New mapping techniques to estimate the preferential loss of small wetlands on prairie landscapes

Serran, Jacqueline N.; Creed, Irena F.

doi:10.1002/hyp.10582

Cited by 60 publications

(44 citation statements)

References 59 publications

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“…() noted that consolidation drainage in the PPR results in landscape changes from many, smaller wetlands to fewer, larger wetlands. Van Meter and Basu (), Serran and Creed (), and Serran et al. () found that smaller wetlands have been disproportionately lost across the Prairie Pothole landscape.…”

Section: Hydrological Physical and Chemical Functions Of Nfwsmentioning

confidence: 99%

Hydrological, Physical, and Chemical Functions and Connectivity of Non‐Floodplain Wetlands to Downstream Waters: A Review

Lane

Leibowitz

Autrey

et al. 2018

J American Water Resour Assoc

105

108

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We reviewed the scientific literature on non‐floodplain wetlands (NFWs), freshwater wetlands typically located distal to riparian and floodplain systems, to determine hydrological, physical, and chemical functioning and stream and river network connectivity. We assayed the literature for source, sink, lag, and transformation functions, as well as factors affecting connectivity. We determined NFWs are important landscape components, hydrologically, physically, and chemically affecting downstream aquatic systems. NFWs are hydrologic and chemical sources for other waters, hydrologically connecting across long distances and contributing compounds such as methylated mercury and dissolved organic matter. NFWs reduced flood peaks and maintained baseflows in stream and river networks through hydrologic lag and sink functions, and sequestered or assimilated substantial nutrient inputs through chemical sink and transformative functions. Landscape‐scale connectivity of NFWs affects water and material fluxes to downstream river networks, substantially modifying the characteristics and function of downstream waters. Many factors determine the effects of NFW hydrological, physical, and chemical functions on downstream systems, and additional research quantifying these factors and impacts is warranted. We conclude NFWs are hydrologically, chemically, and physically interconnected with stream and river networks though this connectivity varies in frequency, duration, magnitude, and timing.

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Section: Hydrological Physical and Chemical Functions Of Nfwsmentioning

confidence: 99%

Hydrological, Physical, and Chemical Functions and Connectivity of Non‐Floodplain Wetlands to Downstream Waters: A Review

Lane

Leibowitz

Autrey

et al. 2018

J American Water Resour Assoc

105

108

View full text Add to dashboard Cite

show abstract

“…For the classification, different scenarios were implemented aiming at assessing the added value of the different derivatives generated in the previous processing steps. All scenarios included NDVI, NDWI and elevation, as their contribution is considered to be effective in recent studies [52][53][54]. Initially (SB), only the original S2 channels were used.…”

Section: Post-classification Correctionsmentioning

confidence: 99%

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

2017

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Abstract:This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes.

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“…; Cheng and Basu ), and biological (e.g., Fairbairn and Dinsmore ; Zamberletti et al. ) services; yet, these systems are being lost at increasing rates (VanMeter and Basu ; Serran and Creed ; Sofaer et al. ; Jones et al.…”

Section: Characterizing Hydrologic Connectivity Of Non‐floodplain Wetmentioning

confidence: 99%

Modeling Connectivity of Non‐floodplain Wetlands: Insights, Approaches, and Recommendations

Jones

Ameli

Neff

et al. 2019

J American Water Resour Assoc

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Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.

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New mapping techniques to estimate the preferential loss of small wetlands on prairie landscapes

Cited by 60 publications

References 59 publications

Hydrological, Physical, and Chemical Functions and Connectivity of Non‐Floodplain Wetlands to Downstream Waters: A Review

Hydrological, Physical, and Chemical Functions and Connectivity of Non‐Floodplain Wetlands to Downstream Waters: A Review

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

Modeling Connectivity of Non‐floodplain Wetlands: Insights, Approaches, and Recommendations

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