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 83702
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.
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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.
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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 1993 – 2006 for the 6,900 km2 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).
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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, DaviesColley 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 1982. 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
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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 km2. 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 km2). 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
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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 300-1000 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 1993 – 2007. 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
2002. The loggers were placed in fourteen streams, with ten loggers in each
stream, except two that had nine, making a total of 138. During JuneSeptember 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
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these data with 152 observations distributed across a representative sample
of small (< 2350 ha contributing area), medium, and large streams (> 10000
ha contributing area) and the full range of elevations within the Boise basin
(1100-2500 m) (Figure 1).
TS2 stream temperatures were sampled with digital thermographs (Hobo and
Tidbit models; Onset Computer Corporation, Pocasset, MA; accuracy = +
0.2°C; iButton; Maxim Integrated Products, Sunnyvale, CA; accuracy = +
0.5°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
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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/m2/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
135–3000 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 118-1038 MJ/m2/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.
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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).
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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 14.08 and 14.58 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
1987.
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
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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,
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corresponding to years 1989 - 2004. Since we could not predict the year
that changes occurred outside of fire perimeters, these were instantiated in
the middle year, 1995. In addition, one fire occurred outside of our imagery
window in 2003. 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/m2/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/m2/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, 10890 HA. In each case, an upper
radiation limit was imposed at 1000 MJ/m2/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
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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:
n
xn =
∑w x
i i
1
(1)
n
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:
wi = e
−
Din
Dc
(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 (R2 = 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
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.
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20
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
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 2003.
Whisker plots are shown in figure 9.
Year
F
p
R2
MWAT
Degrees
of
Freedom
2,11
16.9
0.0004
0.76
MWMT
2,11
18.2
0.0003
0.77
MWAT
2,7
6.2
0.03
0.64
MWMT
2,7
7.0
0.02
0.67
Dependent
Variable
2002
2003
22
Table 4. Regression results of estimated radiation values (MJ/m2yr) versus
both MWAT and MWMT (ºC) obtained for the streams sampled in summers
2002 and 2003. Regression plots are shown in Figures 10 and 11.
Year
2002
2003
N
Depende
nt
Variable
MWAT
R2
F
p
Model
84.7
66.5
<0.0001
0.043R – 14.199
MWMT
86.0
73.5
<0.0001
0.055R – 18.586
MWAT
51.4
8.46
0.02
0.027R – 3.573
MWMT
54.1
9.4
0.02
0.040R – 8.922
14
10
23
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
Figure 2. 181 hemispherical canopy photo locations within the study-1 area,
years 2002 and 2003. Overposting of symbols makes the number of samples
appear reduced.
25
Figure 3. Hemispherical canopy photos and corresponding horizontal photos
for the sites with the highest (38A) and lowest (31B) radiation values.
Figure 4. 1989 Thematic Mapper and 2002 Enhanced Thematic Mapper
satellite imagery encompassing study-1 and study-2 extents.
26
Figure 5. Study-2, 1989 vegetation classification (a), 2002 vegetation
classification (b) and vegetation loss and gain between the two dates (c).
60000
50000
Hectares
40000
Open
Shrub
30000
Conifer
20000
10000
0
1989
2002
Figure 6. Study-2, riparian acreage for the three vegetation classes, 1989
and 2002.
27
1200
June Radiation (MJ/m2/yr)
1000
800
600
400
200
Median
25%-75%
Non-Outlier Range
0
Open
Willow
Conifer
Vegetation Classification
Figure 7: Study-1, box and Whisker plot for the relationships between the
radiation (MJ/m2yr) obtained through the hemispherical photographs and the
three cover types into which each site was classified.
28
y = 105x0.283
r2 = 0.64
2/year)
Radiation
(Mj/m2/ye
Radiation
(µJ/m
1000
800
y = 92.0x0.283
r2 = 0.25
600
y = 71.5x0.283
r2 = 0.31
Open
400
Shrub
Conifer
200
Open - observed
Shrub - observed
Conifer - observed
0
0
1000
2000
3000
Watershed contributing area (ha)
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
4000
Figure 9. Hypothetical 4 km radiation accumulation domain for a cell of
interest.
30
760
June Radiation (MJ/m2/yr)
720
680
640
600
560
Median
25%-75%
Non-Outlier Range
520
480
Undisturbed
Burned
Burned and Scoured
Disturbance
Figure 10: Study-1, box and whisker plot for the relationships between the
radiation (MJ/m2yr) estimated from satellite image processing and the three
disturbance classes into which each stream sampled in 2002 (TS1) and 2003
(TS2) were classified.
31
28
26
Temperature (oC)
24
8
6
MWAT
MWMT
4
22
2
20
0
18
8
16
6
14
4
12
2
10
0
8
8
Undisturbed
Burned & Scoured
Undisturbed
Burned & Scoured
Burned
2003
Burned
2002
Figure 11: Study-1, whisker plot of medians, maximums, and mimimums for
the relationships between the MWAT or MWMT (ºC) and the three
disturbance classes into which each stream sampled in 2002 and 2003 was
classified. See Table 3 for ANOVA statistics.
32
A)
30
MWAT
MWMT
Temperature (oC)
25
20
15
10
5
500
550
600
650
700
750
800
Reach Average Radiation (MJ/m2/yr)
B)
30
MWAT
MWMT
Temperature (oC)
25
20
15
10
5
500
550
600
650
700
750
800
Reach Average Radiation (MJ/m2/yr)
Figure 12: Study-1, regression models for the relationship between reach
average radiation (MJ/m2/yr) estimated from classified vegetation and MWAT
and MWMT for each stream sampled in A) 2002 and B) 2003. See Table 4
for fit statistics.
33
Temperature (MWAT)
Radiation vs. Stream Temperature
25
23
21
19
17
15
13
11
9
7
5
100
200
300
400
500
600
700
Radiation
Figure 13. Study-2, MWMT (TS2) regressed against radiation.
Vegetation Loss and Gain
Percent Change
1989 - 2002
40
30
Loss
20
Gain
10
0
Entire Basin
Within Fire
Outside Fire
Figure 14. Riparian vegetation loss and gain (% change) within and outside
of fire perimeters for study-2.
34
Mean radiation (MJ/m2/yr)
400
390
380
370
1989
360
2004
350
340
330
Entire basin
Within fire
Outside fire
.
Figure 15. Mean radiation. Change between 1989–2004 within and outside
of fire perimeters.
35