Networks of embedded devices that work selleck chem Tubacin together to provide enhanced monitoring across spatial and temporal scales are growing in popularity [10]. Optimizing the performance of WSNs is the focus of ongoing computer science based research [9]. Wireless sensor networks are increasingly being used in terrestrial monitoring applications by ecologists and environmental scientists to collect and transmit data from remote field sites back to base [11,12]. The majority of current WSN deployments utilise sensors at fixed locations [11,13] where each node typically contains multiple sensors to measure a number of environmental parameters, for example, soil moisture or micro-climate. There are also some recent examples of WSN nodes being fitted to animals, creating a collection of mobile nodes within a WSN [9,14].

Inhibitors,Modulators,Libraries Within natural extensive environments communication within such networks of mobile nodes creates a unique set of challenges [15] which will be discussed in this paper.In fragile landscapes domesticated livestock pose a risk to the environment through overuse of particular areas [16-18]. Overgrazing areas of the landscape by herbivores can reduce plant diversity and ground cover, with associated risks of increased erosion [19,20]. Monitoring landscape condition is a prerequisite to implementing appropriate animal management strategies. In extensive grazing environments monitoring landscape condition using traditional observation methods is difficult and costly, as is the management of animals Inhibitors,Modulators,Libraries over large extents. Multi-spectral remotely-sensed images can be used to map the temporal changes in rangeland condition [21].

However, multi-spectral images from satellite-based sensors only provide an indirect measurement of physical characteristics and their usefulness is realised through the interpretation and calibration of the image data.There are many methods for interpreting remotely-sensed images (see [22] and [23] for good overviews); Inhibitors,Modulators,Libraries qualitative methods which combine spatial and spectral analysis include identifying spatial patterns Inhibitors,Modulators,Libraries in the image data, the presence of low- or high-regions, and changes in size or shape of the patches in classified maps.

Useful qualitative information can also be calculated from image data which, depending on the landscape characteristic being studied, ranges from simple vegetation indices such as the widely used Normalized Cilengitide Difference Vegetation Index http://www.selleckchem.com/products/MDV3100.html (NDVI) [24] which is a surrogate for vegetation ��vigour�� or ��greenness��, to more complex indices and analyses depending on whether the image are broadband [25] or hyper-spectral [26]. To determine quantitative information such as biomass requires ground-based calibration of the remotely-sensed image. For example, in temperate regions satellite images are being used to estimate pasture biomass [27] and pasture growth rate [28]. There has been extensive work on using satellite data for calculating net primary productivity [29,30].