scholarly journals Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging

2021 ◽  
Vol 13 (16) ◽  
pp. 3317
Author(s):  
Sourabhi Debnath ◽  
Manoranjan Paul ◽  
D. M. Motiur Rahaman ◽  
Tanmoy Debnath ◽  
Lihong Zheng ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Spectral Ratio ◽  
Support Vector ◽  
Nutrient Deficiencies ◽  
Wine Quality ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Variation Index ◽  
Grapevine Leaves

The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

scholarly journals Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Tasya Vadya Sarira ◽  
Kenneth Clarke ◽  
Philip Weinstein ◽  
Lian Pin Koh ◽  
Megan Lewis
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
South Australia ◽  
Rapid Identification ◽  
Breeding Habitat ◽  
Mapping Accuracy ◽  
Normalised Difference Vegetation Index ◽  
Mosquito Breeding

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen’s Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

scholarly journals Multi-temporal Crop Type and Field Boundary Classification with Google Earth Engine

2020 ◽  
Author(s):  
Michael Marszalek ◽  
Maximilian Lösch ◽  
Marco Körner ◽  
Urs Schmidhalter
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Federal State ◽  
Support Vector ◽  
Data Sets ◽  
Field Boundary ◽  
Data Set ◽  
Crop Type ◽  
Normalised Difference Vegetation Index ◽  
Boundary Classification

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel&rsquo;s raw bands (B1&ndash;B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

scholarly journals Combined effects of altered N:P stoichometry and trees on Mediterranean savanna root dynamics

2018 ◽  
Author(s):  
Richard Nair ◽  
Martin Hertel ◽  
Yunpeng Luo ◽  
Gerardo Moreno ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Community Structure ◽  
Root Biomass ◽  
Vegetation Index ◽  
Near Infrared ◽  
Root Length Density ◽  
Root Traits ◽  
Tree Canopy ◽  
Root Dynamics ◽  
Ingrowth Cores ◽  
Normalised Difference Vegetation Index

Abstract. Mediterranean grasslands are highly seasonal and co-limited by water and nutrients. In such systems little is known about root dynamics which may depend on plant habit and environment as well seasonal water shortages and site fertility. This latter factor is affected by the presence of scattered trees and site management including grazing, as well as chronic nitrogen deposition, which may lead to N:P imbalance. In this study we combined observations from minirhizotrons collected in a Mediterranean tree-grass ecosystem (Spanish Dehesa), with root measurements from direct soil cores and ingrowth cores, and above-ground biomass to investigate seasonal root dynamics and root:shoot ratios. We investigated responses to soil fertility, using a nutrient manipulation (N / NP additions) and microhabitats effects between open pasture and under tree canopy locations. Root dynamics over time were compared with indices of above-ground growth drawn from proximal remote sensing (Normalised Difference Vegetation Index and Green Chromatic Coordinate derived from near-infrared enabled digital repeat photography). Results show distinct differences in root dynamics and biomass between treatments and microhabitats. Root biomass was higher with N additions, but not with NP additions in early spring, but by the end of the growing season root biomass had increased with NP in open pastures but not higher than N alone. In contrast, root length density (RLD) in pastures responded stronger to the NP than N only treatment, while beneath trees RLD tended to be higher with only N. Even though root biomass increased, root:shoot ratio decreased under nutrient treatments.We interpret these differences as a shift in community structure and/or root traits under changing stoichiometry and altered nutrient limitations. The timing of maximum root cover, as assessed by the minirhizotrons, did not match with above-ground phenology indicators at the site as root growth was low during autumn despite the greening up of the ecosystem. In other periods, roots responded quickly to rain events on the scale of days, matching changes in above-ground indices. Our results highlight the need for high resolution sampling to increase understanding of root dynamics in such systems, linkage with shifts in community structure and traits, and targeting of appropriate periods of the year for in-depth campaigns.

scholarly journals Stress Detection in New Zealand Kauri Canopies with WorldView-2 Satellite and LiDAR Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1906 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James D. Shepherd ◽  
John R. Dymond
Keyword(s):  
New Zealand ◽  
Vegetation Index ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Full Range ◽  
Lidar Data ◽  
Stress Monitoring ◽  
Stress Symptoms ◽  
Normalised Difference Vegetation Index ◽  
Stress Levels

New Zealand kauri trees are threatened by the kauri dieback disease (Phytophthora agathidicida (PA)). In this study, we investigate the use of pan-sharpened WorldView-2 (WV2) satellite and Light Detection and Ranging (LiDAR) data for detecting stress symptoms in the canopy of kauri trees. A total of 1089 reference crowns were located in the Waitakere Ranges west of Auckland and assessed by fieldwork and the interpretation of aerial images. Canopy stress symptoms were graded based on five basic stress levels and further refined for the first symptom stages. The crown polygons were manually edited on a LiDAR crown height model. Crowns with a mean diameter smaller than 4 m caused most outliers with the 1.8 m pixel size of the WV2 multispectral bands, especially at the more advanced stress levels of dying and dead trees. The exclusion of crowns with a diameter smaller than 4 m increased the correlation in an object-based random forest regression from 0.85 to 0.89 with only WV2 attributes (root mean squared error (RMSE) of 0.48, mean absolute error (MAE) of 0.34). Additional LiDAR attributes increased the correlation to 0.92 (RMSE of 0.43, MAE of 0.31). A red/near-infrared (NIR) normalised difference vegetation index (NDVI) and a ratio of the red and green bands were the most important indices for an assessment of the full range of stress symptoms. For detection of the first stress symptoms, an NDVI on the red-edge and green bands increased the performance. This study is the first to analyse the use of spaceborne images for monitoring canopy stress symptoms in native New Zealand kauri forest. The method presented shows promising results for a cost-efficient stress monitoring of kauri crowns over large areas. It will be tested in a full processing chain with automatic kauri identification and crown segmentation.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

OENO One ◽  
2020 ◽  
Vol 54 (2) ◽  
pp. 189-197 ◽  
Author(s):  
Marco Sozzi ◽  
Ahmed Kayad ◽  
Francesco Marinello ◽  
James Taylor ◽  
Bruno Tisseyre
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Significance Test ◽  
Support Vector ◽  
Field Scale ◽  
Mixed Pixel ◽  
Normalised Difference Vegetation Index ◽  
Aerial Vehicle ◽  
Sentinel 2

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics.

The patterns of grazed pasture associated with scattered trees across an Australian temperate landscape: an investigation of pasture quantity and quality

2011 ◽  
Vol 33 (2) ◽  
pp. 121 ◽  
Author(s):  
Phoebe Barnes ◽  
Brian R. Wilson ◽  
Mark G. Trotter ◽  
David W. Lamb ◽  
Nick Reid ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Parent Material ◽  
Agricultural Landscapes ◽  
Tree Canopy ◽  
Canopy Reflectance ◽  
Near Infrared Reflectance ◽  
Pasture Production ◽  
Normalised Difference Vegetation Index ◽  
Scattered Trees

Scattered paddock trees occur across agricultural landscapes in Australia. However, in the temperate regions of Australia their numbers are rapidly declining and they may be lost across much of the landscape in 200 years. Here we examined the spatial distribution of green (GDB), senescent (SDB) and total (TDB) dry pasture biomass, and nutrient status of the GDB fraction around scattered Eucalyptus trees on three parent materials (basalt, granite and meta-sediment) in native and sown pastures across a range of grazed temperate landscapes in northern New South Wales. We used a combination of destructive harvests and a handheld active optical canopy reflectance sensor (AOS) with an integrated GPS to examine pasture biomass around scattered trees. The harvested pasture biomass data indicated that under grazed conditions the presence of scattered trees did not introduce significant radial trends in TDB or GDB out to a distance of 3.5 canopy radii regardless of tree species or parent material. The red and near-infrared reflectance-based Normalised Difference Vegetation Index (NDVI), as measured by the AOS, did indicate a consistent azimuthal trend with larger GDB on the southern side of the tree and lower GDB on the northern side in the native pasture. However, this observation must be qualified as the regression coefficient for the relationship between NDVI and GDB was significant but weak (best r2 = 0.42), and SDB reduced its predictive capacity. We also found a higher percentage of GDB under the canopy than in the open paddock. We suggest that the combination of these results may indicate higher grazing pressure under trees than in the open paddock. Pasture nutrient concentration (P, K and S) was higher in both native and sown pastures beneath the tree canopy compared with the open paddock. This study indicates that, in this temperate environment, scattered trees do not adversely affect pasture production, and that they can improve some pasture nutrients.

Sensors key to advances in precision agriculture

Sensor Review ◽  
2017 ◽  
Vol 37 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Robert Bogue
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Agricultural Practices ◽  
Content Type ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Physical And Chemical Variables ◽  
Physical And Chemical ◽  
The Impact ◽  
Agricultural Machines

Purpose This study aims to illustrate the growing role that sensors play in agriculture, with an emphasis on precision agricultural practices. Design/methodology/approach Following a short introduction, this study first provides an overview of agricultural measurements and applications. It then discusses the importance of airborne and land-based optical sensing techniques and the role of the normalised difference vegetation index. Sensors used on conventional and robotic agricultural machines are considered next, and fixed sensors and sensor networks are then discussed. Finally, brief concluding comments are drawn. Findings This shows that much modern agriculture is a high-technology business which relies on a multitude of sensor-based measurements. Sensors are based on a diversity of optical and other technologies and measure a wide range of physical and chemical variables. They are deployed in the air, on agricultural machines and in the field and generate data that can be used to enhance productivity and reduce both costs and the impact on the environment. Originality/value This provides a detailed insight into the important role played by sensors in modern agricultural practices.

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