Publications by Menv Students

Consideration of climate change mitigation in Canadian environmental assessment: Intention and implementation

Hetmanchuk, K. (2019). Consideration of climate change mitigation in Canadian environmental assessment: Intention and implementation. Impact Assessment and Project Appraisal. 

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Quantifying a proposed project’s greenhouse gas (GHG) emissions and scrutinizing their effect on climate change are increasingly required in Canadian environmental assessment (EA) processes. This paper investigates to what degree an EA authority’s intention for the inclusion of GHG considerations has resulted in implementation into environmental impact statements (EISs) by proponents and how these considerations influence the achievement of GHG reduction targets. Fifteen projects across five Canadian jurisdictions were reviewed. The examination revealed that well-developed intentions by EA authorities did not necessarily result in proponents following guidelines for GHG consideration in their EISs due to the absence of regulation or clearly defined policies. Conversely, even though intentions by an EA authority are underdeveloped in some jurisdictions, EISs sometimes exhibited thorough GHG assessments due to mechanisms in the EA process through which GHG consideration by the proponent could be compelled. The examination did not reveal how GHG consideration in EA currently assists in meeting reduction targets. A GHG emissions limit imposed during the EA process could link EA to success in meeting these targets. 

Mapping cumulative impacts on Hong Kong's pink dolphin population 

Marcotte D., Hung D.K., & Caquard S. (2015). Mapping cumulative impacts on Hong Kong's pink dolphin population. Ocean & Coastal Management, 109 (2015), 51-63. 

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Indo-Pacific humpback dolphins have historically inhabited the northern waters off Lantau Island, Hong Kong; however their numbers have significantly decreased over the past decade, while human pressure has simultaneously increased. Based on a spatio-temporal analysis using a Geographic Information System (GIS), this study aims to assess the cumulative human impacts of local activities on this dolphin population since 1996. After introducing and discussing the multiple approaches, difficulties, and limitations to cumulative effects assessments (CEA), this paper outlines our proposed CEA methodology. Our methodology involves mapping and analysis of anthropogenic marine impacts in relation with historical dolphin distributions in the area. Local scale results show evidence of a relationship between the addition of new high-speed ferry (HSF) routes into the cumulative environment and the decrease in dolphins in a specific region known as the Brothers Islands. These results coincide with past research showing that whales and dolphins are significantly disrupted in the presence of high vessel traffic, which continues to grow in the northern waters off Lantau Island, Hong Kong and in many other places around the world.

Spatial decision support system: Controlled tile drainage – Calculate your benefits

Kaur, G.., Kross, A., Callegari, D., Sunohara, M., van Vliet, L., Rudy, H., Lapen, D., & McNairn, H. (2018, June). Spatial decision support system: Controlled tile drainage – calculate your benefits. Proceedings of the 14^th International Conference on Precision Agriculture. Paper presented at the 14^th International Conference on Precision Agriculture.

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Climate projection studies suggest that extreme heat waves and floods will become more frequent, affecting future crop yields by 20%-30%, globally. Managing vulnerability and risk begins at the farm level where best management practices can reduce the impacts associated with extreme weather events. A practice that can assist in mitigating the impact of some extreme events is controlled tile drainage (CTD). With CTD, producers use water flow control structures to manage the drainage of water from their fields, which allows producers to maintain soil water on their fields during periods of crop demand or allows free drainage to facilitate field trafficking and earlier spring seeding. The result is a dampening of the negative impact of extreme events on crop yields. In this study, a spatial decision support system was developed that will 1) allow farmers and other stakeholders to explore potential sites for implementation of tile drainage systems; 2) show predicted yield benefits of crops (corn and soybean) from CTD fields compared to crops associated with uncontrolled tile drainage (UCTD) systems, during varying growing season precipitation. A Multi-Criteria Suitability Analysis was performed to determine potential sites for the implementation of tile drainage systems. Crop yield was characterized by ground yield measurements and satellite-derived Normalized Difference Vegetation Index (NDVI). Yield benefits from CTD fields were determined as the difference between ground yield measurements or NDVI values from CTD compared to UCTD fields. Yield benefits were finally related to precipitation data to enable the creation of yield benefit prediction scenarios under varying precipitation. The results of the suitability analysis and yield difference prediction were combined in the tool, along with additional slope, soil drainage and precipitation layers. The tool development is ongoing and functional on https://demo.gatewaygeomatics.com/ctd/.

Evaluation of an artificial neural network approach for prediction of corn and soybean yield

Kross, A., Znoj, E., Callegari, D., Kaur, G., Sunohara, M., van Vliet., Rudy, H., Lapen, D., & McNairn, H. (2018, June). Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean Yield. Paper presented at the 14^th International Conference on Precision Agriculture.

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The ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based method (Advangeo® Prediction Software) was used to evaluate: 1) the relative importance of predictor variables for corn and soybean yield prediction, and 2) the potential of ANNs for predicting corn and soybean yield. Several satellite derived vegetation indices (e.g. normalized difference vegetation index - NDVI, red edge NDVI, simple ratio - SR, and the land surface water index - LSWI) and slope data were used as crop yield predictor variables, hypothesizing that different vegetation indices reflect different crop and site conditions. The study identified the SR index and the slope as the most important predictor variables for both crop types during both years. The number and dates of the images however were different for the two crop types (earlier dates for corn) and for the wetter (2011) and drier (2012) years. The relative mean absolute errors (RMAEs) were overall smaller for corn compared to soybean and 100% of the corn study sites had errors below 20% in both years. The errors were more variable for soybean. The results are promising and can provide yield estimates at the farm level, unlike current county level approaches.