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Snow hydrology is a specialized field of hydrology that is of particular importance for high latitudes and mountainous terrain. In many parts of the world, river and groundwater supplies for domestic, irrigation, industrial, and ecosystem needs are generated from snowmelt, and an in-depth understanding of snow hydrology is of clear importance. Study of the impacts of global warming has also stimulated interest in snow hydrology because increased air temperatures are projected to have major impacts on the snow hydrology of cold regions. Principles of Snow Hydrology describes the factors that control the accumulation, melting, and runoff of water from seasonal snowpacks over the surface of the earth. The book addresses not only the basic principles governing snow in the hydrologic cycle, but also the latest applications of remote sensing, and principles applicable to modelling streamflow from snowmelt across large, mixed landuse river basins. Individual chapters are devoted to climatology and distribution of snow, ground-based measurements and remote sensing of snowpack characteristics, snowpack energy exchange, snow chemistry, modelling snowmelt runoff (including the SRM model developed by Rango and others), and principles of snowpack management on urban, agricultural, forest, and range lands. There are lists of terms, review questions, and problems with solutions for many chapters available online at www.cambridge.org/9780521823623. This book is invaluable for all those needing an in-depth knowledge of snow hydrology. It is a reference book for practising water resources managers and a textbook for advanced hydrology and water resources courses which span fields such as engineering, Earth sciences, meteorology, biogeochemistry, forestry and range management, and water resources planning.

The simple, empirical degree‐day approach for calculating snowmelt and runoff from mountain basins has been in use for more than 60 years. It is frequently suggested that the degree‐day method be replaced by the more physically‐based energy balance approach. The degree‐day approach, however, maintains its popularity, applicability, and effectiveness. It is shown that the degree‐day method is reliable for computing total snowmelt depths for periods of a week to the entire snowmelt season. It can also be used for daily snowmelt depths when utilized in connection with an adequate snowmelt runoff model for computing the basin runoff. The degree‐day ratio is shown to vary seasonally as opposed to being constant as is often assumed. Additionally, in order to evaluate the degree‐day ratio correctly, the changing snow cover extent in a basin during the snowmelt season must be taken into account. It is also possible to combine the degree‐day approach with a radiation component so that short time interval (<24 hours) computations of snowmelt depth can be made. When snowmelt input is transformed to basin output (runoff) by a snowmelt runoff model, there is little difference between the degree‐day approach and a radiation‐based approach. This is fortuitous because the physically‐based energy balance models will not soon displace the degree‐day methods because of their excessive data requirements.

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 (R 2 = 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.

Imagery acquired with unmanned aerial vehicles (UAVs) has great potential for incorporation into natural resource monitoring protocols due to their ability to be deployed quickly and repeatedly and to fly at low altitudes. While the imagery may have high spatial resolution, the spectral resolution is low when lightweight off-the-shelf digital cameras are used, and the inclusion of texture measures can potentially increase the classification accuracy. Texture measures have been used widely in pixel-based image analysis, but their use in an object-based environment has not been well documented. Our objectives were to determine the most suitable texture measures and the optimal image analysis scale for differentiating rangeland vegetation using UAV imagery segmented at multiple scales. A decision tree was used to determine the optimal texture features for each segmentation scale. Results indicated the following: 1) The error rate of the decision tree was lower; 2) prediction success was higher; 3) class separability was greater; and 4) overall accuracy was higher (high 90% range) at coarser segmentation scales. The inclusion of texture measures increased classification accuracies at nearly all segmentation scales, and entropy was the texture measure with the highest score in most decision trees. The results demonstrate that UAVs are viable platforms for rangeland monitoring and that the drawbacks of low-cost off-the-shelf digital cameras can be overcome by including texture measures and using object-based image analysis which is highly suitable for very high resolution imagery.