Developing species specific vegetation maps using multi-spectral hyperspatial imagery from unmanned aerial vehicles

In remote, rugged or sensitive environments ground based mapping for condition assessment of species is both time consuming and potentially destructive. The application of photogrammetric methods to generate multispectral imagery and surface models based on UAV imagery at appropriate temporal and spatial resolutions is described. This paper describes a novel method to combine processing of NIR and visible image sets to produce multiband orthoimages and DEM models from UAV imagery with traditional image location and orientation uncertainties. This work extends the capabilities of recently developed commercial software (Pix4UAV from Pix4D) to show that image sets of different modalities (visible and NIR) can be automatically combined to generate a 4 band orthoimage. Reconstruction initially uses all imagery sets (NIR and visible) to ensure all images are in the same reference frame such that a 4-band orthoimage can be created. We analyse the accuracy of this automatic process by using ground control points and an evaluation on the matching performance between images of different modalities is shown. By combining sub-decimetre multispectral imagery with high spatial resolution surface models and ground based observation it is possible to generate detailed maps of vegetation assemblages at the species level. Potential uses with other conservation monitoring are discussed.