Examining spectral reflectance features related to foliar nitrogen in forests: Implications for broad-scale nitrogen mapping

Lepine, L. C.; Ollinger, Scott V.; Ouimette, A.; Martin, Mary E.

doi:10.1016/j.rse.2015.11.028

Cited by 69 publications

(75 citation statements)

References 82 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2016

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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 (R 2 CV = 0.79, RMSE CV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R 2 CV = 0.76, RMSE CV = 0.27). In addition, the mean NIR reflectance (800-850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R 2 CV = 0.73, RMSE CV = 0.30). The PLSR model provided a less accurate estimation of %N (R 2 CV = 0.69, RMSE CV = 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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2016

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“…, Lepine et al. ). We suggest that the differences in spectral properties may be due to differences in both forest structure and tree species, which indicate long‐term impacts from past human settlement patterns.…”

Section: Discussionmentioning

confidence: 99%

Ancient Amazonian populations left lasting impacts on forest structure

et al. 2017

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Abstract. Amazonia contains a vast expanse of contiguous tropical forest and is influential in global carbon and hydrological cycles. Whether ancient Amazonia was highly disturbed or modestly impacted, and how ancient disturbances have shaped current forest ecosystem processes, is still under debate. Amazonian Dark Earths (ADEs), which are anthropic soil types with enriched nutrient levels, are one of the primary lines of evidence for ancient human presence and landscape modifications in settings that mostly lack stone structures and which are today covered by vegetation. We assessed the potential of using moderate spatial resolution optical satellite imagery to predict ADEs across the Amazon Basin. Maximum entropy modeling was used to develop a predictive model using locations of ADEs across the basin and satellite-derived remotely sensed indices. Amazonian Dark Earth sites were predicted to be primarily along the main rivers and in eastern Amazonia. Amazonian Dark Earth sites, when compared with randomly selected forested sites located within 50 km of ADE sites, were less green canopies (lower normalized difference vegetation index) and had lower canopy water content. This difference was accentuated in two drought years, 2005 and 2010. This is contrary to our expectation that ADE sites would have nutrient-rich soils that support trees with greener canopies and forests on ADE soils being more resilient to drought. Biomass and tree height were lower on ADE sites in comparison with randomly selected adjacent sites. Our results suggested that ADE-related ancient human impact on the forest is measurable across the entirety of the 6 million km 2 of Amazon Basin using remotely sensed data.

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“…A recent study in northern temperate forests explored the effect of spatial resolution on canopy N [%] estimation. The results showed that, although the prediction accuracy was reduced compared to what was achieved using PLS regression at higher spatial resolution, it was still possible to estimate canopy N[%] with r 2 between 0.34 and 0.81 using various vegetation indices computed from MODIS reflectance data at 500 m spatial resolution (Lepine et al, 2016). In this context, 365 the methodology applied in this article could be a valuable alternative to explore canopy N detection at larger scale.…”

Section: Perspectives For Larger Scale Applicationsmentioning

confidence: 95%

“…Detection of canopy N is often limited to local scale studies due to the spatial restrictions associated with N data acquisition in the field and treatment of high spatial resolution remote sensing imagery with limited spatial coverage (Lepine et al, 2016).…”

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Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Loozen

Rebel

Karssenberg

et al. 2017

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) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant relationships between MTCI and both canopy N concentration and content, r 2 = 0.32 and r 2 = 0.17, respectively.We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N 20 concentration was strongest for deciduous broadleaf and mixed plots (r 2 = 0.25 and r 2 = 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r 2 = 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots (r 2 = 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. capacity (Evans, 1989;Reich et al., 1995;Reich et al., 1997;Reich et al., 1999;Wright et al., 2004), specific leaf area, leaf life span (Reich et al., 1999;Wright et al., 2004) and light use efficiency (Kergoat et al., 2008). Leaf N concentration expressed on a leaf area basis, also called leaf N content (N g m -2 ) has also been linked with chlorophyll content, Rubisco content (Evans, 35 1989) and photosynthetic capacity (Evans, 1989;Reich et al., 1995). At stand scale, canopy nitrogen concentration, which represents the leaf N concentration averaged over the stand canopy, has also been found to correlate with above ground Net Primary Productivity (NPP) (Reich, 2012), while canopy N content has been linked with the canopy light use efficiency (Green et al., 2003).Given their links to many vegetation processes, leaf and canopy N variables could be used to constrain N cycle modules in 40 global vegetation models. At the global scale, ample data is available to constrain models for the C cycle; however, data to constrain the N cycle are limited. Currently, canopy N data is not widely available and canopy N sampling campaigns are timeconsuming and thus expensive tasks. Moreover, upscaling from local sampling campaign measurements represents an additional limitation. In this perspective, local, regional or even global remotely sensed canopy N estimates will be a valuable addition, enabling us to collect information in a less time intensive and expensive manner than traditional on-field sampling 45 campaigns. Such near global canopy N estimates will be beneficial as input in global vegetation models or to calibrate and validate these models.Currently, different remote sensing techniques have been applied to detect canopy N in terrestrial vegetation. Imaging spectrometry from either airborne or spaceborne sensors coupled with different analysis methods, including partial least squares regression (PLS), continuu...

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Examining spectral reflectance features related to foliar nitrogen in forests: Implications for broad-scale nitrogen mapping

Cited by 69 publications

References 82 publications

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

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

Ancient Amazonian populations left lasting impacts on forest structure

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

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