Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

Salt Marsh Elevation and Habitat Mapping Using Hyperspectral and LIDAR Data

Accurate mapping of both elevation and plant distributions in salt marshes is important for management and conservation goals. Although light detection and ranging (LIDAR) is effective at measuring surface elevations, laser penetration is limited in dense salt marsh vegetation. In a previous study, we found that LIDAR-derived digital elevation model (DEM) error varied with vegetation cover. We derived cover-class-specific correction factors to reduce these errors, including separate corrections for three different height classes of Spartina alterniflora, the dominant macrophyte in southeastern U.S. salt marshes. In order to apply these cover class-specific corrections, it is necessary to have information on the distribution of cover classes in a LIDAR-derived DEM. Hyperspectral imagery has been shown to be suitable for the separation of salt marsh vegetation species by spectral signatures, and can be used to determine cover classes; however, there is persistent confusion both among the different height classes of S. alterniflora and between plants and mud (the Spartina problem). This paper presents a method to overcome the respective limitations of LIDAR and hyperspectral imagery through the use of multisensor data. An initial classification of hyperspectral imagery based on the maximum likelihood classification algorithm was used in a decision tree in combination with elevation and normalized difference vegetation index (NDVI) derived from the hyperspectral imagery to map nine salt marsh cover classes. The decision tree appreciably reduced the Spartina problem by reassigning classes using these ancillary data and resulted in a final overall classification accuracy of 90%, with a quantity disagreement of 1% and an allocation disagreement of 9%. The resulting hyperspectral image classification was then used as the basis for applying cover class-specific elevation correction factors to the LIDAR-derived DEM. Applying these correction factors greatly improved the accuracy of the DEM: overall mean error decreased from 0.10 ± 0.12 (SD) to − 0.003 ± 0.10 m, and root mean squared error from 0.15 to 0.10 m. Our results suggest that the use of decision trees to combine elevation and spectral information can aid both hyperspectral image classification and DEM elevation mapping

Landscape Estimates of Habitat Types, Plant Biomass, and Invertebrate Densities in a Georgia Salt Marsh

Salt marshes often contain remarkable spatial heterogeneity at multiple scales across the landscape. A combination of advanced remote-sensing approaches (hyperspectral imagery and lidar) and conventional field survey methods was used to produce detailed quantifications and maps of marsh platform geomorphology, vegetation composition and biomass, and invertebrate patterns in a Georgia (USA) salt marsh. Community structure was largely related to hydrology, elevation, and soil properties. Both abiotic drivers and community patterns varied among subwatersheds and across the landscape at larger spatial scales. The authors conclude that measurements of marsh ecosystem structure and processes are spatially contextual and not scalable without detailed geospatial analysis. Efforts to protect and restore coastal marshes must strive to document, understand, and conserve this inherent spatial complexity

Estimation of Chlorophyll a Content in Inland Turbidity Waters Using WorldView-2 Imagery: A Case Study of the Guanting Reservoir, Beijing, China

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R2, RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation

Author Correction: GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

An author of the paper was omitted in the original version (Ted Conroy, University of Waikato, New Zealand). This has been corrected in the pdf and HTML versions of the paper, and the associated metadata