Mapping and monitoring the extent of submerged aquatic vegetation in the Laurentian Great Lakes with multi-scale satellite remote sensing

Shuchman, Robert A.; Sayers, Michael J.; Brooks, Colin

doi:10.1016/j.jglr.2013.05.006

Cited by 56 publications

(46 citation statements)

References 18 publications

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“…Differing bottom coverage and species-specific structure elements like biomass, chlorophyll content, canopy height and alignment explain the variations of the shape of the spectra within the growing season. The species-specific phenologic development depends on the environmental conditions such as water temperature, water clarity, morphology and nutrient load [8,24,62]. To characterize the response of different macrophyte species to different environmental conditions, the species-specific spectral signatures within two growing seasons within two growing seasons is most informative.…”

Section: Phenologic Development In R Rs Spectra and Water Temperaturementioning

confidence: 99%

“…Environmental parameters such as water clarity, water temperature and nutrient load affect macrophyte expansion and phenologic development [24,62]. Environmental parameters vary from year to year, affecting the prediction accuracy of the model outputs.…”

Section: Reflectance Modelmentioning

confidence: 99%

“…Annual variations of different environmental parameters such as water clarity, temperature, sediment quality and nutrient loading are well known factors controlling the distribution and phenologic development of submersed macrophyte populations [21][22][23][24][25][26][27][28]. For several years, these natural variations have been superimposed by continuously increasing mean annual water temperatures, higher frequencies of heavy rain events, and prolongations as well as other temporal shifts of growing seasons.…”

Section: Introductionmentioning

confidence: 99%

“…As an important variable influencing the growth pattern of submersed macrophytes [24][25][26][27][28], water temperature was chosen in this study. The effect of sediment quality on macrophyte growth [21][22][23] was beyond the scope of this study, mainly because the same patches were analyzed for both years.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Variation in Spectral Response of Submerged Aquatic Macrophytes: A Case Study at Lake Starnberg (Germany)

Fritz

Schneider

Geist

2017

Water

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Submerged macrophytes are important structural components of freshwater ecosystems that are widely used as long-term bioindicators for the trophic state of freshwater lakes. Climate change and related rising water temperatures are suspected to affect macrophyte growth and species composition as well as the length of the growing season. Alternative to the traditional ground-based monitoring methods, remote sensing is expected to provide fast and effective tools to map submerged macrophytes at short intervals and over large areas. This study analyses interrelations between spectral signature, plant phenology and the length of growing season as influenced by the variable water temperature. During the growing seasons of 2011 and 2015, remote sensing reflectance spectra of macrophytes and sediment were collected systematically in-situ with hyperspectral underwater spectroradiometer at Lake Starnberg, Germany. The established spectral libraries were used to develop reflectance models. The combination of spectral information and phenologic characteristics allows the development of a phenologic fingerprint for each macrophyte species. By inversion, the reflectance models deliver day and daytime specific spectral signatures of the macrophyte populations. The subsequent classification processing chain allowed distinguishing species-specific macrophyte growth at different phenologic stages. The analysis of spectral signatures within the phenologic development indicates that the invasive species Elodea nuttallii is less affected by water temperature oscillations than the native species Chara spp. and Potamogeton perfoliatus.

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Section: Phenologic Development In R Rs Spectra and Water Temperaturementioning

confidence: 99%

Section: Reflectance Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Seasonal Variation in Spectral Response of Submerged Aquatic Macrophytes: A Case Study at Lake Starnberg (Germany)

Fritz

Schneider

Geist

2017

Water

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“…SAV is one of the biological quality elements (BQE) used in monitoring Visible Imaging Sensor (MIVIS) to detect the distribution of seagrass (Posidonia oceanica) in a coastal lagoon in Italy. To assess the littoral bottom coverage of inland waters, Brooks et al [38] and Shuchman et al [39] investigated SAV (notably Cladophora spec.) at the Laurentian Great Lakes.…”

Section: Introductionmentioning

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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Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat

et al. 2017

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. 2017. Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat. Ecosphere 8(7):e01883. 10. 1002/ecs2.1883 Abstract. Efficient management and prevention of species invasions requires accurate prediction of where species of concern can arrive and persist. Species distribution models provide one way to identify potentially suitable habitat by developing the relationship between climate variables and species occurrence data. However, these models when applied to freshwater invasions are complicated by two factors. The first is that the range expansions that typically occur as part of the invasion process violate standard species distribution model assumptions of data stationarity. Second, predicting potential range of freshwater aquatic species is complicated by the reliance on terrestrial climate measurements to develop occurrence relationships for species that occur in aquatic environments. To overcome these obstacles, we combined a recently developed algorithm for species distribution modeling-range bagging-with newly available aquatic habitat-specific information from the North American Great Lakes region to predict suitable habitat for three potential invasive species: golden mussel, killer shrimp, and northern snakehead. Range bagging may more accurately predict relative suitability than other methods because it focuses on the limits of the species environmental tolerances rather than central tendency or "typical" cases. Overlaying the species distribution model output with aquatic habitat-specific data then allowed for more specific predictions of areas with high suitability. Our results indicate there is suitable habitat for northern snakehead in the Great Lakes, particularly shallow coastal habitats in the lower four Great Lakes where literature suggests they will favor areas of wetland and submerged aquatic vegetation. These coastal areas also offer the highest suitability for golden mussel, but our models suggest they are marginal habitats. Globally, the Great Lakes provide the closest match to the currently invaded range of killer shrimp, but they appear to pose an intermediate risk to the region. Range bagging provided reliable predictions when assessed either by a standard test set or by tests for spatial transferability, with golden mussel being the most difficult to accurately predict. Our approach illustrates the strength of combining multiple sources of data, while reiterating the need for increased measurement of freshwater habitat at high spatial resolutions to improve the ability to predict potential invasive species.

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Mapping and monitoring the extent of submerged aquatic vegetation in the Laurentian Great Lakes with multi-scale satellite remote sensing

Cited by 56 publications

References 18 publications

Seasonal Variation in Spectral Response of Submerged Aquatic Macrophytes: A Case Study at Lake Starnberg (Germany)

Seasonal Variation in Spectral Response of Submerged Aquatic Macrophytes: A Case Study at Lake Starnberg (Germany)

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

Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat

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