Abstract
Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.
Highlights
Remote sensing applications for natural resources using unmanned aircraft systems (UAS) as the observing platform have grown considerably in recent years
The methods for orthorectification and mosaicking of the multispectral imagery follow closely the approaches we developed for low-cost digital cameras [8,29]
To further improve the band alignment, we developed a new automated band-to-band registration algorithm using a local weighted mean transform (LWMT), which is better able to compensate for locally varying pixel misalignments between bands than a polynomial approach
Summary
Remote sensing applications for natural resources using unmanned aircraft systems (UAS) as the observing platform have grown considerably in recent years. This increase has been observed in practical applications, and in the peer-reviewed literature. Quality lightweight multispectral sensors suitable for use on small UAS have not been widely available in the past, and one alternative has been to alter consumer cameras to acquire images in the near infrared band [14,22,23]. A multispectral sensor that captures data over a range of relatively narrow wavelength bands is preferable for vegetation applications because of the potential for quantitative remote sensing, retrieval of biophysical parameters, better differentiation of vegetation species, and greater suitability for comparison with satellite imagery. Despite the potential of UAS to acquire high spatial resolution multispectral data, research in this area is relatively limited, and only a few applications have been reported in the literature
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