R.N. Handcock

Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites.

Tracking livestock using global positioning systems – are we still lost?

Since the late 1980s, satellite-based global positioning systems (GPS) have provided unique and novel data that have been used to track animal movement. Tracking animals with GPS can provide useful information, but the cost of the technology often limits experimental replication. Limitations on the number of devices available to monitor the behaviour of animals, in combination with technical constraints, can weaken the statistical power of experiments and create significant experimental design challenges. The present paper provides a review and synthesis of using GPS for livestock-based studies and suggests some future research directions. Wildlife ecologists working in extensive landscapes have pioneered the use of GPS-based devices for tracking animals. Wildlife researchers have focussed efforts on quantifying and addressing issues associated with technology limitations, including spatial accuracy, rate of data collection, battery life and environmental factors causing loss of data. It is therefore not surprising that there has been a significant number of methodological papers published in the literature that have considered technical developments of GPS-based animal tracking. Livestock scientists have used GPS data to inform them about behavioural differences in free-grazing experiments. With a shift in focus from the environment to the animal comes the challenge of ensuring independence of the experimental unit. Social facilitation challenges independence of the individual in a group. The use of spatial modelling methods to process GPS data provides an opportunity to determine the degree of independence of data collected from an individual animal within behavioural-based studies. By using location and movement information derived from GPS data, researchers have been able to determine the environmental impact of grazing animals as well as assessing animal responses to management activities or environmental perturbations. Combining satellite-derived remote-sensing data with GPS-derived landscape preference indices provides a further opportunity to identify landscape avoidance and selection behaviours. As spatial livestock monitoring tools become more widely used, there will be a greater need to ensure the data and associated processing methods are able to answer a broader range of questions. Experimental design and analytical techniques need to be given more attention if GPS technology is to provide answers to questions associated with free-grazing animals.

Image sharpening method to recover stream temperatures from ASTER images

Linear unmixing of spectra from daytime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images can be used to improve the spatial resolution of temperatures calculated for streams that are not fully resolved in the 90-m thermal infrared (TIR) data. We first examine ASTER 15-m Visible-Near Infrared (VNIR) data to select three endmembers using a simple automated technique. These endmembers correspond to vegetation, shade/water, and other scene components (e.g. urban/soil/non-photosynthetic vegetation). Then the 15-m VNIR data are unmixed into the three corresponding fraction images. Threshold and adjacency tests are used to separate the shade and water fractions creating a total of four fraction images that together are used to specify the amount of the scene components in each 90-m TIR pixel. The emitted thermal radiance (ETR) from each of the scene components can be estimated if we assume that it is the same as for a

Spatio-Temporal Analysis Using a Multiscale Hierarchical Ecoregionalization

We address the need for spatio-temporally explicit analysis techniques linking the scales of ecosystem, observation, and analysis, using a hierarchical ecoregionalization to examine remotely sensed data at spatial scales of ecological and management significance. Long- and short-term changes in vegetation functioning are a key indicator of ecological processes. We predict net primary production (NPP) at monthly temporal resolution for 16 years (1981-1996) at an 8-km spatial resolution for the approximately 10 6 km 2 area of Ontario, Canada. We calculate landscape-level light use efficiency values that are tuned to monthly and long-term ecoclimates, and the Normalized Difference Vegetation Index from the NOAA-AVHRR sensor. Applying our spatio-temporal analysis tools, we show evidence for increasing NPP across most of the province. This increase varies seasonally and annually across Ontario, and its magnitude and distribution varies with the spatial scales of analysis. Bridging the gap between local and global studies, this research supports spatio-temporal monitoring and analysis of ecosystem functions.

Ecoregionalization assessment: Spatio-temporal analysis of net primary production across Ontario

Abstract An ecoregionalization can be defined as a partitioning scheme that captures landscape patterns by dividing an area into hierarchically nested ecounits based on similar physiographic and ecological characteristics. In this paper, we introduce new spatio-temporally explicit methods to characterize spatio-temporal variability of static, a priori defined, ecounits by their dynamic “spatio-temporal signatures” (STSs) and assess the strength of the ecoregionalization boundaries using this information. To analyze the spatial and temporal patterns of net primary productivity (NPP) at the ecozone, ecoregion, and ecodistrict levels of the National Ecological Framework of Canada (NEF) ecoregionalization, we compute a 15-year monthly series of NPP for Ontario at 8-km by 8-km resolution based on satellite images (NOAA-AVHRR) and a light-use efficiency model. At each level of the NEF hierarchy, within-unit homogeneity of the monthly, annual, and 15-year average NPP of ecounits is characterized by the Getis statistic, and between-unit heterogeneity of these variables is characterized by the boundary contrast (squared difference across the boundary). Similarities across the levels of the hierarchy are assessed by the sum-of-squared differences of monthly, annual, and 15-year average NPP of nested ecounits. Temporal trends of NPP per ecounit are measured using Kendall’s correlation coefficient. The seasonal and annual variations in the growing season, as captured by a time series of NPP aggregated to the ecodistrict, ecoregion, and ecozone level, are shown to vary across Ontario. These results indicate the potential of our spatio-temporal approach for ecoregionalization assessment based on dynamic and spatially distributed data.

Stream-temperature estimation from thermal infrared images

Stream temperature is an important water quality indicator. Spatial gradients of stream temperature can also be used to identify groundwater and surface water input locations in channel systems. Endangered fish populations are sensitive to elevated stream temperature, especially in the summer when low precipitation and high solar insolation increase temperatures beyond established thresholds. The removal of riparian vegetation and increases in surface run-off, that results from land-use change, also contribute to elevated stream temperatures. Thus, if critical watersheds are to be managed properly, accurate and spatially extensive temperature measurements are needed. For these purposes, it is necessary to know water surface temperature within 1/spl deg/C. Thermal infrared images (TIR) have long been used to estimate water surface temperatures, especially of the ocean where split-window techniques have been used to compensate for atmospheric effects. Streams are a more complex environment because 1) most are unresolved in typical thermal infrared images, and 2) stream corridors may consist of tall trees that irradiate the stream surface. Therefore, key additional problems to solve in measuring stream temperatures include both subpixel unmixing and multiple scattering. Over a watershed In the Cascade mountains of southern Washington, USA, we use fine-resolution airborne MODIS/ASTER Simulator (MASTER) data, and coarse-resolution ASTER data, to develop an approach for successful extraction of stream temperatures.

The spectral response of pastures in an intensively managed dairy system

All grazing-based industries require information on their feed resources in order to manage them optimally. Gathering this information through traditional methods for measuring pasture biomass is time-consuming and error-prone, resulting in increased interest in remotely-sensed methods. Remote sensing used to monitor feed resources in farming systems differs from remote sensing of systems such as forestry because of how the time-scale of management practices impacts on the growth rate and accumulation patterns of biomass. Also, in operational systems, designed for near real-time delivery to end-users of quantitative pasture measurements, we are restricted to the commercially available broad-band high-resolution sensors. The goal of this paper is to understand how remotely-sensed observations of pastures in an intensively managed dairy system change in relation to intensive management practices, so that better image analysis and ground-validation methods can be developed for measuring and monitoring such systems. At two dates in the growing season we examined high-resolution (SPOT-5 and Ikonos) images of an intensively managed perennial dairy farm in Victoria (Australia). We showed that the observed spectral response in the images varied with the length of time since the paddock was grazed, consistent with the re-growth of pastures post-grazing. The operational remote sensing of pastures is often restricted by the range of spectral bands that are available on broad-band sensors. However, these results suggest that when choosing a vegetation index for intensively managed dairy pastures it should incorporate the short-wave infrared (SWIR) band to improve observations of recently grazed pastures and tune analyses based on the spectral response.

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is required. This study investigated the utility of high-resolution airborne images (Digital Multi Spectral Imagery (DMSI)) obtained in two seasons to estimate carbon stocks at the plant- and stand-scale. Pixel-scale vegetation indices, sub-pixel fractional green vegetation cover for individual plants, and estimates of the fractional coverage of the grazing plants within entire plots, were extracted from the high-resolution images. Carbon stocks were correlated with both canopy coverage (R2: 0.76–0.89) and spectral-based vegetation indices (R2: 0.77–0.89) with or without the use of the near-infrared spectral band. Indices derived from the dry season image showed a stronger correlation with field measurements of carbon than those derived from the green season image. These results show that in semi-arid environments it is better to estimate saltbush biomass with remote sensing data in the dry season to exclude the effect of pasture, even without the refinement provided by a vegetation classification. The approach of using canopy cover to refine estimates of carbon yield has broader application in shrublands and woodlands.

Three regionalised analyses of a time-series of annual pasture production for southwest Western Australia

A thirteen year time-series (1994 to 2006) of gross annual pasture production (GAPP; representing both pasture and crop) was created for the Mediterranean-climate area in the southwest of Western Australia (SWWA) using a light-use efficiency model, incorporating NOAA-AVHRR and NASA- MODIS images in combination with climate data. Trends across the GAPP time-series were quantified by aggregating pixels to spatial regions (called a partition, unit, or spatial support) so that the effects of local spatial noise were minimized. We compared the GAPP analysis using the three spatial partitioning schemes (precipitation zones, Interim Biogeographic Regionalisation for Australia (IBRA) eco-regions, and Statistical Local Areas), and showed that the aggregation units size & shape impacted on the analysis. Our results demonstrate trends in GAPP that may be indicative of broader trends in climate change for the SWWA.