scholarly journals Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

2020 ◽  
Vol 2 (4) ◽  
pp. 523-543
Author(s):  
Jason Barnetson ◽  
Stuart Phinn ◽  
Peter Scarth
Keyword(s):  
Near Infrared ◽  
Scale Up ◽  
Field Measurements ◽  
Predictive Modelling ◽  
Field Site ◽  
Resource Base ◽  
Pasture Management ◽  
Surface Modelling ◽  
Pasture Quality ◽  
Satellite Platform

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

scholarly journals Estimating Plant Pasture Biomass and Quality from UAV Imaging Across Queensland’s Rangelands

2020 ◽  
Author(s):  
Jason Barnetson ◽  
Stuart Phinn ◽  
Peter Scarth
Keyword(s):  
Scale Up ◽  
Field Measurements ◽  
Nutrient Status ◽  
Predictive Modelling ◽  
Field Site ◽  
Resource Base ◽  
Pasture Management ◽  
Surface Modelling ◽  
Satellite Platform ◽  
Pasture Yield

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV) to map pasture biomass yield and nutrient status, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed predictive models of both pasture yield and pasture nutrient status. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements of pasture yields, to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict pasture yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of pasture yield and on average indicated an error of 0.8 t ha-1 in grasslands and 1.3 t ha-1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale, however further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

scholarly journals The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Michelle C. Stanton ◽  
Patrick Kalonde ◽  
Kennedy Zembere ◽  
Remy Hoek Spaans ◽  
Christopher M. Jones
Keyword(s):  
Image Analysis ◽  
Malaria Control ◽  
Rural Areas ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Temporal Trends ◽  
Scale Up ◽  
Larval Habitat ◽  
Habitat Characteristics ◽  
Rural Environments

Abstract Background Spatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach. Methods Drone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence. Results Imagery covering an area of 8.9 km2 across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence. Conclusions This study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control.

scholarly journals Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

2021 ◽  
Vol 3 (1) ◽  
pp. 73-91
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
Ângelo Carapau ◽  
Ana Elisa Rato
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Optical Sensors ◽  
Near Infrared ◽  
Production Systems ◽  
External Validation ◽  
Neutral Detergent Fiber ◽  
Reference Laboratory ◽  
Pasture Quality ◽  
Animal Grazing

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

scholarly journals Retrieval and Validation of Water Turbidity at Metre-Scale Using Pléiades Satellite Data: A Case Study in the Gironde Estuary

2020 ◽  
Vol 12 (6) ◽  
pp. 946 ◽  
Author(s):  
Yafei Luo ◽  
David Doxaran ◽  
Quinten Vanhellemont
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Quality Parameters ◽  
Satellite Constellations ◽  
Water Turbidity ◽  
Gironde Estuary ◽  
The Difference

This study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems.

scholarly journals Characterising the surface magnetic fields of T Tauri stars with high-resolution near-infrared spectroscopy

2019 ◽  
Vol 630 ◽  
pp. A99 ◽  
Author(s):  
A. Lavail ◽  
O. Kochukhov ◽  
G. A. J. Hussain
Keyword(s):  
Magnetic Field ◽  
High Resolution ◽  
Magnetic Fields ◽  
Large Scale ◽  
Near Infrared ◽  
Field Measurements ◽  
T Tauri Stars ◽  
Small Scale ◽  
Surface Magnetic Field ◽  
T Tauri

Aims. In this paper, we aim to characterise the surface magnetic fields of a sample of eight T Tauri stars from high-resolution near-infrared spectroscopy. Some stars in our sample are known to be magnetic from previous spectroscopic or spectropolarimetric studies. Our goals are firstly to apply Zeeman broadening modelling to T Tauri stars with high-resolution data, secondly to expand the sample of stars with measured surface magnetic field strengths, thirdly to investigate possible rotational or long-term magnetic variability by comparing spectral time series of given targets, and fourthly to compare the magnetic field modulus ⟨B⟩ tracing small-scale magnetic fields to those of large-scale magnetic fields derived by Stokes V Zeeman Doppler Imaging (ZDI) studies. Methods. We modelled the Zeeman broadening of magnetically sensitive spectral lines in the near-infrared K-band from high-resolution spectra by using magnetic spectrum synthesis based on realistic model atmospheres and by using different descriptions of the surface magnetic field. We developped a Bayesian framework that selects the complexity of the magnetic field prescription based on the information contained in the data. Results. We obtain individual magnetic field measurements for each star in our sample using four different models. We find that the Bayesian Model 4 performs best in the range of magnetic fields measured on the sample (from 1.5 kG to 4.4 kG). We do not detect a strong rotational variation of ⟨B⟩ with a mean peak-to-peak variation of 0.3 kG. Our confidence intervals are of the same order of magnitude, which suggests that the Zeeman broadening is produced by a small-scale magnetic field homogeneously distributed over stellar surfaces. A comparison of our results with mean large-scale magnetic field measurements from Stokes V ZDI show different fractions of mean field strength being recovered, from 25–42% for relatively simple poloidal axisymmetric field topologies to 2–11% for more complex fields.

scholarly journals A Visible and Near-Infrared Scanning Photometer for Field Measurements of Spectral Albedo and Irradiance under Polar Conditions

1981 ◽  
Vol 27 (97) ◽  
pp. 476-481 ◽  
Author(s):  
T. C. Grenfell
Keyword(s):  
Solar Radiation ◽  
Near Infrared ◽  
Field Measurements ◽  
Interference Filter ◽  
Arctic Sea Ice ◽  
Spectral Irradiance ◽  
Operating Characteristics ◽  
Infrared Scanning ◽  
Solar Radiation Incident ◽  
Arctic Sea

AbstractThe design and operating characteristics are presented for a visible and near-infrared scanning photometer which can measure incident and reflected spectral irradiance from 400 nm to 2 450 nm. The instrument is designed to record over 98% of the solar radiation incident upon and reflected from the Earth’s surface using a turret-type cosine collector and a circular variable interference filter. The apparatus has been tested successfully on Arctic sea ice at temperatures down to –20°C. It is self-contained and easily portable, and its mass is less than 16 kg. Some preliminary results are presented.

scholarly journals The Aerosol Limb Imager: acousto-optic imaging of limb scattered sunlight for stratospheric aerosol profiling

2015 ◽  
Vol 8 (12) ◽  
pp. 13285-13330
Author(s):  
B. J. Elash ◽  
A. E. Bourassa ◽  
P. R. Loewen ◽  
N. D. Lloyd ◽  
D. A. Degenstein
Keyword(s):  
Particle Size ◽  
Extinction Coefficient ◽  
Near Infrared ◽  
Stratospheric Aerosol ◽  
Tunable Filter ◽  
Low Earth Orbit ◽  
Optical Remote Sensing ◽  
Design Development ◽  
Space Agency ◽  
Satellite Platform

Abstract. The Aerosol Limb Imager (ALI) is an optical remote sensing instrument designed to image scattered sunlight from the atmospheric limb. These measurements are used to retrieve spatially resolved information of the stratospheric aerosol distribution, including spectral extinction coefficient and particle size. Here we present the design, development and test results of an ALI prototype instrument. The long term goal of this work is the eventual realization of ALI on a satellite platform in low earth orbit, where it can provide high spatial resolution observations, both in the vertical and cross-track. The instrument design uses a large aperture Acousto-Optic Tunable Filter (AOTF) to image the sunlit stratospheric limb in a selectable narrow wavelength band ranging from the visible to the near infrared. The ALI prototype was tested on a stratospheric balloon flight from the Canadian Space Agency (CSA) launch facility in Timmins, Canada, in September 2014. Preliminary analysis of the hyperspectral images indicate that the radiance measurements are of high quality, and we have used these to retrieve vertical profiles of stratospheric aerosol extinction coefficient from 650–1000 nm, along with one moment of the particle size distribution. Those preliminary results are promising and development of a satellite prototype of ALI within the Canadian Space Agency is ongoing.

scholarly journals Atmospheric observations of the water vapour continuum in the near-infrared windows between 2500–6600 cm<sup>−1</sup>

2019 ◽  
Author(s):  
Jonathan Elsey ◽  
Marc D. Coleman ◽  
Tom D. Gardiner ◽  
Kaah P. Menang ◽  
Keith P. Shine
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Laboratory Data ◽  
Field Measurements ◽  
The Self ◽  
Atmospheric Conditions ◽  
Sensing Applications ◽  
Atmospheric Observations ◽  
Real Atmosphere ◽  
Remote Sensing Applications

Abstract. Water vapour continuum absorption is potentially important for both closure of the Earth’s energy budget and remote sensing applications. Currently, there are significant uncertainties in its characteristics in the near-infrared atmospheric windows at 2.1 and 1.6 μm. There have been several attempts to measure the continuum in the laboratory; not only are there significant differences amongst these measurements but there are also difficulties in extrapolating the laboratory data taken at room temperature or higher to atmospheric temperatures. Validation is therefore required using field observations of the real atmosphere. There are currently few published observations in atmospheric conditions with enough water vapour to detect a continuum signal within these windows, or where the self-continuum component is significant. We present observations of the near-infrared water vapour continuum from Camborne, UK at sea level using a sun-pointing, radiometrically-calibrated Fourier transform spectrometer in the window regions between 2000–10000 cm−1. Analysis of this data is challenging, particularly because of the need to remove aerosol extinction, and the large uncertainties associated with such field measurements. Nevertheless, we present data that is consistent with recent laboratory datasets in the 4 and 2.1 μm windows (when extrapolated to atmospheric temperatures). These results indicate that the most recent revision (3.2) of the MT_CKD foreign continuum, versions of which are widely used in atmospheric radiation models, requires strengthening by a factor of ~ 5 in the centre of the 2.1 µm window. In the higher-wavenumber window at 1.6 µm, our estimated self and foreign continua are significantly stronger than MT_CKD. The possible contribution of the self and foreign continua to our derived total continuum optical depth is estimated by using laboratory or MT_CKD values of one, to estimate the other. The obtained self-continuum shows some consistency with temperature-extrapolated laboratory data in the centres of the 4 and 2.1 µm windows. The 1.6 μm region is more sensitive to atmospheric aerosol and continuum retrievals and therefore more uncertain than the more robust results at 2.1 and 4 μm. We highlight the difficulties in observing the atmospheric continuum and make the case for additional measurements from both the laboratory and field, with discussion of the requirements for any new field campaign.

scholarly journals Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem

2020 ◽  
Vol 10 (13) ◽  
pp. 4463
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva ◽  
Luís Paixão ◽  
Emanuel Carreira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Service Providers ◽  
Quality Parameters ◽  
Neutral Detergent Fiber ◽  
Pasture Quality ◽  
Reference Methods

Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.

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