Landscape resource mapping for wildlife research using very high resolution satellite imagery

Abstract

Quantifying wildlife-habitat relationships through resource selection analysis (RSA) has traditionally relied on landscape variables extracted at medium-to-coarse scales and general-purpose digital maps. However, RSA at fine scales, facilitated by accurate positional data obtained using GPS-tags, requires improved measures of habitat resources. The combination of cutting-edge remote sensing technology, such as very high spatial resolution (VHSR) satellite imagery and object-based image analysis (OBIA), can provide landscape maps that are suitable for the extraction of detailed variables at fine scale.

We used Quickbird satellite imagery and OBIA to produce a map using the multispectral bands (MULT), and explored the usefulness of the technique for resource identification by combining the panchromatic (highest spatial resolution) and multispectral (spectral information) bands (PAN:MULT) to produce a second map. Each of the mapping methods was used in a heterogeneous braided-river environment in New Zealand to: (1) classify and delimit ground features using an object-based accuracy assessment approach; (2) detect ground features of different sizes; (3) extract independent landscape variables at fine scale (within buffers between 20 and 30 m) for separate RSA for introduced hedgehogs (Erinaceus europaeus) and feral cats (Felis catus).

Per-pixel accuracy assessment produced overall accuracies of 82% (PAN:MULT) and 79% (MULT). The per-object assessment using shrubs as the testing class yielded further information on classification and delimitation of object accuracy, with accuracies of 80% for shrub patches ≥30 m2 in MULT and ≥5 m2 in PAN:MULT. The inclusion of the panchromatic band noticeably improved the identification and delimitation of cover. However, RSA using each of the maps did not yield differences in the best models for cats or hedgehogs.

VHSR imagery and OBIA provide a valuable method to identify smaller ground features and thus to produce more detailed landscape maps to study animal habitat use at fine scale. Improvements in ground feature detection achieved by including the panchromatic band may not justify its cost, as it was shown for the studies on cats and hedgehogs presented here. However, the level of detail offered by the panchromatic layer may be useful for addressing other RSA questions or applications at fine scale, such as remote censusing of colonial species.