Open Collections

Abstract

Commercial cranberry production involves an intensive management system that requires the close monitoring of the crop throughout the entire growing season. This is traditionally accomplished by ground surveying; however, this method is both time consuming and labour intensive and excessive bog traffic can be damaging to the ground-covering vines. In this study, colour near-infrared photography and quantitative image analysis techniques were used to determine the feasibility of using remote sensing to monitor site conditions within cranberry bogs and to relate these conditions to yield. Colour near-infrared images of four cranberry bogs were obtained three times over each of two growing seasons. Correlation and regression techniques were used to measure linear relationships between soil and foliar element concentrations, vine status, yield and remote sensing variables. Images were also examined using both supervised and unsupervised classification techniques to measure spatial relationships between biophysical and remote sensing variables. Sample sites containing high levels of chronic weed stress were also found to have high levels of soil and foliar Al, Fe, and Mn, suggesting that excessive levels of these metals had a negative influence on vine status and consequently fruit production. This was likely a result of the toxic effects on cranberry vine growth of high levels of Al, Fe and Mn made available by the acidic and wet conditions characteristic of bogs. Higher yielding sites were found to contain lower soil and foliar Al, Fe and Mn concentrations and higher Mg levels. Supervised image classification based on high correlation coefficients between yield and the NIR/R ratio was effective in identifying different levels of production within the bog. Unsupervised classification proved to be a fast and effective method of delineating areas containing high levels of weed infestation as well as areas that were poorly drained. Because of the negative impact of these two forms of stress on yield, unsupervised classification was also successful in identifying different production levels within the bog. The results suggest that remote sensing techniques, when used in conjunction with field sampling, can be an effective means of monitoring conditions within the bog and can be useful in improving yield forecasts.