Els Knaeps

Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity spectrum

A method for the detection and correction of water pixels affected by adjacency effects is presented. The approach is based on the comparison of spectra with the near infrared (NIR) similarity spectrum. Pixels affected by adjacency effects have a water-leaving reflectance spectrum with a different shape to the reference spectrum. This deviation from the similarity spectrum is used as a measure for the adjacency effect. Secondly, the correspondence with the NIR similarity spectrum is used to quantify and to correct for the contribution of the background radiance during atmospheric correction. The advantage of the approach is that it requires no a priori assumptions on the sediment load or related reflectance values in the NIR and can therefore be applied to turbid waters. The approach is tested on hyperspectral airborne data (Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS)) acquired above coastal and inland waters at different flight altitudes and under varying atmospheric conditions. As the NIR similarity spectrum forms the basis of the approach, the method will fail for water bodies for which this similarity spectrum is no longer valid.

Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata, Tanimbar, Indonesia

Airborne remote sensing with a CASI‐550 sensor has been used to map the benthic coverage and the bottom topography of the Pulau Nukaha coral reef located in the Tanimbar Archipelago (Southeast Moluccas, Eastern Indonesia). The image classification method adopted was performed in three steps. Firstly, five geomorphological reef components were identified using a supervised spectral angle mapping algorithm in combination with data collected during the field survey, i.e. benthic cover type, percentage cover and depth. Secondly, benthic cover mapping was performed for each of the five geomorphological components separately using an unsupervised hierarchical clustering algorithm followed by class aggregation using both spectral and spatial information. Finally, 16 benthic cover classes could be labelled using the benthic cover data collected during the field survey. The overall classification accuracy, calculated on the biological diverse fore reef, was 73% with a kappa coefficient of 0.63. A reliable bathymetric model (up to a depth of 15 m) of the Pulau Nukaha reef was also obtained using a semi‐analytical radiative transfer model. When compared with independent in‐situ depth measurements, the result proved relatively accurate (mean residual error: −0.9 m) and was consistent with the seabed topography (Pearson correlation coefficient: 86%).

Retrieval of Suspended Sediment from Advanced Hyperspectral Sensor Data in the Scheldt Estuary at Different Stages in the Tidal Cycle

Hyperspectral airborne remote sensing images and in-situ data are combined to assess the spatial and temporal sediment dynamics in the tidal Scheldt river. A log-linear empirical relationship has been developed between a near-infrared reflectance difference and total suspended matter. The relationship was shown to be relatively insensitive to the varying cirrus cloud cover occurring during data acquisition. The produced sediment maps show good agreement with known variations of turbidity over the tidal cycle: maximum turbidity around high water, gradual settling of the sediment in the succeeding slack water and resuspension at the onset of the ebb flow stage.

Improved correction methods for field measurements of particulate light backscattering in turbid waters

Monte Carlo simulations are used to compute the uncertainty associated to light backscattering measurements in turbid waters using the ECO-BB (WET Labs) and Hydroscat (HOBI Labs) scattering sensors. ECO-BB measurements provide an accurate estimate of the particulate volume scattering coefficient after correction for absorption along the short instrument pathlength. For Hydroscat measurements, because of a longer photon pathlength, both absorption and scattering effects must be corrected for. As the standard (sigma) correction potentially leads to large errors, an improved correction method is developed then validated using field inherent and apparent optical measurements carried out in turbid estuarine waters. Conclusions are also drawn to guide development of future short pathlength backscattering sensors for turbid waters.

A Seasonally Robust Empirical Algorithm to Retrieve Suspended Sediment Concentrations in the Scheldt River

A seasonally robust algorithm for the retrieval of Suspended Particulate Matter (SPM) in the Scheldt River from hyperspectral images is presented. This algorithm can be applied without the need to simultaneously acquire samples (from vessels and pontoons). Especially in dynamic environments such as estuaries, this leads to a large reduction of costs, both in equipment and personnel. The algorithm was established empirically using in situ data of the water-leaving reflectance obtained over the tidal cycle during different seasons and different years. Different bands and band combinations were tested. Strong correlations were obtained for exponential relationships between band ratios and SPM concentration. The best performing relationships are validated using airborne hyperspectral data acquired in June 2005 and October 2007 at different moments in the tidal cycle. A band ratio algorithm (710 nm/596 nm) was successfully applied to a hyperspectral AHS image of the Scheldt River to obtain an SPM concentration map.

A Wavelet-Enhanced Inversion Method for Water Quality Retrieval From High Spectral Resolution Data for Complex Waters

Optical remote sensing in complex waters is challenging because the optically active constituents may vary independently and have a combined and interacting influence on the remote sensing signal. Additionally, the remote sensing signal is influenced by noise and spectral contamination by confounding factors, resulting in ill-posedness and ill-conditionedness in the inversion of the model. There is a need for inversion methods that are less sensitive to these changing or shifting spectral features. We propose WaveIN, a wavelet-enhanced inversion method, specifically designed for complex waters. It integrates wavelet-transformed high-spectral resolution reflectance spectra in a multiscale analysis tool. Wavelets are less sensitive to a bias in the spectra and can avoid the changing or shifting spectral features by selecting specific wavelet scales. This paper applied WaveIN to simulated reflectance spectra for the Scheldt River. We tested different scenarios, where we added specific noise or confounding factors, specifically uncorrelated noise, contamination due to spectral mixing, a different sun zenith angle, and specific inherent optical property (SIOP) variation. WaveIN improved the constituent estimation in case of the reference scenario, contamination due to spectral mixing, and a different sun zenith angle. WaveIN could reduce, but not overcome, the influence of variation in SIOPs. Furthermore, it is sensitive to wavelet edge effects. In addition, it still requires in situ data for the wavelet scale selection. Future research should therefore improve the wavelet scale selection.

Spectral reflectance measurement methodologies for Tuz Golu field campaign

A field campaign had been organized in August 2010 on Tüz Gölü salt lake, Turkey, with the aim of characterizing the site for satellite optical sensor vicarious calibration, and of comparing different methodologies of surface reflectance factor characterization. Several teams have made ground-based reflectance measurements with a field spectrometer on different areas of the salt lake of 100 m × 300 m and 1km × 1 km size. Different types of sampling strategies and measurements methods have been used by the participants, and are described in this paper. Preliminary results on one area are presented, that show a good agreement between the different measurements.

Large-scale mapping of the riverbanks, mud flats and salt marshes of the Scheldt basin, using airborne imaging spectroscopy and LiDAR

For maintaining the tidal waterways in the Scheldt basin, including the rivers Rupel and Durme and a large part of the Nete catchment, and for ecological monitoring of the mud flats, salt marshes and riverbank vegetation, the Flemish government needs detailed maps of these rivers and their bank structures. These maps indicate not only vegetation types, plant associations and sediment types but also hard structures, such as quays, locks, sluices and roads. Different remote sensing techniques were used to collect the data necessary to produce the required detailed maps. During the months of July and August 2007 an airborne flight campaign took place to collect hyperspectral and LiDAR data of the Scheldt basin and the Nete catchments. These rivers have a total length of about 240 km. The Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor acquired hyperspectral data in 32 spectral bands covering the visible/near-infrared (VIS/NIR) part of the electromagnetic spectrum with a ground resolution of 1 m. A multiple binary classification algorithm based on Fishers linear discriminant analysis (LDA) was used to map the salt marshes and riverbank vegetation. Ground truth information, that is vegetation and sediment types, together with their geographical locations collected around the time of the flight campaign, was used to train the classifier in the later classification step. Laser scanning was performed using the Riegl LMS-Q560. The LiDAR dataset obtained had a resolution of at least 1 point per m2 and was used to produce a digital elevation model (DEM) that contains all elements of the terrain. From this DEM a digital terrain model (DTM) was derived by applying appropriate filtering techniques. The elevation models were used primarily to derive information on the height, slope and aspect of the banks and dikes, but they also served as expert knowledge in the classification of the mud flats and bank vegetation. Overall, this work illustrates how airborne hyperspectral and LiDAR data can be used to derive highly detailed maps of the sediments, vegetation and hard structures along tidal rivers in large river basins. It also shows how large datasets can be handled in an expert system, in combination with different classification techniques, to produce the required result and accuracy.

Bio-optical Modeling of Total Suspended Solids

Total suspended solids (TSS) play a fundamental role in inland waters as different materials including contaminants and pollutants can aggregate to these solids and brought in suspension. This can alter the state of the aquatic ecosystem and the use of freshwater resources. For instance, excessive suspended sediment might condition primary productivity and can hinder water use in agriculture. Suspended solids are one of the most successful parameters that can be measured by means of remote sensing due to the effect of TSS on backscattering and water leaving radiance. Consequently, a variety of applications have been developed since the eighties; they have generally been build on empirical or semi-empirical methods which use reflectance at appropriate wavebands as correlates, or semi-analytical and quasi-analytical approaches such as the spectral inversion procedures which relies on the matching of spectral data to bio-optical forward models. Forward bio-optical modeling is used to show the response of water leaving reflectance depending on inherent optical properties of particles and TSS concentrations. Then, remotely sensed data acquired by different optical sensors are presented to show the performance of state-of-art algorithms for mapping TSS and turbidity in different aquatic systems located in Northern Italy, which include deep clear lakes, a system of fluvial lakes characterized by highly productive waters and a segment of the longest Italian river prior reaching the delta. Overall, the conclusions presented in this chapter encourage the use of remote sensing technology to improve inland water management, although new research efforts remain open to adapt bio-optical modeling to TSS to the variety of sensors used in inland water applications.

Atmospheric Correction for Inland Waters

Abstract One of the inherent challenges in remote sensing is the effect of atmospheric gases and particles on the light received by the sensor. The effects of atmospheric interference need to be adequately corrected for in order to retrieve quantitative biophysical information about a water body from the remotely sensed signal. Atmospheric correction of remotely sensed data is more challenging for inland waters than for open ocean waters due to a number of factors. The proximity of inland waters to various terrestrial sources of atmospheric pollution results in a more optically heterogeneous atmosphere, which complicates atmospheric modeling; the signal received at the sensor is often contaminated by contribution from the adjacent land, which is particularly problematic in cases of raised topography surrounding the water body and complicates atmospheric correction; non-negligible reflectance of water in the near-infrared region due to high sediment concentrations in inland waters (from terrestrial sources such as soil erosion, surface runoff from rainfall, and agricultural and industrial discharge) makes it difficult to accurately estimate and remove the effect of atmospheric aerosol scattering on the received signal. In spite of these and other challenges, a few algorithms have been developed and used for atmospheric correction of remotely sensed data from inland waters with reasonable success. This chapter contains a discussion of the main challenges in atmospheric correction for inland waters and brief descriptions of a few existing atmospheric correction algorithms that are suitable for inland waters.