Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

Abstract

Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light OPEN ACCESSRemote Sens. 2015, 7 9046 detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R 2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination 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.

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

“…In this mixed temperate forest, functional type and species composition played the dominant role in explaining the variance of canopy foliar nitrogen. This is consistent with findings from different ecosystems, such as temperate, tropical, boreal, and Mediterranean ecosystems [29,[54][55][56]58,60].…”

“…Compared with multiple linear regression models, PLSR avoids the problem of co-linearity of variables which is inherent when using hyperspectral data. PLSR has been widely used in the remote sensing community for predicting vegetation parameters such as nitrogen [18,29,97]. In addition to vegetation indices, PLSR was used to relate the spectral data to field measured canopy foliar %N.…”

“…However, a weak correlation between %N and LAI was observed in this study (R 2 = 0.38, results not shown). A structural parameter, crown closure, was found to determine the relationship between nitrogen concentration and hyperspectral data in a boreal forest study [29], since crown closure represents the amount of green foliage in the canopy and thus controls the spectral response of the canopy. However, that relationship was not observed in this study (R 2 = 0.09, results not shown).…”

“…Leaf nitrogen has been determined in forest [18,23,[28][29][30], grassland [31][32][33], and crop ecosystems [34][35][36]. Empirical techniques are dominant in the retrieval of different vegetation variables from remote sensing data such as nitrogen, ranging from vegetation indices [37] and traditional regression techniques such as stepwise multiple linear regression [17] and partial least square regression [18], to a number of artificial intelligence methods such as support vector regression, neural network, and Bayesian model averaging [32,38,39].…”

“…Additionally, Townsend et al [52] disagreed that the %N-NIR relationship is necessarily spurious, as Wright et al [14] and Ollinger [53] indicated that the canopy structure and leaf properties may co-vary across plant functional types. Species and plant functional types (i.e., broadleaf and coniferous forest types) account for most of the variance of canopy chemistry that has been demonstrated across tropical [54][55][56][57], temperate [58], boreal forests [29,59], and Mediterranean ecosystems [60]. The link between species and canopy biochemistry can be explained by the concept of 'global leaf economics spectrum' [14], which means that the key plant traits such as leaf mass per area, specific leaf area, leaf nitrogen, leaf phosphorous, leaf lifespan, and photosynthesis fall into a spectrum across plant species, and species converge towards the functional traits globally [14,61].…”