The influence of river discharge and wind on Patos Lagoon,Brazil, Suspended Particulate Matter

Eight years (2008–2015) of reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) – Aqua sensor are used to quantify Suspended Particulate Matter (SPM) distribution and variability in Brazil’s Patos Lagoon. After application of an atmospheric correction suitable for extremely turbid coastal water and calculation of SPM concentrations from the reflectance values using an established algorithm, the daily remote sensing data were averaged into eight-day and monthly composites. The climatological patterns show a gradient from higher concentrations in the north to lower concentration in the south, with highest concentrations in austral spring and summer and lowest in autumn and winter. Strong interannual variability shows 2009 and 2012 to have the highest SPM concentrations and 2010 and 2014 to have the lowest. Time series of SPM were then compared with the main forces to the lagoon circulation (wind and river discharge). Peaks in SPM are associated with peaks in river discharge. Maximum SPM occurred following peaks in river discharge combined with strong northeast (NE) winds, suggesting that freshwater input and direction-specific winds are the major mechanisms of sediment transport in Patos Lagoon.     

Seasonal changes in colour ratios and optically active contituents in the optical Case-2 waters of the Menai Strait,North Wales

A time series was conducted in the Menai Strait from March to December 1996 combining optical measurements by a colour sensor with measurements of light-absorbing constituents in the water. In order to improve estimations of pigment concentration in Case-2 waters, an optical model was used to synthesize the spectrum of sea-leaving radiance from the absorption properties of pure sea water, yellow substances, mineral suspended solids and phytoplankton. The original model was tested and improved by using a different set of parameters. Multiple regression was used to empirically relate colour ratios to pigments and total suspended solids.     

Satellite-based products for monitoring optically complex inland waters in support of EU Water Framework Directive

The need to restore and protect waterbodies from further degradation has resulted in formulation of the European Union Water Framework Directive 2000/60/EC (WFD). The directive aims to harmonize European legislation on water; and member states shall establish a programme for monitoring the status of all waterbodies larger than 0.5 km², in order to ensure future quality and quantity of inland waters. The biological and physical–chemical status and ecological potential should be assessed and action plans for a sustainable management and protection of freshwater resources should be established. In practice, this means that extensive and expensive sampling programmes are needed. The ecological status of a waterbody can be described by various biological and physical–chemical quality elements, and several of these important ecological parameters can be monitored by space-based instruments: (1) phytoplankton biomass; (2) chlorophyll-a concentration; (3) water transparency; and (4) frequency and intensity of blooms. The objective of this article is to demonstrate how Environmental Satellite/Medium Resolution Imaging Spectrometer and future Sentinel-3/Ocean Land Colour Instrument data can be effectively used to complement traditional water monitoring programmes by adding information with significantly improved spatial coverage and temporal detail to support the WFD status assessment process. Examples are provided for five large European lakes (Peipsi, Võrtsjärv, Vänern, Vättern, Mälaren). Time series based on satellite data and data collected within national and regional monitoring programmes were compiled and compared, to demonstrate good agreement between the two techniques, but also to discuss natural differences and limitations. Furthermore, the ecological status class based on satellite and in situ data for each waterbody was calculated and analysed.     

Parameterization of the chlorophyll a-specific in vivo light absorption coefficient covering estuarine,coastal and oceanic waters

We evaluated models predicting the spectral chlorophyll-a (Chl a)-specific absorption coefficient (a* _ph (λ)) from Chl a concentration [Chl a] on the basis of 465 phytoplankton absorption spectra collected in estuarine, coastal and oceanic waters. A power model on ln-transformed data provided the best model fit compared to a power model on non-transformed data previously applied to parameterize the relationship between a* _ph (λ) and [Chl a]. The variation in a* _ph (λ) was parameterized over four orders of magnitude in [Chl a] (0.01-100 mg Chl a m^?3) producing a 13-fold range in a* _ph (0.19 to 0.015 m² mg^?1 Chl a) at 440 nm, the peak absorption of Chl a in the blue part of the spectrum. The variations in the modelled a* _ph spectra were within realistic predictions of a* _ph (λ) and the model satisfactorily reproduced the spectral flattening with increasing [Chl a]. The parameterization of a* _ph (λ) confirmed the indirect dependency of a* _ph (λ) on [Chl a] through co-variations between [Chl a] with pigment packaging and pigment composition. Although pigment packaging determined the spectral flattening, analysis of absorption ratios revealed a systematic change in pigment composition with profound influence on the variability of a* _ph in the 440 to 495 nm region. Modelled spectra deviated by approximately 20% from the measured spectra on average and model accuracy was independent of [Chl a]. Although the model cannot fully replace spectral measurements of phytoplankton absorption, it does permit realistic reconstructions of a* _ph (λ) from simple measurements of [Chl a] sampled in estuarine, coastal and oceanic waters.     

Linear spectral mixture analysis of Landsat TM data for monitoring invasive exotic plants in estuarine wetlands

This study assessed the feasibility of spectral mixture analysis (SMA) of Landsat thematic mapper (TM) data for monitoring estuarine vegetation at species level. SMA modelling was evaluated, using χ² test, by comparing SMA fraction images with a precisely classified QuickBird image that has a higher spatial resolution. To clearly understand the strengths and weaknesses of SMA, eight SMA models with different endmember combinations were assessed. When the TM data dimension for SMA and the endmember number required were balanced, a model with three endmembers representing water and two vegetation types was most accurate, whereas a model with five endmembers approximated the actual surface situation and generated a relatively accurate result. Our results indicate that an SMA model with appropriate endmembers had relatively satisfactory accuracy in monitoring vegetation. However, errors might occur in SMA fraction images, especially in models with an inappropriate endmember combination, and the errors were mainly distributed in areas filled with water or near water. Therefore, short vegetation usually submerged during high tide tended to be poorly predicted by SMA models. These results strongly suggest that tide water has a great influence on SMA modelling, especially for short vegetation.     

Spectral irradiance profiles of suspended marine clay for the estimation of suspended sediment concentration in tropical waters

Reflectance spectra of water containing varying concentrations of organic and inorganic sediments isolated from the coastal waters of Singapore were measured using a portable spectroradiometer under controlled experimental conditions and natural sunlight. The effect of different sizes of sediments on the spectral profile of water was also investigated. In the presence of organic sediments, maximum reflectance of water was observed over a broad band between 440-580 nm, with a peak reflectance at about 550 nm, followed by two smaller peaks at 600-645 nm and 665-690 nm. In contrast, inorganic sediments produced a distinct band peak between 595 and 690 nm. For both sediment types, characteristic features in the infra-red region include a reflectance trough at 754 nm and peak at 814 nm. The empirical colour ratios, OD550-OD754 and OD 595-OD754, were used to estimate the organic and inorganic sediment concentrations respectively. In the case of organic sediments, a power function was found to fit the data well ( R 2 =0.89-0.98), whereas a linear fit was found for inorganic sediments ( R 2 =0.53-0.86). In general, decreases in particle size resulted in overall increases in spectral reflectance.     

Comparative analysis of four semi-analytical models for estimating chlorophyll-a concentration in case-2 waters using field hyperspectral reflectance

ABSTRACT

Hyperspectral remote sensing can capture the complicated and variable characteristics of inland waters; thus, it is suited for the water quality assessment of Case-2 waters, and it has the potential to attain high estimation accuracy. In the present study, four improved models adapted from published approaches (three-band index, ΔΦ, BNDBI and TCARI) were investigated to estimate chlorophyll-a (chl-a) for the case of Dianshan Lake, China. Calibration and validation were provided from in situ measured chl-a and field hyperspectral measurements. The improved three-band (ITB) model, ΔΦ model, and BNDBI model yielded satisfactory results and enabled the estimation of chl-a for inland Case-2 waters with coefficients of determination (R²) reaching 0.75, 0.76, and 0.86, respectively. In particular, the TCARI/OSAVI model presented the highest accuracy (R² = 0.94) compared to the other models. All of the results provide strong evidence that the hyperspectral models presented in this paper are promising and applicable to estimate chl-a in eutrophic inland Case-2 waters.     

一种线性动态模型参数估计方法

本文提出一种线性动态模型参数估计方法,该算法改进并推广了Durbin算法.它能在噪声过程结构未知时,给出实际过程和噪声过程参数的全局P相容估值.五个数字仿真例子表明了算法的有效性.     

Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments,chlorophyll a and phycocyanin

Nuisance blue-green algal blooms contribute to aesthetic degradation of water resources by means of accelerated eutrophication, taste and odor problems, and the production of toxins that can have serious adverse human health effects. Current field-based methods for detecting blooms are costly and time consuming, delaying management decisions. Methods have been developed for estimating phycocyanin concentration, the accessory pigment unique to freshwater blue-green algae, in productive inland water. By employing the known optical properties of phycocyanin, researchers have evaluated the utility of field-collected spectral response patterns for determining concentrations of phycocyanin pigments and ultimately blue-green algal abundance. The purpose of this research was to evaluate field spectroscopy as a rapid cyanobacteria bloom assessment method. In-situ field reflectance spectra were collected at 54 sampling sites on two turbid reservoirs on September 6th and 7th in Indianapolis, Indiana using ASD Fieldspec (UV/VNIR) spectroradiometers. Surface water samples were analyzed for in-vitro pigment concentrations and other physical and chemical water quality parameters. Semi-empirical algorithms by Simis et al. [Simis, S., Peters, S., Gons, H. (2005). Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. American Society of Limnology and Oceanography 50(11): 237–245] were applied to the field spectra to predict chlorophyll a and phycocyanin absorption at 665 nm and 620 nm, respectively. For estimation of phycocyanin concentration, a specific absorption coefficient of 0.0070 m² mg PC^-1 for phycocyanin at 620 nm, a_PC^?(620), was employed, yielding an r² value of 0.85 (n = 48, p < 0.0001), mean relative residual value of 0.51 (σ = 1.41) and root mean square error (RMSE) of 19.54 ppb. Results suggest this algorithm could be a robust model for estimating phycocyanin. Error is highest in water with phycocyanin concentrations of less than 10 ppb and where phycocyanin abundance is low relative to chlorophyll a. A strong correlation between measured phycocyanin concentrations and biovolume measurements of cyanobacteria was also observed (r = 0.89), while a weaker relationship (r = 0.66) resulted between chlorophyll a concentration and cyanobacterial biovolume.     

A comparison of experimental energy balance components data and SEBAL model results in Dourados,Brazil

The understanding of surface energy flux is important as regards weather and climate models. Therefore, analysis of remotely sensed data, estimation of surface energy balance components (fluxes), validating experiments and results are discussed in this work. Data extracted from NOAA–AVHRR (National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer) satellite images were used to estimate the fluxes based on the SEBAL (Surface Energy Balance for Land) algorithm, as suggested by Bastiaanssen [Bastiaanssen, W.G.M., 1995, Regionalization of surface flux densites and moisture indicators in composite terrain. PhD thesis, Wageningen Agricultural University, The Netherlands.] To validate the results, one wide-ranging field experiment was organized near Dourados, municipality in the Brazilian state of Mato Grosso do Sul, during the summer of 1999. The experiment involved simultaneous taking of satellite images and in-situ measurements. SEBAL results are shown and discussed. The obtained average errors are less than 4%, 6% and 7% for net surface radiation, surface heat and latent flux estimations, respectively, as compared to the in-situ measurements.     

Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands

Bio-optical algorithms for remote estimation of chlorophyll-a concentration (Chl) in case-1 waters exploit the upwelling radiation in the blue and green spectral regions. In turbid productive waters other constituents, that vary independently of Chl, absorb and scatter light in these spectral regions. As a consequence, the accurate estimation of Chl in turbid productive waters has so far not been feasible from satellite sensors. The main purpose of this study was to evaluate the extent to which near-infrared (NIR) to red reflectance ratios could be applied to the Sea Wide Field-of-View Sensor (SeaWiFS) and the Moderate Imaging Spectrometer (MODIS) to estimate Chl in productive turbid waters. To achieve this objective, remote-sensing reflectance spectra and relevant water constituents were collected in 251 stations over lakes and reservoirs with a wide variability in optical parameters (i.e. 4 ≤ Chl ≤ 240 mg m^− 3; 18 ≤ Secchi disk depth ≤ 308 cm). SeaWiFS and MODIS NIR and red reflectances were simulated by using the in-situ hyperspectral data. The proposed algorithms predicted Chl with a relative random uncertainty of approximately 28% (average bias between − 1% and − 4%). The effects of reflectance uncertainties on the predicted Chl were also analyzed. It was found that, for realistic ranges of R_rs uncertainties, Chl could be estimated with a precision better than 40% and an accuracy better than ± 35%. These findings imply that, provided that an atmospheric correction scheme specific for the red-NIR spectral region is available, the extensive database of SeaWiFS and MODIS images could be used to quantitatively monitor Chl in turbid productive waters.     

A Linear Programming Model for Distribution of Electrical Energy in a Steel Plant

The energy crisis is one of the deterrents of economic growth in a developing country like India. Rapid industrialization and poor capacity utilization of power plants make the operations of energy consuming industries like integrated steel plants extremely difficult. This case study discusses the development and implementation of a mixed integer linear programming model for optimal distribution of electrical energy in an integrated steel plant. The model considers the balance equations of capacity, material, thermal and electrical energy, oxygen. It also considers the constraints of yields, product routes, net realizations, variable costs, market demands and commitments to decide not only the hierarchy of shutdowns in the event of a power crisis but also the optimal product mix in each level of power availability. The round-the-clock implementation of the model increased the net profit per ton of saleable steel by 58% in 1986. Since then, the model, which is generic in nature, has been successfully integrated into the decision-making process. The cumulative benefit from this work will be at least 73 million US dollars.     

The Size and Power of Bootstrap Tests for Spatial Dependence in a Linear Regression Model

In this paper, we define the spatial bootstrap test as a residual-based bootstrap method for hypothesis testing of spatial dependence in a linear regression model. Based on Moran’s I statistic, the empirical size and power of bootstrap and asymptotic tests for spatial dependence are evaluated and compared. Under classical normality assumption of the model, the performance of the spatial bootstrap test is equivalent to that of the asymptotic test in terms of size and power. For more realistic heterogeneous non-normal distributional models, the applicability of asymptotic normal tests is questionable. Instead, spatial bootstrap tests have shown superiority in smaller size distortion and higher power when compared to asymptotic counterparts, especially for cases with a small sample and dense spatial contiguity. Our Monte Carlo experiments indicate that the spatial bootstrap test is an effective alternative to the theoretical asymptotic approach when the classical distributional assumption is violated.     

Robust Adaptive Model Tracking for Distributed Parameter Control of Linear Infinite-dimensional Systems in Hilbert Space

This paper is focused on adaptively controlling a linear infinite-dimensional system to track a finite-dimensional reference model. Given a linear continuous-time infinite-dimensional plant on a Hilbert space with disturbances of known waveform but unknown amplitude and phase, we show that there exists a stabilizing direct model reference adaptive control law with the properties of certain disturbance rejection and robustness. The plant is described by a closed, densely defined linear operator that generates a continuous semigroup of bounded operators on the Hilbert space of states. The central result will show that all errors will converge to a prescribed neighborhood of zero in an infinitedimensional Hilbert space. The result will not require the use of the standard Barbalat0s lemma which requires certain signals to be uniformly continuous. This result is used to determine conditions under which a linear infinite-dimensional system can be directly adaptively controlled to follow a reference model. In particular, we examine conditions for a set of ideal trajectories to exist for the tracking problem. Our results are applied to adaptive control of general linear diffusion systems described by self-adjoint operators with compact resolvent.      

遗传算法用于估值O,O-二甲基硫代磷酰氯合成反应动力学模型的参数

适当简化之下,建立了O-甲基硫代磷酰二氯与甲醇在碱作用下合成O,O-二甲基硫代磷酰氯的复杂反应动力学模型,采用遗传算法处理等温恒定加碱速度的间歇反应实验数据,得相应的动力学模型参数.模拟计算了加碱速度、温度、甲醇与O-甲基硫代磷酰二氯的摩尔配比对产品质量与收率的影响.结果表明:缩短加碱时间、降低反应温度、合适的甲醇与O-甲基硫代磷酰二氯的摩尔配比,有利于提高产品质量与收率.     

An algorithm for estimating systematic measurement errors for air velocity,angle of attack,and sliding angle in flight testing

An identification algorithm for systematic measurement errors of air velocity and the aerodynamic angles of a flight vehicle in the course of flight testing, which uses high precision velocity measurements performed by a satellite navigation system, is proposed. A specific feature of the considered method is that a wide set of flight maneuvers, along with the specialized test regimes used in the practice of flight testing, can be used for solving the problem. The performance of the method and the legitimacy of the assumptions made are proved by processing large volumes of the experimental data obtained in the course of flight testing.     

Spectral indices for remote sensing of phytomass,deciduous shrubs,and productivity in Alaskan Arctic tundra

The relationships among in situ spectral indices, phytomass, plant functional types, and productivity were determined through field observations of moist acidic tundra (MAT), moist non-acidic tundra (MNT), heath tundra (HT), and sedge-shrub tundra (SST) in the Arctic coastal tundra, Alaska, USA. The two-band enhanced vegetation index (EVI2) was found more useful for estimating vascular plant green phytomass, leaf carbon and nitrogen, leaf carbon and nitrogen turnover, and vascular plant net primary productivity (NPP) without root production than the normalized difference vegetation index (NDVI). Deciduous shrub green phytomass was strongly correlated with deciduous shrub index (DSI), defined as EVI2 × (R_blue + R_green – R_red)/(R_blue + R_green + R_red) (with a coefficient of determination (R²) of 0.63). R_blue, R_green, and R_red denote the blue, green, and red bands, respectively. This is because R_blue and R_green values were higher than the R_red values for green leaves, deciduous shrub stems, lichens, and rocks compared with other ecosystem components, and EVI2 values of lichens and rocks were very low. The vascular plant NPP without root production was estimated with an R² of 0.67 using DSI and EVI2. Our results offer empirical evidence that a new spectral index predicts the distribution of deciduous shrub and plant production, which influences the interactions between tundra ecosystems and the atmosphere.     

Individual,social, and organizational contexts for active knowledge sharing in communities of practice

Firms that have implemented knowledge management initiatives are now interested in nurturing voluntary knowledge sharing organizations, called communities of practice (CoPs). Adopting the Triandis model on attitude formation, we identified and validated a set of organizational factors that was anticipated to have effects on knowledge sharing by CoP members such as perceived consequences, affect, social factors and facilitating conditions. One hundred and seventy-nine members from 70 CoPs of a large multinational electronics firm participated in this survey. Based on the PLS analysis, perceived consequences, affect, social factors, and facilitating conditions were found to significantly affect knowledge sharing in CoPs.     

Landsat-TM data for estimating ground temperature and depth of subsurface coal fire in the Jharia coalfield,India

Coal fires are a ubiquitous problem in coal-mines, the world over. They burn our prime energy resource, lead to atmospheric pollution and render mining of coal hazardous. Processes leading to coal combustion and spread of subsurface fires are briefly examined in this paper and the role of remote sensing in surveillance of coal fires is presented.

The present study aims at developing a quick method for estimating the temperature of the ground surface directly above subsurface coal fires. Utility of TM6 and TM7-band data for temperature estimation is briefly reviewed. It is argued that temperature calculations of surface anomalies related to subsurface fires can only be done on the basis of 8–14 μm band data, due to the low temperatures involved.

In the Jharia coalfield, it is noted that subsurface fires in various coal-mines are associated with surface thermal anomalies, as has also been confirmed by ground checks. The pattern of TM6 data distribution and ground truth is used to isolate thermal anomalies, and the TM6 digital numbers are converted into kinetic temperature values. It is observed that for the Landsat-TM scene ( 28 November 1990) the kinetic temperatures range from 16·0°C to 31·6°C in the Jharia coalfield, with a threshold value of 25·6°C associated with the anomalies. Depth estimation of fire has been carried out using field structural geology data and pixel locations of thermal anomalies. It is inferred that the depth of subsurface coal fire ranges between 45–55 m, in most cases, which is in general agreement with the field data. Limitations of the method are indicated.     

Measurement of oak tree density with Landsat TM data for estimating biogenic isoprene emissions in Tennessee,USA

Isoprene emissions from oak trees in the eastern USA play an important role in tropospheric ozone pollution. Oak trees (Quercus) emit an order of magnitude more isoprene than most other emitting tree species, and are by far the largest source of biogenic isoprene in the eastern US. We used Landsat TM data to measure oak tree abundance near Oak Ridge, Tennessee, to estimate fluxes of isoprene. The Landsat classification was performed using multi-date data, supervised classification techniques, and an iterative approach. Training sites were selected based on transect data, and ten vegetation classes were mapped. A supervised classification algorithm called the Spectral Angle Mapper was used to classify the data. Empirical vegetation emission data were used to estimate the isoprene flux from each of the vegetation classes. The resultant isoprene flux maps were compared with concentrations measured in the field, and a good correspondence was observed. We also compare the Landsat classification with three other landcover schemes including the USGS's Global Landcover Classification, which is based on AVHRR data. Results from these landcover classifications are used as input for models that predict tropospheric ozone production and are used to investigate ozone control strategies.