Morphological analysis of underwater sensing for Coast Guard applications /

Final report; April 1978.

Morphological analysis of underwater sensing for Coast Guard applications : appendix C /

Final report; April 1978.

Reducing the height of a circular array of monopoles using top hats and dielectric coatings

Using a Radial Guide Field Matching Method, an investigation is performed into reducing the height of an electronically steered circular array of monopole antennas composed of a central active element surrounded by passive elements being either short- or open-circuited. It is shown that a considerable height reduction can be achieved using top hats attached to monopoles ends and by applying dielectric coating underneath the top hats. The trade-off in achieving height reduction is narrower impedance bandwidth.

Circumarctic wetland dataset based on ENVISAT ASAR Global Monitoring Mode with links to geotiff image and NetCDF files (2005-01 to 2011-12)

A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized. Owing to a lack of comparable regional to circumpolar data with respect to thematic detail, a potential wetland map cannot directly be validated; however, one might claim the validity of such a product by comparison with vegetation maps, which hold some information on the wetness status of certain classes. It was shown that the Envisat ASAR-derived classes are related to wetland classes of conventional vegetation maps, indicating its applicability; 30% of the land area north of the treeline was identified as wetland while conventional maps recorded 1-7%.

Spring snowmelt and midwinter thaw and refreeze north of 60°N based on Seawinds QuikScat 2000-2009

The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.

Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

A fast parallel hyperspectral coded aperture algorithm for compressive sensing using OpenCL

In this paper, we develop a fast implementation of an hyperspectral coded aperture (HYCA) algorithm on different platforms using OpenCL, an open standard for parallel programing on heterogeneous systems, which includes a wide variety of devices, from dense multicore systems from major manufactures such as Intel or ARM to new accelerators such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), the Intel Xeon Phi and other custom devices. Our proposed implementation of HYCA significantly reduces its computational cost. Our experiments have been conducted using simulated data and reveal considerable acceleration factors. This kind of implementations with the same descriptive language on different architectures are very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

Analyse des signaux radars polarimétriques en bandes C et L pour le suivi de l'humidité du sol de sites forestiers

Résumé : Dans les couverts forestiers, le suivi de l’humidité du sol permet de prévenir plusieurs désastres tels que la paludification, les incendies et les inondations. Comme ce paramètre est très dynamique dans l’espace et dans le temps, son estimation à grande échelle présente un grand défi, d’où le recours à la télédétection radar. Le capteur radar à synthèse d’ouverture (RSO) est couramment utilisé grâce à sa vaste couverture et sa résolution spatiale élevée. Contrairement aux sols nus et aux zones agricoles, le suivi de l’humidité du sol en zone forestière est très peu étudié à cause de la complexité des processus de diffusion dans ce type de milieu. En effet, la forte atténuation de la contribution du sol par la végétation et la forte contribution de volume issue de la végétation réduisent énormément la sensibilité du signal radar à l’humidité du sol. Des études portées sur des couverts forestiers ont montré que le signal radar en bande C provient principalement de la couche supérieure et sature vite avec la densité de la végétation. Cependant, très peu d’études ont exploré le potentiel des paramètres polarimétriques, dérivés d’un capteur polarimétrique comme RADARSAT-2, pour suivre l’humidité du sol sur les couverts forestiers. L’effet du couvert végétal est moins important avec la bande L en raison de son importante profondeur de pénétration qui permet de mieux informer sur l’humidité du sol. L’objectif principal de ce projet est de suivre l’humidité du sol à partir de données radar entièrement polarimétriques en bandes C et L sur des sites forestiers. Les données utilisées sont celles de la campagne terrain Soil Moisture Active Passive Validation EXperiment 2012 (SMAPVEX12) tenue du 6 juin au 17 juillet 2012 au Manitoba (Canada). Quatre sites forestiers de feuillus ont été échantillonnés. L’espèce majoritaire présente est le peuplier faux-tremble. Les données utilisées incluent des mesures de l’humidité du sol, de la rugosité de surface du sol, des caractéristiques des sites forestiers (arbres, sous-bois, litières…) et des données radar entièrement polarimétriques aéroportées et satellitaires acquises respectivement, en bande L (UAVSAR) à 30˚ et 40˚ et en bande C (RADARSAT-2) entre 20˚ et 30˚. Plusieurs paramètres polarimétriques ont été dérivés des données UAVSAR et RADARSAT-2 : les coefficients de corrélation (ρHHVV, φHHVV, etc); la hauteur du socle; l’entropie (H), l’anisotropie (A) et l’angle alpha extraits de la décomposition de Cloude-Pottier; les puissances de diffusion de surface (Ps), de double bond (Pd) extraites de la décomposition de Freeman-Durden, etc. Des relations entre les données radar (coefficients de rétrodiffusion multifréquences et multipolarisations (linéaires et circulaires) et les paramètres polarimétriques) et l’humidité du sol ont été développées et analysées. Les résultats ont montré que 1) En bande L, plusieurs paramètres optimaux permettent le suivi de l’humidité du sol en zone forestière avec un coefficient de corrélation significatif (p-value < 0,05): σ[indice supérieur 0] linéaire et σ[indice supérieur 0] circulaire (le coefficient de corrélation, r, varie entre 0,60 et 0,96), Ps (r entre 0,59 et 0,84), Pd (r entre 0,6 et 0,82), ρHHHV_30˚, ρVVHV_30˚, φHHHV_30˚ and φHHVV_30˚ (r entre 0,56 et 0,81) alors qu’en bande C, ils sont réduits à φHHHV, φVVHV et φHHVV (r est autour de 0,90). 2) En bande L, les paramètres polarimétriques n’ont pas montré de valeur ajoutée par rapport aux signaux conventionnels multipolarisés d’amplitude, pour le suivi de l’humidité du sol sur les sites forestiers. En revanche, en bande C, certains paramètres polarimétriques ont montré de meilleures relations significatives avec l’humidité du sol que les signaux conventionnels multipolarisés d’amplitude.

Wind Waves in the Arctic Seas: Envisat and AltiKa Data Analysis

Wind-generated waves in the Kara, Laptev, and East-Siberian Seas are investigated using altimeter data from Envisat RA-2 and SARAL-AltiKa. Only isolated ice-free zones had been selected for analysis. Wind seas can be treated as pure wind-generated waves without any contamination by ambient swell. Such zones were identified using ice concentration data from microwave radiometers. Altimeter data, both significant wave height (SWH) and wind speed, for these areas were further obtained for the period 2002-2012 using Envisat RA-2 measurements, and for 2013 using SARAL-AltiKa. Dependencies of dimensionless SWH and wavelength on dimensionless wave generation spatial scale are compared to known empirical dependencies for fetch-limited wind wave development. We further check sensitivity of Ka- and Ku-band and discuss new possibilities that AltiKa's higher resolution can open.

DopSCAT: A mission concept for simultaneous measurements of marine winds and surface currents

A radar scatterometer operates by transmitting a pulse of microwave energy toward the ocean's surface and measuring the normalized (per-unit-surface) radar backscatter coefficient (σ°). The primary application of scatterometry is the measurement of near-surface ocean winds. By combining σ° measurements from different azimuth angles, the 10 m vector wind can be determined through a Geophysical Model Function (GMF), which relates wind and backscatter. This paper proposes a mission concept for the measurement of both oceanic winds and surface currents, which makes full use of earlier C-band radar remote sensing experience. For the determination of ocean currents, in particular, the novel idea of using two chirps of opposite slope is introduced. The fundamental processing steps required to retrieve surface currents are given together with their associated accuracies. A detailed description of the mission proposal and comparisons between real and retrieved surface currents are presented. The proposed ocean Doppler scatterometer can be used to generate global surface ocean current maps with accuracies better than 0.2 m/s at a spatial resolution better than 25 km (i.e., 12.5 km spatial sampling) on a daily basis. These maps will allow gaining some insights on the upper ocean mesoscale dynamics. The work lies at a frontier, given that the present inability to measure ocean currents from space in a consistent and synoptic manner represents one of the greatest weaknesses in ocean remote sensing.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

Avaliação ambiental de manguezais adjacentes aos campos petrolíferos de Macau e Serra (RN), como subsídio às medidas mitigadoras ao processo erosivo

Among the various effects caused by the climate change and human intervention, the mangrove ecosystem changes through of the years has been worth mentioning, which hasn t known which are the pros and cons for the adjacent coastal and estuarine environments yet. It happens due to the present dynamism in these areas, besides of the difficult understanding of the processes associated with evolution. This study aimed to environmentally evaluate adjacent mangroves from the Macau and Serra oil fields, located on Rio Grande do Norte northern coast, to support the mitigating actions related to the containment of the erosive process, as well as, according to the principles of the Clean Development Mechanism (CDM), to assess the amount of atmospheric carbon sequestered by the studied ecosystem. An inventory was conducted through mangrouve mapping which has supplied this research, especially regarding to the structural characterization of mangrove areas. To understand the local mangrove behavior in a greater level detail, techniques of remote sensing, GIS and GPS were used to make an analogy between the current and past states of the mangrove studied, allowing to make anticipated projections for the future impacts or changes in that region. This study combined data from multispectral LANDSAT 5 TM, Landsat 7 ETM+ with radar microwave data from SAR RADARSAT-1, which increased the interpretation capacity of the data from optical sensor systems. The interpretations have been supported by the data field, representing a better and innovative methodology for the environmental and taxonomic characterization of mangrove forests considered. The results reveal that mangroves of the Ponta do Tubarão Sustainable Development Reserve are biologically representative areas and providing a variety of benefits, especially for local communities, constituting the priority sites for actions development aimed at conservation. They also have been showing the necessity to make mitigating measures in order to recover degraded areas through reforestation or creating new areas of mangrove, as currently 7.1% of the mangrove forests studied are dead or in an advanced state of decomposition. The amount of atmospheric carbon sequestered proved very significant when analyzed for the whole area, which is able to sequester atmospheric 4,294,458 Ton CO2 per year

Comparison and Validation of Tropical Rainfall Measuring Mission (TRMM) Rainfall Algorithms in Tropical Cyclones

Tropical Rainfall Measuring Mission (TRMM) rainfall retrieval algorithms are evaluated in tropical cyclones (TCs). Differences between the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) retrievals are found to be related to the storm region (inner core vs. rainbands) and the convective nature of the precipitation as measured by radar reflectivity and ice scattering signature. In landfalling TCs, the algorithms perform differently depending on whether the rainfall is located over ocean, land, or coastal surfaces. Various statistical techniques are applied to quantify these differences and identify the discrepancies in rainfall detection and intensity. Ground validation is accomplished by comparing the landfalling storms over the Southeast US to the NEXRAD Multisensor Precipitation Estimates (MPE) Stage-IV product. Numerous recommendations are given to algorithm users and developers for applying and interpreting these algorithms in areas of heavy and widespread tropical rainfall such as tropical cyclones.