Search Results (8)

This study investigates the potential of gradient-weighted class activation mapping (Grad-CAM++) in enhancing land cover and land use (LCLU) classification using deep learning models. A U-Net and an Attention U-Net model were trained on Sentinel-2 imagery to classify 10 LCLU classes in a study area in Northern Ontario, Canada (centered at 49.17° N, 83.03° W). The classes included water, wetland, deciduous forest, mixed forest, coniferous forest, barren, urban/development, agriculture, shrubland, and no data (masked areas). The U-Net model achieved overall accuracy of 70.68%, a mean intersection over union (IoU) of 0.4852, and an F1 score of 0.7150, slightly outperforming the Attention U-Net model. Grad-CAM++ visualizations revealed that both models correctly focused on relevant features for each LCLU class, enhancing the interpretability of deep learning models in remote sensing applications. The findings suggest that integrating Grad-CAM++ with deep learning architectures can improve model transparency and guide future enhancements in LCLU classification tasks. Full article

With the recent thrust to convert forests in Ontario’s Clay Belt to agricultural land, a vital need arises to assess the attendant effects on carbon and greenhouse gas (GHG) emissions. This paper examines the possible effect of land conversion on soil organic carbon and GHG emissions within a study area in Northern Ontario, Canada, during the next two decades under different land management schemes. The study established a framework to conduct simulations with the DNDC model for agricultural lands and the CBM for forested areas. The methodology involves a unique change detection method for models’ land cover and disturbance inputs. The work highlights the improvement in carbon simulation accuracy from better inputs to carbon models. Furthermore, it addresses modalities to ensure fewer uncertainties are introduced while merging data from multiple geospatial data sources. The simulations demonstrated that the carbon sequestration potential in the forests was almost double the soil organic carbon accumulation in the agricultural lands. Validations done for the estimation of carbon sequestered included comparisons of the carbon model outputs from field survey data from 2018–2021. In most sites, the carbon amounts from the computer models compared to those from the field survey, within limits of error. The average uncertainties in GHG emissions ranged from ~0.5% to 12.8%. Full article

Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) and Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather and all-day imaging capability to supply dense observations in the early crop season. This study applied the local window attention transformer (LWAT) to time-series SAR data, including RCM and Sentinel-1, for early-season crop classification. The performance of this integration was evaluated over crop-dominated regions (corn, soybean and wheat) in southwest Ontario, Canada. Comparative analyses against several machine learning and deep learning methods revealed the superiority of the LWAT, achieving an impressive F1-score of 97.96% and a Kappa coefficient of 97.08% for the northern crop region and F1-scores of 98.07% and 97.02% for the southern crop region when leveraging time-series data from RCM and Sentinel-1, respectively. Additionally, by the incremental procedure, the evolution of accuracy determined by RCM and Sentinel-1 was analyzed, which demonstrated that RCM performed better at the beginning of the season and could achieve comparable accuracy to that achieved by utilizing both datasets. Moreover, the beginning of stem elongation of corn was identified as a crucial phenological stage to acquire acceptable crop maps in the early season. This study explores the potential of RCM to provide reliable prior information early enough to assist with in-season production forecasting and decision making. Full article

Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and harvest events was explored. The study was conducted for the 2019 growing season in Temiskaming Shores, an agricultural area in Northern Ontario, Canada. Time-series SAR data acquired by Sentinel-1 constellation with the interferometric wide (IW) mode with dual polarizations in VV (vertical transmit and vertical receive) and VH (vertical transmit and horizontal receive) were obtained. interferometric SAR (InSAR) processing was conducted to derive coherence between each pair of SAR images acquired consecutively in time throughout the year. Crop seeding and harvest dates were determined by analyzing the time-series InSAR coherence and SAR backscattering. Variation of SAR backscattering coefficients, particularly the VH polarization, revealed seasonal crop growth patterns. The change in InSAR coherence can be linked to change of surface structure induced by seeding or harvest operations. Using a set of physically based rules, a simple algorithm was developed to determine crop seeding and harvest dates, with an accuracy of 85% (n = 67) for seeding-date identification and 56% (n = 77) for harvest-date identification. The extra challenge in harvest detection could be attributed to the impacts of weather conditions, such as rain and its effects on soil moisture and crop dielectric properties during the harvest season. Other factors such as post-harvest residue removal and field ploughing could also complicate the identification of harvest event. Overall, given its mechanism to acquire images with InSAR capability at 12-day revisiting cycle with a single satellite for most part of the Earth, the Sentinel-1 constellation provides a great data source for detecting crop field management activities through coherent or incoherent change detection techniques. It is anticipated that this method could perform even better at a shorter six-day revisiting cycle with both satellites for Sentinel-1. With the successful launch (2019) of the Canadian RADARSAT Constellation Mission (RCM) with its tri-satellite system and four polarizations, we are likely to see improved system reliability and monitoring efficiency. Full article

Farming in Northern Ontario is limited to less than 1% of the total land area available. With over 2000 farms, this is home to about 6% of the province’s population, concentrated in the five major southern border cities of Thunder Bay, Sault Ste. Marie, Timmins, Sudbury and North Bay, with a significant presence of indigenous (i.e., First Nations) and disadvantaged peoples. This review highlights the challenges and opportunities of agriculture in Northern Ontario and offers a few strategies for establishing and sustaining agricultural operations locally. The challenges of farming in this region include the prevalence of adverse climatic conditions, lack of crop/economic diversification, insufficient infrastructure and support services, presence of small local markets, an aging population and youth out-migration, attitudes of dependency on government and limited investment potential. Nevertheless, this region offers much potential for farming as it contains significant amounts of fertile soils, good road networks and affordable land to start up farm businesses. Furthermore, the changing climate could be a boon to improve growing conditions, with expanded cropping options and increased yields in recent years. Production and consumption of local foods, conducting innovative on-farm research that addresses the needs of local producers including First Nations peoples, fostering regional research centres, building relationships through networking, exchange of ideas through effective use of different extension avenues, and collaboration and assisting local producers with market development may help establish a more competitive and sustainable agrifood sector in Northern Ontario. Favourable government policies to support growers who have experienced damage to their crops, forages and livestock due to adverse climatic conditions will further help sustain and expand their agricultural operations. Full article

Aboriginal people in Canada experience disproportionately high rates of diet-related illnesses, such as obesity and diabetes. Food insecurity has been identified as a contributing factor to these illnesses along with a loss of traditional lifestyle. Current food systems within northern subarctic and arctic regions of Canada rely heavily on imported foods that are expensive (when available), and are environmentally unsustainable. A warming subarctic and arctic climate present challenges, but also offers the opportunity for local agricultural production that can increase food security and promote a more sustainable food system. In this study the feasibility of sustainably growing potatoes (Solanum tuberosum L.) utilizing agroforestry practices to enhance food security in remote subarctic communities is explored through a case study in Fort Albany First Nation in northern Ontario, Canada. Potato crops were grown over a two-year period and rotated into plots that had been planted with green bush beans (Phaseolus vulgaris L.). Results showed that potatoes and bush beans could be grown successfully in the subarctic without the use of greenhouses with yields comparable to more conventional high-input agricultural methods. In subarctic Canada, sustainable local food production can help to promote social capital, healthier lifestyles, and food security. Full article

The purpose of this research is to analyze how changes in acquisition time and incidence angle affect various C-band synthetic aperture radar (SAR) polarimetric intensities, co-polarized phase information, polarimetric response plots and decomposition parameters for various crops typical of Northern Ontario, Canada. We examine how these parameters may be used to monitor the growth stages of five common cash crops, namely, barley (Hordeum vulgare), canola (Brassica napus), oat (Avena sativa), soybean (Glycine max) and wheat (Triticum spp.). In total, nine RADARSAT-2 polarimetric images were analyzed across a 14-week period beginning in June and ending in September 2011 using two incidence angles of approximately 26° and 41°. As expected, the backscatter intensities for all targets were found to show a higher response when acquired at the steeper incidence angle (26°). All cash crop targets showed a rise and fall in backscatter response over the course of the growing season, coinciding with changing growth stages. Slight phase differences were observed for cereal crops, possibly due to one of the polarizations penetrating between the rows allowing double-bounce to occur. The polarimetric response plots and decompositions offered insight into the scattering mechanisms of each crop type, generally showing an increase in volume scattering as the crops reached maturity. Specifically, the contributions of the crops increased towards the volume scattering component and zones 4 and 2, as the crops matured in regards to the Freeman-Durden and Cloude-Pottier decompositions respectively. Overall, soybean and canola showed a more similar response in comparison to the cereal cash crops. Although the study focused on Northern Ontario, it is anticipated that these results would be relevant in investigations of multi-temporal RADARSAT-2 for agricultural zones with similar crop types. Full article

The high prevalence of food insecurity experienced by northern First Nations partially results from dependence on an expensive import-based food system that typically lacks nutritional quality and further displaces traditional food systems. In the present study, the feasibility of import substitution by Agroforestry Community Gardens (AFCGs) as socio-ecologically and culturally sustainable means of enhancing food security was explored through a case study of Fort Albany First Nation in subarctic Ontario, Canada. Agroforestry is a diverse tree-crop agricultural system that has enhanced food security in the tropics and subtropics. Study sites were selected for long-term agroforestry research to compare Salix spp. (willow)-dominated AFCG plots to a “no tree” control plot in Fort Albany. Initial soil and vegetative analysis revealed a high capacity for all sites to support mixed produce with noted modifications, as well as potential competitive and beneficial willow-crop interactions. It is anticipated that inclusion of willow trees will enhance the long-term productive capacity of the AFCG test plots. As an adaptable and dynamic system, AFCGs have potential to act as a more reliable local agrarian system and a refuge for culturally significant plants in high-latitude First Nation socio-ecological systems, which are particularly vulnerable to rapid cultural, climatic, and ecological change. Full article