Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy

Generality of leaf spectroscopic models for predicting key foliar functional traits across continents: A comparison between physically- and empirically-based approaches

Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest

Key leaf functional traits, such as chlorophyll and carotenoids content (Cab and Cxc), equivalent water thickness (EWT), and leaf mass per area (LMA), are essential to the characterization and monitoring of ecosystem function. Spectroscopy provides access to these four leaf traits by relying on their specific spectral absorptions over the 0.4–2.5 µm domain. In this study, we compare the performance of three categories of estimation methods to retrieve these four leaf traits from laboratory directional-hemispherical leaf reflectance and transmittance measurements: statistical, physical, and hybrid methods. To this aim, a dataset pooling samples from 114 deciduous and evergreen oak trees was collected on four sites in California (woodland savannas and mixed forests) over three seasons (spring, summer and fall) and was used to assess the performance of each method. Physical and hybrid methods were based on the PROSPECT leaf radiative transfer model. Physical methods included inversion of PROSPECT from iterative algorithms and look-up table (LUT)-based inversion. For LUT-based methods, two distance functions and two sampling schemes were tested. For statistical and hybrid methods, four distinct machine learning regression algorithms were compared: ridge, partial least squares regression (PLSR), Gaussian process regression (GPR), and random forest regression (RFR). In addition, we evaluated the transferability of statistical methods using an independent dataset (ANGERS Leaf optical properties database) to train the regression algorithms. Thus, a total of 17 estimations were compared. Firstly, we studied the PROSPECT leaf structural parameter N retrieved by iterative inversions and its distribution over our oak-specific dataset. N showed a more pronounced seasonal dependency for the deciduous species than for the evergreen species. For the four traits, the statistical methods trained on our dataset outperformed the PROSPECT-based methods. More particularly, statistical methods using GPR yielded the most accurate estimates (RMSE = 5.0 µg·cm−2; 1.3 µg·cm−2; 0.0009 cm; and 0.0009 g·cm−2 for Cab, Cxc, EWT, and LMA, respectively). Among the PROSPECT-based methods, the iterative inversion of this model led to the most accurate results for Cab, Cxc, and EWT (RMSE = 7.8 µg·cm−2; 2.0 µg·cm−2; and 0.0035 cm, respectively), while for LMA, a hybrid method with RFR (RMSE = 0.0030 g·cm−2) was the most accurate. These results showed that estimation accuracy is independent of the season. Considering the transferability of statistical methods, for the four leaf traits, estimation performance was inferior for estimators built on the ANGERS database compared to estimators built exclusively on our dataset. However, for EWT and LMA, we demonstrated that these types of statistical methods lead to better estimation accuracy than PROSPECT-based methods (RMSE = 0.0016 cm and 0.0013 g·cm−2 respectively). Finally, our results showed that more differences were observed between plant functional types than between species or seasons.

Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1-19.4%; coefficient of determination, 0.28-0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28-81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental-scale functional diversity.

Mapping foliar functional traits and their uncertainties across three years in a grassland experiment

Equivalent water thickness (EWT) and leaf mass per area (LMA) are important indicators of plant processes, such as photosynthetic and potential growth rates and health status, and are also important variables for fire risk assessment. Retrieving these traits through remote sensing is challenging and often requires calibration with in situ measurements to provide acceptable results. However, calibration data cannot be expected to be available at the operational level when estimating EWT and LMA over large regions. In this study, we assessed the ability of a hybrid retrieval method, consisting of training a random forest regressor (RFR) over the outputs of the discrete anisotropic radiative transfer (DART) model, to yield accurate EWT and LMA estimates depending on the scene modeling within DART and the spectral interval considered. We show that canopy abstractions mostly affect crown reflectance over the 0.75–1.3 μm range. It was observed that excluding these wavelengths when training the RFR resulted in the abstraction level having no effect on the subsequent LMA estimates (RMSE of 0.0019 g/cm2 for both the detailed and abstract models), and EWT estimates were not affected by the level of abstraction. Over AVIRIS-Next Generation images, we showed that the hybrid method trained with a simplified scene obtained accuracies (RMSE of 0.0029 and 0.0028 g/cm2 for LMA and EWT) consistent with what had been obtained from the test dataset of the calibration phase (RMSE of 0.0031 and 0.0032 g/cm2 for LMA and EWT), and the result yielded spatially coherent maps. The results demonstrate that, provided an appropriate spectral domain is used, the uncertainties inherent to the abstract modeling of tree crowns within an RTM do not significantly affect EWT and LMA accuracy estimates when tree crowns can be identified in the images.

Synergistic retrieval of mangrove vital functional traits using field hyperspectral and satellite data

Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning

Determination of foliar traits in an ecologically distinct conifer species in Maine using Sentinel-2 imagery and site variables: Assessing the effect of leaf trait expression and upscaling approach on prediction accuracy

Remote sensing provides a consistent form of observation for biodiversity monitoring across space and time. However, the regional mapping of forest species diversity is still difficult because of the complexity of species distribution and overlapping tree crowns. A new method called “spectranomics” that maps forest species richness based on leaf chemical and spectroscopic traits using imaging spectroscopy was developed by Asner and Martin. In this paper, we use this method to detect the relationships among the spectral, biochemical and taxonomic diversity of tree species, based on 20 dominant canopy species collected in a subtropical forest study site in China. Eight biochemical components (chlorophyll, carotenoid, specific leaf area, equivalent water thickness, nitrogen, phosphorus, cellulose and lignin) are quantified by spectral signatures (R2 = 0.57–0.85, p < 0.01). We also find that the simulated maximum species number based on the eight optimal biochemical components is approximately 15, which is suitable for most 30 m × 30 m forest sites within this study area. This research may support future work on regional species diversity mapping using airborne imaging spectroscopy.

Spectra-phenology integration for high-resolution, accurate, and scalable mapping of foliar functional traits using time-series Sentinel-2 data

Estimating nutritive, non-nutritive and defense foliar traits in spruce-fir stands using remote sensing and site data

Climate change disrupts ecosystems and increases extreme weather events. Modeling ecosystem functioning is crucial for effective adaptation strategies. This study focuses on quantifiable vegetation properties, including leaf area index (LAI), chlorophyll content (CHL), leaf mass per area (LMA), and equivalent water thickness (EWT). One of the most effective ways to retrieve plant biophysical and structural properties at a large scale is by using multispectral satellite images. However, accurately quantifying plant biophysical variables from such datasets presents several challenges, including understanding the influence of the vertical distribution of such traits within the canopy on the corresponding signal; the impact of sun-view geometry during image acquisition, the use of genotype-specific relationship or instead the use of genotype biophysical traits in the model. This study estimates biophysical variables from a large measurement dataset obtained on forest plantations in Sao Paulo, Brazil, and analyzes the effect of vertical heterogeneity, solar and viewing geometry, and associated biophysical properties.The dataset comprises in-situ and remote sensing data. In-situ measurements of LAI, CHL, LMA, and EWT were collected from 2019 to 2021 on 25 eucalypt genotypes. Biomass measurements were conducted in 1323 trees, and CHL, LMA, and EWT were measured per vertical third of the canopy. To evaluate the influence of the vertical heterogeneity, we defined a weighted expression of the top and middle thirds of the canopy to average these biophysical variables and relate them to the remote sensing data, which includes Sentinel-2 images acquired from 2019 to 2021, at dates close to the field measurements.  First, 22 vegetation indices (VIs) were used to build regression models, each regressing the weighted target variable to a VI. After determining the optimal weight, we tested the accuracy of the linear models by accounting for sun-sensor geometry and vegetation traits. Both 10-fold cross-validation and an independent test dataset were used to assess model performance together with root mean square (RMSE) and coefficient of determination (R2).Results show that most models using 70% top and 30% medium canopy produced the best performances in estimating CHL. For both LMA and EWT, the optimal percentage was 50%-50%. These outcomes indicate that shaded parts of the canopy play a significant role in the above-canopy reflectance, especially for LMA and EWT, which are particularly sensitive to spectral domains ranging from 1700 to 2400 nm, which has a higher transmittance rate towards the canopy. Concerning the inclusion of sun-sensor geometry, most models, generated with different VIs, benefitted from these variables to predict the target variable, resulting in lower RMSEs. The use of several canopy traits in the model reduced the error but would require to have previous knowledge of them. These empirical results underline the influence of sensor geometry and other biophysical properties on the prediction of LAI, CHL, EWT, and LMA. We believe that these results advocate for further investigation using radiative transfer model inversion.

Characterizing seasonal variation in foliar biochemistry with airborne imaging spectroscopy

Selective logging effects on plant functional traits depend on soil enzyme activity and nutrient cycling in a Pinus yunnanensis forest