT model to create cost functions relating variably illuminated regions within a scene. In this paper, we propose multi-layered regression learning-based recovery of radiance components, i.e., total ground-reflected radiance and path radiance from reflectance and radiance images of the scene. These decomposed components represent terms in the RT equation and enable us to relate variable illumination. Therefore, we assume that Hyperspectral Image (HSI) radiance of the scene is provided and AC can be processed on it, preferably with QUick Atmospheric Correction (QUAC) algorithm. QUAC is preferred because it does not account for surface models. The output from the proposed algorithm is an intermediate map of the scene on which our mathematically derived binary and multi-label threshold is applied to classify shadowed and non-shadowed regions. Results from a satellite and airborne NADIR imagery are shown in this paper. Ground truth (GT) is generated by ray-tracing on a LIDAR-based surface model in the form of contour data, of the scene. Comparison of our results with GT implies that our algorithm’s binary classification shadow maps outperform other existing shadow detection algorithms in true positive, which is the detection of shadows when it is in ground truth. It also has the lowest false negative i.e., detecting non-shadowed region as shadowed, compared to existing algorithms.

scholarly journals Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

2021 ◽  
Vol 13 (9) ◽  
pp. 1697
Author(s):  
Alexander Jenal ◽  
Hubert Hüging ◽  
Hella Ellen Ahrends ◽  
Andreas Bolten ◽  
Jens Bongartz ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Near Infrared ◽  
Imaging System ◽  
N Uptake ◽  
Ground Truth ◽  
Short Wave ◽  
Camera System ◽  
Ground Truth Data ◽  
Single Observation ◽  
Wheat Field

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.

scholarly journals Mapping the Extent of Invasive Phragmites australis subsp. australis From Airborne Hyperspectral Imagery

2021 ◽  
Vol 9 ◽  
Author(s):  
Kathryn Elmer ◽  
Margaret Kalacska ◽  
J. Pablo Arroyo-Mora
Keyword(s):  
Remote Sensing ◽  
Invasive Species ◽  
Early Detection ◽  
Target Detection ◽  
Phragmites Australis ◽  
Hyperspectral Imagery ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Visual Interpretation ◽  
Ground Truth Data

Invasive species pose one of the greatest threats to global biodiversity. Early detection of invasive species is critical in order to prevent or manage their spread before they exceed the ability of land management groups to control them. Optical remote sensing has been established as a useful technology for the early detection and mapping of invasive vegetation populations. Through the use of airborne hyperspectral imagery (HSI), this study establishes a target detection methodology used to identify and map the invasive reed Phragmites australis subsp. australis within the entire extent of Îles-de-Boucherville National Park (Quebec, ON, Canada). We applied the Spectral Angle Mapper (SAM) target detection algorithm trained with a high accuracy GNSS ground truth data set to produce a park-wide map illustrating the extent of detected Phragmites. The total coverage of detected Phragmites was 26.74 ha (0.267 km2), which represents 3.28% of the total park area of 814 ha (8.14 km2). The inherent spatial uncertainty of the airborne HSI (∼2.25 m) was accounted for with uncertainty buffers, which, when included in the measurement of detected Phragmites, lead to a total area of 59.17 ha (0.591 km2), or 7.26% of the park. The overall accuracy of the Phragmites map was 84.28%, with a sensitivity of 76.32% and a specificity of 91.57%. Additionally, visual interpretation of the validation ground truth dataset was performed by 10 individuals, in order to compare their performance to that of the target detection algorithm. The overall accuracy of the visual interpretation was lower than the target detection (i.e., 69.18%, with a sensitivity of 59.21% and a specificity of 78.31%). Overall, this study is one of the first to utilize airborne HSI and target detection to map the extent of Phragmites over a moderately large extent. The uses and limitations of such an approach are established, and the methodology described here in detail could be adapted for future remote sensing studies of Phragmites or other vegetation species, native or invasive, at study sites around the world.

The impact of the OSIRIS grating efficiency on radiance and trace-gas retrievals

2002 ◽  
Vol 80 (4) ◽  
pp. 469-481 ◽  
Author(s):  
C A McLinden ◽  
J C McConnell ◽  
K Strong ◽  
I C McDade ◽  
R L Gattinger ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Diffraction Grating ◽  
Infrared Imaging ◽  
Imaging System ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Optical Absorption Spectroscopy ◽  
Transfer Model ◽  
Efficiency Coefficient ◽  
The Impact

The optical spectrograph and infrared imaging system (OSIRIS), launched in 2001, is a UV–visible diffraction-grating instrument designed to measure light scattered from the Earth's limb. Laboratory measurements of the OSIRIS diffraction-grating efficiency reveal a sensitivity to polarization including an anomalous structure of width 20–30 nm introduced into light polarized in a direction perpendicular to the grooves of the grating. A vector radiative-transfer model was used to generate synthetic OSIRIS spectra in an effort to examine the effect of this on radiances and trace-gas retrievals. Radiances that included grating effects were found to deviate by nearly 10% from those that did not and also contained the anomalous structure. Performing differential optical absorption spectroscopy (DOAS) on these spectra revealed errors in ozone apparent column densities of up to 80 DU. The size of the error was controlled mainly by the difference in polarization between the two DOAS spectra. Two possible correction methods were investigated. The first was to remove the grating effects by applying a correction factor to the raw radiances calculated using the vector radiative-transfer model. The second was to include the efficiency coefficient spectra in the DOAS fit. PACS Nos.: 42.68Mj, 98.55Qf

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