Wireless sensor networks for in-situ image validation for water and nutrient management

Water and Nitrogen (N) are critical inputs for crop production. Remote sensing data collected from multiple scales, including ground-based, aerial, and satellite, can be used for the formulation of an efficient and cost effective algorithm for the detection of N and water stress. Formulation and validation of such techniques require continuous acquisition of ground based spectral data over the canopy enabling field measurements to coincide exactly with aerial and satellite observations. In this context, a wireless sensor in situ network was developed and this paper describes the results of the first phase of the experiment along with the details of sensor development and instrumentation set up. The sensor network was established based on different spatial sampling strategies and each sensor collected spectral data in seven narrow wavebands (470, 550, 670, 700, 720, 750, 790 nm) critical for monitoring crop growth. Spectral measurements recorded at required intervals (up to 30 seconds) were relayed through a multi-hop wireless network to a base computer at the field site. These data were then accessed by the remote sensing centre computing system through broad band internet. Comparison of the data from the WSN and an industry standard ground based hyperspectral radiometer indicated that there were no significant differences in the spectral measurements for all the wavebands except for 790nm. Combining sensor and wireless technologies provides a robust means of aerial and satellite data calibration and an enhanced understanding of issues of variations in the scale for the effective water and nutrient management in wheat.<br /

The function of remote sensing in support of environmental policy

Limited awareness of environmental remote sensing’s potential ability to support environmental policy development constrains the technology’s utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of “remote sensing and policy” revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 versus 8.8% yr−1), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance

Human footprint and protected areas shape elephant range across Africa

Over the last two millennia, and at an accelerating pace, the African elephant (Loxodonta spp. Lin.) has been threatened by human activities across its range. We investigate the correlates of elephant home range sizes across diverse biomes. Annual and 16-day elliptical time density home ranges were calculated by using GPS tracking data collected from 229 African savannah and forest elephants (L. africana and L. cyclotis, respectively) between 1998 and 2013 at 19 sites representing bushveld, savannah, Sahel, and forest biomes. Our analysis considered the relationship between home range area and sex, species, vegetation productivity, tree cover, surface temperature, rainfall, water, slope, aggregate human influence, and protected area use. Irrespective of these environmental conditions, long-term annual ranges were overwhelmingly affected by human influence and protected area use. Only over shorter, 16-day periods did environmental factors, particularly water availability and vegetation productivity, become important in explaining space use. Our work highlights the degree to which the human footprint and existing protected areas now constrain the distribution of the world’s largest terrestrial mammal. A habitat suitability model, created by evaluating every square kilometer of Africa, predicts that 18,169,219 km2 would be suitable as elephant habitat—62% of the continent. The current elephant distribution covers just 17% of this potential range of which 57.4% falls outside protected areas. To stem the continued extirpation and to secure the elephants’ future, effective and expanded protected areas and improved capacity for coexistence across unprotected range are essential

Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis.

The dynamics of foliar chlorophyll concentrations have considerable significance for plant–environment interactions, ecosystem functioning and crop growth. Hyperspectral remote sensing has a valuable role in the monitoring of such dynamics. This study focussed upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Leaf-scale radiative transfer models were used to generate very large spectral data sets with which to develop and rigorously test refinements to the approach and compare it with existing spectral indices. The results demonstrated that by decomposing leaf spectra, the resultant wavelet coefficients can be used to generate accurate predictions of chlorophyll concentration, despite wide variations in the range of other biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and a range of spectral indices. The paper discusses the possibilities for further refining the wavelet approach and for extending the technique to the sensing of a variety of vegetation properties at a range of spatial scales

Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping

The accurate mapping of small, often fragmented and linear vegetation patches is of key importance for natural resource management because of their ecological significance. However, due to their small size and the quantised nature of remote sensing imagery they may be under-represented in the landscape when mapped using earth observation. This paper investigates the effect of patch area and patch elongation on the accurate mapping of these vegetation patches. Using synthetic images to simulate sub-pixel patch location, we investigated classification accuracy and extraction probability resulting from differences in the geometric properties of the raster grid and the feature alone. We simulated the effect of grid position, detectability, feature size and shape on classification. This represents the highest achievable accuracy using the remote sensing raster grid, where other factors influencing classification such as classification algorithm, radiometric calibration and sensor characteristics are excluded. We found that mapping error was highest when the scale of the feature and the raster grid coincided. We showed that the spatial resolution of the grid should be many times finer in order to extract these features accurately. For square patches with a mean classification accuracy of 75%, the grid pixel area was 11 times smaller than patch size. When patches were small and/or elongated, the probability of extraction was reduced, mapping accuracies decreased and variability in accuracy due to the effects of grid position increased. For example, a square shaped patch needed an area of at least 11 pixels to achieve a mean accuracy of 75%, whilst a linear patch with a width to length ratio of 4 needed an area of 12.3 pixels. This paper quantifies the limitations of remote sensing for the accurate detection of small and linear features and provides guidelines on the appropriate spatial resolution required to map these features. Using our results, map users can estimate the probability of a map classifying small and linear features independently of the error matrix. Furthermore, we provide a more precise estimate of the size of the smallest discernable feature taking into account the random position of the remote sensing grid with respect to the feature as well as its shape. An understanding of this phenomenon is critical for making good land management decisions based on a thorough understanding of the limitations of remote sensing data

Wireless sensor networks for in-situ image validation for water and nutrient management

https://www-researchgate-net.ezproxy2.utwente.nl/profile/Glenn_Fitzgerald/publication/228352682_Wireless_sensor_networks_for_in-situ_image_validation_for_water_and_nutrient_management/links/0912f50c911be54ba4000000/Wireless-sensor-networks-for-in-situ-image-validation-for-water-and-nutrient-management.pd

Short-term repeated HRV-16 exposure results in an attenuated immune response <i>in vivo</i> in humans - Fig 5

<p>Plasma IP-10 levels following placebo inoculation and HRV challenge (panel A) and following two HRV challenges separated by one week time (panel B). Panel C shows the peak levels in the first four days post-challenge in the group that received placebo, followed by a HRV challenge (bar 1 and 2) and in the group that were challenged with HRV twice (bar 3 and 4). Data are represented as geometric mean and 95% CI. Lower detection limit was 156 pg/mL.</p

Short-term repeated HRV-16 exposure results in an attenuated immune response <i>in vivo</i> in humans

<div><p>Introduction</p><p>Naturally, development of adaptive immunity following HRV infection affects the immune response. However, it is currently unclear whether or not HRV re-exposure within a short time frame leads to an altered innate immune response. The “experimental cold model” is used to investigate the pathogenesis of HRV infection and allows us to investigate the effects of repeated exposure on both local and systemic innate immunity.</p><p>Methods</p><p>40 healthy male and female (1:1) subjects were nasally inoculated with HRV-16 or placebo. One week later, all subjects received HRV-16. Baseline seronegative subjects (n = 18) were included for further analysis.</p><p>Results</p><p>Infection rate was 82%. Primary HRV infection induced a marked increase in viral load and IP-10 levels in nasal wash, while a similar trend was observed for IL-6 and IL-10. Apart from an increase in IP-10 plasma levels, HRV infection did not induce systemic immune effects nor lower respiratory tract inflammation. With similar viral load present during the second HRV challenge, IP-10 and IL-6 in nasal wash showed no increase, but gradually declined, with a similar trend for IL-10.</p><p>Conclusion</p><p>Upon a second HRV challenge one week after the first, a less pronounced response for several innate immune parameters is observed. This could be the result of immunological tolerance and possibly increases vulnerability towards secondary infections.</p></div

Experimental design of the study.

<p>Experimental design of the study.</p