Characterising spatiotemporal environmental and natural variation using a dynamic habitat index throughout the province of Ontario

Understanding changes in landscape productivity patterns is critical for management of ecosystems, characterising biodiversity, and monitoring climate change. Vegetation productivity, a key functional component of terrestrial ecosystems, can be readily monitored using remote sensing and can also be combined with other spatial information, such as land cover and topography, to provide a more comprehensive understanding, at the landscape scale, of ecosystem dynamics. Ontario is the second largest and most populated province in Canada covering approximately 1 million km2 with a population of 13 million. Befitting such a large and complex area, Ontario is characterised by high environmental and land cover diversity. In order to assess temporal trends in vegetation productivity across the province we utilised a series of 10-day composites of Medium Resolution Imaging Spectroradiometer (MERIS) fraction of Photosynthetically Active Radiation (fPAR) over a 6-year period (2003–2008). Indicators of annual productivity, seasonality and the minimum amount of vegetated cover were all calculated from the fPAR time-series to evaluate vegetation condition. To define the level of observed natural variability and areas which fall within, and beyond, the variability thresholds, we utilised the Theil–Sen's non-parametric statistical trend test. Results indicate that over the 6-year period decreasing trends in productivity dominated the Ontario landscape. Using spatial data layers of potential drivers of vegetation variability, we found that different land cover types exhibited significantly different trend amplitudes and proportions with elevation a significant correlate for most of the observed trends. Distance from nearest road or human settlement, indicative of anthropogenic drivers, was not significantly related to the observed trends. Recent and older burn areas were linked to decreasing and increasing trends in satellite derived productivity respectively. Our findings suggest that characterising trends in landscape productivity patterns over large areas is possible using remote sensing technology and the integration of productivity trends with explanatory spatial data sets can provide insights into drivers of productivity dynamics. These insights can aid in provincial and national monitoring activities as well as be used to focus more detailed analysis to regions of specific interest.