White Paper. Spatial Methods for Estimating Basin-wide Riparian Solar Radiation at the Stream Surface

Transcription

1 White Paper Spatial Methods for Estimating Basin-wide Riparian Solar Radiation at the Stream Surface Boise River Basin Spatial-Statistical Stream Temperature Modeling David E. Nagel Draft February 12, 2009 U. S. Forest Service Rocky Mountain Research Station Boise Aquatic Sciences Laboratory 322 E. Front Street, Suite 401 Boise, ID Important Contributions by: Charles H. Luce, Daniel J. Isaak, Barbara Gutierrez Teira, Bruce E. Rieman and Tom Black Forward This white paper describes methods developed at the U.S. Forest Service, Rocky Mountain Research Station, Boise Aquatic Sciences Laboratory for estimating solar radiation incident to the stream surface, at a basin-wide scale. The methods were developed as part of a larger study aimed at developing a spatial statistical stream temperature model for the Boise River basin in central Idaho. Earlier work exploring solar radiation estimates has been presented by Gutiettez Teira, et al. (2004) and Nagel et al. (2004). Information presented in this paper has not yet been sanctioned through the peer-review process, however a review is underway. As such, the procedures outlined herein should be considered preliminary and should not be replicated for the purposes of resource management. The results and conclusions sections of this document have been omitted in anticipation of future publication. This paper provides a brief overview of the Boise River basin stream temperature modeling study and follows by describing the field data collection, GIS, and remote sensing methods used for estimating solar radiation. 1

2 Procedures and Methods Disclaimer of Liability Neither the United States Government nor any of its employees makes any warranty, express or implied, for any purposes regarding these procedures or methods. This includes warranties of merchantability and fitness for any particular purpose. Furthermore, neither the United States Government nor any of its employees assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information or products derived from these procedures. Disclaimer of Non-endorsement Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government, and shall not be used for advertising or product endorsement purposes. 2

3 Boise River Basin Stream Temperature Modeling Overview We undertook a multi-year study in the Boise River basin to model stream temperatures and predict thermally suitable aquatic habitat for bull trout and rainbow trout. The stream temperature modeling approach used air temperature, stream flow, elevation, and solar radiation as the primary predictor variables. GIS and spatial statistical models that account for network topology (Ver Hoef et al. 2006) were used to predict stream temperatures at 1 km intervals throughout the 2,500 km river network. In an effort to apply these statistical models to thermal habitat predictions, we assembled a large stream temperature database (n = 780 records) spanning a 14 year period from for the 6,900 km 2 Boise River basin in central Idaho. Predictor variables that potentially affected stream temperature were quantified using automated GIS routines to obtain measurements of geomorphic features (e.g., elevation, channel slope, and valley confinement), satellite imagery to estimate solar radiation and radiation changes from wildfire, and climate stations to provide information on stream flow and air temperatures. Spatial models with four fixed effect predictors and a mixed model spatial error structure accounted for 93% and 86% of the variation in summer mean and maximum stream temperatures, respectively, during the 14 year study period. Introduction Solar radiation is an important factor in stream heat budgets (Johnson 2003; Caissie 2006) and has been documented as a fundamental variable influencing stream temperatures (Sinokrot and Stefan, 1993; Webb and Zhang, 1997; Webb and Zhang, 1999; Johnson, 2004, Danehy et al., 2005). The riparian zone and the shade it provides are the main factors regulating the amount of solar radiation that enters a stream and thus have an important influence on water temperatures (Gray and Edington, 1969, Johnson and Jones, 2000; Johnson, 2004), especially in narrow streams (Rutherford et al. 1997). However, there is considerable debate regarding the relative importance of, and the mechanism by which riparian vegetation influences stream temperatures (Johnson, 2004). There is little debate however over the enormous importance of stream temperature for freshwater organisms (Webb, 2008). Radiation flux to streams can increase dramatically when fires alter riparian vegetation and these changes account for the majority of chronic, post-fire summer temperature increases in streams. As riparian vegetation recovers, thermal regimes may also recover, but the process can take decades, depending on prefire vegetation types and local conditions (Dunham et al. 2007). In some instances, riparian alterations could become permanent if postfire conditions are no longer suitable for establishment of more mesic tree species (McKenzie et al. 2004; van Mantgem and Stephenson 2007). 3

4 Estimating the affect of riparian vegetation on the amount of solar radiation reaching the stream surface is difficult because shade varies with vegetation type and density. Moreover, radiation flux to the water surface interacts with stream size where shading is more pronounced in smaller streams and diminishes for larger channels, as the ratio of vegetative height to stream width decreases (Naiman and Sedell 1980; Quinn and Wright-Stow 2008). We attempted to estimate the influence of shade on stream temperatures by using satellite imagery to classify riparian vegetation and linking these cover classes to ground measurements of radiation. Others have computed solar radiation at a basin-wide scale using general solar models and including parameters for topographic shading and cloud cover (Flint and Flint, 2008). The effect of solar radiation upon stream temperature is certainly complex and varies with latitude, season, hour (University of Oregon, 2002), cloud cover, topographic shading (Flint and Flint, 2008), and canopy shading (Webb, 2008). Our approach focused solely on solar radiation input through the vegetation canopy and examined its influence on summer temperatures. Hemispherical canopy photography is one of the best accepted methods for estimating solar radiation from the ground (see Weiss et al., 1991; Robinson and McCarthy, 1999). The technique is well documented and has been used in numerous forest, riparian, and shade studies (Barrie et al., 1990, Davies- Colley and Payne, 1998, Robinson and McCarthy, 1999, Englund et al., 2000, Hale and Edwards, 2002, Bellow and Nair, 2003, Ringold, 2003, Kelly and Krueger, 2005). Attempts to evaluate or model stream shade patterns over river basins with this methodology alone, however, would require measurements over extensive stream lengths, which is impractical, particularly in light of the rugged terrain conditions in many headwater streams. Instead, we used hemispherical canopy photography to estimate mean radiation values through a range of vegetation types and then mapped these vegetation classes at a basin-wide scale using remotely sensed imagery. Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) satellite imagery have been used extensively for mapping vegetation since the first TM sensor was launched on the Landsat 4 satellite in Procedures for doing so are well documented in numerous textbooks (Lillesand, et al. 2003, Jensen, 2004, Campbell, 2006). In addition, many researchers have explored the use of TM and ETM+ imagery for mapping riparian vegetation (Hewitt, 1990, Narumalani et al., 1997, Congalton et al, 2002, Lattin et al., 2004, Sohn and Qi, 2005, Baker et al., 2006). Results have varied depending on the study objectives, however riparian classes associated with rivers, lakes and wetlands have been classified successfully (Hewitt, 1990, Narumalani et al., 1997). Our purpose in this study was to evaluate the utility of remote sensing data over an entire basin for estimating solar radiation along the stream channel and to identify a set of radiation values that might be useful for inferring stream temperature. We also sought to estimate the influence of fire on 4

5 radiation inputs at the basin-wide scale. Specifically, we sought to 1) test the effectiveness of Thematic Mapper satellite imagery for mapping solar radiation along stream channels 2) investigate the amount of variability in stream temperature that can be explained by radiation alone and 3) determine the approximate spatial lag distance where the upstream influence of shade or radiation is most influential to any particular point on the stream. We used hemispherical canopy photography as the basis for measuring radiation and related these radiation values to vegetation type. We then linked the canopy photo radiation to vegetation data derived from satellite imagery. Then we created a map of solar radiation for each stream cell in the study area. Radiation values were accumulated along the stream channel using a 4 km distance decay function that simulated the diminishing importance of more distant radiation affects. From these data we were able to estimate general radiation shifts between 1989 and 2004 resulting from wildfire, at a basin-wide scale. Methods Study Design The results reported herein are the product of two separate, but related studies. The first study (study-1) was conducted specifically to develop vegetation-radiation relationships and link remote sensing derived vegetation to stream incident radiation. This project used a limited set of instream temperature records (148) collected over a two year period, along with a preliminary vegetation map and study area extent of 2,300 km 2. Following the methods developed from study-1, a more ambitious project was undertaken to model stream temperatures using a much larger set of temperature records (780) and more extensive study area (6,900 km 2 ). This second study (study-2), although undertaken to accomplish independent objectives, provided further validation of our methods and helped strengthen our conclusions (Figure 1). Study-1 and study-2 each used a separate set of stream temperature data referred to as temperature set 1 (TS1) and temperature set 2 (TS2) respectively. Study Area The study was conducted in the upper Boise River basin (Boise basin) of central Idaho. The Boise basin covers 6,900 km2 and is drained by approximately 2,500 km of fish-bearing streams that range in elevation from 900-2,500 m. The terrain is forested, mountainous, and administered primarily by the US Forest Service. Vegetation varies along gradients associated with elevation, aspect, and precipitation. More mesic upper elevations and northfacing slopes at lower elevations are timbered, but drier southfacing slopes and lower elevations are characterized by shrubs, grasses, and forbs. Riparian vegetation consists primarily of shrub species such as willow (Salix, spp.) or alder (Alnus, spp.) and tree species such as lodgepole pine (Pinus contorta), douglas fir (Pseudotsuga menziesii), ponderosa pine (Pinus ponderosa), with very limited cottonwood or aspen (Populus, spp.), and birch (Betula, spp.). Lower elevations were historically characterized by 5

6 mixed severity fires with return intervals less than 35 years. Intervals for higher elevations may have been an order of magnitude greater (Brown and Smith 2000). Wildfires were relatively rare within the Boise basin during most of the 20th century (presumably the result of fire suppression), but have been more common in the last 20 years. Approximately 25% of the basin (area within fire perimeters) has burned with a mix of fire severity in this time (Figure 1; Dunham et al. 2007), although only 14% has burned since 1993 when the first stream temperature records are available. Climate is characterized by relatively wet winters and cold temperatures that contrast with hot and dry summers. Mean annual air temperature at 1600 m is about 5 C and precipitation is variable, ranging from mm/year, with snow comprising the largest fraction at higher elevations. Stream hydrographs are characteristic of snowmelt driven systems in the northern Rockies, with high flows occurring from April through June and low flows during late summer and early fall. High flow events can be associated with summer thunderstorms, but are generally limited to smaller watersheds and short durations. Stream Temperature Database We obtained stream temperature data throughout the study area that had been collected over a 15 year period from We used stream temperature data to 1) examine the relative validity of our radiation predictions, assuming that radiation would be positively correlated with stream temperature, 2) determine the amount of variability in stream temperature that can be explained by radiation alone, and 3) estimate the spatial decay distance where the upstream influence of shade or radiation is most influential. We used two sets of temperature records in our investigation. The first set (temperature set one [TS1]) was initially collected for a temperature and natural disturbance study (Dunham et al., 2007), but was also used to fulfill our objectives in study-1. Thermographs (Tidbit models; Onset Computer Corporation, Pocasset, MA) were installed and retrieved from July-September The loggers were placed in fourteen streams, with ten loggers in each stream, except two that had nine, making a total of 138. During June- September 2003, 10 more loggers were placed in ten streams of the watershed for a total of 148 (Figure 1). In both cases, the hottest days of the year were included. Thermograph locations were recorded using global positioning system (GPS) devices. Loggers were set inside PVC cases to protect them from direct solar radiation and collected temperatures every 30 minutes. The second set of stream temperature data (TS2) included records from TS1, but also contained an additional 540 records. This database of stream temperature measurements was assembled from previous studies (Rieman et al. 2006; Dunham et al. 2007) and routine monitoring efforts conducted by several natural resource agencies. In 2006 and 2007, we supplemented 6

7 these data with 152 observations distributed across a representative sample of small (< 2350 ha contributing area), medium, and large streams (> ha contributing area) and the full range of elevations within the Boise basin ( m) (Figure 1). TS2 stream temperatures were sampled with digital thermographs (Hobo and Tidbit models; Onset Computer Corporation, Pocasset, MA; accuracy = C; ibutton; Maxim Integrated Products, Sunnyvale, CA; accuracy = C) that recorded temperatures at a minimum of five times daily (average = 72/day). Thermographs were placed in streams before mid-july and retrieved after mid-september. Locations were either georeferenced with GPS or were recorded on 1:24,000-scale topographic maps and later converted to a digital format. After screening to eliminate potentially anomalous temperature records (e.g., those downstream from reservoirs, hot springs, or beaver (Castor Canadensis) dam complexes), 780 records at 518 unique sites were retained for analysis. Numerous metrics have been developed to summarize the long time-series of measurements that comprise a temperature record, but most are highly correlated (Isaak and Hubert 2001; Dunham et al. 2005). We used somewhat different but correlated metrics for study-1 and study-2, as these studies were conducted independently. For study-1 we used the Maximum Weekly Average Maximum Temperature (MWMT) and the Maximum Value of Average Weekly Temperature (MWAT). MWMT is the highest maximum temperature summarized over a continuous seven-day period, and MWAT is the highest average temperature summarized over each day of a continuous seven-day period. With these values, we calculated the average MWMT and the average MWAT for every stream each year. Data were analyzed for the period July 1 to September 15 for each year. In 2002, this missed the actual peak stream temperatures, which were reached in late June, however a secondary peak in August is represented in this analysis. For study-2 we used two metrics to summarize our data; the mean temperature from July 15 - September 15, and the maximum weekly maximum temperature (MWMT), which was the highest 7-day moving average of the maximum daily temperatures over this same period. The mean provides a good indicator of overall thermal suitability and conditions for growth, whereas a maxima provided an indicator that would better capture transient conditions associated with climatic or wildfire extremes. Temperature maxima are also frequently cited for their importance to aquatic biota and are used in laboratory settings for determination of lethal limits (e.g., Richter and Kolmes 2005; McMahon et al. 2007). The 113 thermographs used for study-1 (TS1) were installed in a linear string along 14 separate stream channels. Since the temperature records were highly correlated within a stream, the string of 9-10 loggers in each 7

8 stream were not considered independent. Rather we combined temperature metrics within each stream and these were used to compare measures between streams, yielding independent samples. For study-2 data (TS2) the number and spatial arrangement of the temperature recorders was so large and complex that all of the records were used to arrive at a general relationship between temperature and radiation. Hemispherical Canopy Photography In order to estimate the amount of incident solar radiation at the stream surface we collected hemispherical canopy photography at 181 field sites. Hemispherical canopy photography (Rich, 1989 and 1990) is a technique used to quantify solar radiation transmitted through a vegetation canopy. The lens used is a fisheye design with a circular viewing angle of 180. The camera is mounted in a vertical upward looking position and images may be acquired either digitally or on analog film. Images are then analyzed to estimate direct and diffuse insolation in energy units, such as megajoules per square meter per year ( MJ/m 2 /yr). The 181 field sites were visited in June, 2003 and distributed among a range of vegetation types representative of the Boise basin (Figure 2). We also distributed the sample sites among an array of stream sizes appropriate for temperature modeling, with contributing areas varying from approximately HA and stream widths ranging from 1-8 m. Photos were acquired using a fisheye lens and panchromatic film with the camera mounted on a tripod at mid-stream, positioned 1 m above the water surface. We also collected horizontal photos a each location to help identify dominant vegetation types (Figure 3). The film was scanned and then analyzed using Hemiview software (Dynamax, Inc. 1 ). We computed total June radiation (direct and diffuse) and values ranged from MJ/m 2 /yr. Stream Network Our procedure for predicting solar radiation required a digital, raster stream network for storing the predicted radiation values along the stream corridor. Our work was completed using a combination of standard tools and custom scripts for ArcGIS Desktop version 9.2 software (ESRI, 2006). The radiation dataset was represented by raster data using ESRI GRID format (ESRI, 2007). We used 1 arc second (30 m) USGS National Elevation Dataset (NED) data (USGS, 2006) for generating a synthetic stream network. The NED data and all grids were co-registered and projected to the UTM, Zone 11, NAD 83 coordinate system. The synthetic stream network for the Boise basin was generated with TauDEM software (Tarboton, 2008) using the NED data as input. The channels were generated using the D8 flow direction (O Callaghan and Mark, 1984) and D8 contributing area (Mark, 1988) routines (Tarboton, 1997). The digital elevation model (DEM) curvature method with an accumulation threshold of 25 pixels was used to initiate the stream channels, which then were traced by the TauDEM program along the DEM flow accumulation path. 8

9 The raster stream network generated in this process was used as the stream channel template for all of our future modeling efforts. Remote Sensing and Vegetation Classification To estimate the amount of solar radiation that influenced each cell of the stream network in the Boise basin, we classified riparian vegetation using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) satellite imagery and linked the vegetation classes to radiation values. Radiation values were derived from field measurements that were obtained using the hemispherical canopy photography. The reported success of using TM and ETM+ data in riparian studies has varied depending on the author s objectives (Hewitt, 1990, Narumalani et al., 1997, Congalton et al, 2002, Lattin et al., 2004, Sohn and Qi, 2005, Baker et al., 2006). Goetz (2006) noted that the use of land cover maps for riparian analysis may be limited unless the data are specifically developed for narrow riparian zones. Heeding this advice, we concentrated specifically on the riparian zone to avoid the confounding affect of the superfluous upland vegetation, which increases overall scene variance. By concentrating only on the riparian zone we found that the satellite imagery provided enough detail to meet our study objectives. We also improved our results over other studies because our narrow objective (mapping solar radiation) only required that we classify a limited set of vegetation types. In addition, we accumulated the average upstream radiation (derived from vegetation) over a distance of approximately 4 km, which mitigated the affect of discrete classification errors and provided a synoptic measure of the general shade pattern upstream from cell of interest. We obtained Landsat 7 ETM+ imagery acquired on July 10, 2002 and Landsat 5 TM data acquired on July 14, 1989 (Figure 4). The imagery covered path/row designators 41/29 and 41/30. These two dates of satellite imagery were acquired in order to track vegetation changes that occurred within the temporal record of our stream temperature data. The 2002 riparian vegetation was classified first, then used as guidance for classifying the 1989 imagery. The riparian vegetation was then modified through a time series between 1989 to 2004 to reflect changes resulting from wildfire and other affects. ERDAS Imagine version 9.1 software was used for all image processing (Leica Geosystems, 2006). Preprocessing of the satellite imagery involved a number of steps including radiometric and geometric correction. Radiometric correction was applied to convert the original sensor digital numbers (DN) to absolute radiance, so that the 1989 and 2002 images would have comparable brightness values for similar vegetation types. Radiance values were then converted to reflectance values to account for minor differences in sun angle between the two images (Irish, 2000). 9

10 Geometric correction involved rectification of the imagery to the UTM coordinate system using 1 meter National Agricultural Imagery Program (NAIP) orthophotography for ground control. In addition, the 30 m NED data was used for terrain correction of the imagery to remove topographic displacement effects (Leica,Geosystems, 2006). This process ensured good registration between the DEM derived stream network and the ETM+/TM derived vegetation. The cubic convolution resampling method (Lillesand et al. 2003) was used for rectification of the ETM+ image and was subsequently used to co-register the ETM+ and TM images. The cubic convolution routine was used previously by Armstron et al., 2002 for coregistering satellite imagery. Root mean square error for the 1989 and 2002 rectifications was and m respectively. The output pixel size was specified as 30 m and the image extent was co-registered to the NED data. Clouds present in the 1989 image were backfilled using image data from Four categories of land cover were classified using the ETM+/TM imagery. The categories were: 1) Open vegetation (open), 2) Riparian shrubland (shrub), 3) Coniferous forest (conifer) and 4) Open water (water). Open vegetation consisted of riparian meadows, burned forest where the canopy was primarily consumed or dead, and exposed earth resulting from debris flows and flooding. Riparian shrubland was primarily comprised of broad leaved shrubs such as willow and alder. Coniferous forest consisted of species such as Douglas-fir, Englemann spruce, subalpine fir, and Ponderosa pine. Our classification system was limited to these three vegetation classes because these categories covered the vast majority of cover types in the Boise basin. In addition, these three classes were easily identifiable from the satellite imagery and covered the full range of radiation values in our study area. Broadleaf tree species are limited in extent within the study area and were not classified. For study-2, vegetation was only classified along the riparian corridor. A buffer distance of 60 m (two pixels) was delineated along each bank of the stream network, yielding a total riparian width of 120 m. Lattin, et al. (2004) used a two pixel width to classify riparian vegetation with TM imagery. We chose a somewhat wider width and obtained acceptable classification results. The study-2, 2002 imagery was classified using the ISODATA and maximum likelihood classifiers. The ISODATA algorithm (Tou, 1974, Leica Geosystems, 2006) is the most commonly used unsupervised classification technique and has been used in previous riparian and wetland mapping studies (Kindscher et al., 1998 and Narumalani et al., 1997). We first input the riparian imagery into the ISODATA classifier to render an initial signature set. Using the 1 m NAIP orthophotography for ground reference information, the signature set was evaluated and pruned of high variance signatures. The remaining set was input into the maximum likelihood classifier (Leica Geosystems, 2006) to arrive at a final vegetation classification. A similar 10

11 combined classification approach was used by Reese, et al. (2002). The final 2002 classified data set produced land cover types in the following proportions: open 41%, shrub 19%, conifer 37%, and water 2%. Accuracy of the resultant land cover data was evaluated by generating 158 random points within the classified riparian areas. These points were compared against the 1 m NAIP photography by a third party, unbiased analyst. The overall classification accuracy was calculated at 80% (Table 1). The 1989 imagery (study-2) was classified using a similar technique, except that the 2002 classification results were used for ground reference guidance rather than the aerial photography. The 1989 data was segmented into 60 unsupervised classes using the ISODATA algorithm. These 60 classes were overlain with the 2002 classification to assign each of the 60 classes to the most appropriate land cover type. Where an ISODATA class did not have good correspondence with any particular land cover class from the 2002 data, the corresponding signature was eliminated. The final 1989 signature set was then input into the maximum likelihood algorithm to arrive at the final classification map. The final 1989 classified data set produced land cover types in the following proportions: open 40%, shrub 13%, conifer 45%, and water 2%. The accuracy of the 1989 classification was not evaluated quantitatively due to the difficulty in obtaining historical aerial photography for the study area. However, a qualitative comparison of the classification against the satellite imagery and an assessment of the change classes between 1989 and 2002 established that the 1989 results were comparable in accuracy to the 2002 results (Figure 5). The 1989 and 2002 classification results are compared in Figure 6. The study-1 vegetation classification used the same 2002 imagery and a similar classification technique. However, for the study-1 work the riparian area was not extracted prior to classification and the overall accuracy was lower, at 75%. The water class was not derived for this study (Table 1). Vegetation Change To estimate stream temperatures for every year in our study time frame (study-2), it was necessary to derive a corresponding vegetation layer (and hence radiation) for each year. We did so by using the 1989 vegetation classification as our year 0 initiator. As each fire burned in the basin, we updated the vegetation layer to reflect the fire induced changes. We assumed that any changes within fire perimeters resulted in a vegetation change to the cover type present in the 2002 classification. Since most fires in the basin occurred after the maximum weekly mean temperature (MWMT) time period each summer, the vegetation change was instantiated in the year following the fire. In this fashion we proceeded through each year in the study period creating a new vegetation layer to represent years 0-15, 11

12 corresponding to years Since we could not predict the year that changes occurred outside of fire perimeters, these were instantiated in the middle year, In addition, one fire occurred outside of our imagery window in Radiation values were initiated in 2004 for this fire and were estimated using fire severity data as a surrogate for vegetation. The vegetation grids (one for each year) were then converted to radiation grids as described below (Figure 5). Linking Vegetation with Radiation In order to link radiation values to vegetation type, we binned each of the 181 hemispherical canopy photo sites into one of the three vegetation classes; open, shrub or conifer. Classes were assigned using a combination of 1 m aerial photography and the horizontal ground collected photos for reference. We then computed the mean radiation for each vegetation class with the following results: open, 786; shrub, 688; and conifer, 476 (MJ/m 2 /yr) (Figure 7; F(2,178)=66.73, p<0.0001). As expected, open sites registered the highest incoming radiation, while conifer locations received the lowest amount of insolation. Shrubs were intermediate. Differences in mean radiation were established as significant based on a single factor analysis of variance (ANOVA) computation (Table 2). To estimate incoming radiation for all cells along the stream channel, we first extracted the vegetation pixels that coincided with the raster stream network. This network consisted of a single cell width chain of pixels representing the channel. Each cell in this set was then assigned a radiation value based on the mean figures identified above (open, 786; shrub, 687; and conifer, 476 (MJ/m 2 /yr)). Next, each radiation cell value in the network was modified to account for variability in stream width. To do so we parameterized simple power-law relationships using log-transformed data to predict total radiation as a function of watershed contributing area for each vegetation type (Figure 8). The contributing area thresholds were: open, 2970 HA; shrub, 4770 HA; and conifer, HA. In each case, an upper radiation limit was imposed at 1000 MJ/m 2 /yr to approximate the level at which riparian vegetation would no longer significantly shade large streams. These relationships conformed to general expectations, with radiation levels increasing as contributing area (stream width) increased, and being higher for open/grass vegetation classes and lower for conifers. Considerable variation in radiation occurred within the vegetation classes, which resulted from a diversity of canopy conditions within each class. These canopy conditions include vegetation height, density, and species composition. Radiation Accumulation and Mapping The temperature at a point on a stream is the result of heat gains and losses that are controlled by upstream conditions (Johnson 2003). Conditions immediately upstream generally have greater influence than those further away, but the spatial domains over which these conditions are most influential are unclear. Therefore, we quantified upstream radiation affect using two techniques, 1) simple averaging and 2) inverse distance-weighted 12

13 averaging, over a range of distance domains. Simple average was used with data from study-1 and inverse distance-weighted averaging with study-2 data. For study-1, we used a simple accumulation scheme that gave all upstream cells equal weight for estimating the average upstream radiation. We tested accumulation distances of 1, 2, 3, 4, and 5 km. For study-2, we used an inverse distance-weighted scheme utilizing inverse exponential weights with distances (the distance at which the weight is 1/e) of 1 km, 4 km, and 15 km. Along an individual stream this can be estimated as: x n n wi xi = 1 n (1) where: nx is the upstream-averaged quantity at the nth cell from the beginning of the stream, xi are the values of the quantity being averaged at each upstream cell, and wi are the weights at each upstream cell. The xi were taken from the radiation grid, and wi are given by: Din Dc w i = e (2) where: Din is the distance between the nth and ith cell along the stream path (using simple 8 direction flow vectors between cells), and Dc is the distance (i.e., 1 km, 4 km, or 15 km). The procedure incorporated upstream cells along the channel including tributaries. For the simple average accumulation scheme we found that an upstream distance of 2 km provided the best correlation between stream temperature and radiation (R 2 = 54.1). We used only the 2003 TS1 data (n = 10) for making this determination. The correlation then declined slightly with increasing distance toward 5 km. Using the inverse distance-weighted scheme (distance decay), we found that 4 km provided the best fit (Figure 9). Acknowledgements Funding for this work was provided by the National Fire Plan, the US Forest Service Rocky Mountain Research Station, and local assistance from the Boise National Forest. We thank Kevin Jones, Dylan Kovis, Iosefa Matagi, and Ray Schofield for their assistance in the field. The US Forest Service Remote Sensing Applications Center provided assistance with TM image processing. 13

14 Stream temperature data were provided by Dan Kinney, Pacfish/Infish Biological Opinion monitoring group, Environmental Protection Agency, University of Idaho, and CH2M HILL. Gwynne Chandler, Dona Horan, and Sharon Parkes at the Boise Aquatic Sciences Lab provided invaluable data processing, technical, and logistical support. References Armston, J. D., T. J. Danaher, B. M. Goulevitch and M. I. Byme, (2002). Geometric correction of Landsat MSS, TM and ETM+ imagery for mapping woody vegetation cover and change detection in Queensland. Proceedings of the 11 th Australasian Remote Sensing and Photogrammetry Conference, Brisbane, Australia, September. Caissie, D The thermal regime of rivers: a review. Freshwater Biology 51: Dunham, J., G. Chandler, B. Rieman, and D. Martin Measuring stream temperature with digital data loggers: a user's guide. General Technical Report RMRS GTR-150WWW. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, USA. Dunham, J. B., A. E. Rosenberger, C. H. Luce, and B. E. Rieman Influences of wildfire and channel reorganization on spatial and temporal variation in stream temperature and the distribution of fish and amphibians. Ecosystems doi: /s ESRI, Inc. (2006). ESRI ArcGIS Desktop 9.2. Redlands, CA, USA. ESRI, Inc. (2007). ArcGIS 9.2 Desktop Help. Accessed: September 15, Baker, C, R. Lawrence, C. Montagne, and D. Patten, (2006). Mapping Wetlands and Riparian Areas Using Landsat ETM+ Imagery and Decision-Tree Based Models. Wetlands, 26(2): Barrie, J., J. N. Greatorex-Davies, R. J. Parsell, and R. H. Marrs, (1990). A Semi-Automated Method for Analysing Hemispherical Photographs for the Assessment of Woodland Shade. Biological Conservation, 54: Bellow, J. G. and P. K. R. Nair, (2003). Comparing common methods for assessing understory light availability in shaded-perennial agroforesty systems. Agricultural and Forest Meteorolgy. 114: Brown, L. E. and D. M. Hannah, Spatial heterogeneity of water temperature across and alpine basin. Hydrological Processes 22:

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18 Northwest GIS Users Conference, Sun Valley, Idaho. September Accessed. September 23, Naiman, R. J., and J. R. Sedell Relationships between metabolic parameters and stream order in Oregon. Canadian Journal of Fisheries and Aquatic Sciences 37: Narumalani, S., Y. Zhou, J. R. Jensen, (1997). Application of remote sensing and geographic information systems to the delineation and analysis of riparian buffer zones. Aquatic Botany, 58: O Callaghan, J. F. and D. M. Mark, (1984). The Extraction of Drainage Networks From Digital Elevation Data. Computer Vision, Graphics and Image Processing, 28: Quinn, J. M., and A. E. Wright-Stow Stream size influences stream temperature impacts and recovery rates after clearfell logging. Forest Ecology and Management 256: Rich, P. M., (1989). A manual for analysis of hemispherical canopy photography. Los Alamos National Laboratory Report LA M. Rich, P. M. (1990). Characterizing plant canopies with hemispherical photographs. In: N.S. Goel and J.M. Norman (eds), Instrumentation for studying vegetation canopies for remote sensing in optical and thermal infrared regions. Remote Sensing Reviews 5: Rieman, B. E., J. T. Peterson, and D. L. Myers Have brook trout Salvelinus fontinalis displaced bull trout Salvelinus confluentus along longitudinal gradients in central Idaho streams? Canadian Journal of Fisheries and Aquatic Sciences 63: Ringold, P. L., J. Van Sickle, K. Rasar, and J. Schacher Use of hemispheric imagery for estimating stream solar exposure. Journal of the American Water Resources Association 39: Reese, H. M., T. M. Lillesand, D. E. Nagel, J. S. Steward, R. A. Goldman, T. E. Simmons, J. W. Chipman, P. A. Tessar, (2002). Statewide land cover derived from multiseasonal Landsat TM data; A retrospective of the WISCLAND project. Remote Sensing of the Environment, 82: Richter, A., and S. A. Kolmes Maximum temperature limits for Chinook, coho, and chum salmon, and steelhead trout in the Pacific Northwest. Reviews in Fisheries Science 13:

19 Robinson, S. A. and B. C. McCarthy, (1999). Potential factors affecting the estimation of light availability using hemispherical photography in oak forest understories. Journal of the Torrey Botanical Society 126(4): Rutherford, J. C., Blackett, S., Blackett, C., Saito, L. and Davies Colley, R Predicting the effects of shade on water temperature. New Zealand Journal of Marine and Freshwater Research 31: Sinokrot, B. A. and Stefan, H. G Stream temperature dynamics: measurements and modeling. Water Resources Research 29: Sohn, Y and J. Qi, (2005). Mapping Detailed Biotic Communities in the Upper San Pedro Valley of Southeastern Arizona Using Landsat 7 ETM+ Data and Supervised Spectral Angle Classifier. Photogrammetric Engineering and Remote Sensing. 71(6): Tarboton, D. G., (1997). A New Method for the Determination of Flow Directions and Contributing Areas in Grid Digital Elevation Models, Water Resources Research, 33(2): Tarboton, D. G., (2003). Terrain Analysis Using Digital Elevation Models in Hydrology. In: Conference Proceedings from the 23 rd ESRI International Users Conference, San Diego, CA, July Tarboton, D. G., (2008). Terrain Analysis Using Digital Elevation Models (TauDEM). Accessed: September 15, Tou, J. T. and Gonzalez, R., Pattern Recognition Principles. Reading, Massechusetts: Addison-Wesley Publishing Company. 377 pp. University of Oregon, Solar Radiation Monitoring Laboratory, Solar radiation basics. Accessed February 9, USGS (2006). National Elevation Dataset. Accessed: September 15, van Mantgem, P. J., and N. L. Stephenson Apparent climatically induced increase of tree mortality rates in a temperate forest. Ecology Letters 10: Ver Hoef, J.M., E. Peterson, and D. Theobald Spatial statistical models that use flow and stream distance. Environmental and Ecological Statistics 13: Webb, B.W. and Y. Zhang Spatial and seasonal variability in the components of the river heat budget. Hydrological. Processes. 11:

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21 Tables Table 1. Study-2, 2002 vegetation classification accuracy results. Bold diagonal values represent the number of sample sites that were correctly classified. Study-1 results were similar. 21

22 Table 2. Single factor ANOVA results for the mean radiation values associated with each vegetation class. Table 3: ANOVA results of the stream disturbance class versus both MWAT and MWMT (ºC) for the streams sampled in summers 2002 and Whisker plots are shown in figure 9. Year Dependent Variable Degrees of Freedom F p R 2 MWAT 2, MWMT 2, MWAT 2, MWMT 2,

23 Table 4. Regression results of estimated radiation values (MJ/m 2 yr) versus both MWAT and MWMT (ºC) obtained for the streams sampled in summers 2002 and Regression plots are shown in Figures 10 and 11. Year N Depende nt R 2 F p Model Variable MWAT < R MWMT < R MWAT R MWMT R

24 Figures Figure 1. Boise River basin study area in Idaho. Study-1 and study-2 perimeters are depicted with corresponding thermograph locations. Study-1 thermographs are represented by solid boxes but are overposted in the map display due to their close proximity on the ground. 24

25 Figure hemispherical canopy photo locations within the study-1 area, years 2002 and Overposting of symbols makes the number of samples appear reduced. 25

26 Figure 3. Hemispherical canopy photos and corresponding horizontal photos for the sites with the highest (38A) and lowest (31B) radiation values. Figure Thematic Mapper and 2002 Enhanced Thematic Mapper satellite imagery encompassing study-1 and study-2 extents. 26

27 Figure 5. Study-2, 1989 vegetation classification (a), 2002 vegetation classification (b) and vegetation loss and gain between the two dates (c) Hectares Open Shrub Conifer Figure 6. Study-2, riparian acreage for the three vegetation classes, 1989 and

28 June Radiation (MJ/m 2 /yr) Median 25%-75% Non-Outlier Range Open Willow Conifer Vegetation Classification Figure 7: Study-1, box and Whisker plot for the relationships between the radiation (MJ/m 2 yr) obtained through the hemispherical photographs and the three cover types into which each site was classified. 28

29 1000 y = 105x r 2 = 0.64 Radiation (µj/m (Mj/m2/ye 2 /year) y = 92.0x r 2 = 0.25 y = 71.5x r 2 = Watershed contributing area (ha) Open Shrub Conifer Open - observed Shrub - observed Conifer - observed FIGURE X. Relationships between radiation, watershed area, and vegetation class used to predict radiation values for the stream network in the Boise River basin. Vegetation classifications were derived from TM imagery and radiation was measured at 181 field sites. 29

30 Figure 9. Hypothetical 4 km radiation accumulation domain for a cell of interest. 30