Canadian Forest Service Publications
Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest. 2015. Gokkaya, K.; Thomas, V.; Noland, T.; McCaughey, H.; Morrison, I.; Treitz, P. Applied Vegetation Science 18: 143-157.
Issued by: Great Lakes Forestry Centre
Catalog ID: 36397
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Questions: Can the ratio of nitrogen to phosphorus (N:P ratio) be predicted at canopy level using imaging spectroscopy (IS) and light detection and ranging (LiDAR) remote sensing data? How do temporal variation and difference in spa tial resolution of these data sources affect prediction accuracy of the canopy N:P ratio? Location: Boreal mixedwood forest, northern Ontario, Canada. Methods: Canopy N:P ratio was estimated using spectral indices calculated from IS data at two spatial resolutions, airborne and space-borne, across two sum mers. The relationship between the canopy N:P ratio and forest structure was investigated through analysis of LiDAR data. The impact of temporal variation on canopy N:P ratio and the different spatial resolution of IS data on prediction accuracy for canopy N:P was addressed. Maps of canopy N:P ratio generated from airborne and space-borne IS data were generated. Results: Airborne and space-borne ISdata explained 70% and 69% of the vari ation in canopy N:P, ratio, with predictions errors of 5.0% and 7.2%, respec tively, in two consecutive years. Predictions differed significantly with changes in spatial resolution. Predictive models obtained from LiDAR data explained 54% and 67% of the variation in canopy N:P ratio, with prediction errors of 6.1 % and 7.5%, respectively, for the 2 yrs. Conclusions: The results show that canopy N:P ratio can be predicted with remote sensing data based on the relationship between canopy N:P ratio and crown closure at this site. The spatial variation due to the mixed deciduous and coniferous forest type is the underlying mechanism that generates the observed spatial pattern in canopy N:P ratio in this ecosystem, and the canopy N:P ratio map displays this variation.
Plain Language Summary
We used spaceborne imaging spectroscopy (IS) and airborne LiDAR data to model macronutrients for a mixedwood boreal forest canopy in northern Ontario. Information on foliar macronutrients is required to understand plant physiological and ecosystem processes. The ability to measure, model and map foliar macronutrients at the forest canopy level provides information on the spatial patterns of ecosystem processes such as carbon exchange and provides insight on forest condition and stress. We found that the spatial distribution of macronutrient concentration at the canopy scale mimics distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using IS, LiDAR, or both demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data.