19 research outputs found
A silicified Early Triassic marine assemblage from Svalbard
peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tjsp2
Carboniferous and Permian Rugosochonetidae (Brachiopoda) from West Spitsbergen
The rugosochonetid brachiopod species Lissochonetes geinitzianus from the Kazimovian of the Nordenskioldbreen Formation, and Dyoros (Dyoros) mucronata sp. nov., Dyoros (Dyoros) spitzbergianus and Lissochonetes superba from the Artinskian to latest Permian Kapp Starostin Formation in West Spitsbergen are described and figured. Dyoros is generally restricted to the Boreal Realm, whereas Lissochonetes is mostly distributed in the Boreal Realm, but occasionally present in the Palaeoequatorial and Gondwanan Realms<br /
A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures
Timely and accurate monitoring of pasture biomass and ground cover is
necessary in livestock production systems to ensure productive and
sustainable management. Interest in the use of proximal sensors for
monitoring pasture status in grazing systems has increased, since data can be
returned in near real time. Proximal sensors have the potential for
deployment on large properties where remote sensing may not be suitable due
to issues such as spatial scale or cloud cover. There are unresolved
challenges in gathering reliable sensor data and in calibrating raw sensor
data to values such as pasture biomass or vegetation ground cover, which
allow meaningful interpretation of sensor data by livestock producers.
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Our goal was to assess whether a combination of proximal sensors could be
reliably deployed to monitor tropical pasture status in an operational beef
production system, as a precursor to designing a full sensor deployment. We
use this pilot project to (1) illustrate practical issues around sensor
deployment, (2) develop the methods necessary for the quality control of the
sensor data, and (3) assess the strength of the relationships between
vegetation indices derived from the proximal sensors and field observations
across the wet and dry seasons.
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Proximal sensors were deployed at two sites in a tropical pasture on a beef
production property near Townsville, Australia. Each site was monitored by a
Skye SKR-four-band multispectral sensor (every 1 min), a digital camera
(every 30 min), and a soil moisture sensor (every 1 min), each of which were operated
over 18 months. Raw data from each sensor was processed to calculate
multispectral vegetation indices. The data capture from the digital cameras
was more reliable than the multispectral sensors, which had up to 67 % of
data discarded after data cleaning and quality control for technical issues
related to the sensor design, as well as environmental issues such as water
incursion and insect infestations. We recommend having a system with both
sensor types to aid in data interpretation and troubleshooting technical
issues. Non-destructive observations of pasture characteristics, including
above-ground standing biomass and fractional ground cover, were made every
2 weeks. This simplified data collection was designed for multiple years of
sampling at the remote site, but had the disadvantage of high measurement
uncertainty.
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A bootstrapping method was used to explore the strength of the relationships
between sensor and pasture observations. Due to the uncertainty in the field
observations, the relationships between sensor and field data are not
confirmational and should be used only to inform the design of future work.
We found the strongest relationships occurred during the wet season period of
maximum pasture growth (January to April), with generally poor relationships
outside of this period. Strong relationships were found with multispectral
indices that were sensitive to the green and dry components of the
vegetation, such as those containing the band in the lower shortwave infrared
(SWIR) region of the electromagnetic spectrum. During the wet season the
bias-adjusted bootstrap point estimate of the <i>R</i><sup>2</sup> between above-ground
biomass and the normalized ratio between the SWIR and red bands (NVI-SR) was
0.72 (95 % CI of 0.28 to 0.98), while that for the percentage of green
vegetation observed in three dimensions and a simple ratio between the near
infrared and SWIR bands (RatioNS34) was 0.81 (95 % CI of 0.53 to 1.00).
Relationships between field data and the vegetation index derived from the
digital camera images were generally weaker than from the multispectral
sensor data, except for green vegetation observations in two and three
dimensions.
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Our successful pilot of multiple proximal sensors supports the design of
future deployments in tropical pastures and their potential for operational
use. The stringent rules we developed for data cleaning can be more broadly
applied to other sensor projects to ensure quality data. Although proximal
sensors observe only a small area of the pasture, they deliver continual and
timely pasture measurements to inform timely on-farm decision-making
Temporal monitoring of groundcover change using digital cameras
Environmental sensor networks (ESN) provide new opportunities for improving our understanding of ecological change. Their strength is an ability to acquire data at high temporal frequencies and in remote locations (autonomously). This paper describes the development of a method for detecting change in groundcover vegetation as a response to grazing exclusion (kangaroo grazing) using visible wavelength digital photography within an environmental sensor network. The study was conducted over six months with imagery captured every 90 minutes and post-processed using supervised image processing techniques. Synchronous manual assessments of groundcover change were also conducted to evaluate the effectiveness of the automated procedures. Results show that for particular cover classes such as live vegetation and bare ground, there is excellent temporal concordance between automated and manual methods. However, litter classes were difficult to consistently differentiate. The paper describes the operational reliability of the ESN, its ability to reliably monitor ground cover change through a range of environmental extremes, and examines limitations of the methodology. A key limitation is the inability of the method to effectively deal with vertical groundcover change. This indicates that species composition and plant traits play an important role in driving future experimental designs. The research is an early attempt to develop an operational autonomous vegetation monitoring system for assessing the effectiveness of ecological restoration activities