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This paper evaluates the potential for remote mapping of river bathymetry by (1) examining the theoretical basis of a simple, ratio-based technique for retrieving depth information from passive optical image data; (2) performing radiative transfer simulations to quantify the effects of suspended sediment concentration, bottom reflectance, and water surface state; (3) assessing the accuracy of spectrally based depth retrieval under field conditions via ground-based reflectance measurements; and (4) producing bathymetric maps for a pair of gravel-bed rivers from hyperspectral image data. Consideration of the relative magnitudes of various radiance components allowed us to define the range of conditions under which spectrally based depth retrieval is appropriate: the remotely sensed signal must be dominated by bottom-reflected radiance. We developed a simple algorithm, called optimal band ratio analysis (OBRA), for identifying pairs of wavelengths for which this critical assumption is valid and which yield strong, linear relationships between an image-derived quantity X and flow depth d. OBRA of simulated spectra indicated that water column optical properties were accounted for by a shorter-wavelength numerator band sensitive to scattering by suspended sediment while depth information was provided by a longer-wavelength denominator band subject to strong absorption by pure water. Field spectra suggested that bottom reflectance was fairly homogeneous, isolating the effect of depth, and that radiance measured above the water surface was primarily reflected from the bottom, not the water column. OBRA of these data, 28% of which were collected during a period of high turbidity, yielded strong X versus d relations (R 2 from 0·792 to 0·976), demonstrating that accurate depth retrieval is feasible under field conditions. Moreover, application of OBRA to hyperspectral image data resulted in spatially coherent, hydraulically reasonable bathymetric maps, though negative depth estimates occurred along channel margins where pixels were mixed. This study indicates that passive optical remote sensing could become a viable tool for measuring river bathymetry. 1040 EARTH SURFACE PROCESSES AND LANDFORMS σ d standard deviation of water depth σ η standard deviation of water surface elevationFigure 4. OBRA of Hydrolight radiative transfer simulations isolating the effect of bottom reflectance R b (λ) or substrate type; c s = 2 g m −3 and U = 0 m s −1 . (a) R 2 (λ 1 , λ 2 ) matrix from OBRA, (b) residuals from optimal band ratio relation, (c) simulated spectra for periphyton substrates at a range of depths, (d) simulated spectra for gravel substrates at the same range of depths, and (e) the difference in reflectance between periphyton and gravel substrates at these depths. This figure is available in colour online at www.interscience.wiley.com/journal/espl Figure 5. OBRA of Hydrolight radiative transfer simulations isolating the effect of water surface roughness, parameterized in terms of wind speed U using Equation (9); substrate is pe...

Abstract. Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.

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Abstract. Ecologists and managers are motivated to predict the distribution of animals across landscapes as well as understand the mechanisms giving rise to that distribution. Satisfying this motivation requires an integrated framework that characterizes multi-scale habitat use and selection, as well as builds predictive models such as resource selection functions. However, the assumption of constant habitat use or selection is often made in such analyses, which ignores the possibility that individuals experiencing different conditions might respond differently. Assessing functional responses in habitat use evaluates how animal behavior changes with differing environmental conditions, which has basic and applied utility. Here, we combined these ideas into an integrated process that characterizes habitat relationships, predicts habitat, and assesses behavioral differences with changing environmental conditions. Our species of interest was Canada lynx (Lynx canadensis) in the Northern Rocky Mountains, which is a rare and federally threatened forest carnivore. Through our process, we developed multi-scale predictions of lynx distribution and learned that across scales and seasons, lynx use more mature, spruce-fir forests than any other structure stage or species. Intermediate snow depths and the distribution of snowshoe hares (Lepus americanus) were the strongest predictors of where lynx selected their home ranges. Within their home ranges, female and male lynx increasingly used advanced regeneration forest structures as they became more available (up to a maximum availability of 40%). These patterns supported the bottom-up mechanisms regulating Canada lynx in that advanced regeneration generally provides the most abundant snowshoe hares, while mature forest is where lynx appear to hunt efficiently. However, lynx exhibited decreasing use of stand initiation structures (up to a maximum availability of 25%). Land managers have an opportunity to promote lynx habitat in the form of advanced regeneration, but are required to go through the stand initiation phase. Thus, managers can apply the relative proportions of forest structure classes along with our response curves to inform landscape actions (e.g., timber harvest) targeted at facilitating the forest mosaic used and selected by Canada lynx. Collectively, the insights gleaned from our approach advance habitat conservation efforts and consequently are of broad utility to applied ecologists and managers.