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.

Non-destructively sensing pork quality using near infrared multispectral imaging technique

RSC Advances ◽  
2015 ◽  
Vol 5 (116) ◽  
pp. 95903-95910 ◽  
Author(s):  
Qiping Huang ◽  
Huanhuan Li ◽  
Jiewen Zhao ◽  
Gengping Huang ◽  
Quansheng Chen
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Imaging Technique ◽  
Water Holding Capacity ◽  
Pork Quality ◽  
Water Holding ◽  
Non Destructive

Near infrared multispectral imaging system based on three wavebands—1280 nm, 1440 nm and 1660 nm—was developed for the non-destructive sensing of the tenderness and water holding capacity of pork.

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 Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2649
Author(s):  
Toshihiro Takamatsu ◽  
Yuichi Kitagawa ◽  
Kohei Akimoto ◽  
Ren Iwanami ◽  
Yuto Endo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Data Sets ◽  
Imaging Device ◽  
Tumor Implantation ◽  
Before And After ◽  
Validation Procedure ◽  
Leave One Out

In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000–1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.

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.

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.

scholarly journals Remote Sensing of Explosives-Induced Stress in Plants: Hyperspectral Imaging Analysis for Remote Detection of Unexploded Threats

2019 ◽  
Vol 11 (15) ◽  
pp. 1827 ◽  
Author(s):  
Paul V. Manley ◽  
Vasit Sagan ◽  
Felix B. Fritschi ◽  
Joel G. Burken
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Vegetation Index ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Field Capacity ◽  
Remotely Sensed ◽  
Control Group ◽  
Red Edge

Explosives contaminate millions of hectares from various sources (partial detonations, improper storage, and release from production and transport) that can be life-threatening, e.g., landmines and unexploded ordnance. Exposure to and uptake of explosives can also negatively impact plant health, and these factors can be can be remotely sensed. Stress induction was remotely sensed via a whole-plant hyperspectral imaging system as two genotypes of Zea mays, a drought-susceptible hybrid and a drought-tolerant hybrid, and a forage Sorghum bicolor were grown in a greenhouse with one control group, one group maintained at 60% soil field capacity, and a third exposed to 250 mg kg−1 Royal Demolition Explosive (RDX). Green-Red Vegetation Index (GRVI), Photochemical Reflectance Index (PRI), Modified Red Edge Simple Ratio (MRESR), and Vogelmann Red Edge Index 1 (VREI1) were reduced due to presence of explosives. Principal component analyses of reflectance indices separated plants exposed to RDX from control and drought plants. Reflectance of Z. mays hybrids was increased from RDX in green and red wavelengths, while reduced in near-infrared wavelengths. Drought Z. mays reflectance was lower in green, red, and NIR regions. S. bicolor grown with RDX reflected more in green, red, and NIR wavelengths. The spectra and their derivatives will be beneficial for developing explosive-specific indices to accurately identify plants in contaminated soil. This study is the first to demonstrate potential to delineate subsurface explosives over large areas using remote sensing of vegetation with aerial-based hyperspectral systems.

Estimation of Eucalypt Forest Leaf Area Index on the South Coast of New South Wales using Landsat MSS Data

1997 ◽  
Vol 45 (5) ◽  
pp. 757 ◽  
Author(s):  
Nicholas Coops ◽  
Antoine Delahaye ◽  
Eddy Pook
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
New South ◽  
New South Wales ◽  
Monitoring Site ◽  
Area Index ◽  
South Wales ◽  
Normalised Difference Vegetation Index

Research over the last decade has shown that regional estimation of Leaf Area Index (LAI) is possible using the ratio of red and near infrared radiation derived from satellite or airborne sensors. At landscape levels, however, this relationship has been more difficult to establish due to (i) logistic difficulties in measuring seasonal variation in LAI across the landscape over an extended period of time and (ii) difficulties in establishing the effect of understorey, canopy closure, and soil on the spectral radiation at fine spatial resolutions (< 100 m). This paper examines the first issue by utilising a temporal sequence of LAI data of a Eucalyptus mixed hardwood forest (E. maculata Hook., E. paniculata Sm., E. globoidea Blakely, E. pilularis Sm., E. sieberi L.Johnson) in south-eastern New South Wales and comparing it to historical Landsat Multi-Spectral Scanner (MSS) data covering a 9 year period. Field LAI was compared to the Normalised Difference Vegetation Index (NDVI) and the Simple Ratio (SR) derived from the MSS data. Linear relationships were shown to be appropriate to relate both transformations to the LAI data with r2 -values of 0.71 and 0.53 respectively. Using the NDVI relationship, LAI values were estimated along a transect originating from the monitoring site and these were compared to percentage canopy cover values derived from aerial photography.

scholarly journals Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

2019 ◽  
Vol 11 (10) ◽  
pp. 1192 ◽  
Author(s):  
Nianxu Xu ◽  
Jia Tian ◽  
Qingjiu Tian ◽  
Kaijian Xu ◽  
Shaofei Tang
Keyword(s):  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Bare Soil ◽  
Vegetation Canopy ◽  
Vegetation Monitoring ◽  
Red Edge ◽  
Positive Correlations

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

A field method for statewide ground-truthing of a spatial pasture growth model

2000 ◽  
Vol 40 (8) ◽  
pp. 1069 ◽  
Author(s):  
R. C. Hassett ◽  
H. L. Wood ◽  
J. O. Carter ◽  
T. J. Danaher
Keyword(s):  
Growth Model ◽  
Vegetation Index ◽  
South Australia ◽  
Basal Area ◽  
Field Method ◽  
Projective Cover ◽  
Efficient Manner ◽  
Related Data ◽  
Normalised Difference Vegetation Index ◽  
Ground Truthing

This paper describes an innovative method, commonly referred to as ‘spider mapping’, that allows pasture biomass and related data to be collected over large areas in a timely and efficient manner. Spider mapping was developed initially to collect data to allow calibration and validation of a spatial and temporal pasture growth model operating across Queensland on a 5 km grid basis. Two field officers made over 220 000 estimates and collected about 1300 samples of pasture biomass between January 1994 and August 1995. A number of selected biomass samples were analysed for nitrogen, phosphorus and carbon content. In addition, data were also collected on foliage projective cover and tree basal area for a range of woodland communities and both variables compared with mean long-term Normalised Difference Vegetation Index values derived from a time series of National Oceanographic and Atmospheric Administration satellite imagery. Both variables were strongly related to the satellite data with overstorey foliage projective cover having the strongest non-linear correlation (r2 = 0.91). The method described here is currently being used in related work in the rangelands of New South Wales, South Australia, Western Australia and the Northern Territory.

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