Dan Walters

GIS^multicriteria evaluation with ordered weighted averaging (OWA): case study of developing watershed management strategies

This paper focuses on the parameterized-ordered weighted averaging (OWA) method. OWA is a family of multicriteria evaluation (or combination) rules. The proposed approach uses a parameter that serves as a mechanism for guiding multicriteria evaluation procedures. The parameter is incorporated into a method for obtaining the optimal order weights and for developing a transformation function. The function provides us with a consistent way of modifying the criterion values so that the multicriteria combination procedures can be guided by specifying a single parameter. The parameterized-OWA method has been implemented in a GIS environment as a GIS–OWA module and it has been tested in a real-world situation for developing management strategies in the Cedar Creek watershed in Ontario, Canada. Given a set of evaluation criteria, the problem is to evaluate areas in the watershed for rehabilitation and enhancement projects. Using the GIS–OWA method, a number of alternative strategies for rehabilitation and enhancement projects have been generated and evaluated.

Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests- A Case Study in Northeastern Ontario, Canada

With the growth of the low altitude remote sensing (LARS) industry in recent years, their practical application in precision agriculture seems all the more possible. However, only a few scientists have reported using LARS to monitor crop conditions. Moreover, there have been concerns regarding the feasibility of such systems for producers given the issues related to the post-processing of images, technical expertise, and timely delivery of information. The purpose of this study is to showcase actual requests by farmers to monitor crop conditions in their fields using an unmanned aerial vehicle (UAV). Working in collaboration with farmers in northeastern Ontario, we use optical and near-infrared imagery to monitor fertilizer trials, conduct crop scouting and map field tile drainage. We demonstrate that LARS imagery has many practical applications. However, 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 the LARS and to process the imagery, and the influence from local weather conditions (e.g. clouds, wind) on image acquisition all need to be considered. Consequently, at present a feasible solution for producers might be the use of LARS service provided by private consultants or in collaboration with LARS scientific research teams.

Integrated Water Resources Management in Canada: the experience of watershed agencies

Abstract Water agencies from 7 of the 10 Canadian provinces shared their experiences regarding history, successes, challenges and lessons learned with integrated watershed management. Based on these contributions, it is clear that an integrated approach does not mean ‘all-encompassing’. Rather, it proposes desirable and feasible solutions through a systems approach based on sound technical information (e.g. biophysical and socio-economic), public engagement and monitoring. The roles of all participants must be clearly defined in order to promote success and facilitate implementation. Enduring and emerging challenges, such as adequate capacity and financing, engagement with Aboriginal communities and other stakeholders, and successful implementation, are identified.

Lessons from implementing integrated water resource management: a case study of the North Bay-Mattawa Conservation Authority, Ontario

Abstract This case study explores the North Bay-Mattawa Conservation Authority’s experience in implementing IWRM. Successes include protecting life and property by mitigating flood and erosion hazards; building capacity through multi-stakeholder collaborations; and fostering community stewardship. Ongoing challenges include limited resources and narrow mandate for addressing broader watershed and natural resources issues; and a need to enhance relationships with First Nations. The NBMCA has learned numerous lessons on how to apply IWRM, including collaborating early and often and fostering community stewardship.

Why Do Some First Nations Communities Have Safe Water and Others Not? Socioeconomic Determinants of Drinking Water Risk

Securing safe and adequate drinking water is an ongoing issue for many Canadian First Nations communities despite nearly 15 years of reports, studies, policy changes, financial commitments, and regulations. The federal drinking water evaluation scheme is narrowly scoped, ignoring community level social factors, which may play a role in access to safe water in First Nations. This research used the 2006 Aboriginal Affairs and Northern Development Canada First Nations Drinking Water System Risk Survey data and the Community Well-Being Index, including labour force, education, housing, and income, from the 2006 Census. Bivariate analysis was conducted using the Spearman’s correlation, Kendall’s tau correlation, and Pearson’s correlation. Multivariable analysis was conducted using an ordinal (proportional or cumulative odds) regression model. Results showed that the regression model was significant. Community socioeconomic indicators had no relationship with drinking water risk characterization in both the bivariate and multivariable models, with the sole exception of labour force, which had a significantly positive effect on drinking water risk rankings. Socioeconomic factors were not important in explaining access to safe drinking water in First Nations communities. Improvements in the quality of safe water data as well as an examination of other community processes are required to address this pressing policy issue.

Visual analytics of high-frequency lake monitoring data

In recognizing the cumulative effects of multiple stressors on altering aquatic ecosystem function, scientists have become increasingly interested in capturing high-frequency response variables using a variety of sensors. This practice has led to a demand for novel ways to visualize and analyze the wealth of data in order to meet policy and management goals. Time series data collected as part of these monitoring activities are not easily analyzed with traditional methods. In this paper, a visual analytics system is described that leverages humans’ innate capability for pattern recognition and feature detection. High-frequency monitoring of weather and water conditions in Lake Nipissing, a large, shallow, inland lake in northeastern Ontario, Canada, is used as a case study. These visualizations are presented as Web-based tools to facilitate community-based participatory research among scientists, government agencies, and community stakeholders. These analytics techniques contribute to collaborative research endeavors and to the understanding of the response of lake conditions to environmental change.

Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas

Abstract Agriculture in northern Ontario, Canada, has not yet reached the level of development of the southern regions of the province. In spite of the increasing desirability of the former region for agricultural expansion, northern agricultural producers – as well as other producers in “emerging” areas – have less access to information and decision support services relative to more established agricultural regions. At the same time, geographic information systems (GIS) are now being integrated into precision agriculture to assess field variability, to ensure optimal use of information, to maximize output, and to increase efficiency. To address this trend, a community-based research initiative based on an interactive web-based information visualization and GIS decision support system has been deployed with the aim of providing northern Ontario producers with access to the data they need to make the best possible decisions concerning their crops. This system employs citizen science and community-based participatory research to build a mutually beneficial partnership between agricultural producers, researchers, and other community stakeholders.