Victor Klemas

Remote Sensing of Wetlands: Case Studies Comparing Practical Techniques

Abstract To plan for wetland protection and sensible coastal development, scientists and managers need to monitor the changes in coastal wetlands as the sea level continues to rise and the coastal population keeps expanding. Advances in sensor design and data analysis techniques are making remote sensing systems practical and attractive for monitoring natural and man-induced wetland changes. The objective of this paper is to review and compare wetland remote sensing techniques that are cost-effective and practical and to illustrate their use through two case studies. The results of the case studies show that analysis of satellite and aircraft imagery, combined with on-the-ground observations, allows researchers to effectively determine long-term trends and short-term changes of wetland vegetation and hydrology.

Remote Sensing Techniques for Studying Coastal Ecosystems: An Overview

Abstract Advances in sensor design and data analysis techniques are making remote sensing systems practical and attractive for use in research and management of coastal ecosystems, such as wetlands, estuaries, and coral reefs. Multispectral and hyperspectral imagers are available for mapping coastal land cover, concentrations of organic/inorganic suspended particles, and dissolved substances in coastal waters. Thermal infrared scanners can map sea surface temperatures accurately and chart coastal currents, while microwave radiometers can measure ocean salinity, soil moisture, and other hydrologic parameters. Radar imagers, scatterometers, and altimeters provide information on ocean waves, ocean winds, sea surface height, and coastal currents, which strongly influence coastal ecosystems. Using airborne light detecting and ranging systems, one can produce bathymetric maps, even in moderately turbid coastal waters. Since coastal ecosystems have high spatial complexity and temporal variability, they frequently have to be observed from both satellite and aircraft in order to obtain the required spatial, spectral, and temporal resolutions. A reliable field data collection approach using ships, buoys, and field instruments with a valid sampling scheme is required to calibrate and validate the remotely sensed information. The objective of this paper is to present an overview of practical remote sensing techniques that can be used in studies of coastal ecosystems.

Tracking Oil Slicks and Predicting their Trajectories Using Remote Sensors and Models: Case Studies of the Sea Princess and Deepwater Horizon Oil Spills

Abstract Oil spills can harm marine life in the oceans, estuaries, and wetlands. To limit the damage by a spill and facilitate cleanup efforts, emergency managers need information on spill location, size and extent, direction and speed of oil movement, and wind, current, and wave information for predicting oil drift and dispersion. The main operational data requirements are fast turn-around time and frequent imaging to monitor the dynamics of the spill. Remote sensors on satellites and aircraft meet most of these requirements by tracking the spilled oil at various resolutions, over wide areas, and at frequent intervals. They also provide key inputs to drift prediction models and facilitate targeting of skimming and booming efforts. Satellite data are frequently supplemented by information provided by aircraft, ships, and remotely-controlled underwater robots. The Sea Princess tanker grounding off the coast of Wales and the explosion on the Deepwater Horizon rig in the Gulf of Mexico provide good examples for studying the effectiveness of remote sensors during oil-spill emergencies.

The Role of Remote Sensing in Predicting and Determining Coastal Storm Impacts

Abstract Storm-induced flooding and other damage present a major problem as the coastal population continues to increase rapidly and sea level keeps rising. To predict the path and landfall of a hurricane or other coastal storm and assess the damage, emergency managers and scientists need continuous information on the storms path, strength, predicted landfall, and expected damage over large areas. Satellite and airborne remote sensors can provide the required information in a timely and reliable way, as demonstrated by a case study of hurricane Katrinas impact on New Orleans and surrounding areas. Satellite images and hurricane hunter planes were used to track hurricane Katrina, with modelers predicting accurately its path, strength, surge level, and landfall location. Shore-based radars were used to confirm the data as the hurricane approached land. Medium- and high-resolution satellite sensors, helicopters, and aircraft were employed to assess damage to the city, including transportation, power, and communication infrastructures, and to adjacent wetlands and other coastal ecosystems. The lessons learned from hurricane Katrina are helping to optimize future approaches for tracking hurricanes and predicting their impact on coastal ecosystems and developed areas.

Coastal and Environmental Remote Sensing from Unmanned Aerial Vehicles: An Overview

ABSTRACT Klemas, V.V., 2015. Coastal and environmental remote sensing from unmanned aerial vehicles: An overview. Unmanned aerial vehicles (UAVs) offer a viable alternative to conventional platforms for acquiring high-resolution remote-sensing data at lower cost and increased operational flexibility. UAVs include various configurations of unmanned aircraft, multirotor helicopters (e.g., quadcopters), and balloons/blimps of different sizes and shapes. Quadcopters and balloons fill a gap between satellites and aircraft when a stationary monitoring platform is needed for relatively long-term observation of an area. UAVs have advanced designs to carry small payloads and integrated flight control systems, giving them semiautonomous or fully autonomous flight capabilities. Miniaturized sensors are being developed/adapted for UAV payloads, including hyperspectral imagers, LIDAR, synthetic aperture radar, and thermal infrared sensors. UAVs are now used for a wide range of environmental applications, such as coastal wetland mapping, LIDAR bathymetry, flood and wildfire surveillance, tracking oil spills, urban studies, and Arctic ice investigations.

Remote Sensing of Algal Blooms: An Overview with Case Studies

Abstract High concentrations of nutrients from agricultural and urban runoff, or those produced by coastal upwelling, are causing algal blooms in many estuaries and coastal waters. Algal blooms induce eutrophic conditions, depleting oxygen levels needed by organic life, limiting aquatic plant growth by reducing water transparency, and producing toxins that can harm fish, benthic animals, and humans. The magnitude and frequency of phytoplankton blooms have increased globally in recent decades, as shown in data from ocean-color sensors on-board satellites. Satellite and airborne measurements of spectral reflectance (ocean color) represent an effective way for monitoring phytoplankton by its proxy, chlorophyll-a, the green pigment that is present in all algae. This article reviews the use of remote sensing techniques for detecting phytoplankton and mapping algal blooms. Two case studies are presented, illustrating the advantages and limitations of satellite and airborne remote sensing.

Future Retrievals of Water Column Bio-Optical Properties using the Hyperspectral Infrared Imager (HyspIRI)

Interpretation of remote sensing reflectance from coastal waters at different wavelengths of light yields valuable information about water column constituents, which in turn, gives information on a variety of processes occurring in coastal waters, such as primary production, biogeochemical cycles, sediment transport, coastal erosion, and harmful algal blooms. The Hyperspectral Infrared Imager (HyspIRI) is well suited to produce global, seasonal maps and specialized observations of coastal ecosystems and to improve our understanding of how phytoplankton communities are spatially distributed and structured, and how they function in coastal and inland waters. This paper draws from previously published studies on high-resolution, hyperspectral remote sensing of coastal

Interpretation of scatterometer ocean surface wind vector EOFs over the Northwestern Pacific

Abstract Satellite scatterometer winds over the northwestern Pacific were analyzed with the vector empirical orthogonal function (VEOF) method. The Hilbert–Huang transform (HHT), a newly developed non-linear and non-stationary time series data processing method, was also employed in the analysis. A combination of European Remote Sensing Satellite (ERS) −1/2 scatterometer, NASA Scatterometer (NSCAT) and NASAs Quick Scatterometer (QuikSCAT) winds covering the period from January 1992 to April 2000 and the area of 0–50°N, 100–148°E constitutes the baseline for this study. The results indicate that annual cycles dominate the two leading VEOF modes. The first VEOF shows the East Asian monsoon features and the second represents a spring–autumn oscillation. We removed the annual signal from the data set and calculated the interannual VEOFs. The first interannual VEOF represents the interannual variability existing in the spring–autumn oscillation. The temporal mode is correlated with the Southern Oscillation Index (SOI), but has a half-year lag with respect to the SOI. The spatial mode of the first interannual VEOF reflects the response of the tropical and extratropical winds to ENSO events. The second interannual VEOF is another ENSO related mode, and the temporal VEOF mode is correlated with the SOI with a correlation coefficient of 0.78, revealing the wind variability over mid-latitudes, which is associated with ENSO events. Further analysis indicated that the wind variability over the coast of East Asia represents anomalies of a Hadley cell. The quasi-biennial oscillation (QBO) was found in the temporal mode, indicating and verifying that the QBO in the wind fields is related to ENSO events. The third VEOF shows the interannaul variability in the winter–summer mode and displays the interannual variability of the East Asian monsoon. The three leading interannual VEOFs are statistically meaningful as confirmed by a significance test.

Airborne Remote Sensing of Coastal Features and Processes: An Overview

ABSTRACT Klemas, V., 2013. Airborne remote sensing of coastal features and processes: an overview. Coastal ecosystems tend to be spatially complex and exhibit high temporal variability. Observing them requires the ability to monitor their biophysical features and controlling processes at high spatial and temporal resolutions, which can be provided by airborne remote sensors. High-resolution satellite data are now also available, yet the finer resolution and frequent, flexible overflights offered by airborne sensors can be more effective in a range of coastal research and management applications, such as wetlands mapping, coastal bathymetry, and tracking coastal plumes, salinity gradients, tidal fronts, and oil slicks. The airborne imagery is also useful for the interpretation of satellite data. This article reviews estuarine and coastal remote sensing applications that require the high spatial and temporal resolutions provided by airborne sensors.

Remote sensing of wetlands : applications and advances

Submerged aquatic vegetation (SAV) refers to all underwater flowering plants, of which seagrass is the most important from a marine ecosystem perspective. Being submerged underwater, the remote sensing of SAV, as well as coral reefs, is considerably more challenging than for terrestrial targets. The air-water interface and overlying water column form a dynamic medium that strongly influences the transfer of electromagnetic radiation (e.g., visible sunlight) that is used in a remote sensing situation to communicate information about submerged targets. As a result, the spectral differentiation of SAV and coral habitats using optical remote sensing demands specialized strategies, even if the depth of submergence is only a few meters. When submerged features are visible within remote sensing imagery, a situation termed “optically shallow water,” SAV, and coral habitats can be mapped. Conversely, if the lake or seabed is invisible due to excessive turbidity or water depth, termed “optically deep water,” benthic features cannot be mapped using satellite or airborne optical remote sensing. Therefore, optically deep and optically shallow waters require different application of technologies and/or also integration of field and image-based datasets. This chapter will provide insight into the use of remote sensing to map submerged features found in coral reef and SAV environments.