ABSTRACT
The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.
ABSTRACT
Macrophyte net primary productivity (NPP) is a significant but understudied component of the carbon budget in large Amazonian floodplains. Annual NPP is determined by the interaction between stem elongation (vertical growth) and plant cover changes (horizontal expansion), each affected differently by flood duration and amplitude. Therefore, hydrological changes as predicted for the Amazon basin could result in significant changes in annual macrophyte NPP. This study investigates the responses of macrophyte horizontal expansion and vertical growth to flooding variability, and its possible effects on the contribution of macrophytes to the carbon budget of Amazonian floodplains. Monthly macrophyte cover was estimated using satellite imagery for the 2003-2004 and 2004-2005 hydrological years, and biomass was measured in situ between 2003 and 2004. Regression models between macrophyte variables and river-stage data were used to build a semiempirical model of macrophyte NPP as a function of water level. Historical river-stage records (1970-2011) were used to simulate variations in NPP, as a function of annual flooding. Vertical growth varied by a factor of ca. 2 over the simulated years, whereas minimum and maximum annual cover varied by ca. 3.5 and 1.5, respectively. Results suggest that these processes act in opposite directions to determine macrophyte NPP, with larger sensitivity to changes in vertical growth, and thus maximum flooding levels. Years with uncommonly large flooding amplitude resulted in the highest NPP values, as both horizontal expansion and vertical growth were enhanced under these conditions. Over the simulated period, annual NPP varied by ca. 1.5 (1.06-1.63 TgC yr(-1) ). A small increasing trend in flooding amplitude, and by extension NPP, was observed for the studied period. Variability in growth rates caused by local biotic and abiotic factors, and the lack of knowledge on macrophyte physiological responses to extreme hydrological conditions remain the major sources of uncertainty.
Subject(s)
Floods , Models, Theoretical , Plant Development , Brazil , Climate Change , Computer Simulation , Rivers , Satellite ImageryABSTRACT
Aquatic vegetation is an important component of wetland and coastal ecosystems, playing a key role in the ecological functions of these environments. Surveys of macrophyte communities are commonly hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. Also, many vegetation characteristics can be estimated from reflectance measurements, such as species composition, vegetation structure, biomass, and plant physiological parameters. However, proper use of these methods requires an understanding of the physical processes behind the interaction between electromagnetic radiation and vegetation, and remote sensing of aquatic plants have some particular difficulties that have to be properly addressed in order to obtain successful results. The present paper reviews the theoretical background and possible applications of remote sensing techniques to the study of aquatic vegetation.
Subject(s)
Geographic Information Systems , Plants , WaterABSTRACT
A técnica de análise derivativa de dados espectrais foi usada para estudar a variação dos constituintes opticamente ativos (COAs) na água, por meio de dados de campo e de imagens do sensor orbital Hyperion/EO-1. A imagem Hyperion usada neste estudo foi adquirida no dia 23 de junho de 2005, no final do período de cheia. Uma campanha de campo foi realizada entre 23 e 29 de junho de 2005, para coletar dados espectrais e limnológicos in situ. A imagem foi pré-processada visando eliminar faixas de pixels anômalos e convertida de valores de radiância para reflectância de superfície, portanto, corrigidos dos efeitos de absorção e espalhamento atmosféricos. Uma análise da correlação foi realizada para examinar a associação da reflectância e de sua primeira derivada espectral com as concentrações dos COAs. Melhores resultados foram obtidos após a diferenciação dos espectros, o que ajudou a reduzir a influência de efeitos indesejáveis, provindos de diferentes fontes de radiância, sobre as medidas de reflectância da superfície da água realizadas em ambos os níveis de aquisição de dados. Por meio de ajustes de regressões empíricas, considerando o conjunto de dados Hyperion, a primeira derivada espectral em 711 nm explicou 86 por cento da variação da concentração de sedimentos inorgânicos em suspensão (µg.l-1) e a primeira derivada espectral em 691 nm explicou 73 por cento da variação na concentração da clorofila-alfa (µg.l-1). As relações de regressão foram não-lineares, pois, em geral, as águas que se misturam na planície de inundação Amazônica se tornam opticamente complexas. A técnica de análise derivativa hiperespectral demonstrou potenciais para mapear a composição dessas águas.
Derivative analysis of spectral data was used as a technique to study the variation of optically active constituents (OACs) of water, using field data and hyperspectral imagery of EO-1 Hyperion orbital sensor. The Hyperion image used in this study was acquired on June 23, 2005, at the end of the high water period for the Amazon River. A field campaign was carried out between June 23 and 29, 2005 to collect spectral and limnological in situ data. The image was pre-processed to remove stripes of abnormal pixels and converted from radiance to surface reflectance values, thus, correcting the effects of atmospheric absorption and scattering. A correlation analysis was carried out to examine the association of the spectral reflectance and its first derivative to the concentrations of OACs. Better results were obtained after spectra differentiation, which helped to reduce the influence of undesirable effects, coming from different sources of radiance, on the measurements of water surface reflectance taken at both data acquisition levels. Through empirical regression fits, considering the Hyperion dataset, the first spectral derivative at 711 nm explained 86 percent of the variation of suspended inorganic sediment concentration (µg.l-1), and the first derivative at 691 nm explained 73 percent of the variation of chlorophyll-a concentration (µg.l-1). The regression relations were nonlinear because, generally, the water masses that mix in the Amazon floodplain become optically complex. The hyperspectral derivative analysis demonstrated potential for mapping the composition of these waters.
