higher-resolution information with decreased costs. The development of remote-sensing technology to be used with unmanned (drone) aircraft also reduces the risk associated with flying planes and helicopters.
High-resolution remote-sensing imagery can be used to observe unique coat patterns and to detect identifying marks or scars for horse identification. Aerial images taken from manned and unmanned aircraft can produce images with centimeter-level resolution. In addition to color or color-infrared imagery, forward-looking infrared (FLIR) cameras can detect body heat from more than one-fourth of a mile above the ground (Millette et al., 2011). Those cameras have the potential to distinguish horses from the surrounding environment and provide an accurate method for counting animals. Quickbird and Ikonos are satellite sensors that acquire data with resolution of 0.5 to 1 m. These midlevel resolution sensors may be effective for detecting horses and for monitoring change in population densities. Higher-resolution satellite images have been developed and in time will be more readily available.
There are limitations that should be considered when selecting the appropriate remote-sensing platform with respect to estimating populations of free-ranging horses and burros (Millette et al., 2011). First, the spatial resolution of the data must be fine enough to detect individual animals (especially when animals are moving or in a herd) and reduce misidentification with other animal species. Insufficient resolution can be a problem with many satellite-based sensors. Second, data acquisition may be untimely because some technologies rely on orbiting satellites that pass over a given landscape only at intervals of a few days to a few weeks. Third, many remote-sensing technologies are expensive. Fourth, some cameras have too small a field of view and may need to pan back and forth (such as FLIR and handheld cameras). Fifth, the detectability of animals may depend on weather, time of day, vegetation composition and structure, or local topography in a survey area, and quantification of detection probability can be difficult. For example, radiant heat from the earth’s surface (in particular during the daytime) can camouflage the heat produced from a horse or burro when FLIR sensors are used. Sixth, weather patterns, particularly cloud cover, can preclude data collection with many remote-sensing technologies and can add risk to aircraft operators. Finally, current Federal Aviation Administration restrictions limit the use of unmanned aerial vehicles.
A number of studies have used molecular markers to identify animals in noninvasively collected samples to estimate population size. That approach is particularly effective for populations in which individuals are difficult to detect because of vegetative cover or elusive behavior. Traditionally, such populations were surveyed with indirect methods, or indexes, such as sign counts (e.g., feces and tracks), which were corrected for estimates of the rates at which the signs are deposited and decay. In many cases, however, those estimates have relatively large confidence intervals, which limit their usefulness in managing or monitoring populations (Barnes, 2002). For such populations, multilocus genotypes derived from noninvasively collected samples (e.g., feces, hair, and scent marks) have been used as genetic tags for individuals. With a capture-mark-recapture design, populations have been surveyed and the resulting data have been analyzed to estimate population sizes. Genetic tags have advantages over traditional tagging systems in that animals retain their genotypes throughout their lives (thus, tags cannot be lost), and there is no reason to believe that a noninvasively assigned tag will affect the ability to resample the animal (the animals cannot become trap-happy or trap-shy). For dangerous or difficult-to-observe