Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest

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

Reviews and syntheses: remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery

Foliar nitrogen is a critical factor in leaf physiological processes, plant growth, and ecosystem functioning, which has been proposed as one of the essential biodiversity variables. Nitrogen has been quantified by a number of empirical approaches using hyperspectral data, but the retrieval of nitrogen through a physically based approach remains a challenge. A recent study by Wang et al. (2015a) has revealed that leaf protein can be successfully estimated from fresh leaf spectra using a revised leaf radiative transfer model PROPECT-5 which incorporated the effects of leaf protein and cellulose + lignin on leaf reflectance and transmittance. This provides a potential approach of estimating nitrogen using radiative transfer models given the correlation between protein and nitrogen. However, such a revised leaf model has not been tested for the estimation of leaf nitrogen at the canopy level. In this study, a canopy reflectance model INFORM, coupled with the revised PROSPECT-5 model, was used to retrieve leaf and canopy nitrogen content in a mixed temperate forest using the wavelengths of 8002500 nm from airborne hyperspectral imagery. Ecological criteria were applied to the parameterization of the model to reduce unrealistic combinations of input parameters. Global sensitivity analysis showed that leaf protein played a small but distinct role in driving the variation of canopy reflectance in the INFORM model. More accurate estimation was obtained for canopy nitrogen content (R2 = 0.64, RMSE = 1.90, NRMSE = 0.18) than leaf nitrogen content (R2 = 0.46, RMSE = 3.79e-05, NRMSE = 0.19). Moreover, inversion techniques, particularly regularized look-up tables, further improved the estimation accuracies compared to the original tables. Our results indicate that leaf and canopy nitrogen content can be retrieved successfully at the canopy level by inversion of INFORM. Both the direct and indirect effects of nitrogen on canopy reflectance are important for nitrogen estimation

Applicability of the PROSPECT model for estimating protein and cellulose plus lignin in fresh leaves

Hyperspectral remote sensing of leaf biochemicals is critical for understanding many biochemical processes. Leaf biochemical contents (e.g., protein, cellulose and lignin) in fresh and dry leaves have been quantified from hyperspectral data using empirical models. However, they cannot be retrieved for fresh leaves by inverting radiative transfer models. We demonstrated the applicability of PROSPECT leaf optical properties model in the separation of specific absorption coefficients for protein and cellulose + lignin following a newly proposed algorithm, and evaluated the feasibility in estimating leaf protein and cellulose + lignin content through model inversion. Assessment was performed across a large variety of plant species benefiting from the Leaf Optical Properties Experiment (LOPEX) dataset. To alleviate ill-posed problems, inversion was performed over different spectral subsets. The PROSPECT model with newly calibrated specific absorption coefficients was able to accurately reconstruct leaf reflectance and transmittance. Leaf protein and cellulose + lignin were estimated at moderate to good accuracies for both fresh and dry leaves. The spectral subset of 2100-2300 nm yielded the most accurate estimation of leaf cellulose + lignin (R2 = 0.70, RMSE = 5.21E-04 g/cm2 ) and protein (R2 = 0.47, RMSE = 2.75E-04 g/cm2 ) in fresh leaves, which were comparable with those obtained from stepwise multiple linear regressions (protein: R2 = 0.83, RMSE = 3.91E-04 g/cm2 ; cellulose + lignin: R2 = 0.66, RMSE = 2.02E-04 g/cm2 ). Our results confirm the importance of selecting a proper spectral subset that contains sufficient information for a successful inversion. For the first time, we provide promising estimations of leaf protein in fresh leaves through inversion of a radiative transfer model, which can be applied at canopy level for regional mapping if coupled with a canopy reflectance model and air- or spaceborne hyperspectral imaging

Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning

Leaf area index (LAI) has frequently been measured in the field using traditional optical methods such as digital hemispherical photography (DHP). However, in the DHP retrieved LAI, there is always contribution of woody components due to the difficulty in distinguishing woody and foliar materials. In addition, the leaf angle distribution which strongly affects the estimation of LAI is either ignored while using the convergent angle 57.5° or inversed simultaneously with LAI using multiple directions. Terrestrial laser scanning (TLS) provides a 3-dimensional view of the forest canopy, which we used in this study to improve LAI estimation by directly retrieving leaf angle distribution, and subsequently correcting foliage clumping and woody effects. The leaf angle distribution was retrieved by estimating the angle between the leaf normal vectors and the zenith vectors. The clumping index was obtained by using the gap size distribution method, while the woody contribution was evaluated based on an improved point classification between woody and foliar materials. Finally, the gap fraction derived from TLS was converted to effective LAI, and thence to LAI. The study was conducted for 31 forest plots including deciduous, coniferous and mixed plots in Bavarian Forest National Park. The classification accuracy was improved by approximately 10% using our method. Results showed that the clumping caused an underestimation of LAI ranging from 1.2% to 48.0%, while woody contribution led to an overestimation from 3.0% to 31.9% compared to the improved LAI. The combined error ranged from -46.2% to 32.6% of the leaf area index (LAI) measurements. The error was largely dependent on forest types. The clumping index of coniferous plots on average was lower than that of deciduous plots, whereas deciduous plots had a higher woody-to-total area ratio. The proposed method provides a more accurate estimate of LAI by eliminating clumping and woody effects, as well as the effect of leaf angl

Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects

A statistical relationship between canopy mass-based foliar nitrogen concentration (%N) and canopy bidirectional reflectance factor (BRF) has been repeatedly demonstrated. However, the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen. The canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF) has recently been suggested for estimating %N as it suppresses the canopy structural effects on BRF. However, estimation of %N using the scattering coefficient has not yet been investigated for longer spectral wavelengths (>855 nm). We retrieved the canopy scattering coefficient for wavelengths between 400 and 2500 nm from airborne hyperspectral imagery, and then applied a continuous wavelet analysis (CWA) to the scattering coefficient in order to estimate %N. Predictions of %N were also made using partial least squares regression (PLSR). We found that %N can be accurately retrieved using CWA (R2 = 0.65, RMSE = 0.33) when four wavelet features are combined, with CWA yielding a more accurate estimation than PLSR (R2 = 0.47, RMSE = 0.41). We also found that the wavelet features most sensitive to %N variation in the visible region relate to chlorophyll absorption, while wavelet features in the shortwave infrared regions relate to protein and dry matter absorption. Our results confirm that %N can be retrieved using the scattering coefficient after correcting for canopy structural effect. With the aid of high-fidelity airborne or upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling of ecosystem processes as well as ecosystem-climate feedbacks