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Dan Walters

Researcher at Nipissing University

Publications -  31
Citations -  1080

Dan Walters is an academic researcher from Nipissing University. The author has contributed to research in topics: Government & Integrated water resources management. The author has an hindex of 14, co-authored 30 publications receiving 820 citations. Previous affiliations of Dan Walters include University of Western Ontario.

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Assessment of red-edge vegetation indices for crop leaf area index estimation.

TL;DR: In this paper, the authors explored the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the VIS-based VIs.
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Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

TL;DR: In this paper, an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarIMetric images of an agricultural area in North-eastern Ontario, Canada.
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GIS–Multicriteria Evaluation with Ordered Weighted Averaging (OWA): Case Study of Developing Watershed Management Strategies

TL;DR: This paper focuses on the parameterized-ordered weighted averaging (OWA) method, a family of multicriteria evaluation (or combination) rules, which uses a parameter that serves as a mechanism for guiding multicritical evaluation procedures.
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Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data

TL;DR: In this paper, the authors investigated the sensitivity of Synthetic Aperture Radar (SAR) signatures to crop biophysical parameters or phenological stages, such as emergence, flowering, fruiting, maturing and senescence, for crop production surveillance and yield prediction.
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Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario, Canada

TL;DR: It is demonstrated that LARS imagery has many practical applications, and several obstacles remain, including the costs associated with both the LARS system and the image processing software, the extent of professional training required to operate and to process the imagery, and the influence from local weather conditions.