Abstract
Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of techniques. Vegetation indices (VIs) are a simple means of retrieving foliar nitrogen. Despite their popularity, few studies have been conducted to examine the utility of VIs for mapping canopy foliar nitrogen in a mixed forest context. In this study, we assessed the performance of 32 vegetation indices derived from HySpex airborne hyperspectral images for estimating canopy mass-based foliar nitrogen concentration (%N) in the Bavarian Forest National Park. The partial least squares regression (PLSR) was performed for comparison. These vegetation indices were classified into three categories that are mostly correlated to nitrogen, chlorophyll, and structural properties such as leaf area index (LAI). %N was destructively measured in 26 broadleaf, needle leaf, and mixed stand plots to represent the different species and canopy structure. The canopy foliar %N is defined as the plot-level mean foliar %N of all species weighted by species canopy foliar mass fraction. Our results showed that the variance of canopy foliar %N is mainly explained by functional type and species composition. The normalized difference nitrogen index (NDNI) produced the most accurate estimation of %N (R2CV = 0.79, RMSECV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R2CV = 0.76, RMSECV = 0.27). In addition, the mean NIR reflectance (800–850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R2CV = 0.73, RMSECV = 0.30). The PLSR model provided a less accurate estimation of %N (R2CV = 0.69, RMSECV = 0.32). We argue that the good performance of all three categories of vegetation indices in %N estimation can be attributed to the synergy among plant traits (i.e., canopy structure, leaf chemical and optical properties) while these traits may converge across plant species for evolutionary reasons. Our findings demonstrated the feasibility of using hyperspectral vegetation indices to estimate %N in a mixed temperate forest which may relate to the effect of the physical basis of nitrogen absorption features on canopy reflectance, or the biological links between nitrogen, chlorophyll, and canopy structure.
Highlights
Nitrogen is a primary regulator of many leaf physiological processes, such as photosynthesis, respiration, and transpiration [1,2,3], and is strongly linked to chlorophyll content, light use efficiency, and net primary production [4,5,6]
This study evaluated the performance of three categories of vegetation indices, mostly correlated to nitrogen, chlorophyll. and structural properties, derived from airborne hyperspectral imagery for to nitrogen, chlorophyll. and structural properties, derived from airborne hyperspectral imagery for canopy foliar nitrogen estimation in a mixed temperate forest
The canopy foliar nitrogen varied by canopy foliar nitrogen estimation in a mixed temperate forest
Summary
Nitrogen is a primary regulator of many leaf physiological processes, such as photosynthesis, respiration, and transpiration [1,2,3], and is strongly linked to chlorophyll content, light use efficiency, and net primary production [4,5,6]. Nitrogen has been retrieved with good accuracy using leaf- and canopy-level hyperspectral data despite the fact that it is only a relatively small constituent (0.2%–6.4%) in leaves [14,15]. Efforts have been made to enhance the absorption features of nitrogen and reduce the sensitivity of the aforementioned parameters to canopy reflectance. Spectral transformation is one of the approaches, such as using first/second derivatives and log transformation of reflectance [22,23] Other approaches, such as continuum removal [17,24], water removal [25,26], and wavelet analysis [27], improved the nitrogen retrieval
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