Accuracy Assessments of Airborne Hyperspectral Data for Mapping Opportunistic Plant Species in Freshwater Coastal Wetlands

Lopez, Ricardo D.; Edmonds, Curtis M.; Neale, Anne C.; Slonecker, Terrence; Jones, K. Bruce; Heggem, Daniel T.; Lyon, J. G.; Jaworski, Eugene; Garofalo, Donald; Williams, David F.

doi:10.1201/9780203497586.ch18

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…Several studies have been conducted to address the impact of the suspended sediments on the spectral signature of the surface waters (12,13,14,15). Researchers have usually addressed the general trends in the spectral responses of water samples with varying concentrations of organic and inorganic suspension.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing of the Coastal Environment

Kohler¹,

Bissett²,

Steward³

et al. 2007

Fourier Transform Spectroscopy/ Hyperspectral Imaging and Sounding of the Environment

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Remotely sensed earth observation (EO) has revolutionized our understanding of our dynamic environment. Hyperspectral remote sensing is, in many ways, the ultimate optical remote sensing technology. Hyperspectral remote sensing is suited especially well for environmental studies due to it's capability to discriminate between species and quantify the abundance of different materials and chemicals. In this thesis remote sensing methods for hyperspectral data, applicable in environmental monitoring of coastal environment are developed and tested. The planning of hyperspectral flight campaign HYPE08 in SouthWest Finland raised the need to validate and develop data processing methods for HYPE08 as well as for future hyperspectral flight campaigns. The research presented in this thesis can be largely considered as a response to this need. The study concentrates on four main topics: wetlands mapping, benthic mapping, water quality and atmospheric correction. The study was done using airborne hyperspectral data and field spectroscopy measurements. The results of this study emphasize the importance of local calibration and validation of methods used. Water quality retrieval algorithms developed in local environmental conditions outperformed those validated elsewhere. The results also show that hyperspectral remote sensing of benthic cover types is limited to rather shallow areas, indicating the need to use state of the art methodology in order to increase operational depth range. The proposed atmospheric correction algorithm produced very good results. The accuracy of model-based algorithm increases when Empirical-Line (EL) correction using spectral field measurements was applied to spectra generated by the model.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing of the Coastal Environment

Kohler¹,

Bissett²,

Steward³

et al. 2007

Fourier Transform Spectroscopy/ Hyperspectral Imaging and Sounding of the Environment

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“…The classification of reed beds showed good overall accuracy of 81.4%. It was found, however, that the small size and spatial arrangement of Phragmites stands was less than optimal considering Hyperion's spatial resolution of 30 m. Lopez et al [15] studied the discrimination of Phragmites using airborne hyperspectral data collected by the Probe-1 sensor. The study produced Phragmites maps showing an estimated accuracy of 80%.…”

Section: Introductionmentioning

confidence: 99%

Spectral Characteristics of Common Reed Beds: Studies on Spatial and Temporal Variability

Tuominen

Lipping

2016

Remote Sensing

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Reed beds are the second largest producer of biomass in Olkiluoto Island. Quantitative information on the extent and amount of reed stands is an integral part of the biosphere assessment related to long-term safety analysis of nuclear fuel repository site currently under construction. The major challenge in reed bed mapping is discrimination between reed and other green vegetation. Spectral field measurements were used to study the temporal and spatial variability of spectral characteristics of reed beds. Feasibility of discriminating reed beds from other vegetation based on hyperspectral measurements was studied as well. Results indicate that there is large temporal variation of reed bed spectra and the optimal time for data acquisition differs for old and new reed bed types. Comparing spectral characteristics of the reed bed and meadow classes in a local neighborhood indicated that the classes have high within-class spectral variability and similar mean spectra, however, 10 out of 11 targets had lower angle to the mean spectrum of the corresponding class than that of the other class when Spectral Angle Mapper (SAM) was used. Comparing the spectral characteristics of reed beds at four test sites within the Olkiluoto Island indicated that while some of the sites had similar spectra, the difference between others was remarkable. This is partly explained by different density and height of dead and live reed stems at the four sites.

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