Project Title: Mapping current conditions and modeling the dynamic responses of
riparian vegetation and salmon habitat in Oregon.
Proposed Project Period: Oct 1, 2007 to Sep 30, 2009
Principal Investigator:
Dr. Steve Wondzell
USDA Forest Service, PNW Research Station
3625 93rd. Ave., S.W.
Olympia, WA 98512
Phone: 360-753-7691
Fax: 360-753-7737
Email: swondzell@fs.fed.us
Co-Principal Investigator(s):
Dr. Kelly Burnett
USDA Forest Service, PNW Research Station
3200 Jefferson Way
Corvallis, Oregon 97331
Phone: 541-750-7309
Email: kelly.burnett@oregonstate.edu
Dr. Janet Ohmann
USDA Forest Service, Pacific Northwest
Research Station
3200 SW Jefferson Way
Corvallis, Oregon 97331
Phone: 541-750-7487
Fax: 541-758-7760
Email: janet.ohmann@oregonstate.edu
Dr. Warren Cohen
UDSA Forest Service, PNW Research Station
3200 SW Jefferson Way
Corvallis, Oregon 97331
Phone: 541-750-7322
Fax: 541-758-7760
Email: warren.cohen@oregonstate.edu
Mr. Jimmy Kagan
Oregon State University, Institute for Natural
Resources
1322 SE Morrison St.
Portland, Oregon 97214
Phone: 503-731-3070 ext. 111
Fax: 503-731-3070 ext. 118
Email: jimmy.kagan@oregonstate.edu
Dr. Miles Hemstrom
USDA Forest Service, PNW Research Station
620 SW Main St.
Suite 400
Portland, OR 97205
USA
Phone: 503-808-2006
Fax: 503-808-2020
Email: mhemstrom@fs.fed.us
Dr. Peter Bisson
USDA Forest Service, PNW Research Station
3625 93rd Ave., S.W.
Olympia, WA 98512
USA
Phone: 360-753-7671
Fax: 360-753-7737
Email: pbisson@fs.fed.us
Proposal Summary:
Key Words (1000 characters):
Riparian zone; restoration planning; riparian and aquatic assessment; effectiveness
monitoring; intensively monitored watershed; Nehalem River, Middle Fork John Day
River; lidar; NAIP digital photography; Landsat; GIS; salmon; steelhead; bull trout;
habitat quality; active restoration; passive restoration; vegetation composition and
structure.
Objectives (2000 characters):
The proposed research integrates riparian zone mapping with dynamic models to project
the response of riparian zones, stream channels and salmon habitat to natural disturbance
and land-use activities. The overall objective of this work is to produce a decision support
tool for habitat restoration planning that incorporates advanced remote-sensing
technology and information about disturbance-recovery processes with existing
knowledge of critical habitat needs for salmonids. The proposal has two components: 1)
remote sensing and riparian mapping, 2) riparian and aquatic modeling. The objective of
the mapping component is to explore different methods for mapping riparian and instream conditions using Landsat, lidar, and NAIP imagery and to use these methods to
delineate, classify and map the attributes of riparian zones needed for riparian assessment
and monitoring and to support the modeling component. The objective of the modeling
component is to develop a Dynamic Riparian and Aquatic Integrated Network (DRAIN)
planning model that includes background processes and land-use activities to simulate
temporal dynamics of riparian vegetation, channel conditions, and habitat quality for
salmonids. We will apply the remote sensing and mapping methods to two intensively
monitored watersheds – Nehalem and Middle Fork John Day (MFJD) Rivers – and apply
the DRAIN model to the to examine current conditions relative to the historic range of
variability, examine potential of passive restoration to meet recovery goals, and examine
the potential of active restoration to accelerate recovery.
Methodology (2000 characters):
Remote sensing imagery, including Landsat, lidar, and digital airphotos, will be
combined with field data to characterize riparian vegetation. Spatial modeling techniques
will be used to classify and map vegetation composition and structure for riparian
characterization and for initializing the DRAIN model. A time-series of Landsat data will
be used to characterize disturbance patterns over recent decades and their impact on
riparian habitat. Riparian maps will be integrated with basin-wide maps of existing
upland vegetation. A high-resolution DEM (<10 M) will be built from lidar imagery. We
will extract watershed area, longitudinal gradient, and valley-floor width and use these to
delineate and classify stream reaches into the potential channel morphologic groups for
the DRAIN model. Finally, we will assess the accuracy of the mapped riparian attributes
using a variety of established methods. We will compare the quality of maps developed
with and without lidar data to determine the feasibility and cost for scaling up from
watershed to the entire state. The mapping component of our research will be linked to
the modeling component through the delineation, classification, and assessment of
current condition of streams and riparian zones in each watershed. Development of the
DRAIN planning model is based on a prototype aquatic and riparian model we developed
using the Vegetation Development Dynamics Tool (VDDT). We will develop additional
VDDT models for riparian vegetation and channel types missing from our suite of
prototype models. We will also expand the pathways and transition probabilities still
needed to characterize many historic and current land-use activities. We will use TELSA
to develop a dynamic stream network model that is linked with spatially explicit upland
models. We will modify and automate our habitat evaluation model and, using known
habitat preferences of salmonids, project the effects of changes in riparian and channel
condition on fish species.
Rationale (2000 characters):
Pacific salmon and steelhead have declined in abundance or been eliminated from large
parts of their historical range, and many populations are now listed under the U.S.
Endangered Species Act. Maintenance of existing high-quality habitat and restoration of
degraded habitat have become cornerstones of many salmon recovery efforts. To support
these efforts, there is a critical need for sound information on riparian areas – particularly
over the broad spatial extents that are relevant to recovering salmonid populations.
However, providing relatively detailed information on current conditions and likely
future riparian conditions for large watersheds (e.g., 4th-field HUC) over large regions
poses substantial logistical and technical challenges. Combining remote sensing and
ground-based surveys offers a potential method for characterizing current conditions of
riparian zones over large areas. However, much of the available remotely sensed imagery
and DEMs are generally too coarse in spatial resolution to adequately characterize the
detail of narrow riparian zones. A variety of fine-scale remotely sensed data are
becoming widely available (e.g., lidar, NAIP, QuickBird) and have great potential for
detailed landscape assessments. While combining newly available remotely-sensed and
field-collected data offers a means of assessing the current condition of riparian areas at
fine detail over large areas, these assessments only provide a static “snapshot” of a
watershed at a single point in time. We need the ability to use this snapshot as a starting
point for projecting changes in habitat resulting from processes that shape streams,
riparian forests, and the upland systems to which they are connected. Thus, there is also
critical need for an integrated stream network planning model that can support the design,
implementation and monitoring of restoration projects in large watersheds.
Expected Outcomes (2000 characters):
The mapping methods and decision support model we develop will allow watershed
councils and OWEB to assess current riparian conditions and evaluate how conditions
may change in response to land-use activities and restoration efforts. These products will
contribute to whole-watershed restoration planning, examining both passive and active
restoration approaches to develop long-term restoration strategies for large, intensively
monitored watersheds. Our expected outcomes are: 1) maps displaying geomorphic and
vegetation attributes of the Nehalem and Middle Fork John Day watersheds with
associated accuracy assessment, a time-series of disturbance events in the watersheds
since 1972, and vegetation and land cover of the surrounding uplands; 2) methods for
integrating both remotely-sensed and field data for mapping riparian, in-stream, and
associated upland conditions, including recommendations for use of tested remote
sensing products for riparian monitoring; 3) the Dynamic Riparian-Aquatic Integrated
Network (DRAIN) planning model will be made publicly available along with a PNW
General Technical Report fully documenting the model and describing its application for
whole-watershed riparian assessment and restoration planning; and 4) the results of our
final, validated analyses of habitat potential, current stream and riparian conditions and
projected trends of tributary habitats of the Nehalem and Middle Fork John Day River
Watersheds. We will establish a web site where the mapping tools and both the DRAIN
model and model documentation can be freely downloaded by interested parties. We will
archive collected field and remote-sensing data, either on our project’s web site or other
relevant data archival site. Finally, the study’s findings will be reported in peer reviewed
literature and at scientific and professional meetings. Results will also be presented to
land managers, watershed councils, and others interested in stream restoration planning.
7.A. Introduction:
The proposed research integrates riparian zone mapping with dynamic models to project the response
of riparian zones, stream channels and salmon habitat to natural disturbance and land-use activities. The
intent of this work is to produce a decision support tool for habitat restoration planning that incorporates
advanced remote-sensing technology and information about disturbance-recovery processes with existing
knowledge of critical habitat needs for salmonids.
Pacific salmon and steelhead have declined in abundance or been eliminated from large parts of their
historical range (Nehlsen et al., 1991), and many populations are now listed under the U.S. Endangered
Species Act (USDA and USDI, 2000). Multiple factors have contributed to these declines, including the
degradation of spawning and rearing habitat in tributary streams (Federal Caucus, 2000). Maintenance of
existing high-quality habitat and restoration of degraded habitat have become cornerstones of many
salmon recovery efforts. Science generally supports that riparian protection and restoration are effective
ways to conserve stream ecosystems (NRC 2002). However, there is a critical need for sound information
on riparian areas – particularly over the broad spatial extents that are relevant to recovering salmonid
populations. Better understanding is necessary on the role of riparian areas in creating and maintaining
salmon habitats and how these habitats change over time in response to natural and anthropogenic
disturbance. Landscape-scale perspectives of historical, current, and potential future conditions of upland,
riparian, and aquatic systems resulting from plant succession, natural disturbances, and land-use practices
can help inform policy directions. Further, such information is essential to developing strategic restoration
approaches for large regions (e.g., Oregon Plan for Salmon and Watersheds for coho salmon) that make
the most of limited funds. However, providing relatively detailed information on current conditions and
likely future riparian conditions for large watersheds (e.g., 4th-field HUC) over large regions poses
substantial logistical and technical challenges.
Combining remote sensing and ground-based surveys offers a potential method for characterizing
current conditions of riparian zones over large areas (Gould, 2000). Such a baseline could be compared to
available historical information to estimate the relative health of watersheds. Further, the development of
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standardized methods based on remotely sensed data would allow trend assessments of habitat conditions
in the future. Remote sensing is a rapidly growing discipline, and several newer approaches have not been
fully exploited for analyses of riparian zones. Widely used Landsat data are generally too coarse to
adequately characterize the detail of narrow riparian zones. A variety of fine-scale remotely sensed data
are becoming more available (e.g., lidar, NAIP, QuickBird) and have greater potential for detailed
landscape assessments. In particular, lidar (light detecting and ranging) technology, which can both create
a high-resolution digital elevation model and landform map along with a three-dimensional picture of the
vegetation, is becoming more available and affordable. A planned interagency purchase may make lidar
available for most of western Oregon within a few years. In addition, statewide, high-resolution (0.5
meter pixel), geo-referenced, digital air photography was acquired in 2005 through the National
Agricultural Imagery Program (NAIP).
We propose to develop and test methods for integrating remote sensing, other spatial data, and field
plots to characterize current riparian and in-stream conditions. As context for current conditions, we will
combine the riparian data with information on current upland vegetation and historical patterns of
disturbance and vegetation change. We will test the methods for two large, intensively monitored
watersheds – the Nehalem and Middle Fork of the John Day (MFJD) Rivers – with the goal of developing
rigorous and repeatable methods that should be scalable statewide to meet a variety of information needs.
In particular, the methods will deliver maps showing the extent and condition of riparian vegetation, a
high-resolution map of the valley floor and riparian-related landforms, and the range of vegetation
structure and ecological diversity of the riparian areas within these basins. We also will test the capability
of methods for mapping riparian wetlands and wetland conditions. These methods will be specifically
directed toward supplying data for aquatic habitat assessments and for parameterizing the aquatic-riparian
models we are developing. A secondary goal will be to create data directed at monitoring the
effectiveness of restoration projects in these basins, as well as testing monitoring methods to evaluate
changes resulting from restoration efforts.
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The mapping and classification of riparian zones using a combination of remotely-sensed and fieldcollected data offers a means of assessing the current condition of riparian areas at fine detail over large
areas. However, these assessments only provide a static “snapshot” of a watershed at a single point in
time. We need the ability to use this snapshot as a starting point from which we can project changes in
habitat resulting from processes that shape streams, riparian vegetation, and the upland systems to which
they are connected. Therefore we propose to develop a Dynamic Riparian and Aquatic Integrated
Network (DRAIN) planning model designed to support design, implementation and monitoring of
restoration projects in large watersheds. We will build from a prototype state and transition aquatic and
riparian model we developed using the Vegetation Development Dynamics Tool (VDDT; Beukema et al.
2003). VDDT models are now commonly used for upland forest and rangeland assessments (Hann et al.,
1997; Hemstrom et al., 2001 & 2006). In our previous work, we developed a prototype framework
extending VDDT to include aquatic and riparian ecosystems (further details can be found in Wondzell et
al., 2006). We propose to further develop our prototype model into a functional planning tool.
The DRAIN model will be designed to forecast network-scale changes resulting from plant
succession, hydro-geomorphic processes, and natural disturbances. Simulations using a background
natural disturbance regime can be used to “hind-cast” the historical range of variability and to forecast the
outcomes of “passive restoration”, i.e., the strategy of allowing natural ecosystem processes to dictate the
pace and trajectory of habitat recovery. The model will also accommodate land-use activities such as
forest harvest, grazing, and stream, riparian, and upland restoration activities (e.g., large wood additions
to streams, riparian fencing and fuels treatment projects). The model can thus be used to forecast the
outcome of alternative management policies and to project the benefits of “active restoration” actions
focused on key habitats, i.e., those activities designed to create missing habitat elements or to support
future habitat-forming events.
The DRAIN model will be designed to include upland components and will thereby include processes
that control conditions of streams and watersheds, as a whole, in addition to the conditions of specific
reaches or project areas. Critical watershed processes that will be included in our model are: 1) riparian
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vegetation dynamics stemming from land-use activities, natural disturbance, and plant succession; 2)
episodic disturbances (e.g., floods and wildfires) that shape stream channels and valley floors; and 3)
linkages to landscape-scale factors (e.g., background upland processes and management policies).
The assessment of current riparian conditions represented by mapping and classification of riparian
zones and models that can project future conditions are essential tools for restoration planning,
implementation, and monitoring. Our research and development of mapping tools will test the relative
value of expensive, high-resolution, remotely sensed data (lidar and digital airphotos) versus more
affordable but coarser-scale data (Landsat) that are already available for watershed assessment and can
provide time-series information for recent decades. The mapping tools, used alone, will be configured to
provide an accurate assessment of current conditions. Comparing current conditions with vegetation
disturbance and dynamics over the past 30 years from Landsat data, and with model “hind-casts” of the
historical range of variability in habitat from natural disturbance regimes, will support watershed-scale
assessment of the extent of present-day habitat alteration and help set realistic restoration goals.
Alternatively, current conditions can be used as a starting point to forecast likely outcomes of alternative
land-use decisions, including restoration activities. For example, a watershed council could project likely
trajectories of habitat conditions given “passive” restoration resulting from management policies as
determined by the State Forest Practices Act or the Northwest Forest Plan and compare those with
projections that employ “active” restoration projects. The DRAIN model can thus be used to examine
both passive and active restoration approaches (alone or in combination) to develop a long-term
restoration strategy for large, intensively monitored watersheds.
7.B. Objectives:
7.B.1. Objective 1 – Mapping: We will explore different methods to map the distribution and condition of
riparian and upland vegetation. Vegetation and related geomorphic attributes will be used to define
riparian and channel conditions for stream reaches throughout the stream network in two pilot basins.
Methods will include integration of the two types of vegetation information historically collected in
riparian areas: (1) plot data collected to classify riparian vegetation (Crowe et al 2004, McCain 2004); and
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(2) riparian and stream habitat survey data from several agencies (ODFW, EPA, USFS, BLM) which
provide data on channel and valley floor characteristics. Specific objectives are to:
1. Explore methods for mapping riparian and in-stream conditions using Landsat, lidar, and NAIP
imagery. Evaluate the potential to characterize and map riparian and aquatic wetlands with these data.
2. Integrate detailed maps of riparian conditions (from #1, above) with existing maps of vegetation
composition and structure for surrounding uplands, to provide additional basin-level information
important to riparian habitat assessment.
3. Use time series of Landsat data (1972- present) to characterize recent disturbances and vegetation
succession to inform riparian assessment for potential stream sedimentation and recruitment of large
in-stream wood.
4. Develop map attributes specifically designed to support the DRAIN model.
7.B.2. Objective 2 – Modeling: The overall objective of the modeling component is to develop a Dynamic
Riparian and Aquatic Integrated Network (DRAIN) planning model that includes background processes
and land-use activities to simulate temporal dynamics of riparian vegetation, channel conditions, and
habitat quality for salmonids. The proposed research builds on a foundation of existing state-andtransition models we have developed for the upper Grande Ronde River basin over the last 4 years
(Wondzell et al., 2006; Hemstrom et al., 2006). The models are not yet linked into a spatially explicit,
dynamic aquatic-riparian network model that can be used as a functional planning tool. We stress that
these accomplishments serve only as a proof of concept and provide the building blocks for the work
proposed here. Our work is focused on three specific objectives:
1. Adapt the existing aquatic and riparian state and transition models to another setting in eastern
Oregon (Middle Fork John Day River) and to a setting in western Oregon (Nehalem River).
2. Develop a spatially explicit framework in which reach-scale models are linked to a dynamic stream
network and incorporate existing upland models to link riparian zones with adjacent uplands.
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3. Apply the DRAIN model to the Nehalem and MFJD Rivers to examine current conditions relative to
the historical range of variability, examine potential of passive restoration to meet recovery goals, and
examine the potential of active restoration to accelerate recovery.
The DRAIN model will be designed for use by natural resources specialists and land-use planners to
compare the effects of alternative management scenarios on riparian restoration. The modeling
framework makes the models relatively easy to modify and use in a variety of landscape analyses. Further,
the models are based on geomorphic processes and riparian vegetation dynamics that are well described
for eco-regions across Oregon. Thus, the models will be broadly portable to land planning efforts
throughout the state.
Analysis of restoration outcomes will be supported by designing representative active restoration
transitions into the model. These transition pathways and probabilities will be based on the watershedand reach-scale restoration priorities described in the Oregon Plan for Salmon and Watersheds and the
John Day sub-basin plan (2005), and on specific restoration activities conducted within the Nehalem and
MFJD intensively monitored watersheds.
7.C. Approach and Methods:
7.C.1. Mapping Methods: To characterize vegetation physiognomy, cover, and structure we will use a
combination of remote sensing datasets: Landsat, lidar, and digital airphotos. Although each dataset has
its own advantages for estimating and mapping vegetation characteristics, their synergy will provide more
detailed and accurate measures.
We also will test spatial modeling techniques such as classification trees and gradient imputation
(Ohmann and Gregory 2002) for mapping classes of vegetation composition and structure (including
successional status) that are suitable for general riparian characterization and for initializing the DRAIN
model. We will evaluate and modify existing riparian vegetation classifications (National Vegetation
Classification System, Plant Association Group, Ecological Systems, etc.) for our mapping efforts. The
selected classification will be cross-walked to riparian communities already described for northwest
Oregon (McCain 2004) and eastern Oregon (Crowe et al. 2004).
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Target physiognomic types include trees (conifer vs. broadleaf) and shrubs, and to the extent possible
a grass/forb/herbaceous complex. Percent cover will be characterized by physiognomic type. Structure is
a more complex variable set, and will be quantified in a number of ways. For woody plants we will
determine density, size, biomass, etc; for trees we will also calculate basal area.
Landsat data have spectral properties that enable distinguishing between needleleaf and broadleaf
vegetation, but do not do very well at distinguishing between shrubs and trees if they share broad
physiognomic characteristics (Cohen et al. 2001). NAIP digital airphotos
(http://165.221.201.14/NAIP.html), at 0.5 m resolution, can be used to assist in this, but lidar data are
superior to NAIP in that they provide a direct measure of height. Similarly, percent vegetation cover can
be estimated from Landsat (Cohen and Goward 2004), but not as well as from lidar or NAIP data.
Significant challenges exist when using digitally processed airphoto data to derive cover information, but
with use of modern morphological filtering tools such as eCognition this is becoming easier (Arroyo et al.
2006). Lidar data have significant advantages over Landsat and airphoto data for deriving cover and
structure information (Lefsky et al. 2001), and will be the primary source of this information for our work.
We will investigate several means of integrating the three primary datasets to take advantage of the
unique and complementary contributions of each to prove detailed, current vegetation information.
Changes in vegetation cover over time are informative of riparian condition, in terms of
sedimentation, stream chemistry, and accumulation of large dead wood. We will use time series of
Landsat data (1972- present) to define both disturbance (Cohen et al 2002, Healey et al. 2005) and
succession (Schroeder et al in press). This will include translation of time series spectral data into
vegetation cover trajectories and fitting of curves to these trajectories to automatically define disturbance
data and severity (Kennedy et al. in press).
From the lidar data we will derive a high-resolution DEM (<10 M) from which we can extract
watershed area, longitudinal gradient, and valley-floor width needed to delineate and classify stream
reaches into the potential channel morphologic groups for the habitat modeling. Other attributes we will
attempt to extract from the DEM for use in the modeling component include: channel gradient, width, and
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sinuosity; channel incision; frequency of pools, riffles, and off-channel habitat; riparian forest structure
and canopy shade; channel-road crossings (e.g., culverts) and waterfalls that limit fish passage. We will
use existing algorithms to extract these patterns from the digital elevation data (e.g., Miller 2003; Benda
et al. 2006). We will also evaluate the potential to use lidar data and NAIP imagery to identify and
quantify in-stream large wood, and to locate and map riparian-associated wetlands.
Field data to be used as reference for the riparian mapping include plots established in riparian areas
by the regional ecology program of the Pacific Northwest Region (McCain 2004, Crowe et al. 2004).
Other field data sources (e.g., riparian and stream surveys of the USDA FS, EPA, INR, ODFW, and
BLM) will be evaluated and used if adequate for our purposes. We will collect additional validation data
from field plots we will measure as part of this study.
We will assess the accuracy of the mapped riparian attributes using a variety of established methods.
We will compare the quality of maps developed with and without lidar data as one means of determining
feasibility and cost for scaling our methods up from intensively monitored watersheds to statewide.
We will combine the detailed maps of riparian vegetation with existing maps of upland vegetation
developed using gradient imputation (Ohmann and Gregory 2002) and classification and regression trees
(CART) (www.fsl.orst.edu/lemma/gnnpac). These maps are now available for all of eastern Oregon, and
western Oregon will be completed by late 2007.
7.C.2. Linking the Modeling and Mapping Components: The riparian mapping component of our research
will be linked to the modeling component through the delineation, classification, and assessment of
current condition of stream reaches in the stream network of each intensively monitored watershed. The
stream network will be subdivided into reaches following the classification of Montgomery and
Buffington (1997 & 1998) using watershed area, longitudinal gradient, and valley-floor width
(Montgomery et al., 1999). The reaches will be classified into the following potential channelmorphologic type: 1) cascade, 2) step-pool, 3) plane-bed, and 4) pool-riffle (Montgomery et al. 1999).
Each reach will be further classified into a discreet state based on riparian and channel-morphologic
features described in the mapping component – namely active channel width and sinuosity, channel
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incision, frequency of pools, riffles, and off-channel habitat, riparian forest structure and canopy shade,
channel-road crossings (e.g., culverts), and waterfalls that limit fish passage, and if available, in-stream
large wood.
7.C.3. Modeling Methods: We have developed prototype aquatic and riparian planning models using
VDDT (Wondzell et al., 2006). These models describe changes in riparian vegetation and channel
condition that occur through vegetative succession, natural disturbance, and a limited group of land-use
activities. Our prototype models are not spatially explicit, they are designed to provide the building blocks
for constructing a dynamic landscape and network model. These models remain under development. They
require more complete treatment of the effects of land-use practices, development of a modeling
framework that stitches individual reach-scale models into a dynamic stream network, and links to
parallel landscape modeling efforts in Oregon using VDDT-based state and transition models such as the
Interagency Mapping and Assessment Project (IMAP: www.reo.gov/ecoshare/mapping/index-issues.asp).
Task 1 – complete additional reach-scale state-and-transition models following methods described in
Wondzell et al., (2006). We will develop additional models for channel morphologic groups missing from
our suite of prototype models. We will expand the pathways and associated transition probabilities still
needed to characterize many historical and current land-use activities. Critical missing transitions include
the effects of historical logging of riparian areas, splash dams and log drives, wood removal from river
networks, prescribed fire and fuel treatments, logging and other silvicultural treatments, and active
restoration practices. The final models will characterize disturbance regimes for 1) the historical range of
variation; 2) recent land management, and 3) restoration, including effects for both passive restoration and
active restoration practices. Finally, we also will develop new VDDT models, or adapt existing models,
for riparian vegetation types missing from our prototype models, especially for Coast Range streams.
Task 2 – develop a spatially explicit framework. The existing VDDT models simulate local-scale
processes and larger-scale disturbances impacting a given stream reach. Because the models are not
spatially explicit, hydro-geomorphic disturbances, such as floods, cannot propagate downstream nor can
large-scale upland disturbances, such as fire, propagate into riparian areas. There are several modeling
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approaches that would allow translation of the current VDDT model framework into a spatially explicit,
dynamic stream network (e.g., discrete event simulation, Fonnesbeck, 2006). However, a major goal of
the proposed work is to develop models that can be linked to existing, large-scale landscape analyses
(USFS R6 Forest Plan Revisions, IMAP). These models are built on the VDDT framework and utilize the
Tool for Exploratory Landscape Scenario Analysis (TELSA; Kurz et al. 2000) for spatially explicit
simulations. New revisions to TELSA allow simulation of directional movements of disturbances across
landscapes. Our work under this task will focus on developing a dynamic stream network model that is
linked with spatially explicit upland models.
Task 3 – develop stream habitat evaluation models. We developed a prototype expert systems
model to evaluate channel conditions and habitat suitability for Chinook salmon (Oncorhynchus
tshawytscha) and steelhead (O. mykiss) (Wondzell et al., 2006). Channel conditions were evaluated using
14 indicator variables commonly measured in aquatic assessment programs in the Pacific Northwest
(MacDonald et al., 1991; Moore et al., 2002; Kershner et al., 2004; Reeves et al., 2004). These variables
are related to habitat suitability for Chinook salmon and steelhead using a qualitative 4-factor scale based
on professional expertise and available literature. Currently, the use of our habitat suitability models is
limited because they are not directly linked with the VDDT models. Therefore, we will modify the
existing modeling framework to automate habitat evaluations. We have made preliminary projections of
habitat suitability with these models (Wondzell et al., 2006), however, the models are not yet validated.
We will test model predictions with a retrospective examination of actual long-term changes in stream
habitat condition using National Forest inventories, Oregon Department of Fish and Wildlife surveys, or
tribal records.
Task 4 – apply the DRAIN model to the Nehalem and MFJD Rivers. We will use the maps of current
riparian and in-channel conditions derived from the mapping component of our research as a starting
point for model simulations. We will use model analyses to compare and contrast future trends expected
from: 1) continuing current land-use practices; 2) wide-scale passive restoration on public lands, and 3)
combining the projected changes in land-use management called for in the Unified Aquatic Conservation
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Strategy currently being developed for USFS and BLM lands with high priority active restoration
treatments called for in restoration plans for the intensively monitored watersheds (i.e., Oregon Plan for
Salmon and Watersheds and the John Day Sub-Basin Plan). The historical range of variability in the
distribution of riparian and channel conditions will be projected from model runs using only the
background natural disturbance regime. The historical range of variability will be used as the baseline for
model comparisons.
7.C.4. Field Data Collection Methods: Models for both quantitative mapping of riparian conditions and
for projecting aquatic-riparian conditions require field data for parameterization and validation. To the
extent possible, we will use existing data. However, the distribution of available data within our
intensively monitored watersheds may be limited. Our sampling strategy must balance the requirement to
provide a statistically representative distribution of current channel and riparian conditions with the
logistical limitations forced by the resources available and time needed to sample each reach. Therefore,
our sampling will be focused on those channel morphologic groups that have the highest potential to
provide spawning and rearing habitat for salmonids, and where existing data are most limited..
We will sample the channel and riparian conditions at each sample reach using sampling protocols
widely used in stream and riparian assessments in the Pacific Northwest (MacDonald et al., 1991; Moore
et al., 2002; Kershner et al., 2004; Reeves et al., 2004) to measure the variables used in our models. These
sampling protocols have been extensively tested for application to status and trend monitoring (e.g.,
Archer et al., 2004). Our sampling protocol is specifically designed to measure composition and structure
of vegetation for riparian mapping and to measure the indicator variables used in the DRAIN model. The
data will be used to refine thresholds used in the GIS-based classification and reach delineation of
potential channel morphologic groups. The data will also be summarized to characterize the current
distribution of riparian and channel conditions and then compared to predictions from the DRAIN model
to see if they support the discrete states and transformation pathways used in the VDDT models or if these
models need to be modified or if new models need to be developed. The vegetation data will be used as
11
reference data for training the remotely sensed data, and in accuracy assessment of resulting vegetation
maps.
7.D. Work Plan & Time Table:
7.D.1. Mapping – In the first year we will concentrate on acquiring existing plot and spatial data,
acquiring new lidar data for the two basins, developing plot and spatial databases for analysis, and
identifying additional field sampling needs (Table 1). Starting in the first quarter of 2008, we will begin
developing the DEM, stream topology, and vegetation information from the lidar data. We will then
explore methods for integrating plot and spatial data to characterize riparian and in-stream habitat. In the
final phase of the study we will examine the history of disturbance and succession in the basins using
Landsat data, and integrate riparian and upland (disturbance and GNN/ReGAP) map products to
characterize conditions over entire basins. All phases of the work will involve close collaboration with the
modeling component to assure data needs for project integration are met.
7.D.2. Modeling – In the first year of this study we will complete the development of the vegetation
and hydro-geomorphic VDDT models and the spatially-explicit dynamic-network modeling framework.
We will also acquire existing stream survey data and fish abundance data. Finally we will use data from
the mapping component to design our summer field sampling campaign for the Nehalem and MFJDR
watersheds. We will spend the summer field season collecting stream and riparian data (Table 1). During
the following winter, we will use the DEM, stream topology, and vegetation information extracted from
the lidar data to parameterize the DRAIN model and make initial model runs. Model projections will be
tested against our collected field data, stream survey data, and fish abundance data to test the model
projections. We will modify and recalibrate models as needed. Once models are completed, we will
conduct a final analysis of the watersheds, projecting alternative future states expected from alternative
management strategies.
12
Table 1. Schedule of proposed work.
Benchmarks & Milestones
Q
4
Q
1
2008
Q Q
2 3
Q
4
2009
Q Q Q
1 2 3
Mapping:
Acquire existing remote sensing and other spatial data
Acquire existing plot and survey data and develop database
Fly lidar over Middle Fork John Day River
Identify additional field sampling needs
Develop DEM, stream, and vegetation layers from lidar
Integrate spatial and plot data to generate rasters of vegetation
attributes and classified riparian network
Examine history of disturbance and succession (Landsat)
Integrate riparian and upland maps (GNN and ReGAP)
Assess accuracy and error structures of map products
Modeling:
Complete VDDT models (vegetation + hydro-geomorphic)
Develop spatially-explicit dynamic modeling framework
Acquire existing fish population monitoring data
Automate stream-habitat evaluation modeling component
Complete initial watershed analysis and locate field sites
Collect validation field data
Validate & finalize model analyses
Generate a holistic, long-term restoration strategy for each basin
Final report, documentation, manuscripts
►
7.E. Project Management, Collaboration and Matching Funds:
7.E.1. Matching Funds: Matching funds exceed 29% (see budget justification for details).
7.E.2. Working on private land: Not applicable. We will not be collecting field data on private lands.
7.E.3. Collaborations with others:
Mapping - The mapping of riparian vegetation will be coordinated with other ongoing programs to
map existing vegetation of Oregon and the Pacific Coast States. These regional mapping efforts span all
land ownerships, at 30-m resolution using Landsat imagery, and do not target riparian areas. Ecological
Systems of all land cover types are being mapped for the USGS Gap Analysis Program by the USDA
Forest Service (USFS) Pacific Northwest Research Station (PNW), the Institute for Natural Resources at
Oregon State University, and the Oregon Natural Heritage Information Center. The composition and
structure of forest vegetation is being mapped using Gradient Nearest Neighbor imputation by PNW in
13
collaboration with the USFS Western Wildland Environmental Threats Assessment Center, the
Interagency Mapping and Assessment Project (IMAP), the Oregon Department of Forestry, the USFS
Region 6 (Pacific Northwest Region), Effectiveness Monitoring for the Northwest Forest Plan, and
PNW's Forest Inventory and Analysis Program. The proposed riparian mapping and modeling will benefit
directly from this additional information on the uplands. The riparian project will utilize extensive plot
and spatial data developed for the other projects, in addition to final map products.
Modeling – The models developed here will be designed for use by natural resources specialists and
land-use planners to compare the effects of alternative management scenarios on riparian restoration. The
DRAIN models will be designed to link with existing, broad-scale landscape modeling efforts that also
use VDDT models such as the USFS Region 6 Forest Plan Revisions and the multi-agency IMAP project.
Development of the DRAIN model will allow riparian areas to be included in these ongoing efforts.
The models we are developing will contribute substantially to planning and assessment efforts for
salmon recovery. OWEB, Watershed Councils, and private partners; other State and Federal Agencies;
the Tribes; and many NGO’s have invested heavily in salmon recovery efforts. Recovery efforts are
typically focused on active restoration treatments. But to be successful, these efforts must be integrated
with improved tributary habitat and habitat-forming processes throughout the whole watershed. Thus
there is critical need for a large-scale planning and assessment model that can evaluate alternative
management scenarios being considered by landowners or land management agencies and link these to
on-going and planned restoration activities within intensively monitored watersheds.
Our models will provide useful feedbacks between planned and ongoing monitoring and evaluation
efforts within the intensively monitored watersheds. Trends in stream, riparian, and watershed conditions
measured in these monitoring programs can be compared to trajectories projected in our simulation
models. These comparisons will help determine if revised management direction and restoration efforts
are resulting in expected habitat recovery and thereby help inform adaptive management decisions.
14
7.G. Expected Outcomes and Deliverables:
The mapping methods and decision support model we develop will allow watershed councils and
OWEB to assess current riparian conditions and evaluate how conditions may change in response to landuse activities and restoration efforts. These products will contribute to whole-watershed restoration
planning, examining both passive and active restoration approaches to develop long-term restoration
strategies for large, intensively monitored watersheds.
Mapping 1: We will provide maps with accuracy assessment for use in assessing and monitoring
riparian and upland conditions that affect riparian and in-stream habitat. The maps will display:
geomorphic and vegetation attributes of the two study basins; time series of disturbance events in the
basins since 1972; vegetation and land cover of the surrounding uplands.
Mapping 2: We will provide methods for integrating remotely-sensed and field data for mapping
riparian, in-stream, and associated upland conditions, including recommendations for use of tested remote
sensing products for riparian monitoring. We will also provide information about alternative imagery
types, evaluating the quality of information provided vs. costs of acquisition and processing.
Modeling 1: We will make the Dynamic Riparian-Aquatic Integrated Network (DRAIN) planning
model publicly available. We will publish a PNW General Technical Report documenting the model and
describing its application for whole-watershed riparian assessment and restoration planning.
Modeling 2: We will report the results of our final, validated analyses of habitat potential, current
stream and riparian conditions and projected trends of tributary habitats of the Nehalem and Middle Fork
John Day River Watersheds.
Overall 1: We will establish a web site where the mapping tools and both the DRAIN model and
model documentation can be freely downloaded by interested parties. We will archive collected field and
remote sensing data either on our project’s web site or other relevant data archival sites (e.g., H. J.
Andrews LTER data base or the Puget Sound Lidar Consortium web sites).
Overall 2: Study findings will be reported in peer reviewed literature and at scientific and professional
meetings. Results will also be presented to land managers, watershed councils, and others.
15
7.H. References:
Archer, E. K., Roper, B. B., Henderson, R. C., Bouwes, N., Mellison, S. C., and Kershner, J. L. 2004.
Testing common stream sampling methods for broad-scale, long-term monitoring. USDA Forest
Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-122
http://www.fs.fed.us/rm/pubs/rmrs_gtr122.pdf
Arroyo, L.A., S. Healey, W.B. Cohen, D. Cocero, J.A. Manzanera. 2006. Using object-oriented
classification and high-resolution imagery to map fuel types in the Mediterranean region, Journal of
Geophysical Research.
Benda, L. E., D. J. Miller, K. Andras, P. Bigelow, G. Reeves, and D. Michael. In press. NetMap: A new
tool in support of watershed science and resource management. Forest Science.
Beukema, S. J., Kurz, W. A., Pinkham, C. B., Milosheva, K., and Frid, L. 2003. Vegetation Dynamics
Development Tool, User's Guide, Version 4.4c. Vancouver, BC, Canada: ESSA Technologies Ltd.
239 pp.
Cohen , W. B. and S. N. Goward. 2004. "Landsat's role in ecological applications of remote sensing."
BioScience 54: 535-545.
Cohen, W. B., T. K. Maiersperger, T. A. Spies and D. R. Oetter. 2001. "Modeling forest cover attributes
as continuous variables in a regional context with Thematic Mapper data." International Journal of
Remote Sensing 22: 2279-2310.
Cohen, W. B., T. A. Spies, R. J. Alig, D. R. Oetter, T. K. Maiersperger and M. Fiorella. 2002.
"Characterizing 23 years (1972-1995) of stand replacement disturbance in western Oregon forests
with Landsat imagery." Ecosystems 5: 122-137.
Crowe, E.A., B.L. Kovalchik, and M. Kerr. 2004. Riparian and Wetland Vegetation of Central and
Eastern Oregon. Oregon Natural Heritage Program, Portland, OR. 483 pp.
Federal Caucus. 2000. Vols. 1-2. Conservation of Columbia basin fish, final basinwide salmon recovery
strategy. Portland, OR: Federal Caucus/Bonneville Power Administration.
http://www.salmonrecovery.gov/Final_Strategy_Vol_1.pdf,
http://www.salmonrecovery.gov/Final_Strategy_Vol_2.pdf. (June 9, 2003).
Fonnesbeck, C. J. 2006. Spatial modeling of riparian state dynamics in eastern Oregon, USA by using
discrete event simulation. Landscape and Urban Planning, DOI:10.1016/j.landurbplan.2006.10.007
(Published on-line 11 December, 2006).
Gould, W. 2000. Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots.
Ecological Applications. 10:1861-1870.
Healey, S.P., Y. Zhiqiang, W.B. Cohen, and J. Pierce. 2006. Application of two regression-based methods
to estimate the effects of partial harvest on forest structure using Landsat data, Remote Sensing of
Environment 101:115-126.
Hemstrom, M. A., Merzenich, J., Reger, A., and Wales, B. C. 2006. Integrated analysis of landscape
management scenarios using state and transition models in the upper Grande Ronde River Subbasin,
16
Oregon, USA. Landscape and Urban Planning DOI:10.1016/j.landurbplan.2006.10.004 (Published
on-line 28 November, 2006).
Hemstrom, M., Ager, A. A., Vavra, M., Wales, B. C., and Wisdom, M. J. 2004. Chapter 2: A state and
transition approach for integrating landscape models. Pgs. 17-32. in J. L. Hayes, A. A. Ager, and R. J.
Barbour, Technical Editors. Methods for integrating modeling of landscape change: Interior
Northwest Landscape Analysis System. PNW-GTR-610. USDA Forest Service, Pacific Northwest
Research Station, General Technical Report 610.
Herger, L.G. and G. Hayslip. 2000. Ecological Condition of Streams in the Coast Range Ecoregion of
Oregon and Washington. EPA-910-R-00-002. U.S. Environmental Protection Agency, Region 10,
Seattle, Washington.
John Day Sub-Basin Revised Draft Plan. 2005. Report to the Northwest Power and Conservation Council.
Prepared by the Columbia-Blue Mountain Resource Conservation and Development Area., and
available at: http://www.nwcouncil.org/fw/subbasinplanning/johnday/plan/
Kershner, J. L., Archer, E. K., Coles-Ritchie, M., Cowley, E. R., Henderson, R. C., Kratz, K., Quimby, C.
M., Turner, D. L., Ulmer, L. C., and Vinson, M. R. 2004. Guide to effective monitoring of aquatic
and riparian resources. USDA Forest Service, Rocky Mountain Research Station, General Technical
Report RMRS-GTR-121.
Kurz, W. A., Beukema, S. J., Klenner, W., Greenough, J. A., Robinson, D. C. E., Sharpe, A. D., and
Webb, T. M. 2000. TELSA: the tool for exploratory landscape scenario analysis. Computers and
Electronics in Agriculture 27:227-242.
Lefsky, M. A., W. B. Cohen and T. A. Spies. 2001. "An evaluation of alternative remote sensing products
for forest inventory, monitoring, and mapping in Douglas-fir forests of western Oregon." Canadian
Journal of Forest Research 31: 78-87.
MacDonald, L. H., Smart, A. W., and Wissmar, R. C. 1991. Monitoring guidelines to evaluate effects of
forestry activities on streams in the Pacific Northwest and Alaska. US Environmental Protection
Agency, Water Division, EPA/910.9-91-001. 166 p.
McCain, C. 2004. Riparian plant communities of northwest Oregon: streamside plant communities.
USDA Forest Service, Pacific Northwest Region, Technical Paper R6-NR-ECOL-TP-10-04.
Miller, D.J. 2003. Programs for DEM analysis. In Landscape dynamics and forest management. Gen.
Tech. Rep. RMRS-GTR-101CD. USDA Forest Service, Rocky Mountain Research Station, Fort
Collins, CO. CD-ROM. (http://www.fsl.orst.edu/clams/prj_wtr_millerprg.html. Last Accessed on
October 28, 2006)
Montgomery, D. R. and Buffington, J. M. 1998. Chapter 2: Channel processes, classification, and
response. Pages 13-42. In: R. J. Naiman and R. E. Bilby (eds.) River Ecology and Management Lessons from the Pacific Coastal Ecoregion. Springer-Verlag. New York, NY.
Montgomery, D. R. and Buffington, J. M. 1997. Channel-reach morphology in mountain drainage basins.
Geological Society of America Bulletin 109:596-611.
17
Montgomery, D. R., Beamer, E. M., Pess, G. R., and Quinn, T. P. 1999. Channel type and salmonid
spawning distribution and abundance. Canadian Journal of Fisheries and Aquatic Sciences. 56:377387
Moore, K. M. S., Jones, K. K., and Dambacher, J. M. 2002. Methods for stream habitat surveys, version
12.1. Oregon Department of Fish and Wildlife, Aquatic Inventories Project, Natural Production
Program, Corvallis, OR. 59 p.
National Research Council (NRC). 2002. Riparian areas: functions and strategies for management.
National Academy Press, Washington, DC. 428p.
Nehlsen, W., Williams, J. E., and Lichatowich, J. A. 1991. Pacific salmon at the crossroads: Stocks at risk
from California, Oregon, Idaho and Washington. Fisheries 16:4-21.
Ohmann, J.L., and M.J. Gregory. 2002. Predictive mapping of forest composition and structure with
direct gradient analysis and nearest neighbor imputation in coastal Oregon, USA. Canadian Journal of
Forest Research 32:725-741.
Reeves, G. H., Hohler, D. B., Larsen, D. P., Busch, D. E., Kratz, K., Reynolds, K., Stein, K. F., Atzet, T.,
Hays, P., and Tehan, M. 2004. Effectiveness monitoring for the aquatic and riparian component of the
Northwest Forest Plan: conceptual framework and options. Gen. Tech. Rep. PNW-GTR-577. Portland,
OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 71 p.
Schroeder, T.A. W.B. Cohen, Z. Yang. In press. Patterns of forest regrowth following clearcutting in
western Oregon as determined from multi-temporal Landsat data, Forest Ecology and Management.
U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior, Bureau of Land
Management. 2000. Interior Columbia basin supplemental draft environmental impact statement. The
Interior Columbia Basin Ecosystem Management Project. Washington, DC: U.S. Government
Printing Office. [Irregular pagination].
Wondzell, S.M., Hemstrom, M. A., and Bisson, P. A. 2006. Simulating riparian vegetation and aquatic
habitat dynamics in response to natural and anthropogenic disturbance regimes in the Upper Grande
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(Published on-line 6 December, 2006).
18
Grant Administration:
The overall grant budget is split into two separate contracts. While this is a tightly
integrated joint project, the mapping component will be centered in the Corvallis Forestry
Science Laboratory’s – Laboratory for Application of Remote Sensing in Ecology –
under the direction of Dr. Warren Cohen; the modeling component will be centered at the
Olympia Forestry Sciences Laboratory’s – Riparian Ecology and EcoHydrology Group –
under the direction of Dr. Steve Wondzell. Logistical considerations and the high degree
of independence between the two groups in the USFS Pacific Northwest Research
Station’s organizational structure will create accounting problems making the grant
difficult to administer under a single contract. Therefore, we request that the overall grant
budget be broken into two separate contracts.
The Statement of Work, shown below, reflects the joint accomplishments of both groups
and contracts.
Objectives:
The proposed research integrates riparian zone mapping with dynamic models to project
the response of riparian zones, stream channels and salmon habitat to natural disturbance
and land-use activities. The overall objective of this work is to produce a decision support
tool for habitat restoration planning that incorporates advanced remote-sensing
technology and information about disturbance-recovery processes with existing
knowledge of critical habitat needs for salmonids. The proposal has two components: 1)
remote sensing and riparian mapping, 2) riparian and aquatic modeling. The objective of
the mapping component is to explore different methods for mapping riparian and instream conditions using Landsat, lidar, and NAIP imagery and to use these methods to
delineate, classify and map the attributes of riparian zones needed for riparian assessment
and monitoring and to support the modeling component. The objective of the modeling
component is to develop a Dynamic Riparian and Aquatic Integrated Network (DRAIN)
planning model that includes background processes and land-use activities to simulate
temporal dynamics of riparian vegetation, channel conditions, and habitat quality for
salmonids. We will apply the remote sensing and mapping methods to two intensively
monitored watersheds – Nehalem and Middle Fork John Day (MFJD) Rivers – and apply
the DRAIN model to the to examine current conditions relative to the historic range of
variability, examine potential of passive restoration to meet recovery goals, and examine
the potential of active restoration to accelerate recovery.
Methods:
Remote sensing imagery, including Landsat, lidar, and digital airphotos, will be
combined with field data to characterize riparian vegetation. Spatial modeling techniques
will be used to classify and map vegetation composition and structure for riparian
characterization and for initializing the DRAIN model. Riparian maps will be integrated
with basin-wide maps of existing upland vegetation. A high-resolution DEM (<10 M)
will be built from lidar imagery. We will extract watershed area, longitudinal gradient,
and valley-floor width and use these to delineate and classify stream reaches into the
potential channel morphologic groups for the DRAIN model. Finally, we will assess the
accuracy of the mapped riparian attributes using a variety of established methods. We
will compare the quality of maps developed with and without lidar data to determine the
feasibility and cost for scaling up from watershed to the entire state. The mapping
component of our research will be linked to the modeling component through the
delineation, classification, and assessment of current condition of streams and riparian
zones in each watershed. Development of the DRAIN planning model is based on a
prototype aquatic and riparian model we developed using the Vegetation Development
Dynamics Tool (VDDT). We will develop additional VDDT models for riparian
vegetation and channel types missing from our suite of prototype models. We will also
expand the pathways and transition probabilities still needed to characterize many
historic and current land-use activities. We will use TELSA to develop a dynamic stream
network model that is linked with spatially explicit upland models. We will modify and
automate our habitat evaluation model and, using known habitat preferences of salmonids,
project the effects of changes in riparian and channel condition on fish species.
Rationale:
Pacific salmon and steelhead have declined in abundance or been eliminated from large
parts of their historical range, and many populations are now listed under the U.S.
Endangered Species Act. Maintenance of existing high-quality habitat and restoration of
degraded habitat have become cornerstones of many salmon recovery efforts. To support
these efforts, there is a critical need for sound information on riparian areas – particularly
over the broad spatial extents that are relevant to recovering salmonid populations.
However, providing relatively detailed information on current conditions and likely
future riparian conditions for large watersheds (e.g., 4th-field HUC) over large regions
poses substantial logistical and technical challenges. Combining remote sensing and
ground-based surveys offers a potential method for characterizing current conditions of
riparian zones over large areas. However, much of the available remotely sensed imagery
and DEMs are generally too coarse in spatial resolution to adequately characterize the
detail of narrow riparian zones. A variety of fine-scale remotely sensed data are
becoming widely available (e.g., lidar, NAIP, QuickBird) and have great potential for
detailed landscape assessments. While combining newly available remotely-sensed and
field-collected data offers a means of assessing the current condition of riparian areas at
fine detail over large areas, these assessments only provide a static “snapshot” of a
watershed at a single point in time. We need the ability to use this snapshot as a starting
point for projecting changes in habitat resulting from processes that shape streams,
riparian forests, and the upland systems to which they are connected. Thus, there is also
critical need for an integrated stream network planning model that can support the design,
implementation and monitoring of restoration projects in large watersheds.
Expected Outcomes:
The mapping methods and decision support model we develop will allow watershed
councils and OWEB to assess current riparian conditions and evaluate how conditions
may change in response to land-use activities and restoration efforts. These products will
contribute to whole-watershed restoration planning, examining both passive and active
restoration approaches to develop long-term restoration strategies for large, intensively
monitored watersheds. Our expected outcomes are: 1) maps displaying geomorphic and
vegetation attributes of the Nehalem and Middle Fork John Day watersheds with
associated accuracy assessment, a time-series of disturbance events in the watersheds
since 1972, and vegetation and land cover of the surrounding uplands; 2) methods for
integrating both remotely-sensed and field data for mapping riparian, in-stream, and
associated upland conditions, including recommendations for use of tested remote
sensing products for riparian monitoring; 3) the Dynamic Riparian-Aquatic Integrated
Network (DRAIN) planning model will be made publicly available; and 4) the results of
our final, validated analyses of habitat potential, current stream and riparian conditions
and projected trends of tributary habitats of the Nehalem and Middle Fork John Day
River Watersheds. We will establish a web site where the mapping tools and both the
DRAIN model and model documentation can be freely downloaded by interested parties.
We will archive collected field and remote-sensing data, either on our project’s web site
or other relevant data archival site. Finally, the study’s findings will be reported in peer
reviewed literature and at scientific and professional meetings. Results will also be
presented to land managers, watershed councils, and others interested in stream
restoration planning.