Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake

Giardino, Claudia; Bresciani, Mariano; Valentini, Emiliana; Gasperini, Luca; Bolpagni, Rossano; Brando, Vittorio E.

doi:10.1016/j.rse.2014.04.034

Cited by 105 publications

(81 citation statements)

References 63 publications

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“…Focusing on simulated reflectances, according to the spectral configuration of OLI, OLCI and MSI for the three lakes, Fig. 4 highlights that reflectance values, have peak in the green region (about 550 nm), which is typical in turbid productive waters, such as those in the Mantua and Trasimeno lakes [Bresciani et al, 2013;Giardino et al, 2014]. In Lake Garda the simulated spectra are lower due to the lower concentrations of SPM of Lake Garda compared to Mantua and Trasimeno.…”

Section: Resultsmentioning

confidence: 96%

“…This result highlights how the SPM variability affects water reflectance. Indeed SPM has a spatial pattern concentration, which consequently causes different spectral responses of the water reflectance [Giardino et al, 2014]. In all bands, the sum of the main effects of WQPs in Lake Trasimeno are always lower than 100%, which means that there are coupling effects (4-6%).…”

Section: Resultsmentioning

confidence: 99%

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Sensitivity analysis of a bio-optical model for Italian lakes focused on Landsat-8, Sentinel-2 and Sentinel-3

Manzo

Bresciani²,

Giardino³

et al. 2015

European Journal of Remote Sensing

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We analysed the sensitivity of a Case-2 bio-optical model where the water reflectance is computed as a function of concentrations of three optical water quality parameters (WQPs) of three Italian lakes (Garda, Mantua and Trasimeno) and their specific absorption and backscattering coefficients. The modelled reflectance is computed based on the spectral characteristics of three optical sensors, on-board Landsat-8, Sentinel-2 and Sentinel-3. The variance-based analysis was able to quantify the lake-dependence for all (50,000 runs) the simulated reflectance. The results confirmed that Sentinel-3 water reflectance is sensitive to WQPs in all the trophic conditions investigated.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Sensitivity analysis of a bio-optical model for Italian lakes focused on Landsat-8, Sentinel-2 and Sentinel-3

Manzo

Bresciani²,

Giardino³

et al. 2015

European Journal of Remote Sensing

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“…Additional irradiance reflectance spectra (R 0+ (λ) [-]) of bare sediment and vegetation (Potamogeton pectinatus) were collected ex situ with an ASD LabSpec4515 (Analytical Spectral Devices Inc.; range: 350 nm to 2500 nm; interval: 1 nm). Following the method of Giardino et al [27] SAV was harvested and its spectral response was then being measured at the beach (ex situ). Mean and standard deviations of single measurements were computed and resampled to RapidEye spectral response curves.…”

Section: Data Collection and Processingmentioning

confidence: 99%

Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany)

Fritz

Dörnhöfer

Schneider

et al. 2017

Water

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Submersed aquatic vegetation (SAV) is sensitive to changes in environmental conditions and plays an important role as a long-term indictor for the trophic state of freshwater lakes. Variations in water level height, nutrient condition, light availability and water temperature affect the growth and species composition of SAV. Detailed information about seasonal variations in littoral bottom coverage are still unknown, although these effects are expected to mask climate change-related long-term changes, as derived by snapshots of standard monitoring methods included in the European Water Framework Directive. Remote sensing offers concepts to map SAV quickly, within large areas, and at short intervals. This study analyses the potential of a semi-empirical method to map littoral bottom coverage by a multi-seasonal approach. Depth-invariant indices were calculated for four Atmospheric & Topographic Correction (ATCOR2) atmospheric corrected RapidEye data sets acquired at Lake Kummerow, Germany, between June and August 2015. RapidEye data evaluation was supported by in situ measurements of the diffuse attenuation coefficient of the water column and bottom reflectance. The processing chain was able to differentiate between SAV and sandy sediment. The successive increase of SAV coverage from June to August was correctly monitored. Comparisons with in situ and Google Earth imagery revealed medium accuracies (kappa coefficient = 0.61, overall accuracy = 72.2%). The analysed time series further revealed how water constituents and temporary surface phenomena such as sun glint or algal blooms influence the identification success of lake bottom substrates. An abundant algal bloom biased the interpretability of shallow water substrate such that a differentiation of sediments and SAV patches failed completely. Despite the documented limitations, mapping of SAV using RapidEye seems possible, even in eutrophic lakes.

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“…To distinguish between optically-deep and -shallow waters (Figure 2) a threshold of 10% [37] of the optimization error associated to spectral inversion procedure of optically-deep model was used. Then, in shallow waters pixels, BOMBER was run for estimating bottom depth and a linear unmixing of two-benthic classes: one for sand-rubble substrate, the other assembling seagrass and corals, whilst the water optical properties were held constant across the imagery consistently with previous studies [38,39].…”

Section: Image Pre-processing and Classification Strategymentioning

confidence: 99%

Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations

Giardino¹,

Bresciani²,

Fava

et al. 2015

Remote Sensing

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Abstract:In this study we produced the first thematic maps of submerged and coastal habitats of Lampi Island (Myanmar) from in situ and satellite data. To focus on key elements of bio-diversity typically existing in tropical islands the detection of corals, seagrass, and mangrove forests was addressed. Satellite data were acquired from Landsat-8; for the purpose of validation Rapid-Eye data were also used. In situ data supporting image processing were collected in a field campaign performed from 28 February to 4 March 2015 at the time of sensors overpasses. A hybrid approach based on bio-optical modeling and supervised classification techniques was applied to atmospherically-corrected Landsat-8 data. Bottom depth estimations, to be used in the classification process of shallow waters, were in good agreement with depth soundings (R 2 = 0.87). Corals were classified with producer and user accuracies of 58% and 77%, while a lower accuracy (producer and user accuracies of 50%) was found for the seagrass due to the patchy distribution of meadows; accuracies more than 88% were obtained for mangrove forests. The classification indicated the presence of 18 mangroves sites with extension larger than 5 km 2 ; for 15 of those the coexistence of corals and seagrass were also found in the fronting bays, suggesting a significant rate of biodiversity for the study area.

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Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake

Cited by 105 publications

References 63 publications

Sensitivity analysis of a bio-optical model for Italian lakes focused on Landsat-8, Sentinel-2 and Sentinel-3

Sensitivity analysis of a bio-optical model for Italian lakes focused on Landsat-8, Sentinel-2 and Sentinel-3

Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany)

Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations

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