High-resolution spatial analysis of mountain landscapes using a low-altitude remote sensing approach

Mountain landscapes are characterized by great spatial diversity. One basic problem is that there are few high-resolution data for secluded mountain areas. We present a new approach towards topographic mapping and vegetation monitoring: low-altitude remote sensing using Kite Aerial Photography (KAP). The study was conducted in the Norwegian mountains above the treeline. We assessed this approach under specific alpine circumstances. Following the collection of data, we derived a digital elevation model (DEM) from two overlapping images. The model was evaluated by the statistical correlation of 265 random field points and extracted heights from (i) linear contour line interpolation of a topographic map of scale 1:50000, (ii) photogrammetric analysis of kite aerial photographs, and (iii) kriging interpolation of approximately 1000 measured field points. Finally, the vegetation was classified, using both supervised and unsupervised methods. The accuracy of the classification results was evaluated by comparing 265 random points, derived from terrestrial mappings, to classified vegetation types by an error matrix. The generation of derived data compared well with data obtained from high-resolution field surveys and was better than data derived from public-domain government cartography and moderate-scale satellite remote sensing data. Our results demonstrate the economic and logistic advantages of this new KAP-based methodology. The flexibility and outstanding high resolution of our new low-altitude remote sensing approach proved to be particularly suitable for closing the gap between terrestrial investigations and high-altitude remote sensing. Hence, our KAP approach addresses the challenge of multiscale research in mountain landscapes.