Development of inexpensive, automated ground methods is necessary to advance precision plant phenology monitoring across large spatial extents. We propose the use of free, publicly available, Internet-connected cameras, often associated with nonscientific monitoring, to monitor plant phenology at continental scales. We provide a methodology to detect changes in vegetation greenness and determine timing of spring and fall events from over 1100 public cameras across North America from February 2008 - 2009. Manual image segmentation facilitated spring detection for both deciduous and understory vegetation occurring within a single camera view. Deciduous spring green-up was highly correlated with visual ground truths, despite signal noise introduced by varying image exposure and automatic color correction. Compared to co-occurring satellite remote sensing products, public cameras had an equivalent or higher ability to detect spring with fewer days lost to cloud cover.

Networked embedded acoustic sensors and imagers allow scientists to observe biological and environmental phenomena at high sampling rates and multiple scales. Such sampling can create large data sets that often require some form of automated processing to extract useful information. However, to guarantee the accuracy of the data, the scientist must be included in the processing, rather than treating it as a black box, an approach we call interactive environmental sensing. In this paper we describe the challenges of such an approach and motivate it with several examples from bioacoustics, plant phenology and avian biology.

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.