Alternative representations of in‐stream habitat: classification using remote sensing, hydraulic modeling, and fuzzy logic

Legleiter, Carl J.; Goodchild, Michael F.

doi:10.1080/13658810412331280220

Cited by 54 publications

(63 citation statements)

References 33 publications

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“…Wright et al (2000) extended this approach to fuzzy classifications of in-stream units, while Legleiter and Goodchild (2005) investigated fuzzy classifications coupled with unsupervised classification. Techniques used to extract ratio-level data rather than nominal categories include the spectral unmixing of fine particle sizes by Rainey et al (2003) and the spectral angle mapper approach to habitat classification of Leckie et al (2005).…”

Section: Remote Mapping Methodsmentioning

confidence: 99%

Optical remote mapping of rivers at sub‐meter resolutions and watershed extents

Marcus

Fonstad

2007

Earth Surf Processes Landf

250

198

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At watershed extents, our understanding of river form, process and function is largely based on locally intensive mapping of river reaches, or on spatially extensive but low density data scattered throughout a watershed (e.g. cross sections). The net effect has been to characterize streams as discontinuous systems. Recent advances in optical remote sensing of rivers indicate that it should now be possible to generate accurate and continuous maps of in‐stream habitats, depths, algae, wood, stream power and other features at sub‐meter resolutions across entire watersheds so long as the water is clear and the aerial view is unobstructed. Such maps would transform river science and management by providing improved data, better models and explanation, and enhanced applications. Obstacles to achieving this vision include variations in optics associated with shadows, water clarity, variable substrates and target–sun angle geometry. Logistical obstacles are primarily due to the reliance of existing ground validation procedures on time‐of‐flight field measurements, which are impossible to accomplish at watershed extents, particularly in large and difficult to access river basins. Philosophical issues must also be addressed that relate to the expectations around accuracy assessment, the need for and utility of physically based models to evaluate remote sensing results and the ethics of revealing information about river resources at fine spatial resolutions. Despite these obstacles and issues, catchment extent remote river mapping is now feasible, as is demonstrated by a proof‐of‐concept example for the Nueces River, Texas, and examples of how different image types (radar, lidar, thermal) could be merged with optical imagery. The greatest obstacle to development and implementation of more remote sensing, catchment scale ‘river observatories’ is the absence of broadly based funding initiatives to support collaborative research by multiple investigators in different river settings. Copyright © 2007 John Wiley & Sons, Ltd.

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Section: Remote Mapping Methodsmentioning

confidence: 99%

Optical remote mapping of rivers at sub‐meter resolutions and watershed extents

Marcus

Fonstad

2007

Earth Surf Processes Landf

250

198

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“…Defuzzification involves the process of transposing the fuzzy crisp outputs in the form of image data. The fuzzy logic technique has been used in many image classification applications [32,56,57]. The advantage of fuzzy logic over traditional and non-traditional classification techniques is its suitability and ability to deal with uncertainty and imprecision in a decision-making process, and thus offers a new approach for classifying remotely sensed images [32,56,57].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

et al. 2009

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Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE OPEN ACCESSRemote Sensing 2009, 1 4 6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.

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“…This incorporation of hydraulic variables can only be accomplished if hydraulic modeling is combined with remote sensing. For example, Legleiter and Goodchild [22] incorporated hydraulic modeling into the remote sensing process with a combination of supervised classification and fuzzy clustering to delineate channel habitat units. Such combined approaches, however, have rarely been applied, especially on tropical rivers.…”

Section: Discussionmentioning

confidence: 99%

Remote-Sensing Hydraulic Characterization of Channel Habitat Units in a Tropical Montane River: Bladen River, Belize

Praskievicz

Buege

2017

Remote Sensing

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Abstract:The physical characteristics of river systems exert significant control on the habitat for aquatic species, including the distribution of in-stream channel habitat units. Most previous studies on channel habitat units have focused on midlatitude rivers, which differ in several substantive ways from tropical rivers. Field delineation of channel habitat units is especially challenging in tropical rivers, many of which are remote and difficult to access. Here, we developed an approach for delineating channel habitat units based on a combination of field measurements, remote sensing, and hydraulic modeling, and applied it to a 4.1-km segment of the Bladen River in southern Belize. We found that the most prevalent channel habitat unit on the study segment was runs, followed by pools and riffles. Average spacing of channel habitat units was up to twice as high on the study segment than the typical values reported for midlatitude rivers, possibly because of high erosion rates in the tropical environment. The approach developed here can be applied to other rivers to build understanding of the controls on and spatial distribution of channel habitat units on tropical rivers and to support river management and conservation goals.

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Alternative representations of in‐stream habitat: classification using remote sensing, hydraulic modeling, and fuzzy logic

Cited by 54 publications

References 33 publications

Optical remote mapping of rivers at sub‐meter resolutions and watershed extents

Optical remote mapping of rivers at sub‐meter resolutions and watershed extents

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

Remote-Sensing Hydraulic Characterization of Channel Habitat Units in a Tropical Montane River: Bladen River, Belize

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