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Hyperspectral Remote Sensing of Vegetation Functions

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 33230

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Special Issue Editors

Prof. Dr. Quan Wang

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Guest Editor

Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
Interests: hyperspectral RTM; ecophysiology; gas exchange; ecological modelling; remote sensing applications
Special Issues, Collections and Topics in MDPI journals

Dr. Jia Jin

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Guest Editor

Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China
Interests: quantitative remote sensing; plant physiology; biochemistry; ecosystem monitoring; radiative transfer model
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Special Issue Information

Dear Colleagues,

Hyperspectral information remotely sensed from different platforms at multiple spatial, temporal, and spectral scales offers more unprecedented data sources for revealing the properties of vegetation than ever before, opening the door for not only retrieving vegetation’s biophysical (structural), biochemical, and physiological traits, but also the possibility of tracing the dynamics of functions that are impossible with previous remote sensing activities. However, lacking the profound mechanical understanding of involved physical and physiological processes of hyperspectral data, which are scale-dependent, prevents their proper applications and needs to be explicitly addressed. This Special Issue is, thus, calling for state-of-the-art studies on processing and analyzing hyperspectral information acquired from different platforms (leaf spectroscopy, tower-based proximal remote sensing, UAV mounts, airplane/satellite-borne devices), with the target fof clarifying the underlying physical and physiological mechanisms and for accurately tracking the dynamics of vegetation functions. Special focus will be placed on, but is not limited to:

Novel techniques (statistical/RTM/machine-learning or deep-learning) for retrieving and tracing vegetation functions (especially ecophysiological processes) from hyperspectral data.
Novel research on clarifying the physical and physiological bases of hyperspectral information using field monitoring, laboratory-controlled experiments, or RTM simulation datasets.
Insightful research on upscaling/downscaling mechanisms of the relationships between hyperspectral information and vegetation functions from leaf to canopy and plot levels.

Prof. Dr. Quan Wang
Dr. Jia Jin
Guest Editors

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Keywords

leaf spectroscopy
proximal
hyperspectral imaging
RTM
physical and physiological mechanisms
ecological processes
scaling
inversion
machine-learning
deep-learning

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Published Papers (9 papers)

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18 pages, 4814 KiB

Open AccessArticle

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

by Jia Jin, Mengjuan Wu, Guangman Song and Quan Wang

Remote Sens. 2022, 14(20), 5204; https://doi.org/10.3390/rs14205204 - 18 Oct 2022

Cited by 11 | Viewed by 2340

Abstract

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. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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18 pages, 5683 KiB

Open AccessArticle

Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN Model to Classify Plant Species from Reflectance

by Shaoxiong Yuan, Guangman Song, Guangqing Huang and Quan Wang

Remote Sens. 2022, 14(16), 3972; https://doi.org/10.3390/rs14163972 - 16 Aug 2022

Cited by 13 | Viewed by 2743

Abstract

Leaf-level hyperspectral-based species identification has a long research history. However, unlike hyperspectral image-based species classification models, convolutional neural network (CNN) models are rarely used for the one-dimensional (1D) structured leaf-level spectrum. Our research focuses on hyperspectral data from five laboratories worldwide to test the general use of effective identification of the CNN model by reshaping 1D structure hyperspectral data into two-dimensional greyscale images without principal component analysis (PCA) or downscaling. We compared the performance of two-dimensional CNNs with the deep cross neural network (DCN), support vector machine, random forest, gradient boosting machine, and decision tree in individual tree species classification from leaf-level hyperspectral data. We tested the general performance of the models by simulating an application phase using data from different labs or years as the unseen data for prediction. The best-performing CNN model had validation accuracy of 98.6%, prediction accuracy of 91.6%, and precision of 74.9%, compared to the support vector machine, with 98.6%, 88.8%, and 66.4%, respectively, and DCN, with 94.0%, 85.7%, and 57.1%, respectively. Compared with the reference models, CNNs more efficiently recognized Fagus crenata, and had high accuracy in Quercus rubra identification. Our results provide a template for a species classification method based on hyperspectral data and point to a new way of reshaping 1D data into a two-dimensional image, as the key to better species prediction. This method may also be helpful for foliar trait estimation. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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25 pages, 109946 KiB

Open AccessArticle

Drone-Sensed and Sap Flux-Derived Leaf Phenology in a Cool Temperate Deciduous Forest: A Tree-Level Comparison of 17 Species

by Noviana Budianti, Masaaki Naramoto and Atsuhiro Iio

Remote Sens. 2022, 14(10), 2505; https://doi.org/10.3390/rs14102505 - 23 May 2022

Cited by 4 | Viewed by 4243

Abstract

Understanding the relationship between leaf phenology and physiological properties has important implications for improving ecosystem models of biogeochemical cycling. However, previous studies have investigated such relationships only at the ecosystem level, limiting the biological interpretation and application of the observed relationships due to the complex vegetation structure of forest ecosystems. Additionally, studies focusing on transpiration are generally limited compared to those on photosynthesis. Thus, we investigated the relationship between stem sap flux density (SFD) and crown leaf phenology at the individual tree level using the heat dissipation method, unmanned aerial vehicle (UAV)-based observation, and ground-based visual observation across 17 species in a cool temperate forest in Japan, and assessed the potential of UAV-derived phenological metrics to track individual tree-level sap flow phenology. We computed five leaf phenological metrics (four from UAV imagery and one from ground observations) and evaluated the consistency of seasonality between the phenological metrics and SFD using Bayesian modelling. Although seasonal trajectories of the leaf phenological metrics differed markedly among the species, the daytime total SFD (SFD_day) estimated by the phenological metrics was significantly correlated with the measured ones across the species, irrespective of the type of metric. Crown leaf cover derived from ground observations (CLC_ground) showed the highest ability to predict SFD_day, suggesting that the seasonality of leaf amount rather than leaf color plays a predominant role in sap flow phenology in this ecosystem. Among the UAV metrics, Hue had a superior ability to predict SFD_day compared with the other metrics because it showed seasonality similar to CLC_ground. However, all leaf phenological metrics showed earlier spring increases than did sap flow in more than half of the individuals. Our study revealed that UAV metrics could be used as predictors of sap flow phenology for deciduous species in cool, temperate forests. However, for a more accurate prediction, phenological metrics representing the spring development of sap flow must be explored. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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16 pages, 10787 KiB

Open AccessArticle

Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer

by Adenan Yandra Nofrizal, Rei Sonobe, Hiroto Yamashita, Haruyuki Seki, Harumi Mihara, Akio Morita and Takashi Ikka

Remote Sens. 2022, 14(9), 1997; https://doi.org/10.3390/rs14091997 - 21 Apr 2022

Cited by 8 | Viewed by 2948

Abstract

Leaf chlorophyll content is used as a major indicator of plant stress and growth, and hyperspectral remote sensing is frequently used to monitor the chlorophyll content. Hyperspectral reflectance has been used to evaluate vegetation properties such as pigment content, plant structure and physiological features using portable spectroradiometers. However, the prices of these devices have not yet decreased to consumer-affordable levels, which prevents widespread use. In this study, a system based on a cost-effective fingertip-sized spectrometer (Colorcompass-LF, a total price for the proposed solution was approximately 1600 USD) was evaluated for its ability to estimate the chlorophyll contents of radish and wasabi leaves and was compared with the Analytical Spectral Devices FieldSpec4. The chlorophyll contents per leaf area (cm²) of radish were generally higher than those of wasabi and ranged from 42.20 to 94.39 μg/cm² and 11.39 to 40.40 μg/cm² for radish and wasabi, respectively. The chlorophyll content was estimated using regression models based on a one-dimensional convolutional neural network (1D-CNN) that was generated after the original reflectance from the spectrometer measurements was de-noised. The results from an independent validation dataset confirmed the good performance of the Colorcompass-LF after spectral correction using a second-degree polynomial, and very similar estimation accuracies were obtained for the measurements from the FieldSpec4. The coefficients of determination of the regression models based on 1D-CNN were almost same (with R² = 0.94) and the ratios of performance to deviation based on reflectance after spectral correction using a second-degree polynomial for the Colorcompass-LF and the FieldSpec4 were 4.31 and 4.33, respectively. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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15 pages, 3242 KiB

Open AccessArticle

Proximal Remote Sensing-Based Vegetation Indices for Monitoring Mango Tree Stem Sap Flux Density

by Jia Jin, Ning Huang, Yuqing Huang, Yan Yan, Xin Zhao and Mengjuan Wu

Remote Sens. 2022, 14(6), 1483; https://doi.org/10.3390/rs14061483 - 18 Mar 2022

Cited by 13 | Viewed by 3647

Abstract

Plant water use is an important function reflecting vegetation physiological status and affects plant growth, productivity, and crop/fruit quality. Although hyperspectral vegetation indices have recently been proposed to assess plant water use, limited sample sizes for established models greatly astricts their wide applications. In this study, we have managed to gather a large volume of continuous measurements of canopy spectra through proximally set spectroradiometers over the canopy, enabling us to investigate the feasibility of using continuous narrow-band indices to trace canopy-scale water use indicated by the stem sap flux density measured with sap flow sensors. The results proved that the newly developed D (520, 560) index was optimal to capture the variation of sap flux density under clear sky conditions (R² = 0.53), while the best index identified for non-clear sky conditions was the D (530, 575) (R² = 0.32). Furthermore, the bands used in these indices agreed with the reported sensitive bands for estimating leaf stomatal conductance which has a critical role in transpiration rate regulation over a short time period. Our results should point a way towards using proximal hyperspectral indices to trace tree water use directly. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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15 pages, 1823 KiB

Open AccessArticle

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

by Guangman Song and Quan Wang

Remote Sens. 2022, 14(6), 1324; https://doi.org/10.3390/rs14061324 - 9 Mar 2022

Cited by 18 | Viewed by 4733

Abstract

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. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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17 pages, 3113 KiB

Open AccessArticle

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

by Guangman Song and Quan Wang

Remote Sens. 2021, 13(21), 4467; https://doi.org/10.3390/rs13214467 - 6 Nov 2021

Cited by 7 | Viewed by 3078

Abstract

Accurate knowledge of photosynthetic capacity is critical for understanding the carbon cycle under climate change. Despite the fact that deep neural network (DNN) models are increasingly applied across a wide range of fields, there are very few attempts to predict leaf photosynthetic capacity (indicated by maximum carboxylation rate, Vcmax, and maximum electron transport rate, Jmax) from reflected information. In this study, we have built a DNN model that uses leaf reflected spectra, alone or together with other leaf traits, for the reliable estimation of photosynthetic capacity, accounting for leaf types and growing periods in cool–temperate deciduous forests. Our results demonstrate that even though DNN models using only the reflectance spectra are capable of estimating both Vcmax and Jmax acceptably, their performance could nevertheless be improved by including information about other leaf biophysical/biochemical traits. The results highlight the fact that leaf spectra and leaf biophysical/biochemical traits are closely linked with leaf photosynthetic capacity, providing a practical and feasible approach to tracing functional traits. However, the DNN models developed in this study should undergo more extensive validation and training before being applied in other regions, and further refinements in future studies using larger datasets from a wide range of ecosystems are also necessary. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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28 pages, 5184 KiB

Open AccessArticle

Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes

by Salah Elsayed, Salah El-Hendawy, Mosaad Khadr, Osama Elsherbiny, Nasser Al-Suhaibani, Majed Alotaibi, Muhammad Usman Tahir and Waleed Darwish

Remote Sens. 2021, 13(9), 1679; https://doi.org/10.3390/rs13091679 - 27 Apr 2021

Cited by 25 | Viewed by 4372

Abstract

Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R² = 0.80–0.92) and moderate to weak relationships with BDW (R² = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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15 pages, 4678 KiB

Open AccessTechnical Note

UAV LiDAR and Hyperspectral Data Synergy for Tree Species Classification in the Maoershan Forest Farm Region

by Bin Wang, Jianyang Liu, Jianing Li and Mingze Li

Remote Sens. 2023, 15(4), 1000; https://doi.org/10.3390/rs15041000 - 11 Feb 2023

Cited by 18 | Viewed by 2914

Abstract

The accurate classification of single tree species in forests is important for assessing species diversity and estimating forest productivity. However, few studies have explored the influence of canopy morphological characteristics on the classification of tree species. Therefore, based on UAV LiDAR and hyperspectral data, in this study, we designed various classification schemes for the main tree species in the study area, i.e., birch, Manchurian ash, larch, Ulmus, and mongolica, in order to explore the effects of different data sources, classifiers, and canopy morphological features on the classification of a single tree species. The results showed that the classification accuracy of a single tree species using multisource remote sensing data was greater than that based on a single data source. The classification results of three different classifiers were compared, and the random forest and support vector machine classifiers exhibited similar classification accuracies, with overall accuracies above 78%. The BP neural network classifier had the lowest classification accuracy of 75.8%. The classification accuracy of all three classifiers for tree species was slightly improved when UAV LiDAR-extracted canopy morphological features were added to the classifier, indicating that the addition of canopy morphological features has a certain relevance for the classification of single tree species. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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