温帯落葉樹林における光合成パラメータのダイナミクスとハイパースペクトル分光反射特性による評価

博士（理学）doctoral創造科学技術大学院静岡大学甲第1210号non

Including Leaf Traits Improves a Deep Neural Network Model for Predicting Photosynthetic Capacity from Reflectance

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Developing Hyperspectral Indices for Assessing Seasonal Variations in the Ratio of Chlorophyll to Carotenoid in Deciduous Forests

Leaf pigments are sensitive to various stress conditions and senescent stages. Variation in the ratio of chlorophyll to carotenoid content provides valuable insights into the understanding of the physiological and phenological status of plants in deciduous forests. While the use of spectral indices to assess this ratio has been attempted previously, almost all indices were derived indirectly from those developed for chlorophyll and carotenoid contents. Furthermore, there has been little focus on the seasonal dynamics of the ratio, which is a good proxy for leaf senescence, resulting in only a few studies ever being carried out on tracing the ratio over an entire growing season by using spectral indices. In this study, we developed a novel hyperspectral index for tracing seasonal variations of the ratio in deciduous forests, based on a composite dataset of two field measurement datasets from Japan and one publicly available dataset (Angers). Various spectral transformations were employed during this process in order to identify the most robust hyperspectral index. The results show that the wavelength difference (D) type index, using wavelengths of 540 and 1396 nm (calculated from the transformed spectra that were preprocessed by the combination of extended multiplicative scatter correction (EMSC) and first-order derivative), exhibited the highest accuracy for the estimation of the chlorophyll/carotenoid ratio (R2 = 0.57, RPD = 1.52). Further evaluation revealed that the index maintained a good performance at different seasonal stages and can be considered a useful proxy for the ratio in deciduous species. These findings provide a basis for the usage of hyperspectral information in the assessment of vegetation functions. Although promising, extensive evaluations of the proposed index are still required for other functional types of plants

Exploring the instability of the relationship between maximum potential electron transport rate and maximum carboxylation rate in cool-temperate deciduous forests

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Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs

Timely acquisition of forest structure is crucial for understanding the dynamics of ecosystem functions. Despite the fact that the combination of different quantitative structure models (QSMs) and point cloud sources (ALS and DAP) has shown great potential to characterize tree structure, few studies have addressed their pros and cons in alpine temperate deciduous forests. In this study, different point clouds from UAV-mounted LiDAR and DAP under leaf-off conditions were first processed into individual tree point clouds, and then explicit 3D tree models of the forest were reconstructed using the TreeQSM and AdQSM methods. Structural metrics obtained from the two QSMs were evaluated based on terrestrial LiDAR (TLS)-based surveys. The results showed that ALS-based predictions of forest structure outperformed DAP-based predictions at both plot and tree levels. TreeQSM performed with comparable accuracy to AdQSM for estimating tree height, regardless of ALS (plot level: 0.93 vs. 0.94; tree level: 0.92 vs. 0.92) and DAP (plot level: 0.86 vs. 0.86; tree level: 0.89 vs. 0.90) point clouds. These results provide a robust and efficient workflow that takes advantage of UAV monitoring for estimating forest structural metrics and suggest the effectiveness of LiDAR in temperate deciduous forests

Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

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Including leaf trait information helps empirical estimation of jmax from vcmax in cool-temperate deciduous forests

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Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

A clear understanding of the dynamics of photosynthetic capacity is crucial for accurate modeling of ecosystem carbon uptake. However, such dynamical information is hardly available and has dramatically impeded our understanding of carbon cycles. Although tremendous efforts have been made in coupling the dynamic information of photosynthetic capacity into models, using “proxies” rooted from the close relationships between photosynthetic capacity and other available leaf parameters remains the popular selection. Unfortunately, no consensus has yet been reached on such “proxies”, leading them only applicable to limited cases. In this study, we aim to identify if there are close relationships between the photosynthetic capacity (represented by the maximum carboxylation rate, Vcmax) and leaf traits for mature broadleaves within a cold temperature deciduous forest. This is based on a long-term in situ dataset including leaf chlorophyll content (Chl), leaf nitrogen concentration (Narea, Nmass), leaf carbon concentration (Carea, Cmass), equivalent water thickness (EWT), leaf mass per area (LMA), and leaf gas exchange measurements from which Vcmax was derived, for both sunlit and shaded leaves during leaf mature periods from 2014 to 2019. The results show that the Vcmax values of sunlit and shaded leaves were relatively stable during these periods, and no statistically significant interannual variations occurred (p > 0.05). However, this is not applicable to specific species. Path analysis revealed that Narea was the major contributor to Vcmax for sunlit leaves (0.502), while LMA had the greatest direct relationship with Vcmax for shaded leaves (0.625). The LMA has further been confirmed as a primary proxy if no leaf type information is available. These findings provide a promising way to better understand photosynthesis and to predict carbon and water cycles in temperate deciduous forests

Genetic Algorithm Captured the Informative Bands for Partial Least Squares Regression Better on Retrieving Leaf Nitrogen from Hyperspectral Reflectance

Nitrogen is a major nutrient regulating the physiological processes of plants. Although various partial least squares regression (PLSR) models have been proposed to estimate the leaf nitrogen content (LNC) from hyperspectral data with good accuracies, they are unfortunately not robust and are often not applicable to novel datasets beyond which they were developed. Selecting informative bands has been reported to be critical to refining the performance of the PLSR model and improving its robustness for general applications. However, no consensus on the optimal band selection method has yet been reached because the calibration and validation datasets are very often limited to a few species with small sample sizes. In this study, we address the question based on a relatively comprehensive joint dataset, including a simulation dataset generated from the recently developed leaf scale radiative transfer model (PROSPECT-PRO) and two public online datasets, for assessing different informative band selection techniques on the informative band selection. The results revealed that the goodness-of-fit of PLSR models to estimate LNC could be greatly improved by coupling appropriate band-selection methods rather than using full bands instead. The PLSR models calibrated from the simulation dataset with informative bands selected by genetic algorithm (GA) and uninformative variable elimination (UVE) method were reliable for retrieving the LNC of the two independent field-measured datasets as well. Particularly, GA was more effective to capture the informative bands for retrieving LNC from hyperspectral data. These findings should provide valuable insights for building robust PLSR models for retrieving LNC from hyperspectral remote sensing data

Genetic Algorithm Captured the Informative Bands for Partial Least Squares Regression Better on Retrieving Leaf Nitrogen from Hyperspectral Reflectance

Nitrogen is a major nutrient regulating the physiological processes of plants. Although various partial least squares regression (PLSR) models have been proposed to estimate the leaf nitrogen content (LNC) from hyperspectral data with good accuracies, they are unfortunately not robust and are often not applicable to novel datasets beyond which they were developed. Selecting informative bands has been reported to be critical to refining the performance of the PLSR model and improving its robustness for general applications. However, no consensus on the optimal band selection method has yet been reached because the calibration and validation datasets are very often limited to a few species with small sample sizes. In this study, we address the question based on a relatively comprehensive joint dataset, including a simulation dataset generated from the recently developed leaf scale radiative transfer model (PROSPECT-PRO) and two public online datasets, for assessing different informative band selection techniques on the informative band selection. The results revealed that the goodness-of-fit of PLSR models to estimate LNC could be greatly improved by coupling appropriate band-selection methods rather than using full bands instead. The PLSR models calibrated from the simulation dataset with informative bands selected by genetic algorithm (GA) and uninformative variable elimination (UVE) method were reliable for retrieving the LNC of the two independent field-measured datasets as well. Particularly, GA was more effective to capture the informative bands for retrieving LNC from hyperspectral data. These findings should provide valuable insights for building robust PLSR models for retrieving LNC from hyperspectral remote sensing data