scholarly journals Optics and remote sensing of Bahamian carbonate sediment whitings and potential relationship to wind-driven Langmuir circulation

2008 ◽  
Vol 5 (6) ◽  
pp. 4777-4811 ◽  
Author(s):  
H. M. Dierssen ◽  
R. C. Zimmerman ◽  
D. J. Burdige
Keyword(s):  
Remote Sensing ◽  
Water Column ◽  
Natural Waters ◽  
Spatial Scales ◽  
Light Attenuation ◽  
Sediment Resuspension ◽  
Visible Spectrum ◽  
Index Of Refraction ◽  
Langmuir Circulation ◽  
Langmuir Cells

Abstract. Regions of milky white seas or "whitings" periodically occur to the west of Andros Island along the Great Bahama Bank where the bottom sediment consists of fine-grained aragonite mud. We present comprehensive measurements of inherent optical properties within a whiting patch and discuss the potential for monitoring the frequency, extent, and quantity of suspended matter from ocean colour satellite imagery. Sea spectral reflectance measured in situ and remotely from space revealed highly reflective waters elevated across the visible spectrum (i.e., "whitened") with a peak at 490 nm. Particulate backscattering was an order of magnitude higher than that measured at other stations throughout the region. The whiting also had one of the highest backscattering ratios measured in natural waters (0.05–0.06) consistent with water dominated by aragonite particles with a high index of refraction. Regular periodicity of 40 and 212 s evident in the light attenuation coefficient over the sampling period indicated patches of fluctuating turbidity on spatial scales that could be produced from regular rows of Langmuir cells penetrating the 5-m water column. We suggest that previously described mechanisms for sediment resuspension in whitings, such as tidal bursting and fish activity, are not fully consistent with these data and propose that wind-driven Langmuir cells reaching the full-depth of the water column may represent a plausible mechanism for sediment resuspension and subsequent whiting formation. Optics and remote sensing provide important tools for quantifying the linkages between physical and biogeochemical processes in these dynamic shallow water ecosystems.

scholarly journals Optics and remote sensing of Bahamian carbonate sediment whitings and potential relationship to wind-driven Langmuir circulation

2009 ◽  
Vol 6 (3) ◽  
pp. 487-500 ◽  
Author(s):  
H. M. Dierssen ◽  
R. C. Zimmerman ◽  
D. J. Burdige
Keyword(s):  
Remote Sensing ◽  
Water Column ◽  
Natural Waters ◽  
Spatial Scales ◽  
Light Attenuation ◽  
Sediment Resuspension ◽  
Visible Spectrum ◽  
Index Of Refraction ◽  
Langmuir Circulation ◽  
Langmuir Cells

Abstract. Regions of milky white seas or "whitings" periodically occur to the west of Andros Island along the Great Bahama Bank where the bottom sediment consists of fine-grained aragonite mud. We present measurements of inherent optical properties within a sediment whiting patch and discuss the potential for monitoring the frequency, extent, and quantity of suspended matter from ocean colour satellite imagery. Sea spectral reflectance measured in situ and remotely from space revealed highly reflective waters elevated across the visible spectrum (i.e., "whitened") with a peak at 490 nm. Particulate backscattering was an order of magnitude higher than that measured at other stations throughout the region. The whiting also had one of the highest backscattering ratios measured in natural waters (0.05–0.06) consistent with water dominated by aragonite particles with a high index of refraction. Regular periodicity of 40 and 212 s evident in the light attenuation coefficient over the sampling period indicated patches of fluctuating turbidity on spatial scales that could be produced from regular rows of Langmuir cells penetrating the 5-m water column. We suggest that previously described mechanisms for sediment resuspension in whitings, such as tidal bursting and fish activity, are not fully consistent with these data and propose that wind-driven Langmuir cells reaching the full-depth of the water column may represent a plausible mechanism for sediment resuspension and subsequent whiting formation. Optics and remote sensing provide important tools for quantifying the linkages between physical and biogeochemical processes in these dynamic shallow water ecosystems.

scholarly journals Seabed Resuspension in the Chesapeake Bay: Implications for Biogeochemical Cycling and Hypoxia

2020 ◽  
Vol 44 (1) ◽  
pp. 103-122
Author(s):  
Julia M. Moriarty ◽  
Marjorie A. M. Friedrichs ◽  
Courtney K. Harris
Keyword(s):  
Organic Matter ◽  
Chesapeake Bay ◽  
Water Column ◽  
Primary Productivity ◽  
Environmental Changes ◽  
Light Attenuation ◽  
Sediment Resuspension ◽  
Nitrogen Dynamics ◽  
Biogeochemical Model ◽  
Order Of Magnitude

AbstractSediment processes, including resuspension and transport, affect water quality in estuaries by altering light attenuation, primary productivity, and organic matter remineralization, which then influence oxygen and nitrogen dynamics. The relative importance of these processes on oxygen and nitrogen dynamics varies in space and time due to multiple factors and is difficult to measure, however, motivating a modeling approach to quantify how sediment resuspension and transport affect estuarine biogeochemistry. Results from a coupled hydrodynamic–sediment transport–biogeochemical model of the Chesapeake Bay for the summers of 2002 and 2003 showed that resuspension increased light attenuation, especially in the northernmost portion of the Bay, shifting primary production downstream. Resuspension also increased remineralization in the central Bay, which experienced larger organic matter concentrations due to the downstream shift in primary productivity and estuarine circulation. As a result, oxygen decreased and ammonium increased throughout the Bay in the bottom portion of the water column, due to reduced photosynthesis in the northernmost portion of the Bay and increased remineralization in the central Bay. Averaged over the channel, resuspension decreased oxygen by ~ 25% and increased ammonium by ~ 50% for the bottom water column. Changes due to resuspension were of the same order of magnitude as, and generally exceeded, short-term variations within individual summers, as well as interannual variability between 2002 and 2003, which were wet and dry years, respectively. Our results quantify the degree to which sediment resuspension and transport affect biogeochemistry, and provide insight into how coastal systems may respond to management efforts and environmental changes.

scholarly journals Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

2019 ◽  
Vol 11 (14) ◽  
pp. 1664 ◽  
Author(s):  
Heidi M. Dierssen ◽  
Kelley J. Bostrom ◽  
Adam Chlus ◽  
Kamille Hammerstrom ◽  
David R. Thompson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Column ◽  
Hyperspectral Imagery ◽  
Sediment Resuspension ◽  
Google Earth ◽  
Imaging Spectrometer ◽  
Remote Imaging ◽  
Elkhorn Slough ◽  
Tidal Embayment ◽  
Eelgrass Beds

Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals Imaging Spectroscopy for Conservation Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 292
Author(s):  
Megan Seeley ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Case Studies ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Decision Makers ◽  
High Spectral Resolution ◽  
Conservation Areas ◽  
Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

scholarly journals Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

2014 ◽  
Vol 369 (1643) ◽  
pp. 20130194 ◽  
Author(s):  
Michael D. Madritch ◽  
Clayton C. Kingdon ◽  
Aditya Singh ◽  
Karen E. Mock ◽  
Richard L. Lindroth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Populus Tremuloides ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Fine Scale ◽  
Below Ground ◽  
Landscape Scales ◽  
Spectrometer Data

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

scholarly journals Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011

2014 ◽  
Vol 7 (9) ◽  
pp. 3095-3112 ◽  
Author(s):  
P. Sawamura ◽  
D. Müller ◽  
R. M. Hoff ◽  
C. A. Hostetler ◽  
R. A. Ferrare ◽  
...  
Keyword(s):  
Remote Sensing ◽  
In Situ Measurements ◽  
Index Of Refraction ◽  
High Spectral Resolution ◽  
Lidar Data ◽  
Data Set ◽  
Microphysical Properties ◽  
Lidar Measurements ◽  
Multiwavelength Lidar

Abstract. Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore–Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.

scholarly journals Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

2018 ◽  
Vol 10 (10) ◽  
pp. 1601 ◽  
Author(s):  
Carl Talsma ◽  
Stephen Good ◽  
Diego Miralles ◽  
Joshua Fisher ◽  
Brecht Martens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Laboratory Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Input Variables ◽  
Added Uncertainty ◽  
Model Components ◽  
The Individual

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

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