Plant phenology is highly sensitive to changes in environmental conditions and can vary widely across landscapes. Current observation methods are either manual for small-scale, high precision measurements or by satellite remote sensing for large-scale, low spatial resolution measurement. The development of inexpensive approaches is necessary to advance large scale, high precision phenology monitoring. The use of publicly available, Internet-connected cameras, often associated with airports, national parks, and roadway conditions, for detecting and monitoring plant phenology at a continental scale can augment existing ground and satellite-based methodologies. We collected twicedaily images from over 1100 georeferenced public cameras across North America from February 2008 to 2009. Using a test subset of these cameras, we compared modeled spring 'green-up' with that from co-occurring remote sensing products. Although varying image exposure and color correction introduced noise to camera measurements, we were able to correlate spring green-up across North America with visual validation from images and detect a latitudinal trend. Public cameras had an equivalent or higher ability to detect spring compared with satellite-based data for corresponding locations, with fewer numbers of poor quality days, shorter continuous bad data days, and significantly lower errors of spring estimates. Manual image segmentation into deciduous, evergreen, and understory vegetation allowed detection of spring and fall onset for multiple vegetation types. Additional advantages of a public camera-based monitoring system include frequent image capture (subdaily) and the potential to detect quantitative responses to environmental changes in organisms, species, and communities. Public cameras represent a relatively untapped and freely available resource for supporting large-scale ecological and environmental monitoring.

No abstract

No abstract

The performance of a sensor network may be best judged by the quality of application specific information return. The actual sensing performance of a deployed sensor network depends on several factors which cannot be accounted at design time, such as environmental obstacles to sensing. We propose the use of mobility to overcome the effect of unpredictable environmental influence and to adapt to run time dynamics. Now, mobility with its dependencies such as precise localization and navigation is expensive in terms of hardware resources and energy constraints, and may not be feasible in compact, densely deployed and widespread sensor nodes. We present a method based on low complexity and low energy actuation primitives which are feasible for implementation in sensor networks. We prove how these primitives improve the detection capabilities with theoretical analysis, extensive simulations and real world experiments. The significant coverage advantage recurrent in our investigation justifies our own and other parallel ongoing work in the implementation and refinement of self-actuated systems.

a b s t r a c tThe characterization of the solar radiation environment under a forest canopy is important for both understanding temperature-dependent biological processes and validating energy balance models. A modified sinusoidal model of soil heat conductivity was used to estimate subsurface temperature and heat flux from the uneven but periodic solar heating of the soil surface due to sun flecks from a forest canopy. Using a mobile sensor platform with an infrared thermometer along an 11 m transect, a sunfleck model of soil surface temperature was tested using soil surface temperature maxima, air temperatures, and photodiodes placed on the soil surface to measure sunflecks. A pan-tilt-zoom digital camera on a 10 m tower above the site was then used to capture a time series of panoramic images of sunflecks reflected from the soil surface and to scale the sunfleck temperature model to a wide area. Finally, this image-based model of surface temperatures was combined with the modified sinusoidal model for heat conduction to estimate soil subsurface temperatures and heat flux over a wide area due to sunflecks from a forest canopy.