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

Plants ◽  
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.

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

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuai Che ◽  
Guoying Du ◽  
Ning Wang ◽  
Kun He ◽  
Zhaolan Mo ◽  
...  
Keyword(s):  
High Throughput ◽  
Vegetation Index ◽  
Multispectral Imaging ◽  
Vegetation Indices ◽  
Spectral Imaging ◽  
Shielding Effect ◽  
Quadratic Model ◽  
Biomass Estimation ◽  
Marine Aquaculture ◽  
Significant Difference

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.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

2019 ◽  
Vol 11 (5) ◽  
pp. 1410 ◽  
Author(s):  
Suman Moparthy ◽  
Dominique Carrer ◽  
Xavier Ceamanos
Keyword(s):  
Remote Sensing ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Satellite Observations ◽  
Atmospheric Effects ◽  
Forest Regions ◽  
Aerosol Emissions

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2013 ◽  
Vol 10 (10) ◽  
pp. 6279-6307 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Leaf Nitrogen ◽  
Agricultural Landscapes ◽  
Nitrogen Pools ◽  
Spectral Vegetation Indices

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.

Combining tree ring analysis and remote sensing to assess the 2017 black pine dieback in Vinschgau/Val Venosta (Italy)

2020 ◽  
Author(s):  
Nikolaus Obojes ◽  
Jennifer Klemm ◽  
Ruth Sonnenschein ◽  
Francesco Giammarchi ◽  
Giustino Tonon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tree Ring ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Black Pine ◽  
Growth Data ◽  
Tree Vitality ◽  
Mountain Terrain ◽  
Ring Analysis

<p>To prevent further erosion of pastures along the south slopes of the Vinschgau/Val Venosta (South Tyrol/Italy) about 900 ha of non-native black pine (Pinus nigra) have been afforested there between 1900 and the 1960s. This drought-tolerant Mediterranean species was supposed to be able to cope with the dry climate at degraded soils in the inner-alpine dry valley. Nevertheless, black pine in the Vinschgau has been affected by reoccurring tree vitality decline and diebacks in the last 20 years linked to repeated droughts and heat waves. Observing growth trends via tree ring analysis is usually restricted to single stands. On the other hand, remote sensing data to track tree vitality was not available in sufficient spatial and temporal resolution to be applied to complex mountain terrain until recently. This has changed with the launch of the Sentinel-2 A and B satellites in 2015 and 2017 with a spatial resolution of 10 to 20 m and a revisiting period of 5 days. To analyse the accordance of remote sensing-based vegetation indices to tree-ring based growth data, we compared twelve sites across the Vinschgau/Val Venosta with a differing degree of vitality loss in 2017 for a four-year period from 2015 to 2018. In general, less vital sites were located at lower elevation and on steeper slopes. Radial tree growth was positively correlated to spring precipitation and strongly decreased during earlier hot and dry years such as 1995 and 2003. We found high and statistically significant correlations between site-average basal area increment as well as tree ring width indices and multiple vegetation indices (Normalized Difference Vegetation Index NDVI, Green Normalized Difference Vegetation Index GNDVI, Normalized Difference Infrared Index NDII, Moisture Stress Index MSI) especially for the dry 2017 growing season and the 2018 recovery year, which had large gradients in tree vitality between sites. Overall, these results show that remote sensing-based vegetation indices can be used to scale up stand level growth data also in complex mountain terrain.</p>

scholarly journals Evaluation of Organic Camelina Crop Under Different Tillage Systems and Fertilization Types Using Proximal Remote Sensing

2020 ◽  
Vol 77 (1) ◽  
pp. 1
Author(s):  
Foteini ANGELOPOULOU ◽  
Evangelos ANASTASIOU ◽  
Spyros FOUNTAS ◽  
Dimitrios BILALIS
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth And Yield ◽  
Tillage Systems ◽  
Minimum Tillage ◽  
Spectral Vegetation Indices ◽  
Crop Parameters

A field experiment was conducted in Southern Greece to assess Normalized Difference Vegetation Index (NDVI) and Red-Edge Normalized Difference Vegetation Index (NDRE) in estimating Camelina’s crop growth and yield parameters under different tillage systems (conventional and minimum tillage) and organic fertilization types (compost, vermicompost and untreated control). A proximal canopy sensor was used to measure the aforementioned Spectral Vegetation Indices (SVIs) at different days after sowing (DAS). Camelina presented the highest values of NDVI and NDRE under compost fertilization (0.63 and 0.22 accordingly) and minimum tillage system (0.50 and 0.18 accordingly). Additionally, the highest correlations between the measured crop parameters and NDVI, NDRE were achieved at leaf development to early flowering stage. Moreover, NDRE presented the highest correlation with seed yield (R2=0.60, p<0.05) and thus it is suggested for estimating Camelina’s productivity instead of NDVI. Finally, further research is needed for adopting the use of remote sensing technologies on predicting Camelina’s crop growth and yield.

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

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.

Development of a Mobile Platform for Wheat Phenotyping

2020 ◽  
Author(s):  
Majid Khak Pour ◽  
Reza Fotouhi ◽  
Pierre Hucl
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Mobile Platform ◽  
Mechatronic System ◽  
High Quality ◽  
Wheat Field ◽  
Crop Monitoring ◽  
High Throughput Phenotyping

Abstract Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, vegetation indices and is able to take high quality photos of crops. The system includes developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.

scholarly journals Mining and Restoration Monitoring of Rare Earth Element (REE) Exploitation by New Remote Sensing Indicators in Southern Jiangxi, China

2020 ◽  
Vol 12 (21) ◽  
pp. 3558
Author(s):  
Lifeng Xie ◽  
Weicheng Wu ◽  
Xiaolan Huang ◽  
Penghui Ou ◽  
Ziyu Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Rapid Expansion ◽  
Open Pit ◽  
Open Pit Mining ◽  
Chemical Extraction

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km2 in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area.

scholarly journals Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

2017 ◽  
Vol 6 (1) ◽  
pp. 149-158 ◽  
Author(s):  
Mohamed Elhag ◽  
Jarbou A. Bahrawi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Landsat 8 ◽  
Arid Environments ◽  
Crop Water ◽  
Sensing Applications

Abstract. Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

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