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 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 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 Assessing Salt Marsh Vulnerability Using High-Resolution Hyperspectral Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 2938
Author(s):  
Sarah B. Goldsmith ◽  
Rehman S. Eon ◽  
Christopher S. Lapszynski ◽  
Gregory P. Badura ◽  
David T. Osgood ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Salt Marsh ◽  
Salt Marshes ◽  
Multiple Stressors ◽  
Hyperspectral Imagery ◽  
Spatial Scales ◽  
Spectral Response ◽  
Accurate Determination ◽  
Foliar Nitrogen ◽  
Coastal Salt Marshes

Change in the coastal zone is accelerating with external forcing by sea-level rise, nutrient loading, drought, and over-harvest, leading to significant stress on the foundation plant species of coastal salt marshes. The rapid evolution of marsh state induced by these drivers makes the ability to detect stressors prior to marsh loss important. However, field work in coastal salt marshes can be challenging due to limited access and their fragile nature. Thus, remote sensing approaches hold promise for rapid and accurate determination of marsh state across multiple spatial scales. In this study, we evaluated the use of remote sensing tools to detect three dominant stressors on Spartina alterniflora. We took advantage of a barrier island salt marsh chronosequence in Virginia, USA, where marshes of different ages and level of stressor exist side by side. We collected hyperspectral imagery of plants along with salinity, sediment redox potential, and foliar nitrogen content in the field. We also conducted a greenhouse study where we manipulated environmental conditions. We found that models developed for stressors based on plant spectral response correlated well with salinity and foliar nitrogen within the greenhouse and field data, but were not transferable from lab to field, likely due to the limited range of conditions explored within the greenhouse experiments and the coincidence of multiple stressors in the field. This study is an important step towards the development of a remote sensing tool for tracking of ecosystem development, marsh health, and future ecosystem services.

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 Error simulations of uncorrected NDVI and DCVI during remote sensing measurements from UAS

2014 ◽  
Vol 18 (2) ◽  
pp. 35-45 ◽  
Author(s):  
Michał T. Chiliński ◽  
Marek Ostrowski
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Vegetation Index ◽  
Vegetation Mapping ◽  
Radiative Transfer Model ◽  
Unmanned Aerial Systems ◽  
Transfer Model ◽  
Digital Cameras ◽  
Aerial Systems

Abstract Remote sensing from unmanned aerial systems (UAS) has been gaining popularity in the last few years. In the field of vegetation mapping, digital cameras converted to calculate vegetation index (DCVI) are one of the most popular sensors. This paper presents simulations using a radiative transfer model (libRadtran) of DCVI and NDVI results in an environment of possible UAS flight scenarios. The analysis of the results is focused on the comparison of atmosphere influence on both indices. The results revealed uncertainties in uncorrected DCVI measurements up to 25% at the altitude of 5 km, 5% at 1 km and around 1% at 0.15 km, which suggests that DCVI can be widely used on small UAS operating below 0.2 km.

scholarly journals Effect of OH emission on the temperature and wind measurements derived from limb-viewing observations of the 1.27 µm O<sub>2</sub> dayglow

2020 ◽  
Vol 13 (4) ◽  
pp. 1817-1824
Author(s):  
Kuijun Wu ◽  
Weiwei He ◽  
Yutao Feng ◽  
Yuanhui Xiong ◽  
Faquan Li
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spectral Region ◽  
Emission Lines ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Altitude Range ◽  
Band Emission ◽  
Near Space ◽  
Wind Measurements

Abstract. The O2(a1Δg) emission near 1.27 µm is well-suited for remote sensing of global wind and temperature in near-space by limb-viewing observations to its bright signal and extended altitude coverage. However, vibrational–rotational emission lines of the OH dayglow produced by the hydrogen–ozone reaction (H+O3→OH•+O2) overlap the infrared atmospheric band emission (a1Δg→X3Σg) of O2. The main goal of this paper is to discuss the effect of OH emission on the wind and temperature measurements derived from the 1.27 µm O2 dayglow limb-viewing observations. The O2 dayglow and OH dayglow spectrum over the spectral region and altitude range of interest is calculated by using the line-by-line radiative transfer model and the most recent photochemical model. The method of four-point sampling of the interferogram and sample results of measurement simulations are provided for both O2 dayglow and OH dayglow. It is apparent from the simulations that the presence of OH dayglow as an interfering species decreases the wind and temperature accuracy at all altitudes, but this effect can be reduced considerably by improving OH dayglow knowledge.

scholarly journals An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment

2020 ◽  
Vol 12 (17) ◽  
pp. 2752
Author(s):  
Christopher O. Ilori ◽  
Anders Knudby
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Complete Solution ◽  
Atmospheric Correction ◽  
Florida Keys ◽  
Radiative Transfer Model ◽  
Inversion Method ◽  
Transfer Model ◽  
Landsat 8 ◽  
Depth Range

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.

scholarly journals VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

2020 ◽  
Vol 12 (14) ◽  
pp. 2254 ◽  
Author(s):  
Hafiz Ali Imran ◽  
Damiano Gianelle ◽  
Duccio Rocchini ◽  
Michele Dalponte ◽  
M. Pilar Martín ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sharp Change ◽  
Full Potential ◽  
Area Index ◽  
Red Edge ◽  
Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

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.

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