Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

Transactions of the ASABE ◽

10.13031/trans.13661 ◽

2020 ◽

Vol 63 (4) ◽

pp. 1133-1146

Author(s):

Beichen Lyu ◽

Stuart D. Smith ◽

Yexiang Xue ◽

Katy M. Rainey ◽

Keith Cherkauer

Keyword(s):

Data Processing ◽

High Throughput ◽

Large Scale ◽

Vegetation Index ◽

Vegetation Indices ◽

Unmanned Aerial Systems ◽

Growth Stages ◽

Processing Pipeline ◽

High Throughput Phenotyping ◽

Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

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Generalization of Vegetation Indices for Monitoring the Terrestrial Biosphere

10.5194/egusphere-egu21-14263 ◽

2021 ◽

Author(s):

Gustau Camps-Valls ◽

Manuel Campos-Taberner ◽

Alvaro Moreno-Martinez ◽

Sophia Walther ◽

Grégory Duveiller ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Multispectral Imaging ◽

Spectral Band ◽

Vegetation Indices ◽

Observation Data ◽

Terrestrial Biosphere ◽

Time Resolved ◽

Area Index ◽

Nonlinear Generalization

Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data. Reference: "A Unified Vegetation Index for Quantifying the Terrestrial Biosphere", Gustau Camps-Valls, Manuel Campos-Taberner, &#193;lvaro Moreno-Mart&#305;&#769;nez, Sophia Walther, Gr&#233;gory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Mu&#241;oz-Mar&#305;&#769;, Francisco Javier Garc&#305;&#769;a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. Science Advances, in press, 2021

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Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

Remote Sensing ◽

10.3390/rs12193164 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3164

Author(s):

Bikram Pratap Banerjee ◽

German Spangenberg ◽

Surya Kant

Keyword(s):

High Throughput ◽

Plant Traits ◽

Structural Information ◽

Vegetation Indices ◽

Ground Truth ◽

Breeding Population ◽

Aerial Images ◽

Biomass Estimation ◽

Growth Stages ◽

Crop Species

Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R2 = 0.96 and SEE = 69.2 g/m2) and FW (R2 = 0.89 and SEE = 333.54 g/m2). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.

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Selection of Vegetation Indices in Hierarchical Structural Based Vegetation Species Classification

Jurnal Teknologi ◽

10.11113/jt.v71.3853 ◽

2014 ◽

Vol 71 (5) ◽

Author(s):

W. C. Chew ◽

A. M. S. Lau ◽

T. Okuda

Keyword(s):

Vegetation Index ◽

Research Question ◽

Vegetation Indices ◽

Species Group ◽

Species Classification ◽

Classification Procedure ◽

Significance Level ◽

Significant Difference ◽

Multi Level ◽

Vegetation Species

A pilot study has been carried out to test the effectiveness and feasibility of a multi-level classification procedure in handling a large number of vegetation species and aims to improve the classification accuracy. In this study, the research question is whether a selected vegetation index has its significance towards a specific vegetation species group. Ten vegetation indices extracted from in-situ hyperspectral remote sensing data has been applied and one-way ANOVA analysis (with significance level, α = 0.01, 0.05, and 0.1) has been carried out to evaluate significant difference of vegetation indices in pair-wise vegetation species. In the multi-level classification procedure, vegetation species were classified continuously from one level to the next level until a good classification result has been achieved. Results indicated that multi-level classification procedure has effectiveness in handling vegetation species where accuracy has been improved from the first classification level to the second level. The study also has highlighted the significance of selected vegetation indices subsets toward different vegetation species in the multi-level classification procedure where the highest accuracy has been given by the selection in dedicated vegetation species groups.

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Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery

Plants ◽

10.3390/plants10122726 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2726

Author(s):

Yaping Xu ◽

Vivek Shrestha ◽

Cristiano Piasecki ◽

Benjamin Wolfe ◽

Lance Hamilton ◽

...

Keyword(s):

Remote Sensing ◽

Data Collection ◽

High Throughput ◽

Panicum Virgatum ◽

Vegetation Index ◽

Lignin Content ◽

Vegetation Indices ◽

Rust Disease ◽

Bioenergy Feedstock ◽

First Time

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

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High-Throughput UAV Image-Based Method Is More Precise Than Manual Rating of Herbicide Tolerance

Plant Phenomics ◽

10.34133/2019/6036453 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Hema S. N. Duddu ◽

Eric N. Johnson ◽

Christian J. Willenborg ◽

Steven J. Shirtliffe

Keyword(s):

High Throughput ◽

Human Error ◽

Vegetation Index ◽

Block Design ◽

Vegetation Indices ◽

Herbicide Tolerance ◽

Plant Phenotyping ◽

Study Objective ◽

Ground Sample ◽

Visual Rating

The traditional visual rating system is labor-intensive, time-consuming, and prone to human error. Unmanned aerial vehicle (UAV) imagery-based vegetation indices (VI) have potential applications in high-throughput plant phenotyping. The study objective is to determine if UAV imagery provides accurate and consistent estimations of crop injury from herbicide application and its potential as an alternative to visual ratings. The study was conducted at the Kernen Crop Research Farm, University of Saskatchewan in 2016 and 2017. Fababean (Vicia faba L.) crop tolerance to nine herbicide tank mixtures was evaluated with 2 rates distributed in a randomized complete block design (RCBD) with 4 blocks. The trial was imaged using a multispectral camera with a ground sample distance (GSD) of 1.2 cm, one week after the treatment application. Visual ratings of growth reduction and physiological chlorosis were recorded simultaneously with imaging. The optimized soil-adjusted vegetation index (OSAVI) was calculated from the thresholded orthomosaics. The UAV-based vegetation index (OSAVI) produced more precise results compared to visual ratings for both years. The coefficient of variation (CV) of OSAVI was ~1% when compared to 18-43% for the visual ratings. Furthermore, Tukey’s honestly significance difference (HSD) test yielded a more precise mean separation for the UAV-based vegetation index than visual ratings. The significant correlations between OSAVI and the visual ratings from the study suggest that undesirable variability associated with visual assessments can be minimized with the UAV-based approach. UAV-based imagery methods had greater precision than the visual-based ratings for crop herbicide damage. These methods have the potential to replace visual ratings and aid in screening crops for herbicide tolerance.

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Evaluating Multispectral Imaging for Assessing Bacterial Leaf Blight Damage in Indonesian Agricultural Insurance

E3S Web of Conferences ◽

10.1051/e3sconf/202123203008 ◽

2021 ◽

Vol 232 ◽

pp. 03008

Author(s):

Yuti Giamerti ◽

Chiharu Hongo ◽

Daiki Saito ◽

Oliver Caasi ◽

Pepi Nur Susilawati ◽

...

Keyword(s):

Vegetation Index ◽

Rice Field ◽

Multispectral Imaging ◽

Vegetation Indices ◽

Leaf Blight ◽

Bacterial Leaf Blight ◽

Multispectral Images ◽

Agricultural Insurance ◽

Average Value ◽

Red Edge

Bacterial Leaf Blight (BLB) is one of the main diseases in Indonesia that causes a 90% reduction in grain weight. Multispectral imaging may be used as a quick and effective method for damage assessment and is expected to utilize on agricultural insurance in Indonesia. Data were collected at the rainy season and dry season 2018 in Farmers rice field at Bali Province. Vegetation indices (NDVI, GNDVI, and VARIred-edge) was analyzed using QGIS 2.18 from multispectral images. Some vegetation index shows positive correlation with SPAD and negative correlation with DSI (%). VARIred-edge has a higher relationship with DSI (R2: 0.8443) than NDVI (R2: 0.8291) and GNDVI (R2: 0.5463) at the average value on each location, but the relation seems to be affected by that relation between SPAD and LAI. Further data and analysis are required.

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Use of aerial image in the estimation of volume and biomass of Eucalyptus sp. forest stand

Australian Journal of Crop Science ◽

10.21475/ajcs.20.14.02.p2183 ◽

2020 ◽

pp. 286-294

Author(s):

Thallita R. S. Mendes ◽

Eder P. Miguel ◽

Pedro G. A. Vasconcelos ◽

Marco B. X. Valadão ◽

Alba V. Rezende ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Basal Area ◽

Aerial Image ◽

Biomass Estimation ◽

Fit Statistics ◽

Remote Sensing Techniques ◽

The Individual

Assessing forest stands is crucial for managing and planning the use of these resources. Forest inventory is the instrument that provides information about the stand situation, which can be costly and time consuming. In order to facilitate and reduce the time spent obtaining these data, the main objective of this work was to evaluate the accuracy of volume and biomass estimates per unit area with data from remote sensing. Forty sample units were allocated and georeferenced, in which all trees with diameter at breast height (DBH) ≥ 5 cm were inventoried. Sequentially, the cubage was performed in order to obtain individual biomass, volume, and adjustment of the individual models. With data from georeferenced images of the study area, the vegetation indices MSAVI (Modified Soil-Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) were obtained. The volume and biomass estimation using remote sensing variables were carried out through the adjustment of sigmoidal models by regression analysis, which used a combination of the average values of the vegetation indices and the basal area of the plot/hectares as an independent variable. The fit statistics and the accuracy of the tested models presented consistent results to estimate forest production. The results showwd that indices derived from remote sensing techniques associated with forest variables information could accurately estimate the volume and biomass of Eucalyptus spp. plantations.

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Automated Hyperspectral Vegetation Index Derivation Using A Hyperparameter Optimization Framework for High-Throughput Plant Phenotyping

10.1101/2021.10.27.466056 ◽

2021 ◽

Author(s):

Joshua Koh ◽

Bikram Banerjee ◽

German Spangenberg ◽

Surya Kant

Keyword(s):

High Throughput ◽

Vegetation Index ◽

Sugar Content ◽

Vegetation Indices ◽

Plant Phenotyping ◽

Least Squares Regression ◽

Optimization Framework ◽

Band Index ◽

Rapid Generation ◽

Covering Model

Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple 2-band indices which limits the net performance and often do not generalize well for other traits than they were originally designed for. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel 2- to 6-band trait-specific indices in a streamlined process covering model selection, optimization and evaluation driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results show that AutoVI can rapidly generate complex novel VIs (≥4-band index) which correlated strongly (R2 > 0.8) with measured chlorophyll and sugar contents in wheat. AutoVI-derived indices were used as features in simple and stepwise multiple linear regression for chlorophyll and sugar content estimation, and outperformed results achieved with existing 47 VIs and those provided by partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable in high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface of AutoVI is herein provided.

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Remote Sensing for Detection and Monitoring of Vegetation Affected by Oil Spills

10.20944/preprints201609.0081.v1 ◽

2016 ◽

Author(s):

Bashir Adamu ◽

Booker Ogutu ◽

Kevin Tansey

Keyword(s):

Oil Spill ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Oil Spills ◽

Vegetation Indices ◽

P Value ◽

Landsat 8 ◽

Subtle Change ◽

Spectral Techniques ◽

Significant Difference

This study is aimed at demonstrating application of vegetation spectral techniques for detection and monitoring of impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G/NIR) and green-shortwave infrared (G/SWIR) from the spill sites (SS) and non-spill (NSS) sites in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G/NIR and G/SWIR indicated certain level difference between vegetation condition at the SS and the NSS were significant with p-value <0.5 in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G/NIR - p-value 0.01 and GSWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G/NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post—spill analysis show that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique is essential for real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in the mangrove forest.

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