Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters

Increased frequency and extent of potentially harmful blooms in coastal and inland waters world-wide require the development of methods for operative and reliable monitoring of the blooms over vast coastal areas and a large number of lakes. Remote sensing could provide the tool. An overview of the literature in this field suggests that operative monitoring of the extent of some types of blooms (i.e. cyanobacteria) is relatively straightforward. Operative monitoring of inland waters is currently limited to larger lakes or using airborne and hand-held remote sensing instruments as there are no satellite sensors with sufficient spatial resolution to provide daily coverage. Extremely high spatial and vertical variability in biomass during blooms of some phytoplankton species and the strong effects of this on the remote sensing signal suggest that water sampling techniques and strategies have to be redesigned for highly stratified bloom conditions, especially if the samples are collected for algorithm development and validation of remote sensing data. Comparing spectral signatures of different bloom-forming species with the spectral resolution available in most satellites and taking into account variability in optical properties of different water bodies suggests that developing global algorithms for recognizing and quantitative mapping of (harmful) algal blooms is questionable. On the other hand some authors cited in the present paper have found particular cases where satellites with coarse spectral and spatial resolution can be used to recognize phytoplankton blooms even at species level. Thus, the algorithms and methods to be used depend on the optical complexity of the water to which they will be applied. The aim of this paper is to summarize different methods and algorithms available in an attempt to assist in selecting the most appropriate method for a particular site and problem under investigation.     

Estimating phytoplankton biomass in coastal waters of Alaska using airborne remote sensing

Empirical airborne remote-sensing relationships were examined to estimate chlorophyll a concentration in the first optical depth (chl_FOD) of coastal waters of Afgonak/Kodiak Islands during July-August 2002. Band-ratio and spectral-curvature models were tested using satellite remote-sensing reflectance (R_rs(λ)) measurements. Additional shipboard and airborne R_rs(λ) data were also analysed to evaluate consistency of proposed chl_FOD-R_rs(λ) relationships. Validation of chlorophyll algorithms was performed using data collected in the northern-part of the Gulf of Alaska and Bering Sea during 1996, 2002, and 2003 cruises. Likewise, oceanographic conditions during the surveys were typified to interpret variability of chl_FOD fields. The SeaWiFS band-ratio algorithm OC2d was the most sensitive R_rs combination (R_rs(509)/R_rs(553)) to detect chl_FOD variability. Conversely, OC2a (R_rs(412)/R_rs(553)) had the lowest performance to derive chl_FOD values. No valid statistical regressions were established for spectral-curvature relationships in the blue spectrum (< 500 nm). Fertile waters (> 5 mg m^− 3) were preferentially located over shallow banks (∼50 m) and at the entrance of the bays. The approach used in this study to derive chl_FOD values could be universal for Alaskan coastal waters. However, chl_FOD-R_rs(λ) relationships must be calibrated locally for a given season.     

Estimation of net primary productivity by integrating remote sensing data with an ecosystem model

This paper describes a method for integrating leaf area index (LAI) derived from remote sensing data with an ecosystem model for accurate estimation of net primary productivity (NPP). The ecosystem model used in this study was Sim-CYCLE, with which LAI retrieved from the data acquired by MODIS sensor (MODIS-LAI) was integrated. Global annual NPP was estimated as 59.6 Gt C year⁻¹ by MOD-Sim-CYCLE (Sim-CYCLE after integration of MODIS-LAI), whereas it was 62.7 Gt C year⁻¹ in case of Sim-CYCLE for the year 2001. Both models predicted highest NPP around the equator with another smaller peak occurring around 60°N. These two regions represented the tropical and boreal forests biomes, respectively. The NPP estimated by MOD-Sim-CYCLE exceeded the NPP estimated by Sim-CYCLE in these two regions. Other than the tropical and boreal forests biomes, NPP values estimated by the MOD-Sim-CYCLE were typically lower than Sim-CYCLE across the latitudes. Validations of results in Australia and USA showed that MOD-Sim-CYCLE estimated NPP more accurately than Sim-CYCLE. Our results demonstrate the utility of combining satellite-observation with an ecosystem process model to achieve improved accuracy in estimates and monitoring global net primary productivity.     

Orbital remote sensing of phytoplankton functional types: a new review

Phytoplankton functional types (PFTs) are groups of species that have specific roles in the biogeochemical cycles and trophic flow. These groups have been pointed out as keys to improve the knowledge on ecosystem dynamics and effects of climate and anthropogenic changes in the marine environment. Orbital remote sensing is stated as the only means capable of analysing spatio-temporal distribution of PFTs, over regional and global scales. Many research teams have focused their efforts on the development of PFT bio-optical models, presenting a great variety of approaches. Considerable advances have been achieved in a few years of research; however, challenges are still great. Further developments rely on improvements in the knowledge of PFTs' bio-optical characteristics and the possibility of integrating different bio-optical and ecological information. This article presents a new review of the state of the art of PFTs' characterization from space, with examples of the main models and a discussion of achievements, gaps and future perspectives.     

Diurnal variability of ocean optical properties during a coastal algal bloom: implications for ocean colour remote sensing

Understanding the diurnal variability of ocean optical properties is critical for better interpretation of satellite ocean colour data and characterizing biogeochemical processes. The daytime variability of ocean optical properties throughout an algal bloom event is analysed in this article based on in situ observations from dawn to dusk at a fixed coastal site in the South China Sea. Diurnal variability during the sunlit period of the ocean optical properties is found to be significant. During the 6 hours around noon, the temporal variability (defined by the coefficient of variation) of phytoplankton absorption, coloured dissolved organic matter and non-algal particle absorption, and particle backscattering at 443 nm can reach 21% ± 15%, 12% ± 9%, and 17% ± 9%, respectively. The diurnal variability during the bloom is much more pronounced than that of the non-bloom phase. With atmospheric radiative transfer modelling, it is further demonstrated that the geostationary satellite detection of within-day optical variability in algae-dominated waters depends on the reliability of the aerosol retrieval. The implications of the diurnal bio-optical variability for the retrieval, validation, and interpretation of satellite ocean colour products are also discussed.     

The implementation of the ecosystem module of a coastal environmental model: Port Phillip Bay,Australia

When implemented as a computer program, an ecosystem model is only a part of a larger programming environment. This programming environment includes other programs, non-model program components, program design rules, data files, and associated analysis analytical tools. These components should be divided to allow programmers to focus on their areas of expertise, but must then be rejoined in such a way as to minimise debugging and execution overheads. We describe this larger programming environment as it surrounds a model of the ecosystem of Port Phillip Bay, Australia.The ecosystem model requires a transport model to allow spatial modelling; this transport model uses currents from a computationally intensive hydrodynamic model. Implementation of the ecosystem model also requires non-model code, such as routines to initialise parameters or the integration method. Their design determines program reliability and performance. A modular structure allows different parts of the model to be independently modified; this makes for efficient programming. We describe formal design rules used to enhance readability and information content of the model's parameter names. To execute, the model must access data files and a record of the run must be kept — a Unix shell program serves both these functions. The data files may require software tools for generation or manipulation. Output from the model also requires post-processing for visualisation and analysis. The model is thus only a part of a network of software, whose development must be coordinated to ensure reliability and efficiency.     

利用区域信息融合混合活动轮廓模型的 河流遥感图像分割

目的 河流遥感图像是背景复杂的非匀质图像,利用传统的活动轮廓模型进行分割往往不够准确。针对这一问题,提出了基于区域信息融合的混合活动轮廓模型来分割河流遥感图像。方法 该混合模型将Chan-Vese(CV)模型和基于交叉熵的活动轮廓模型的外部能量约束项相结合,并赋予归一化调节比例系数。通过计算轮廓曲线内外区域像素灰度的方差和交叉熵,指导曲线逼近目标边缘。为了加速混合模型的演化,引入曲线内外区域像素灰度的类内绝对差,取代原有的内外区域能量权值,以提高混合模型的分割效率。结果 大量实验结果表明,相较于CV模型、测地线模型、基于交叉熵的活动轮廓模型、CV模型和测地线模型的混合模型以及局部全局灰度拟合能量模型(LGIF),本文混合模型分割河流遥感图像的灵敏度和上述方法都接近于100%,准确率大幅提升,在90%以上,虚警率则下降了约50%,且所需迭代次数和运行时间更少。结论 本文提出的混合模型主要适用于具有一定对比度的河流遥感图像,在分割性能和分割效率两个方面,都有明显的优势。     

Capability representation model for heterogeneous remote sensing sensors: Case study on soil moisture monitoring

Sensor capability information can be used as a basis for the integrated management of vast and heterogeneous remote sensing sensors in Global Earth Observation System of Systems environment. However, the existing representation of this information shows an inconsistent pattern, incomplete capability aspects, and casual expression forms, resulting in information silos among different systems. A sensor capability representation model (SCRM) is proposed in this study. Based on the Meta Object Facility architecture, a five-tuple hierarchical SCRM framework is formulated. Five specific representation element collections for typical remote sensing sensor types are developed to satisfy the requirements of detailed capability expression. The Open Geospatial Consortium Sensor Model Language is used as the expression form of the proposed SCRM. A prototype system is developed and a case study is conducted for a soil moisture monitoring application in Baoxie Town. The SCRM can also be extensively utilised for other environmental monitoring and modelling situations.     

高光谱海岸带区域分割的活动轮廓模型

近年来,高光谱遥感图像的分割作为地物识别和异常目标探测等应用的基础工作而受到重视,而高光谱遥感图像的海量数据和复杂结构使其分割技术成为一项挑战性的工作.在对海岸带高光谱遥感图像的光谱特性进行分析的基础上,提出一种基于光谱特性的海岸带水、陆区域分割的偏微分方程活动轮廓模型:首先以高光谱海岸带图像的海域像元光谱信息为参照点,构建海岸带高光谱图像的能量偏差矩阵;在此基础上建立适应该能量偏差矩阵的水、陆区域分割的活动轮廓模型.模型通过引入基于梯度的边缘引导函数,提升了对水、陆区域边缘的捕捉能力和抗噪声干扰能力.实验结果表明,与传统活动轮廓模型相比,本文模型不仅能够保证水、陆区域分割的精度,而且具有更快的计算速度.     

Modelling the distribution of radionuclides in a Mediterranean coastal ecosystem

The present paper deals with the modelling process in relation to the class of well-mixed compartment models (box models).A box model has been described and tested for radionuclides released by a BWR Nuclear Power plant (2 × 1000 MWe; under construction at Montalto di Castro, Central Italy) into the Tyrrhenian Sea.The model predicts the radionuclide transport between the most important components of the coastal marine ecosystem (water, pore-water, sediments and biota), but only the possibility of determining radionuclide concentrations associated with abiotic components is discussed.In the context of modelling, the requirements for information concerning the behaviour of radionuclides in a coastal ecosystem are discussed, with reference to chemical, physical and biological considerations.A validation for the radionuclide transport model was made using literature data on the Sellafield (Windscale) site. The major sources of uncertainty in the model were evaluated by sensitivity analysis and parameter imprecision analysis using site-specific parameter values obtained at Montalto di Castro.     

Modelling carbon fluxes of different forests by coupling a remote-sensing model with an ecosystem process model

A new model (GLOPEM-CEVSA) to determine terrestrial carbon budgets was developed by coupling remote sensing with ecosystem process simulation, and was validated with reference to the carbon fluxes of three forests. MODIS FPAR (MOD15A2 product) was applied together with meteorological data on flux towers. The seasonal variances of modelled gross primary production and ecosystem respiration were significantly correlated with observed values (correlation coefficient, r > 0.9). The seasonal dynamics of the modelled net ecosystem production over the plant-growth season showed significant agreement with observed values with a r range of 0.64 to 0.87. This work demonstrates the potential of GLOPEM-CEVSA to quantify the spatial patterns and temporal dynamics of terrestrial ecosystem carbon sources and sinks with consideration of the spatial heterogeneity of ecosystems based on remote sensing.     

Spectral variations of coastal water irradiance as a measure of phytoplankton pigments

Spectral irradiance data were collected along the coast of British Columbia, Canada, using a submersible spectroradiometer. The corresponding hydrographic observations show that in general, the surface layer where the irradiance measurements were taken, is a well mixed layer. The spectral variations of the data are interpreted with respect to the changes of chlorophyll α and phaeo-pigment concentrations measured at the same time. Optical properties such as spectral attenuation and back scattering coefficients are estimated from the data. The back scattering coefficient is approximated by an expression obtained from theoretical analysis.

For remote sensing applications, the intercomparisons of algorithms of Gordon et al. (1980) and Kim et al. (1980) are presented. The results indicate that a linear form of the relationship is suitable for estimating the amount of chlorophyll α + phaeopigments in the coastal water of the inshore region. This may be explained by the variations of diffuse reflectance ratio between wavelengths 420 and 560 nm corresponding to the absorption maximum and minimum due to the presence of phytoplankton pigments in the sea water.     

A computationally efficient inverse modelling approach of inherent optical properties for a remote sensing model

Inverse modelling of inherent optical properties (IOP) is an alternative to the in situ measurements of IOP requiring specialized instruments. However, inverse modelling using Monte Carlo models may require very large computational time due to a large number of dynamic model runs needed to search the optimum parameter values. We present a new approach to reduce this computational time. Mathematical relationships were developed for wavelength and concentration dependence of IOP values of suspended mineral based on four parameters. Optimal values of these four parameters were calculated by minimizing the mean sum of error between the physical hyperspectral optical-Monte Carlo (PHO-MC) model predicted reflectance to measured reflectance values for selected 33 reflectance measurements for a set of 11 wavelengths and three suspended sediment concentrations. The computation time was significantly reduced by several orders of magnitude by: (1) replacing the PHO-MC model with 11 wavelengths specific pseudo-simulator models developed by applying artificial neural network approach; and (2) using a nondominated sorted genetic algorithm –II (NSGA II) to search the global optimal solution of four parameters of IOP equations. Determined IOP values of suspended minerals were then successfully validated by using them as input to PHO-MC model to predict reflectance values for an independent set of 287 combinations of 41 wavelengths and seven suspended sediment concentrations.     

基于蚀刻工艺的光纤曲率传感器的研究

针对薄结构应变测量中因应变幅值小,需有极高灵敏度的检测元件,而采用一种具有蚀刻结构的曲率传感器用于薄结构的应变测量。通过理论研究以及相应的实验证明:该传感器用于薄结构的应变测量是可行的,并具有独特的优势。     

Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities†

Growing interest in urban systems as ecological entities calls for some standards in parameterizing biophysical composition of urban environments. A vegetation-impervious surface-soil ( V-I-S) model is presented as a possible basis for standardization. The V-I-S model may serve as a foundation for characterizing urban/near-urban environments universally, and for comparison of urban morphology within and between cities. Inasmuch as the model may be driven by satellite digital data, it may serve as a global model of urban ecosystem analysis and comparison world-wide. The V-I-S model may prove useful for urban change detection and growth modelling, for environmental impact analysis from urbanization, for energy- and water-related investigations, and for certain dimensions of human ecosystem analysis of the city as well.     

A forward image model for passive optical remote sensing of river bathymetry

By facilitating measurement of river channel morphology, remote sensing techniques could enable significant advances in our understanding of fluvial systems. To realize this potential, researchers must first gain confidence in image-derived river information, as well as an appreciation of its inherent limitations. This paper describes a forward image model (FIM) for examining the capabilities and constraints associated with passive optical remote sensing of river bathymetry. Image data are simulated “from the streambed up” by first using information on depth and bottom reflectance to parameterize models of radiative transfer within the water column and atmosphere and then incorporating sensor technical specifications. This physics-based framework provides a means of assessing the potential for spectrally-based depth retrieval from a particular river of interest, given a sensor configuration. Forward image modeling of both a hypothetical meander bend and an actual gravel-bed river indicated that bathymetric accuracy and precision vary spatially as a function of channel morphology, with less reliable depth estimates in pools. A simpler, more computationally efficient analytical model highlighted additional controls on bathymetric uncertainty: optical depth and the ratio of the smallest detectable change in radiance to the bottom-reflected radiance. Application of the FIM to a complex, natural channel illustrated how the model can be used to quantify the effects of various sensor characteristics. Bathymetric accuracy was determined primarily by spatial resolution, due to mixed pixels along the banks and sub-pixel scale variations in depth, whereas depth retrieval precision depended on the sensor's ability to resolve subtle changes in radiance. This flexible forward modeling approach thus allows the utility of image-derived river information to be evaluated in the context of specific investigations, leading to more efficient, more informed use of remote sensing methods across a range of fluvial environments.     

Mapping the distribution of coral reefs and associated sublittoral habitats in Pacific Panama: a comparison of optical satellite sensors and classification methodologies

This research compared the ability of Landsat ETM+, Quickbird and three image classification methods for discriminating amongst coral reefs and associated habitats in Pacific Panama. Landsat ETM+ and Quickbird were able to discriminate coarse and intermediate habitat classes, but this was sensitive to classification method. Quickbird was significantly more accurate than Landsat (14% to 17%). Contextual editing was found to improve the user's accuracy of important habitats. The integration of object‐oriented classification with non‐spectral information in eCognition produced the most accurate results. This method allowed sufficiently accurate maps to be produced from Landsat, which was not possible using the maximum likelihood classifier. Object‐oriented classification was up to 24% more accurate than the maximum likelihood classifier for Landsat and up to 17% more accurate for Quickbird. The research indicates that classification methodology should be an important consideration in coral reef remote sensing. An object‐oriented approach to image classification shows potential for improving coral reef resource inventory.     

Analysis of energy fluxes and land surface parameters in a grassland ecosystem: a remote sensing perspective

A remote sensing‐based land surface characterization and flux estimation study was conducted using Landsat data from 1997 to 2003 on two grazing land experimental sites located at the Agricultural Research Services (ARS), Mandan, North Dakota. Spatially distributed surface energy fluxes [net radiation (R _n), soil heat flux (G), sensible heat (H), latent heat (LE)] and surface parameters [emissivity (ε), albedo (α), normalized difference vegetation index (NDVI) and surface temperature (T _sur)] were estimated and mapped at a pixel level from Landsat images and weather information using the Surface Energy Balance Algorithm for Land (SEBAL) procedure as a function of grazing land management: heavily grazed (HGP) and moderately grazed pastures (MGP). Energy fluxes and land surface parameters were mapped and comparisons were made between the two sites. Over the study period, H, ε and T _sur from HGP were higher by 6.7%, 18.2% and 2.9% than in MGP, respectively. The study also showed that G, LE and NDVI were higher by 1.3%, 1.6% and 7.4% for MGP than in HGP, respectively. The results of this study are beneficial in understanding the trends of land surface parameters, energy and water fluxes as a function of land management.     

Modelling pollution dispersion,the ecosystem and water quality in coastal waters: a review

This review is intended as a comprehensive but concise summary of present capabilities in coastal pollutant, ecosystem and water quality modelling. It reflects the recent rapid developments in multidisciplinary modelling in shelf seas.The behaviour of conservative pollutants that act as passive tracers is contrasted with those that have more complex behaviours, including oil spills. The importance of sediment modelling is emphasised, since contaminants commonly exist in both a dissolved and a particulate state, or adhere to sediments.Recently developed ecological models can have great complexity, reflecting the complexity of the real ecosystem. These models are now being linked to physical models of coastal waters and run with the same resolution. This has become possible only recently because of increases in computer power, particularly the availability of parallel systems at reasonable cost.The main advances in physical modelling are likely to come through greater understanding of turbulence and other sub-grid-scale processes as well as increased resolution.In the coastal seas there is often a lack of oceanographic data, which is even greater for the many biological and chemical variables than it is for physical variables. This is probably the single most important factor limiting the progress of operational water quality models.     

A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of field, AVIRIS and MODIS data

Using field data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery, and Moderate-Resolution Imaging Spectroradiometer (MODIS) data, a multi-scale analysis of ecosystem optical properties was performed for Sky Oaks, a Southern California chaparral ecosystem in the spectral network (SpecNet) and FLUXNET networks. The study covered a 4-year period (2000-2004), which included a severe drought in 2002 and a subsequent wildfire in July 2003, leading to extreme perturbation in ecosystem productivity and optical properties. Two vegetation greenness indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)), and a measure of the fraction of photosynthetically active radiation absorbed by vegetation (fPAR), were compared across sampling platforms, which ranged in pixel size from 1 m (tram system in the field) to 1000 m (MODIS satellite sensor). The three MODIS products closely followed the same seasonal trends as the tram and AVIRIS data, but tended to be higher than the tram and AVIRIS values, particularly for fPAR and NDVI. Following a wildfire that removed all green vegetation, the overestimation in MODIS fPAR values was particularly clear. The MODIS fPAR algorithm (version 4 vs. v.4.1) had a significant effect on the degree of overestimation, with v. 4.1 improving the agreement with the other sensors (AVIRIS and tram) for vegetated conditions, but not for low, post-fire values. The differences between MODIS products and the products from the other platform sensors could not be entirely attributed to differences in sensor spectral responses or sampling scale. These results are consistent with several other recently published studies that indicate that MODIS overestimates fPAR and thus net primary production (NPP) for many terrestrial ecosystems, and demonstrates the need for proper validation of MODIS terrestrial biospheric products by direct comparison against optical signals at other spatial scales, as is now possible at several SpecNet sites. The study also demonstrates the utility of in-situ field sampling (e.g. tram systems) and hyperspectral aircraft imagery for proper interpretation of satellite data taken at coarse spatial scales.