Kate Colleen Fickas for the degree of Master of Science in Forest Ecosystems and
Society presented on March 20, 2014.
Title: Landsat-based Monitoring of Annual Wetland Change in the Main-Stem
Willamette River Floodplain of Oregon, USA from 1972 to 2012.
Abstract approved:
______________________________________________________
Warren B. Cohen
Despite holding substantial ecological value, wetlands in the United States have experienced a significant decline in both area and function over the past century with the majority of freshwater wetland loss attributed to agricultural conversion.
Agriculture is the second largest industry in the State of Oregon and the State places substantial emphasis in its land use planning goals on the preservation of agricultural land. Oregon’s Willamette Valley accounts for the majority of agricultural output with 53% of the valley bottom classified as agricultural land. Additionally, the valley houses 70% of the state’s population. The valley was once comprised of extensive wet prairies and abundant riparian forests along the Willamette River floodplain, but native ecosystems have been reduced to a fraction of their original distribution since
Euro-American settlement in the mid 1800s. The few wetlands that remain are at high risk to loss and degradation from agricultural activity. Following national wetland conservation policies, Oregon has since attempted to monitor and regulate losses due to disturbance and modification of the State’s remaining wetlands through a "no-net-

loss" policy aiming to decrease wetland losses and replace disturbed wetlands through mitigation. The National Wetlands Inventory (NWI) was designed to produce detailed maps and status reports of the characteristics and extent of the nation’s wetlands and help determine the efficacy of no-net-loss policy implementation on the nation’s wetlands. In some cases, the NWI has been found to have low categorical and spatial accuracy and coarse temporal resolution, with some maps over two decades old.
Although Landsat satellite imagery was originally found to lack the needed spatial resolution for classification detail and wetness designation that aerial photography provided, Landsat has 40 years of freely available, high quality annual imagery and should be explored for use in annual wetland change detection. Our objectives were to: (1) Quantify and characterize spatial and ecological trends in annual wetland change through gain, loss, and conversion in the Willamette Valley; (2) Evaluate the effect of the no-net-loss federal wetland conservation policy change enacted in 1990 on trends in net wetland area; and (3) Describe a new methodology that reaches back through the over 40-year Landsat archive to map fine scale wetland and related landuse changes from 1972-2012. We used annual Landsat MSS and TM/ETM+ images from 1972 to 2012 to manually interpret loss, gain, and type conversion of wetland area in the two-year inundation floodplain of the Main-Stem Willamette River using
TimeSync, Google Earth, and ArcMap. By creating Tasseled Cap Brightness,
Greenness, and Wetness indices for MSS data that visually match TM/ETM+
Tasseled Cap images, we were able to construct a complete and consistent annual time series and utilize the entire Landsat archive. Additionally, with an extended time series, we were able to compare trends of annual net change in wetland area before

and after the no-net-loss policy established under Section 404 of the Clean Water Act in 1990. We found that wetlands experienced annual loss, gain, and type conversion across the entire study period. Vegetated wetlands (emergent and riparian wetlands) experienced a 314 ha net loss of wetland area across the 40 year study period whereas non-vegetated wetlands (lacustrine and riverine wetlands) experienced a 393 ha net gain. All wetland types combined saw a 79 ha net increase in wetland area across the full study period. The majority of both gain and loss in the study area was attributed to and from agricultural conversion followed by urban land use. Time series analysis of the rate of change of net wetland area was calculated using the Theil-Sen (TS)
Slope estimate analysis. For annual change of wetland area before and after 1990 nonet-loss policy implementations, the rate of annual wetland area lost slowed for riparian wetlands and reversed into trends of annual net gain in area of emergent wetlands. The rate of annual net area gained for lacustrine wetlands was slowed postpolicy. Accuracy assessment of land use change polygons in the field was only able to capture 12% of our interpretations due to access restrictions associated with private land. In spite of a low sample size (n=45), overall accuracy of land use classification through wetland change polygons was at 80%. This accuracy increased to 91.1% when land use classes were aggregated to either wetland or upland categories, indicating that our methodology was more accurate at distinguishing between general upland and wetland than finer categorical classes.

©Copyright by Kate Colleen Fickas
March 20, 2014
All Rights Reserved

Landsat-based Monitoring of Annual Wetland Change in the Main-Stem Willamette
River Floodplain of Oregon, USA from 1972 to 2012 by
Kate Colleen Fickas
A THESIS submitted to
Oregon State University in partial fulfillment of the requirements for the degree of
Master of Science
Presented March 20, 2014
Commencement June, 2014

Master of Science thesis of Kate Colleen Fickas presented on March 20, 2014
APPROVED:
Major Professor, representing Forest Ecosystems and Society
Head of the Department of Forest Ecosystems and Society
Dean of the Graduate School
I understand that my thesis will become part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my thesis to any reader upon request.
Kate Colleen Fickas, Author

I wish convey sincere appreciation, first, to Dr. Warren Cohen for giving me the opportunity to express my passion for conservation through remote sensing, and hiring me to be a part of the amazing and dynamic Laboratory of Remote Sensing of
Applications in Ecology. Your guidance, mentorship, and patience have made me a better and more confident scientist.
Next, I want to express my endless gratitude to Zhiqiang Yang for your help on the more technical aspects of the methodology in this project. Having you as an engaging and patient co-author has increased the standard and quality of this work.
Lastly, I wish to thank my family and friends for your compassion, excitement and undying support of my work. Most importantly, I could not have done this without my husband, Steven Naleway. Your love and support as both a partner and a fellow scientist knows no bounds and for that I am eternally grateful and humbled.
We call upon the waters that rim the earth, horizon to horizon, that flow in our rivers and streams, that fall upon our gardens and fields, and we ask that they teach us and show us the way.
Chinook Blessing

Page
Introduction ................................................................................................................... 1
Methods .......................................................................................................................... 9
Study Area ................................................................................................................. 9
Image Pre-Processing .............................................................................................. 10
Manual Interpretation of Wetland Loss and Gain ................................................... 13
Accuracy Assessment .............................................................................................. 15
Wetland Change ...................................................................................................... 16
Results .......................................................................................................................... 24
Wetland Loss, Gain, and Type Conversion Across the Full Study Period ............. 24
Annual Change, Pre- and Post-Policy ..................................................................... 27
Error Analysis ......................................................................................................... 28
Discussion .................................................................................................................... 35
Wetland Loss ........................................................................................................... 37
Wetland Gain .......................................................................................................... 39
Ecological and Spatial Trends of Wetland Change ................................................. 43
Mapping Annual Wetland Change with Landsat Imagery ...................................... 47
Conclusions .................................................................................................................. 58
References .................................................................................................................... 60

Figure Page
1. Map of Study Area……………………………………….………………………..19
2. Predicted Tasseled Cap Correlation Scatter Plots……….………………………..20
3. Workflow Diagram.................................................................................................21
4. Map of Gridded Study Area…………...………………………………………….22
5. Cumulative, Annual change in net Wetland Area by Year...…………..…………32
6. Cumulative Annual Precipitation Data for the Willamette Valley by Year...........55
7. Wetland Mitigation Permits by Year…………………………………………......56
8. Size of Change Polygons Capture by MSS vs. TM data……………………........57

1
2
Table Page
Land Use Categories…………………………………………………….. .23
Wetland Loss, Gain, and Type Conversion by Category..............………..30
3 Wetland Area Gain and Loss Across 40 Year Study Period…………...... 31
4
5
6
Pre-Policy and Post-Policy Theil-Sen Slopes……………………………. 31
Error Matrix for All Land Use Categories……………………….....……. 33
Error Matrix for Aggregated Upland and Wetland Land Use
Categories……………………….....……................................................... 34

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Introduction
Wetlands are some of the most important and valuable ecosystems on the planet.
They are known to cleanse polluted waters, protect shorelines, recharge groundwater aquifers, buffer flood and drought severity, and provide unique habitat to a wide variety of plants and animals (Mitsch and Gosselink 2000). Even though wetlands are so important, the planet has lost roughly 50% of its wetlands since 1900 (UNWWAP 2003;
Nicholls 2004). In the U.S. specifically, 22 states have lost more than 50% of their wetland area between the 1780s and the 1980s (Dahl 1990) with losses continuing into the 21 st century (Frayer et al. 1983, Dahl and Johnson 1991; Dahl 2000; Dahl 2006; Dahl
2011). The majority (87%) of freshwater wetland loss in the U.S. is attributed to agricultural conversion (Hefner and Brown 1984), and in states where agriculture is a significant source of economic output, remaining wetlands are increasingly threatened. In
Oregon, agriculture is the second largest industry behind technology and accounts for an estimated 12% of all jobs in the State (Searle 2012). The State thusly places substantial importance in its land use planning goals on the preservation of agricultural land
(Bernasek 2006). Oregon’s Willamette Valley accounts for the majority of agricultural output with 53% of the valley bottom classified as agricultural land (Morlan et al. 2010).
Agriculture, combined with 70% of the State’s population residing in the valley, places
Willamette Valley wetlands in a dangerous setting.
Wetlands of the Willamette Valley have a long history of disturbance, alteration and removal. The valley was once comprised of extensive wet prairies and abundant riparian forests along the Willamette River floodplain. However, over the past 150 years, these ecosystems have been greatly reduced in area and connectivity (Daggett et al. 1998,
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Baker et al. 2004, Christy and Alverson 2011). Historic accounts suggest that preceding
European settlement, a majority of the Willamette Valley was covered by expansive native habitats such as woodlands, oak savanna, grassland, riparian, and wet prairie
(Christy and Alverson 2011). The indigenous Kalapuya!
people set regular fires to improve foraging, hunting, and travel and consequently facilitated maintenance of the valley’s open habitats (Pendergrass et al. 1998). With Euro-American settlement came the loss of natural vegetation through development (both agricultural and urban) and the suppression of fire that allowed woody coniferous vegetation to encroach on grassland and wet prairie habitat (Clark and Wilson 2001).
The Willamette River itself is the thirteenth largest river (by volume) in the contiguous United States and is comprised of a series of individual reaches, each with distinct geomorphic and disturbance histories (Kammerer 1990). Historically, the
Willamette was a dynamic, anastomose river traveling through dense riparian forest, but today is channelized to a single thread surrounded by agricultural fields and development
(Wallick et al. 2007). The Willamette River’s past flood dynamics have included a natural range of flood severities but the magnitude of peak discharge events has been steadily decreasing since settlement and flood protection measures were established
(Benner and Sedell 1997, Gutowsky 2000, Gregory et al. 2002, Wallick et al. 2007).
Natural flood events and the anastomose nature of the Willamette River were deemed essential to maintaining an ecologically diverse mosaic of floodplain wetlands across the valley. While agricultural conversion of wetlands in the valley has been the most expansive destructive force on these ecosystems, the subtle stress due to a decline in flood events and magnitude has also contributed (Cline and McAllister 2012).
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The valley’s floodplain wetlands are dependent on a wet-dry cycle that allows highly productive and dynamic plant communities to interact at different temporal and environmental scales (D’Amore et al. 2000, D’Amore et al. 2004). Hydrologic alteration combined with increased isolation from the Willamette River and its floodplain has converted many wet prairies and riparian forests to less productive and less diverse grasslands, pastures, and dry woodland (Rocchio 2011). Urbanization, agricultural activities, logging, and channelization of the Willamette River have reduced the valley’s wetlands up to 57% of their original range, with a 98% loss of wet prairies and a 72% loss of floodplain riparian forest (Taft and Haig 2003, Oetter et al. 2004, Christy and
Alverson 2011). Today, with 70% of the state’s population residing in the valley and 96% of the valley privately owned, monitoring the abundance and arrangement of wetlands and other critical habitats have become a top priority to planners and conservationists
(Morlan 2000, Baker et al. 2004, Oregon Department of Fish and Wildlife 2006).
Following the nation’s Clean Water Act of 1972 and Presidents Bush and
Clinton’s policies in the early 1990s, Oregon has since attempted to monitor and regulate losses due to disturbance and modification of the State’s remaining wetlands through a
"no-net-loss" policy aiming to decrease wetland losses and replace disturbed wetlands through creation of new wetlands and restoration, enhancement, or conservation of existing wetlands when disturbance is approved (Kentula et al. 1992, Bernert et al. 1999,
Votteler and Muir 2002). Following policy implementations, several studies have examined the efficacy of wetland conservation in the Willamette Valley through the State of Oregon’s wetland strategy by evaluating policy compliance through investigation of permit requirements associated with the no-net-loss standard, by examining trends and
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patterns in permitting and compensatory mitigation records, and two-date change detection of spatial wetland patterns and extent through wetland classification of aerial photography (Kentula et al. 1992, Bernert et al. 1999, Morlan et al. 2010, Christy and
Alverson 2011).
The U.S. also has its own wetland mapping protocol to aid in monitoring wetland trends. Run under the U.S. Fish and Wildlife Service, The National Wetlands Inventory
(NWI) was designed to produce detailed maps and status reports of the characteristics and extent of the nation’s wetlands (Wilen and Bates 1995). These maps are established through a mixture of stereoscopic color infrared photographs to identify and delineate wetlands (emphasizing color, texture, and pattern), fieldwork, and ancillary cover class maps such as soils and land use (Wilen et al. 1996). These techniques are aimed toward efficiency and balance the need for detail and accuracy not possible using time consuming ground surveys. Priorities for mapping have been centered on the informational needs of the U.S. Fisheries and Wildlife Service (USFWS) and other agencies, and the availability of funding and high-quality aerial photographs.
Geographically, mapping and inventory have been focused on coastal zones, prairie wetlands, playa lakes, flood plains of major rivers, and areas of importance to the nation’s waterfowl (Wilen et al. 1996). While it is critical to the success of the no-netloss wetland conservation strategy that wetlands be mapped and monitored, the NWI has been found to be variable in its accuracy (with low categorical and spatial accuracy), contain high errors of omission, and of coarse temporal resolution, with some maps over two decades old (Stolt and Baker 1995, Kudray and Gale 2000). Agencies and individuals attempting to explore changes and trends in wetlands using aerial imagery are
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often limited in their temporal scope by the availability of NWI maps or aerial imagery.
Moreover, the two-date, decadal interval change detection approaches used are too coarse a temporal grain to allow for insight into finer temporal-scale trends in wetland change and disturbance before and after specific policy implementations and environmental events (such as floods).
Utilizing Landsat satellite imagery to aid in federal wetland mapping was initially proposed and tested by the Federal Geographic Data Committee, but found to lack the needed spatial resolution for classification detail and wetness designation that aerial photography provided (Federal Geographic Data Committee 1992). However, for monitoring regional scale processes over longer time periods at a finer temporal resolution, Landsat satellite imagery has several advantages over aerial photography.
Landsat now has over 40 years of freely available, high quality annual imagery (offering the longest running time series of systematically collected remote sensing data) and a spatial resolution that has yielded accurate classifications of many specific land use types and land use change (Cohen and Goward 2004). Dense (at least annual), multi-decadal
Landsat time series imagery is becoming the norm for change detection in forested landscapes and should be explored for use in other ecosystems and land cover and use types such as wetlands (Cohen et al. 2010).
Wetland ecosystems are notoriously difficult to classify using remote sensing satellite data compared to their upland counterparts (Adam et al. 2010). Most work in wetland change detection using satellite imagery (from a variety of different sensors) has been done using automated classification techniques (Baker et al. 2006, Baker et al. 2007,
Adam et al. 2010, Klemas 2011). While there are numerous advantages with automated
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analyses, a considerable issue with the common approach of deriving independent classifications for separate years and then comparing them to derive change is that the independent errors from each map are compounded, yielding a significantly greater error in the change domain. Direct change detection approaches such as image differencing or multitemporal analysis (Coppin and Bauer 1996) have greater potential for reducing wetland change-mapping errors. However, promising newer approaches that detect and classify change directly from the dense time series are under development and being tested over larger (especially forested) areas (Jin and Sader 2005, Kennedy et al. 2007,
Huang et al. 2010), but have only just begun to be explicitly tested in wetland applications.
Many space-borne sensors have been used to create wetland maps at the landscape to regional scale (Hardisky et al. 1986, Ozesmi and Bauer 2002), but the literature suggests that Landsat provides increased accuracy and is more cost-effective over other sensors at those scales (Civco 1989, Hewitt 1990, Bolstad and Lillesand 1992,
Baker et al. 2006, Baker et al. 2007). Most wetland mapping using Landsat data has been single-date classification, two-date change detection, or short-term (0-5 years) change detection using annual images (Johnston and Barson 1993, Ramsey et al. 1997, Lunetta and Balogh 1999, Baker et al. 2007, Frohn et al. 2009, Huang et al. 2014), but dense time series algorithms for temporally and categorically detailed characterization of changes in wetlands is inevitable. However, accurate results will likely remain elusive during the development and testing phases, and there is an urgent need to begin to characterize wetland changes using dense Landsat time series interpretations across large, important wetland areas such as the Willamette Valley.
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In a recent study to map changes and develop temporal spectral profiles for wetlands delineated by the NWI in northern Virginia, USA Kayastha et al. (2012) used the Vegetation Change Tracker (VCT) (Huang et al. 2010). The VCT data consisted of pixel-level spectral trajectories and detected disturbances throughout a near-annual 25year Landsat time series from 1984-2009. VCT was designed to reconstruct forest disturbance history, but was nonetheless able to capture both dramatic and subtle changes in wetland temporal spectral profiles associated with processes such as urban development and wetland to wetland type conversion.
In the study presented here we build on the work of Kayastha et al. (2012) by extending the use of annual Landsat time series to quantify wetland losses and type conversions to a large and important western U.S. wetland ecosystem. Further, we extend the analysis back through the complete Landsat archive, examine wetland area gained, and focus on ecology- and policy-specific contextual information to both aid in wetland identification and interpret the significance of observed changes. To collect the best data possible, we used a methodology that applies long-standing and accurate geovisual cognition approaches associated with manual aerial photointerpretation to dense Landsat time series (Cohen et al. 2010). Our objectives are to:
(1) Quantify and characterize spatial and ecological trends in annual wetland change through gain, loss, and conversion in the Willamette Valley;
(2) Evaluate the effect of the no-net-loss federal wetland conservation policy change enacted in 1990 on trends in net wetland area; and
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(3) Describe a new methodology that reaches back through the over 40-year
Landsat archive to map fine scale wetland and related land-use changes from
1972-2012.
Visual interpretation of Landsat time series to derive accurate change information is not new. Cohen et al. (1998) compared this technique against use of multi-date aerial photos and polygon spatial databases and found that the three approaches yielded nearly identical results in the context of accuracy assessment for Landsat-based mapping of forest stand replacement disturbances. Following Cohen et al. 1998, Hayes and Sader
(2001) and Hayes and Cohen (2007) successfully used visual interpretation to generate reference points from Landsat color composite images for accuracy assessment in the absence of adequate historical reference data. More recently, Cohen et al. (2010) developed the TimeSync methodology to validate and calibrate change detection maps derived using automated algorithms with annual Landsat time series (Kennedy et al.
2010). TimeSync is based on visual, contextual interpretation of plot-level disturbance and recovery sequences throughout a pixel’s (or plot’s) spectral trajectory and has already been successfully used with early Landsat data (Pflugmacher et al. 2012).
Although the spatial resolution of Landsat data is considerably less than that of aerial photos, at the patch level (multiple 30 m pixels) Landsat data should provide a capability for accurate characterization of wetland change. For the pre-Thematic Mapper
(TM) era (before 1982) we rely on integration of Multispectral Scanner (MSS) data to complete the time series, which may require a larger minimal mapping unit (multiple 60 m pixels) for accurate interpretations. Additionally, the spectral quality is sub-TM level, which could reduce the quality of interpretations. We informally examine the difference
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in quality of interpretations from MSS and TM and the latter sensor ETM+ (Enhanced
TM +) to provide insights into the value of MSS in this context.
METHODS
Study Area
At approximately 14,400 km 2 , the Willamette Valley Ecoregion (Omernik 1987) lies between the Coast Range Mountains to the west and the Cascade Mountains to the east (Fig. 1). Agriculture dominates the ecoregion’s landscape utilizing 41% of the geographic area, with 35% forest and woodland, 10% pasture and grassland, and the remaining comprised of rural, urban development, and natural features such as wetlands and lakes (Oregon Department of Fish and Wildlife 2006). The valley ranges from 32 to
64 km wide and 195 km in length with elevations varying between 4 and 122 m. Climate in the Willamette Valley consists of mild, wet winters and hot, dry summers with most precipitation falling in the coldest months between fall and early spring. Annual precipitation totals vary considerably depending on elevation. Eugene, for example, sits at 110 m above sea level and receives an annual average precipitation of 117 cm while
Portland, at 7 m, receives 94 cm (Taylor and Bartlett 1993). Average high temperatures in the valley range from 3-5 °C in the coldest months to 25-30 °C in the summer and average lows are generally between -1 and 2 °C in winter and 10 to 13 °C in summer with a growing season (days between freezing temperatures) ranging between 110 to 180 days depending on location (Taylor and Bartlett 1993).
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To focus our change detection effort on areas with the highest probability of containing historic and present wetlands, a floodplain inundation map was used (Fig. 1).
Developed by River Design Group (RDG) in Corvallis, OR, this map estimates the extent of the floodplain inundation area associated with the two-year discharge of the main stem
Willamette River by utilizing 1-meter lidar data and “bath tub” hydrologic modeling
(River Design Group 2012). The map extends from the confluence of the Middle Fork and Coast Fork Willamette River in the City of Eugene in the south to Willamette Falls near Oregon City to the north. This map was selected for the ecological importance of a regulated two-year inundation discharge for floodplain wetlands. We theorized that a two-year inundation flood frequency represents the spatial extent within which wetlands could potentially persist (if they do not already) if not obstructed through agricultural or urban land alteration.
Image Pre-Processing
To compile a complete time series, we collected annual summer date Landsat images of the study area from 1972 to 2012. These data included both MSS World
Reference System (WRS) 1 path/row 50/29 images from 1972 to 1983 and TM and
ETM+ WRS 2 path/row 46/29 images from 1984 to 2012, with all data processed to L1T
(terrain-corrected and radiometrically calibrated; http://landsat.usgs.gov/Landsat_Processing_Details.php). The geometric accuracy of
MSS L1T data was less than desirable for time series analysis (~120 m RMSE for some images). To increase geometric accuracy, we individually co-registered MSS images to a reference TM image using an automated, correlation-based identification of image “tie
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points” with a maximum RMSE of 30 m and a first-order polynomial transformation
(Kennedy and Cohen 2003, Pflugmacher et al. 2012). To match the spatial resolution of the TM and ETM+ images for standardized interpretation, we resampled the MSS images to 30 m pixel size using a nearest neighbor approach.
The orthogonal Tasseled Cap (TC) transformation has been widely used for change detection because of its usefulness in revealing changes in vegetation (Dymond et al. 2002, Parmenter et al. 2003, Hui et al. 2009, Kennedy et al. 2010, Pflugmacher et al.
2012). The TM TC transformation of the six Landsat TM reflectance bands results in three vegetation indices known as brightness, greenness, and wetness (Crist et al. 1984).
Since the Willamette River floodplain is predominantly a mosaic of agricultural land with scattered natural vegetation, we chose the TC transformation, in part, because it has shown to be particularly useful in capturing differences in natural versus cultivated vegetation (Lobser and Cohen 2007). Additionally, a combination of the brightness and wetness indices is correlated with moisture content, giving supplementary insight to wetland disturbance and land use change distinction between wetland and upland vegetation (Crist et al. 1984, Hodgson et al. 1988, Nielsen et al. 2008, Ordoyne and
Friedl 2008, Kayastha et al. 2012). Lastly, by reducing the image data to three bands, valuable spectral and textural information is more parsimoniously displayed on a computer screen, allowing for easier manual interpretation of annual change.
The TC transformation was originally created for Landsat MSS imagery (Kauth and Thomas 1976) and was subsequently reformulated for higher quality Landsat TM images (Crist et al. 1984). We used TC coefficients for TM reflectance factor data (Crist
1985) for both the TM and ETM+ data (Cohen et al. 2003) after first deriving surface
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reflectance for each TM and ETM+ image using LEDAPS (Masek et al. 2006). There is no similar set of coefficients for MSS reflectance factor data, requiring a calibration of the MSS data to the TM/ETM+ time series. Pflugmacher et al. (2012) worked out a detailed technical approach to spectrally align the MSS and TM/ETM+ data for time series analyses based on new MSS calibration coefficients, newly derived MSS TC coefficients, scene-level radiometric normalization, and pixel level bias removal.
For this study we took a simpler, but similarly effective approach for integrating
MSS data with TM/ETM+, following a method developed by Main-Knorn et al. (2013).
They used linear regression analysis to model Landsat shortwave infrared (SWIR) with
1000 training samples from two visible and one near-infrared Landsat bands. They then applied this model to the corresponding SPOT-1 bands to predict a statistically strong (P
< 0.001 and R 2 of 0.9) pseudo-SWIR band for SPOT-1 data. Following this, we used random forest regression (Breiman 2001) to predict TM TC brightness, greenness, and wetness (y-variables) directly from the MSS band 1-4 spectral data (x-variables) with coincidently acquired Landsat 4 MSS and Landsat 5 TM images from 1987. The derivation of pseudo-wetness for MSS data is new, and was chosen over the derivation of
TC angle (Gómez et al. 2012, Pflugmacher et al. 2012) for our study because we wanted to maintain the wetness index from TM/ETM+ for our time series. When evaluating the goodness of fit for the predicted TC coefficients, we examined scatterplots for general correlation (Fig. 2), but put most emphasis on image appearance consistency both within the MSS images themselves and compared to the TM/ETM+ images through time.
Although imperfect, the results were more than sufficient for consistent visual
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interpretation across the time series. Once the time series was compiled, we applied a two-standard deviation stretch to each image to enhance visual image contrast.
Manual Interpretation of Wetland Loss and Gain
To manually interpret annual wetland losses and gains associated with land use change, a specific workflow was implemented for consistency and quality assurance (Fig.
3). The interpreter had extensive experience with TimeSync-based visual vegetation disturbance detection using Landsat TC time series and spent additional time visually training and calibrating on TC color patterns within known wetlands. For increased interpretation functionality, we spatially segmented the study area into 40 different subareas, each a 255x255 pixel block (7650 m x 7650 m in size), that spanned the entire inundation study area (Fig. 4).
During interpretations of a given subarea, three software applications were open:
TimeSync (Cohen et al. 2010), ArcMap, and Google Earth (Fig. 3). TimeSync and
ArcMap were complementary in their use. Within TimeSync the 41 annual Landsat TC image “chips” for the subarea were simultaneously displayed, and was thus best for interpretation of wetland gain and loss and land use change (given ArcMap does not have this functionality). TimeSync does not facilitate digitization of change polygons, thus
ArcMap was used for this function. Additionally, within ArcMap were two data layers that assisted in the interpretation process by spatially limiting the interpretations, including the 1 m lidar inundation map and a shapefile of the main stem of the
Willamette River and its tributaries within the floodplain created from the Pacific
Northwest Hydrogaphy Framework’s Oregon Water Courses shapefile. Within Google
Earth were historic image snapshots of each subarea and the inundation map (defined by
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kmz files), which assisted in confirming and identifying wetland gain and loss and land use change within the relevant interpretation area.
We interpreted the study area one subarea at a time moving east to west from south to north. Wetland gain was interpreted as new wetland land use derived from a different land use category and loss was any wetland area that was converted to a different land use during the time period defined by the Landsat time series. Wetland-towetland conversion was also interpreted and classified as wetland area that was converted to a different wetland type (e.g. riparian to emergent). We did not note upland-to-upland land use conversion in our interpretations because that did not represent an explicit gain, loss, or conversion of wetland area. When interpreting change polygons, three attributes were selected from drop down menus in the attribute table: date when change was first detected, starting land use, and ending land use.
For this study, we broadly defined a wetland as transitional land between terrestrial and aquatic systems where the water table is usually at or near the surface or the land is covered by shallow water (Dahl and Bergeson 2009). Corresponding to the
U.S. Fish and Wildlife Service’s Technical procedures for conducting status and trends of the Nation’s wetlands (Dahl and Bergeson 2009), we used a consolidated, modified version of the Cowardin wetland classification system in order to accommodate the use of Landsat Tasseled Cap imagery as our primary data source (Cowardin et al. 1979). For example, water chemistry, water depth, and detailed differences in vegetative species cannot necessarily be assessed reliably from the imagery data alone and detailed classifications reliant on these characteristics were not possible. Cowardin et al. 1979 defines deepwater habitats (deep, permanent water bodies such as lakes and rivers)
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separately from wetlands. While specific structural and functional differences exist between wetlands and deepwater habitats, the Willamette River and its tributaries, as well as floodplain ponds and lakes, hold significant hydrological and ecological value in the context of our study area and objectives and were included as wetlands.
Wetland land use classes included emergent vegetation, lacustrine, riparian vegetation, and riverine (Table 1). Although farmed wetlands may hold seasonal ecological significance for waterfowl and other species (Taft and Haig 2003, Tiner 2003), the highly variable agricultural disturbance regime of the Willamette Valley was not consistent with our target of monitoring annual, discrete disturbance and related events and would have introduced a high potential for false change of wetland disturbance.
Thus, farmed wetlands were not a focus of our study. We adapted upland vegetation land use categories from the Anderson land use classification system (Anderson et al. 1976), and included agricultural, upland vegetated, and urban categories (Table 1). Although some changes spanned multiple years, only the first year of detection was used in annual analysis of wetland change.
Area and perimeter of each drawn polygon were calculated within ArcMap. Our workflow required an average of 39 minutes per subarea, ranging between 10 to 90 minutes, with the biggest factors being the geometric area of the floodplain located within the subarea and the number and complexity of wetland losses and gains.
Accuracy Assessment
Accuracy of our Landsat time series interpretations was checked in several ways.
First, as already described, all wetland loss and gain interpretations and land use and
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wetland class interpretations were checked against high spatial resolution true-color aerial and satellite images in Google Earth. Second, we conducted fieldwork to compare our interpretations of current land use and wetland type, with the assumption that accuracy of these interpretations was consistent through time. Fieldwork was conducted in June, 2013 at the tail end of the region’s wet season. This was when water associated with ephemeral wetlands was still present and the growing season had commenced, permitting identification of wetland vegetation.
Using state, county, and city maps we sorted through all demarcated change polygons (365) to identify only those that were on public land or potentially visible from public land or roads. Because the majority of the Willamette River floodplain is privately owned and not accessible by public roads, this narrowed the number of possible polygons to visit to 67. Once in the field, only 45 sites were accessible. Sites were located in the field using a recreation grade GPS and checked against imagery from Google Earth and maps. At each visited location, land use and wetland class were noted. We also noted the presence of running or standing water and determined if dominate over-story and understory vegetation was identified as wetland vegetation (i.e. obligate wetland, facultative wetland, and facultative categories as described by U.S. Army Corps (2010) and Lichvar (2013)). For all locations visited, a site was marked as correctly interpreted if the majority of the polygon contained the correct wetland and/or land use class.
Wetland Change
To characterize temporal trends in wetland loss and gain, we calculated amount of wetland gain and loss, and net change (gain – loss), for each of the 40 annual intervals of
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the time series from 1972-2012. This was done across the four wetland types and separately for each type. Wetland gain and loss were defined by wetland land use change not associated with wetland-to-wetland (e.g. riparian to emergent) conversion. Wetlandto-wetland conversion can be an ecologically significant transition, but as it represents a net neutral change in wetland area it was not included in gain and loss calculations.
For insight into the land use transitions associated with wetland loss and gain, and transitions among wetland-to-wetland types, we organized our data into relevant transitions tables. One table highlights wetland loss, one highlights wetland gain, and the third shows wetland type conversion. Because we did not interpret upland-to-upland type conversion in our analyses we did not combine all transitions into a single table.
In addition to examining the categorical changes in wetland area lost and gained, we also sought to explore the possible effects of the no-net loss policy change on the rate at which net wetland area changed over the study period. First, we calculated the annual, cumulative sum of net change in area for each wetland type and across all wetland types and plotted it over time. To determine if there were trends related to the no-net-loss policy change, we first split our annual net change data into two periods to represent the years prior to federal policy mandating no-net-loss (“pre-policy”, 1972-1989) and the period after (“post-policy”, 1990-2012). Time series analysis of the rate of change of net wetland area was calculated using the Theil-Sen (TS) Slope estimate analysis (Theil
1950; Sen 1968). TS is a non-parametric linear regression method that estimates the slope of a dataset by calculating the median slope of all pair-wise sample points in the forward direction. This analysis yielded a pre-policy and post-policy slope for each wetland type and for all wetland categories combined. The slope values indicate the strength of a trend
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in the change in net area for each wetland category. If the slope had a negative sign for a given time period, a trend in net loss was observed, and visa versa. A permutation test
(Good 2000) with 10,000 iterations was used to create a p-value that represents the significance of the difference between the pre- and post-policy TS slopes. The one-sided p-value is calculated as the proportion of sampled permutations where the difference between pre- and post-policy change was greater than or equal to the observed slope difference for the difference between the two slopes.
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Figures and Tables
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Figure 1. Map of the Willamette Valley Ecoregion with the Main Stem Willamette
River two-year inundation floodplain.
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Figure 2. Scatter plots showing pixel density correlation (high to low) of Tasseled Cap brightness, greenness, and wetness indices using Crist et al. 1984 coefficients for TM reflectance factor data (y-axis) versus TC BGW indices predicted from random forest regression using MSS band 1-4 spectral data with coincidently acquired Landsat 4 MSS and Landsat 5 TM images (x-axis).
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Figure 3. Depiction of wetland change interpretation workflow using three software applications. When a change in wetland area was observed in TimeSync (1), it was then verified with aerial imagery in Google Earth (2) and drawn and classified in
ArcMap (3). This figure shows classification of an agricultural pond (seen in white box in TimeSync) that first appeared in
2004 and continued to expand through 2011.
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Figure 4. Map of inundation study area spatially segmented into 40 different subareas, each a 255x255 pixel block (7650 m x 7650 m in size). The extent of the inundation study area was varied depending on spatial context of the Willamette
River. In metropolitan areas (top right) the floodplain was restricted to a small margin along the river. In agricultural areas (bottom right), the floodplain extended far beyond the riverbank.
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Table 1. Land use categories used in wetland land use change interpretations.
Land Use
Category
Emergent
Vegetation
(wetland)
Lacustrine
(wetland)
Riparian
Vegetation
(wetland)
Riverine
(wetland)
Agriculture
(upland)
Upland
Vegetated
(upland)
Urban (upland)
Description
Vegetated land that includes non-woody, perennial, rooted herbaceous hydrophytes associated with wetlands such as marshes, bogs, fens, or wet prairies. This category is a combination of the Cowardin et al. 1979
Palustrine system and the emergent vegetation associated with Riverine and
Lacustrine systems.
Non-vegetated land that includes permanently flooded lakes, reservoirs, and ponds as defined by Cowardin et al. 1979 Lacustrine deepwater system.
Vegetated land that includes scrub-shrub and forested wetlands associated with Cowardin et al. 1979 Riverine, Lacustrine, and Palustrine systems.
Non-vegetated land with running water or a substrate associated with a stream or river such as rock, cobbles, gravel, or sand associated with the
Cowardin et al. 1979 Riverine deepwater system.
Mixed land associated with the attributes of Anderson’s Agricultural Land class.
Non-wetland vegetated land associated with the Anderson et al. 1979
Rangeland and Forested Land class.
Non- or partially-vegetated land associated with the Anderson et al. 1979
Urban land use class.
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RESULTS
Wetland Loss, Gain, and Type Conversion across the Full Study Period
Within the two-year inundation floodplain of the main stem of the Willamette
River (approximately 28,890 ha), only a fraction of the area underwent wetland gain, loss, or type conversion between 1972-2012. Adding the totals from all loss (442 ha,
Table 2a), gain (521 ha, Table 2b), and type conversion (289 ha, Table 2c), we observed that 1252 ha of total wetland change occurred between 1972-2012, which represents
4.3% of the study area.
Across the full study period, emergent wetlands experienced the second greatest decrease in area, 85 ha or 19% of total wetland area lost (Table 2a). Emergent wetlands had the lowest increase in area, 45 ha or 8.6% of total wetland area gained (Table 2b) among the four wetland types. This translates to a 40.5 ha net loss in emergent wetland area (Table 3). Of the total emergent wetland area lost, 41.5% was from conversion to agriculture and 58.5% from conversion to urban land use (Table 2a). Of total emergent wetland area gained, 100% came from conversion from agricultural land use (Table 2b).
Gain in lacustrine wetlands was proportionally the largest among the four types at
67% (348 ha) of total gains (Table 3), with losses being the smallest at 1% (5.1 ha) of total wetland area lost, next to riverine wetland for which there was no loss (Table 3).
Lacustrine wetlands experienced 68 times more gain in area compared to loss, resulting in a 343 ha net gain in area (Table 3). Of the 5.1 ha of lacustrine area lost, 39% came from conversion to agricultural land use and 61% from conversion to urban land use
(Table 2a). Of the total lacustrine wetland area gained, 81% was attributed to conversion
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from agricultural land use and the remaining 19% came from conversion from urban land use (Table 2b).
Loss in riparian wetlands was the largest decrease in area among the four wetland types, accounting for 80% (352 ha) of all wetland area lost (Table 3). Although riparian wetlands had the second largest increase in area with 15% (78 ha) of total wetland area gained (Table 2b), losses in area were 4.5 times greater than gains resulted in a net loss of
274 ha (Table 3). From the riparian wetland area lost, 83% came from conversion to agricultural land use and 17% from conversion to urban land use (Table 2a). Of the total riparian wetland area gained, 95% came from conversion from agricultural land use, 4% from conversion from urban land use, and 1% (0.8 ha) from conversion from upland herbaceous land (Table 2b).
While gain in riverine wetlands area accounted for only 9.6% of total wetland area gained (Table 3), this wetland category experienced no losses during the study period (Table 2a) resulting in a net gain of 50 ha (Table 3). Of the riverine area gained,
100% came from conversion from agricultural land (Table 2b).
Within the full study period and across all wetland types, the study area experienced 1.1 times more gain than loss in wetland area, resulting in a net gain of 78 ha
(Table 3). Conversion to agricultural land use was the main cause of wetland area lost for all individual categories and accounted for 74% all wetland area lost across all categories
(Table 2a). Conversion to urban land use was the second highest cause of wetland area lost and accounted for 26% of area lost across all categories (Table 2a). Conversion from agricultural land to wetland was the leading land use change across gains and losses
(449.8 ha) and resulted in 86% of all wetland area gained, with the remaining gain
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attributed to conversion from urban land use at 13% and less than 1% from conversion to upland herbaceous (Table 2b).
Although the study area saw a net gain in total wetland area, gains and losses were not distributed evenly across wetland types. Vegetated wetlands (emergent and riparian) had individual net losses across the study period and when combined, had an overall net loss of 314 ha (Table 3). Non-vegetated wetlands (lacustrine and riverine) had individual net gains in wetland area across the study period and when combined, had an overall net gain of 484 ha (Table 3). Therefore, while calculations across all wetland types aggregate to a net gain in wetland area, this is the result of vegetated wetland area being replaced with non-vegetated wetland area relative to net wetland area changed.
Although, not representative of net gain or loss in wetland area, there was a significant amount of wetland type change within the individual wetland categories
(Table 2c). The largest conversion between wetland types was riparian wetland area converted to riverine wetland (89.1 ha). This was followed by, in descending order of area converted, lacustrine to emergent, riverine to riparian, riparian to emergent, riparian to lacustrine, emergent to lacustrine, riverine to emergent, lacustrine to riparian, emergent to riverine, and riverine to lacustrine. Combined, vegetated wetlands (emergent and riparian) gained 120.3 ha from conversion from non-vegetated wetlands (lacustrine and riverine) and lost 133.6 ha to non-vegetated wetlands, resulting in a net loss of 13.3 ha for vegetated wetlands to non-vegetated wetlands. Therefore, in both conversion to and from both upland and wetland land use types, vegetated wetlands show a net loss of area.
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Annual Change, Pre- and Post-Policy
All four wetland types, as well as all wetland types combined, had different trends in annual net change in wetland area from the pre-policy and post-policy eras (Table 4, Fig.
5). In the pre-policy era, net change in emergent wetlands resulted in a negative TS slope of 3.48, representing a trend in which the study area lost 3.48 ha more net emergent wetland area than it gained per year (Table 4). Post-policy, this trend reversed with a positive TS slope, signifying a trend in which emergent wetlands gained 1.38 ha more wetland area than lost per year. While emergent wetlands experienced a reversal of the negative pre-policy trend into a positive post-policy one, the positive trend observed postpolicy is less than half the strength of the preceding negative trend and, thusly, did not completely reverse trend in net losses in wetland area that occurred pre-policy.
Lacustrine wetlands had strong positive pre-policy TS slope resulting in a trend of
8.8 ha net gain in wetland area per year (Table 4). The post-policy slope of annual change in net area for lacustrine wetlands was still positive, but weaker, at 4.5 ha net gain in wetland area per year (Table 4). While the strength of the positive trend for annual net change in wetland area decreased post-policy, the positive post-policy trend was still the strongest of any other post-policy trend across all wetland types. Therefore, while the amount of net lacustrine area added per year slowed during the post-policy years, lacustrine wetland area is still gained at a high rate.
The pre-policy TS slope for riparian wetlands was the strongest and most negative across all wetland types for both pre- and post-policy years, with a net loss of 9.32 ha of wetland area annually (Table 4). The post-policy trend was still negative, but weaker, with a net loss of 3.59 ha of riparian wetland per year. Although the negative post-policy
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trend in net wetland area was 61% weaker than the pre-policy trend, is still the second strongest of all negative trends across all wetland types and both eras, indicating these wetlands are still losing area at a high annual rate, even after policy implementation.
Riverine wetlands only had four non-zero data points for the entire time series and are less interpretable.
Across all wetland types combined, the study area experienced a net loss of 2.59 ha of wetland area per year in the pre-policy era of 1972-1989 (Table 4). In the postpolicy years, this trend was reversed into a stronger, positive trend of a net gain of 3.29 ha of all wetland area per year from 1990-2012. However, similar to analysis of the wetland loss, gain, and type conversion across the full study period, the pre- and postpolicy trends observed across all wetland types are the result of an imbalance between vegetated and non-vegetated wetland types. The strong, positive annual trend observed in post-policy lacustrine wetlands tips the post-policy trend across all wetland types into the positive spectrum, despite a weak positive trend from emergent wetlands, a strong negative trend in riparian wetlands, and no trend from riverine wetlands. Removing lacustrine and riverine wetlands, vegetated wetlands had a very strong negative trend and lost -13.3 ha of net wetland area annually in the pre-policy era. Post-policy, the trend in net change in wetland area was still negative but weaker with 2.31 ha of net wetland area lost annually.
Error Analysis
Error matrices for the seven land use categories, based on the field visits to 45 change polygons yielded an overall accuracy of 80% (Table 5). With our methodology,
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we overestimated riparian, agricultural, and lacustrine land use types most frequently with errors of commission of 25%, 28%, and 40% respectively. Urban, emergent, upland vegetation, and riverine land use types all had 0% errors of commission. We underestimated agricultural, urban, and upland land use types most frequently with errors of omission at 13%, 40%, and 100% respectively and errors of omission equaling 0% for all other land use types. Examining our ability to distinguish only between wetland and upland land use types, overall accuracy was 91.1% and omission and commission rates were low (Table 6). The aggregated wetland land use category had both the highest error of omission (13%) and commission (22%), indicating it was more difficult to classify compared to upland categories, which had 0% errors of omission and commission.
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Figures and Tables
Table 2a. Loss of wetland area (ha), by type, to the three upland land use classes.
Starting Wetland Class
(ha)
Emergent
Lacustrine
Riparian
Riverine
Agriculture
35.3
2
291.1
0
Ending Upland Class (ha)
Upland
Vegetation Urban
0
0
49.8
3.1
0
0
60.9
0
Total
85.1
5.1
352
0
Total 328.4 0 113.8 442.2
Table 2b. Gain of wetland area (ha), by type, from the three upland land use classes.
Starting Upland
(ha)
Agriculture
Upland Vegetation
Urban
44.6
0
0
Ending Wetland Class (ha)
Emergent Lacustrine Riparian Riverine Total
281.4
0
66.6
73.9
0.8
3.4
49.9
0
0
449.8
0.8
70
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Total 44.6 348 78.1 49.9 520.6
Table 2c. Type conversion of one wetland category to another. Grey cells indicate a conversion from vegetated wetland category to non-vegetated wetland category, or from non-vegetated wetland to vegetated wetland.
Ending Wetland Class (ha)
Starting Wetland Class
(ha)
Emergent
Lacustrine
Riparian
Riverine
Total
Emergent Lacustrine Riparian Riverine Total
0
49.9
33.8
17.2
100.9
19.3
0
22.6
1.4
43.3
0
13.8
0
39.4
53.2
2.6
0
21.9
63.7
89.1 145.5
0 58.0
91.7 289.1
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Table 3. Wetland area gain and loss across the 40-interval period of observation.
Wetland
Type
Emergent
Lacustrine
Riparian
Riverine
Total
Area
Gained (ha)
44.6
348.0
78.1
49.9
520.5
Percent of
Total Gain
8.6
66.9
15.0
9.6
100.0
Area Lost
(ha)
85.1
5.1
352.1
0.0
442.2
Percent of Total
Loss
19.2
1.15
79.6
0.0
100.0
Net Change
(ha)
-40.5
342.9
-273.9
49.9
78.3
Table 4. Calculated Theil-Sen (TS) slope for cumulative net change in wetland area for pre-policy (1972-1989) and post-policy (1990-2012) time series for each wetland category and all wetland categories combined. Also shown are p-value for the significance of the difference between the two slopes.
Wetland Type p-Value
Emergent
Lacustrine
Riparian
Riverine
All
Vegetated Only
Pre-Policy TS
Slope
-3.48
8.82
-9.32
1.59
-2.59
-13.32
Post-Policy TS
Slope
1.38
4.45
-3.59
0
3.29
-2.14
0.006
0.15
0.057
0.13
0.26
0.004
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Figure 5. Cumulative, annual change in net area for individual wetland categories as well as all wetland categories combined.
Dashed line at 1990 represents no net loss federal policy implementation.
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Table 5. Error matrix for all land use categories based on wetland loss and gain polygon classifications validated in the field.
Observed Land Use
Predicted Land Use
Emergent Lacustrine Riparian Riverine Agriculture Upland Vegetation Urban Column Total Error of Omission (%)
Emergent 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0
Lacustrine
Riparian
Riverine
Agriculture
Upland Vegetation
Urban
Row Total
Error of Commission (%) 0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
1.0
1.0
0.0
8.0
25.0
0.0
6.0
0.0
1.0
0.0
1.0
5.0
40.0
3.0
0.0
0.0
13.0
0.0
5.0
18.0
27.8
0.0
0.0
0.0
0.0
0.0
0.0
3.0
0.0
0.0
0.0
3.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9.0
9.0
0.0
0.0
0.0
0.0
15.0
1.0
15.0
45.0
3.0
6.0
3.0
13.3
100.0
40.0
100.0
0.0
0.0
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Table 6. Error matrix for aggregated wetland and upland land use categories based on loss and gain polygon classifications validated in the field.
Observed Land Use
Upland
Wetland
Row Total
Error of Commission (%)
Upland
27
0
27
0
Wetland
4
14
18
22.2
Predicted Land Use
Column Total
31
14
45
Error of Omission
(%)
12.9
0
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Discussion
Globally, wetlands have been recognized as some of the most valuable providers of essential ecosystem services such as water quality regulation, groundwater recharge, erosion control, natural hazard mitigation, and carbon storage (de Groot et al. 2012, Russi et al. 2012) and have been compared to agricultural lands and forests in their role as a key life support system for the planet (Ramsar Convention Secretariat 2013). Despite their importance, the planet has lost roughly 50% of its wetlands since 1900 (UNWWAP
2003) through effects such as agricultural conversion, irrigation, pollution, urbanization, and climate change with recent loss in areas such as East Asia up to 1.6% a year (Gong et al. 2010). Signed in 1971, in recognition for the need of a global strategy on wetland conservation, the Ramsar Convention on Wetlands was created with a mission of
“conservation and wise use of all wetlands through local, regional and national actions and international cooperation, as a contribution towards achieving sustainable development throughout the world” (Ramsar Convention Secretariat 2013). To date, 168 nations have agreed to participate in the guidelines for wetland preservation with a total of nearly 207 million ha designated as Wetlands of International Importance (Ramsar
2014).
With 35 sites and just over 1.8 million ha designated for wetland conservation, the
United States is ranked 28th globally in the Ramsar Convention list of contracting nations
(Ramsar 2014). Along with treaties in the Ramsar Convention, the U.S. federal government regulates wetland policy through Section 404 of the Clean Water Act, striving for no-net-loss of wetland area. The goal of no-net-loss of as an official administrative policy was originally announced by President George H.W. Bush during
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an EPA press conference in 1989 (Sibbing 2008). This goal was then put into written federal policy in 1990 under the U.S. Army Corps of Engineers and Environmental
Protection Agency memorandum of agreement on mitigation (USACE and EPA 1990,
Turner et al. 2001).
With a no-net-loss of wetlands national standard, efforts must be made to mitigate wetland area in place of area destroyed. Mitigation is the attempt to alleviate the destruction of wetlands by replacing an existing wetland or its functions by creating a new wetland, restoring a former wetland, or enhancing or preserving an existing wetland.
Although mitigation and wetland regulation under Section 404 is a federal policy, the implementation is left to the state. In Oregon, wetland mitigation is regulated through the
Department of State Lands (ODSL) with several other agencies involved in wetland regulation and some areas implementing more intensive goals at the watershed, city, or community scale (ODSL 2011). The Willamette Valley is particularly dense in localized wetland conservation goals with some agencies operating at the sub-city level. Oregon had regulatory programs in place to conserve wetlands even before the 1990 federal policy implementation. The Oregon Removal-Fill Law was established in 1967 and mandated a permit requirement for the fill or removal of wetlands with updates to the law broadening the types of wetlands protected and increasing regulations continuing through present day. Although wetland regulations had been in place since 1967, in similar timing to the federal no-net-loss regulations, Oregon implemented its most comprehensive wetland protection and monitoring act in 1989 (Shaich 2000).
To achieve a broader view of ecological and spatial changes in Willamette Valley wetlands, we employed a new methodology that reaches back through the over 40-year
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Landsat archive to map fine scale wetland land-use changes. First, we were able to quantify and characterize trends in annual wetland change through gain, loss, and conversion in the Willamette Valley from 1972-2012, and, subsequently, evaluate the effect of the no-net-loss federal wetland conservation policy enacted in 1990, on annual trends in net wetland area.
Across the full study period, we observed annual wetland loss, gain, and type conversion across all wetland types in the two-year inundation floodplain of the main stem of the Willamette River. While the area of observed wetland change is on a much smaller scale than the total study area, even one hectare of change to an ecologically significant and vulnerable wetland is important to characterize. From 1972-2012, and across all wetland types, net change in area resulted in a net gain of 78 ha, but individually, emergent and riparian (i.e. vegetated) wetlands each experienced a net loss across the entire study period.
Wetland Loss
The largest decrease in wetland area across all wetland types combined was attributed to conversion to agricultural land with a loss of 328 ha (74% of total loss). As noted, the Willamette Valley places high value on its agricultural industry. Extensive loss of wetland area to agricultural expansion is consistent with previous studies that have examined wetland trends in the Willamette Valley and is likely attributed to the continuing expansion of agricultural production and commerce in the valley (Gedrey and
Hiserote 1989, Bernert et al. 1999, Morlan et al. 2010). To compound this, regulatory monitoring of spatial and ecological wetland changes within private agricultural settings
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is not straightforward. A study of regulatory compliance of wetland change in the
Willamette Valley from 1982-1994 conducted by ODSL found that 81% of all unauthorized wetland changes were attributed to agricultural conversions. Further, they found that no wetland changes due to agricultural conversions that required a permit were authorized by OSDL permit (Shaich 2000). Both Section 404 of the Clean Water Act and the ODSL operate on a complaint driven enforcement program of unauthorized agricultural wetland change. While some activity using heavy equipment closer to urban areas may be visible to the public, wetland change activities on expansive agricultural land in sparsely populated areas are less likely to be observed and reported (Shaich
2000). This lends great value to studies such as this one that use remote sensing to monitor wetland losses.
The next largest decrease in wetland area across all wetland types was conversion to urban land use, which accounted for 26% (114 ha) of all wetland lost to upland conversion. Oregon’s population has increased 2.5 times since 1950 with the population for the middle and southern Willamette Valley increasing by 11.6% in the past decade alone (State of Oregon 2011, State of Oregon 2012). With increase in population, comes an increase in urbanization and development. Destruction of wetlands in urban settings is often driven by the shortage of remaining undeveloped areas and the economic value of property adjacent to aesthetically pleasing wetland settings (Hall 1988, Holland 1995).
Even though urbanization poses an increased threat to Willamette River floodplain wetlands, our study observed less urban conversion than agricultural. Through Oregon’s
Statewide Planning Goals 3 (Agricultural Lands), 5 (Natural Resources, Scenic and
Historic Areas, and Open Spaces), 6 (Air, Water and Land Resources Quality), and 15
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(Willamette River Greenway) there is explicit emphasis on protecting the area surrounding the Willamette River floodplain from urbanization and development
(Macpherson and Paulus 1973). These goals, implemented first in 1973, could explain why more wetlands were lost to agricultural conversion compared to urbanization and development. Additionally, Goal 14 (Urbanization) outlines how land use laws should limit development outside of city-defined urban growth boundaries (UGB), and only 7% of our study area intersected with any city’s UGB, limiting area for larger scale urban expansion at the expense of wetlands observable within the our study area.
Although conversion to urban land use was second to agricultural conversion, it accounted for the majority of wetland area lost from emergent and lacustrine wetland types. Further, conversion to urban land use represents a significantly different ecological change compared to conversion to agriculture. Wetlands converted to agriculture still have the possibility to function as wetlands in varying ecological capacities depending on precipitation levels, terrain, and the intensity of agricultural activities. For example, wetland shore birds are able to utilize Willamette Valley agricultural lands as foraging habitat where poorly drained soils facilitate natural ponding (Taft and Haig 2006).
However, wetland conversion to urban land use represents a much more permanent change and a significant decrease in ecological function.
Wetland Gain
For each individual wetland type and across all wetland types combined, conversion from agriculture resulted in the largest increase (86.5%) in wetland area for the full study period. Almost all wetlands observed in our two-year inundation study area
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were directly adjacent or closely connected to an agricultural field, making these fields spatially and ecologically relevant for connecting current wetlands to future restored wetlands. Gain in wetland area can occur both naturally or purposefully through official mitigation and restoration or creation from land owners or groups not affiliated with government agencies.
Nearly all (96%) of vegetated wetland area gain was attributed to conversion from agriculture. For emergent and riparian wetlands, agricultural fields in the Willamette
Valley are well suited for wetland restoration, enhancement, or creation. Since these fields were likely prairie or wet prairie ecosystems in the past, relic soil and hydrologic conditions may remain for potential recapture of wetland ecosystem function through restoration (Wold et al. 2011). Streams running through agricultural land offer additional opportunity for riparian wetland restoration, creation, and enhancement. Along with programs such as the Wetland Reserve Program that offer private landowners the opportunity to protect, restore, and enhance wetlands on their agricultural property
(United States Congress 1990), many other land trust organizations in the Willamette
Valley have begun to buy private agricultural land either outright or as easements with the goal of future wetland restoration (Wold et al. 2011).
Conversion from agricultural land to lacustrine wetlands accounted for 54% of all wetland area gained. The majority of agricultural land converted to lacustrine wetlands appeared to be from anthropogenic activities and the construction of on-farm ponds.
Ponds constructed in an agricultural setting can be utilized to increase the efficiency of rainfall use and surface runoff collection for supplemental irrigation to crops during periods of water stress (Krishna et al. 1987, Arnold and Stockle 1991). While these
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temporary ponds do have the potential to be ecologically significant for Willamette
Valley plant and wildlife species, they function far below that of a wetland with natural vegetation and hydrology and without exposure to agricultural disturbance (Lippert and
Jameson 1964). We also observed permanent ponds created on agricultural land as the result of heavy flooding where inundation levels never receded entirely, leaving behind small ponds directly adjacent to river.
All increase in riverine wetland area came from agricultural conversion. Riverine wetlands had three distinct time periods that accounted for all gain in area: 1983, 1990, and 1996-1997 and resulted from incision or erosion of channel banks and agricultural fields directly adjacent to the river. Comparing these events with annual precipitation data in the Willamette Valley (Fig. 6) shows that these gains come from periods of a relative increase in annual precipitation that are preceded by periods of relatively lower precipitation years, resulting in increased discharge of the Willamette River. This increase in discharge could create events in which the river overtakes a channel bank for a gain in area. The 1996 precipitation event, in particular, was a notable 100-year flood event of the Willamette River resulting from a rain-on-snow precipitation event (Colle and Mass 2000) and created rearrangement of river course on a small scale and, from our observations, took several years to stabilize.
In addition to riverine area gained from agricultural land, 89 ha of riparian wetland area was converted to riverine wetland area over the full study period through the same mechanisms as riverine conversion of agricultural land. Gutowsky 2000 found that riparian forests along the Willamette River have seen a net increase in stand maturity over the past several decades attributed to an increase in flood control measures. This
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suggests that during periods of increased inundation and discharge of the Willamette
River, riparian forests are vulnerable to capture by the flooded channels. Additionally, our observed loss of riparian wetlands to both riverine and agricultural conversion, and a continued annual net loss of riparian wetlands, suggest that riparian forests along the
Willamette River are being spatially compressed from both sides into narrow strips that when lost, are not replaced in area. Due to their smaller size, these smaller, narrow stands are more susceptible to complete riverine conversion during periods of heavy inundation.
Both riparian and lacustrine wetlands had an increase in wetland area resulting from conversion from urban land use. For lacustrine wetlands, the majority of this conversion either came from ponds created on private residences and golf courses or from partially developed or barren land converted to quarry land with quarry ponds.
Similar to agricultural ponds, ponds in an urban setting have the potential to provide wildlife species with habitat in an otherwise altered landscape (Failey et al. 2007 and others). However, often surrounded by non-native vegetation and impervious surfaces along with disturbances associated with urban settings, these wetlands do not compare in ecological function to that of natural wetlands.
Quarry ponds connected to gravel mining are particularly prevalent along the
Willamette River floodplain landscape. These quarries transform vegetated wetland or agricultural land (that has first been cleared and converted into barren land classified as urban land use) into open pits directly adjacent to the Willamette River and are often separated from the main channel by only a narrow strip of unmined land. During times of heavy flow, the active river channel can capture quarry ponds due to their proximity to the river and connectivity to the floodplain water table. This capture creates either a new
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side channel that offers the river a shorter course than the currently active channel, or expands existing ponds in size and consequently increases either riverine or lacustrine wetland area, possibly at the expense of vegetated wetlands (Kondolf 1997). As they stand, gravel ponds offer little wetland habitat. However, future reclamation of quarry ponds into higher functioning wetlands is possible (Andrews and Kinsman 1990; Kondolf
1993). While restoration is possible, within our study period we found that restoration into vegetated wetlands is not happening on the same aerial scale as creation of deepwater quarry ponds.
Ecological and Spatial Trends of Wetland Change
As noted previously, most studies of net change in wetland area over time have not used annual data to investigate changes and trends in wetland area, and instead interpolate trends from two-date change detection. Using historical Landsat imagery for comprehensive analysis of the effect of policy on spatial and temporal ecological trends has been used as a tool before. Healey et al. 2008, for example, used incremental Landsat
MSS and TM/ETM+ data from 1972 to 2002 to examine the effect of the Northwest
Forest Plan (NWFP), a policy shift away from the economic yield of timber and towards conserving biodiversity and endangered species (Thomas et al. 2006), on the spatial and temporal patterns of harvest in old-growth forests. They found that in just one decade following the NWFP, federal harvest of old-growth forest declined significantly, adding to the efficacy of the NWFP policy.
No-net-loss policies under section 404 of the Clean Water Act were created in an attempt to slow the amount of wetland area lost and replace area lost with creation, restoration and enhancement. Our observations suggest that in the Willamette River two-
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year floodplain, this type of protection was necessary. We found that before State and
Federal policy implementations, all wetlands experienced annual loss of area with emergent and riparian wetlands decreasing at an even higher annual magnitude. After policy implementation in 1990 and through 2012, loss of wetland area across all wetland types was not just slowed, but reversed into a trend of annual gain across all wetlands
(Table 4 and Fig. 5). However, like trends seen across the entire study period, individual wetland types were affected differently by policy implementation.
Emergent wetlands saw a reversal of annual cumulative loss into an annual gain of wetland area, but at a lower magnitude. Riparian wetlands continued to see cumulative annual losses even after policy implementations but at a slower rate. Lacustrine wetlands added net area annually both pre-policy and post-policy but saw at a slower rate after
1990. Zooming into a finer scale and looking at more recent data, we see no net loss and very little change in wetland area from 2009-2012 with the only change attributed to gain in emergent wetland area (Fig. 5). This could be a promising stabilization of wetland change and may point to stricter regulations and enforcement of wetland conservation laws as well as a lessening of wetland disturbance in our study area.
Across all wetland types and individually, it appears that wetland conservation policies were effective at slowing loss of wetland area and in some cases, were able to facilitate trends in annual gain. This is consistent with an increase in mitigation permits associated with Willamette River received by ODSL after 1990 compared to 1972-1989
(Fig. 7). An increase in permits can be seen as a representation of regulations imposed and enforced on wetland area changed, and limitations on illegal or unregulated wetland change.
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Although Oregon has had wetland regulatory programs in place since the 1960s and more intensive regulations since 1990, we found that for both our 40-year study period, and since 1990 wetland conservation policies, vegetated wetland area was lost at a greater rate than, and was replaced by, non-vegetated wetlands. Other studies investigating both Willamette Valley wetlands and national wetlands have found the same trend with an increase in shallow, non-vegetated and open water ponds and lakes
(Bernert et al 1999, Morlan et al. 2010, Dahl 2006). Along with loss in area to upland land use, we also observed net loss of vegetated wetlands from conversion to nonvegetated wetlands. Thus, it is important to recognize that conversion of one type of wetland to another either directly or through mitigation can/may represent a net loss of ecosystem services and function.
Willamette Valley vegetated wetland habitats hold substantial ecological significance for many plant and wildlife species. Of the twenty species that occur in the
Willamette Valley that are listed under the federal Endangered Species Act and the 155 that are imperiled, wet prairies provide habitat for 31 for at least some portion of their lifecycle (Floberg et al. 2004). Wet prairies alone also provide critical reproductive habitat for 38 different wildlife species and an additional 54 breeding species (Primozich and Bastasch 2004). In addition to plant and wildlife species, these naturally vegetated wetlands provide important ecosystem services to the human population Willamette
Valley. Both high functioning in-stream riparian wetlands and floodplain riparian wetlands are valuable in the removal of nonpoint-source pollution such as sediment, nutrient, and pesticide from agricultural runoff, protecting the drinking water of the valley’s residents, and reducing damage from flooding (Mitsch 1992, Gilliam 1994,
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Darby 1999). If higher ecologically functioning wetlands are lost and replaced in area with lower quality wetlands, mitigation does not represent a net neutral change in wetland function.
Nationally, the overwhelming consensus in the literature is that the no-net-loss policy has failed in regards to both to no net loss of wetland area and no net loss of wetland function, as wetlands destroyed are not always replaced with equally valuable wetland types. A national review of mitigation permits conducted by Turner et al. 2001 found that only 21% of the mitigation sites evaluated met various tests of ecological equivalency to the functions lost and the replacement wetlands ranged from 0 to 67 percent in ecological functionality. Studies from individual states have similar results.
For example, individual investigations from Illinois (BenDor 2009), Pennsylvania (Cole and Brooks 2000; Cole and Brooks 2001), Ohio (Gutrich and Hitzhusen 2004), California
(Allen and Feddama 1996, Malakoff 1998, Sudol and Ambrose 2002), and Wisconsin
(Reinartz and Warne 1993) all concluded that although mitigated wetlands create wetland area, they do not match the ecosystem functionality lost when natural wetlands are destroyed.
Spatial extent is another issue to consider when evaluating trends in wetland area.
Our study area was located in the Willamette Valley but was stratified by the main-stem
Willamette River two-year inundation floodplain as determined by 1m lidar hydrologic modeling. Oregon’s wetlands are officially regulated on a statewide scale with conservation goals to monitor at finer scales such as city, county, and watershed. Hectare for hectare we do not know which, if any, gain in wetland area we observed was meant to replace wetland area lost that we observed or which was subject to regulatory permits.
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While there are over two dozen wetland mitigation banks within the Willamette Basin, only one was located within our study area. Therefore, we must consider that wetland area destroyed in our study area could have been compensated with mitigation banks elsewhere. This raises the issue of spatial extent in relation to compensatory wetland mitigation; if a wetland is destroyed in a particular area, managers should consider the spatial and ecological bounds of where to rebuild, restore, or enhance elsewhere.
Mapping Annual Wetland Change with Landsat Imagery
Our accuracy assessment of wetland land use change was polygon-based as opposed to pixel-based and was only able to capture 12% of our interpretations due to access restrictions associated with private land. In spite of a low sample size (n=45), overall accuracy of land use classification through gain and loss polygons was at 80%.
This accuracy increased to 91.1% when land use classes were aggregated to either wetland or upland categories, indicating that our methodology was more accurate at distinguishing between general upland and wetland than finer categorical classes. Both accuracies are on par with other studies examining Landsat’s capability to accurately map and classify wetland land use (Sader et al. 1995, Hewitt 1990, Harvey and Hill 2001).
The high temporal resolution (1 image per year) of our image data not only yielded insight into annual trends of wetland gain and loss, but was also crucial to interpretation of wetlands using Landsat data. Landsat data does not provide the same spatial resolution as aerial imagery, making it more difficult to classify finer categorical wetland and land use classes. To make up for this informational deficiency, we augmented our analysis with high temporal resolution.
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Wetlands in our study area sit in a landscape mosaic of dynamic agricultural land and naturally vegetated upland area making it an additional challenge to identify them among other land uses. With annual imagery, the interpreter was able to gain insight into spatial and temporal context associated with land use dynamics that would not be possible with single date interpretations. For example, depending on specific crop type, a plot of agricultural land bordering a stream or the Willamette River often gave a Tasseled
Cap spectral signal visually matching a specific wetland type but a different spectral signal the following year. To determine if this signified a loss of wetland area versus agricultural crop rotation, the interpreter was able to access a 40+ year history of the site and use temporal context to see if that particular site had variable spectral signals for a significant amount of time in the past, indicating agricultural land use. If it had maintained the same spectral wetland appearance for a longer time period, this indicated natural vegetation. Additionally, temporal context was crucial in distinguishing farmed emergent wetlands from persistent ones. If a particular site showed the spectral, textural, and spatial qualities of an emergent wetland, but was located in the middle of an agricultural field, the interpreter was able to access the entirety of the time series, both past and future, to determine if that wetland remained naturally vegetated for a consistent time period or if it was frequently or occasionally farmed.
As noted with other studies utilizing Landsat imagery to map and classify wetland area (Sader et al. 1995, Lawrence et al. 2004, Baker et al. 2006), ancillary data layers add valuable spatial context not available through spectral satellite data alone. For example, the study area contained a significant amount of hardwood fruit and nut orchards as well as some Christmas tree farms. These orchards displayed a similar Tasseled Cap spectral
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signal and texture as the riparian wetland category. When a specific site gave a spectral signal indicating a mixed hardwood/coniferous forest and was located directly adjacent to the Willamette River, the interpreter was more easily able to classify that patch as riparian wetland if it was eventually disturbed. However, the study area also contained many smaller streams connected to the river that were spectrally occluded due to vegetation canopy cover or an extent too small for the sensor to capture. Isolated patches of forest scattered throughout the study area that were located significantly far from the main river channel and lacked a spectral signal indicating presence of water were difficult to distinguish from upland hardwood stands without the spectral context of the presence of water. However, by utilizing the streams shapefile overlaid on top of the Landsat imagery in ArcMap, the interpreter was able to see if the forest was adjacent to a water source and subsequently distinguish wetland from upland. Ancillary soils data (i.e. hydrolic soils) are often used in wetland mapping but were omitted from our workflow because they only represent a discrete view of soil distribution in time and were, thus, not useful in evaluating annual change in wetland area through our study period.
In addition to ancillary data, Google Earth was a valuable tool for classifying wetland change in our study area. Many remote sensing studies use aerial imagery to validate land use after classification, but using it within the context of initial classifications helped the interpreter to continuously and dynamically validate land use classifications and change detection. Through a feature that allows the user to access a dataset of all available historical imagery, the occurrence of a wetland change event was often validated through the higher resolution, true color imagery seen in Google Earth.
The availability and temporal resolution of historical imagery in Google Earth is
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inconsistent depending on location and cannot be reliably used for annual change detection. However, when used in combination with interpretations from other remote sensing data, it is very useful for assisting in interpretation of wetland change.
Assessing Landsat’s entire time series was crucial for evaluating long-term trends in our study area. MSS imagery (1972-1983) is underutilized, if not discounted, in recent studies that use Landsat imagery for temporal analysis (Sloan 2011) due to a perceived inconvenience and increased labor associated with pre-processing (Maxwell and
Sylvester 2012). Comparing wetland change polygons captured and drawn using MSS imagery (1972-1983) to those captured with TM/EMT+ imagery (1984-2012), we found that MSS polygons had a slightly higher median (4.6 ha compared to 2.7 ha) but a smaller range than TM polygons (0.5ha – 55.0 ha compared to 0.3 ha – 70.1 ha) (Fig. 8). A single
30m square pixel and a 60m square pixel represent 0.09 ha and 0.36 ha respectively. The minimum polygon captured from TM imagery (0.3 ha) is only slightly smaller than the minimum single-pixel polygon you could capture using MSS data and does not represent a large spatial or informational gap between the two data sets. We can also not factor out other variables that could affect the size of wetland change polygons observed such as climate, policy, or other natural and anthropogenic factors.
While our methodology showed Landsat’s successful capacity to distinguish between wetland and upland land use types for the purposes of change detection, there are several notable deficiencies. Landsat’s spatial resolution limited our ability to distinguish between wetland classes at a finer scale, such as Cowardin subclasses
(Cowardin et al. 1979) or hydrogeomorphic (HGM) classification (Brinson 1993), and led to an aggregation into coarser land use classes. Although annual monitoring of broad
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wetland classes is a good first step, those interested in gain and loss of specific ecological functions require more detailed cover class information.
We were, similarly, not able to include farmed/agricultural wetlands into our study because of limits in spatial and spectral resolution and an inability to differentiate farmed wetlands from agricultural fields with high soil moisture content, which appear spectrally and spatially similar, especially in summer. Additionally, their disturbance regime often entails loss of wetland area every other year through agricultural activities, making it difficult to interpret change in the context of our monitoring objectives. While farmed wetlands may be difficult to classify using 30 m Landsat imagery, these ecosystems could benefit from annual monitoring. The Highly Erodible Land
Conservation and Wetland Conservation Compliance provisions (Swampbuster) were introduced in the 1985 Farm Bill with the purpose of removing specific incentives to produce agricultural commodities on previously converted wetlands. Swampbuster penalizes landowners that choose to convert farmed wetland area to cropland by removing farm program benefits until wetland hydrologic function has been restored
(Blumm and Zaleha 1989). While we were able to make annual observations of more stable wetland (e.g. riparian wetland) converted to agriculture land, we were not able to view annual change of farmed wetlands. Similar to our comparison of pre-1990 policy implementations, future studies could make use of annual data to detect signals in farmed wetland change before and after the 1985 Swambuster provision, and its additional provisions in 1990, 1996, and 2002. This is especially relevant in areas such as the
Willamette Valley where farmed wetlands are vulnerable to permanent loss due to the prioritization of agricultural land for its economic importance.
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Resulting from the imminent need to develop a methodology that utilizes Landsat imagery to monitor annual wetland change, and the accuracy associated with manual classification, we chose to use visual interpretation instead of an automated approach.
However, an automated approach has the potential for increased efficiency, reliability, and repeatability and is necessary if this type of change detection is to be implemented on a larger scale (Hui et al. 2009). The temporal, spectral, and spatial variability of wetlands makes them difficult to detect and classify and it is often challenging to distinguish the boundaries between vegetation habitat types (Schmidt and Skidmore 2003, Zomer et al.
2008, Adam et al. 2010). Ecological variability, combined with the frequent occlusion of wetland vegetation by the reflectance spectra of soil, atmospheric, and hydrologic regimes often associated with these ecosystems, automated classification becomes even more complicated (Guyot 1990, Yuan and Zhang 2006).
The most recent, promising results of automated classification of wetlands has been the inclusion of ancillary data such as soils, topography (with increasing usage of lidar data), and hydrology (Rebelo et al. 2009). Our study also indicates that temporal and spatial contextual information is a necessity to correctly identify wetlands when they are located in a matrix of other, similar, vegetated cover classes. Rule-based classifiers such as decision trees and object oriented analysis have both been successful in accurately mapping wetlands by using ancillary environmental data (Li and Chen 2005, Baker et al.
2006, Wright and Gallant 2007 and others). Object oriented analysis has been primarily used to detect and classify isolated wetlands by utilizing spatial and spectral factors exclusive to wetlands (Frohn et al. 2009, Halabisky 2011), but inclusion of temporal data could help to classify wetlands within the context of a larger matrix of vegetation types
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using rules on persistence to distinguish it from perennially harvested agricultural vegetation cover (Daniels 2006, Schroeder et al. 2011).
In addition to including ancillary environmental data, inclusion of intra-annual
Landsat images could help improve classification of annual wetland change detection.
Wetlands are a land condition, not exclusively cover, and are, therefore, subject to both intra- and inter-annual variations based on hydrology, precipitation, and other climatic factors which could result in false-change detections when using annual imagery
(Schroeder et al. 2011). Zhu et al. 2012 developed the Continuous Monitoring of Forest
Disturbance Algorithm (CMFDA) and subsequently the Continuous Change Detection and Classification (CCDC) algorithm (Zhu and Woodcock 2014) that use all available cloud-free Landsat pixels in for any usable image (even those with high cloud cover) to characterize forest disturbance (CMFDA) and land cover change (CCDC). When utilizing Landsat 5 and 7 (now 7 and 8), the highest temporal resolution is every 8 days when the two sensors are used and every 16 days for dates when the two sensors are not coincident. CMFDA and CCDC algorithms were created for capturing land cover change, but the concept could be applied for classifying temporally variable wetlands. Inclusion of wet season imagery together with growing season imagery could help to both distinguish between wetland classes and wetlands from upland cover classes and help in classifying and monitoring seasonally and annually variable wetlands such as farmed wetlands. Addition of every clear pixel in monitoring change detection could also give insight to the trends in intra-annual ecosystem variability and link them to policy changes or changing climatic factors.
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The launch of Landsat Program’s newest satellite, Landsat 8, in February of 2013 holds additional promise in interpreting more complex and detailed wetland classification using Landsat data. The new satellite’s Operational Land Imager (OLI) sensor is an upgrade in radiometric resolution with 12-bit quantization (4096 potential grey levels) compared to 8-bit (256 grey levels) in previous sensors
(http://landsat.usgs.gov/landsat8.php). Enhanced signal to noise performance will allow improvements in classification of land cover dynamics and should be explored for more detailed mapping of distinct wetland ecosystems.
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Figures and Tables
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Figure 6. Cumulative annual precipitation data for the Willamette Valley from 1972-2012. Red lines highlight years associated with riverine wetland area gained: 1983, 1990, and 1996-1997.
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Figure 7. Number of wetland mitigation permits submitted to the State of Oregon associated with the Main-Stem Willamette
River. Source: Oregon Department of State Lands 2013.
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Figure 8. Boxplot displaying 1 st (25%) and 3 rd
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(75%) quartiles, median, and the minimum and maximum for the size of individual wetland change polygons captured for MSS (1972-1983) and TM (1984-2012) data.
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Conclusions
By evaluating wetland change from 1972-2012 in the Willamette River two-year inundation floodplain using annual Landsat imagery we found that:
• Despite wetland conservation laws dating back to the 1960s, wetlands in our study area experienced annual loss, gain, and type conversion across the entire study period.
• Federal and State laws in 1990 mandating no-net-loss of wetland area helped to slow the rate of annual loss of wetland area as well as create trends in annual gain in area for some wetland types with change in wetland area slowing even more significantly for all wetland types near the end of the study period.
• Higher functioning vegetated wetlands (emergent and riparian) experienced greater loss and less gain than and were replaced in area by non-vegetated
(lacustrine and riverine) both before and after stricter policy regarding wetland conservation.
• Manual interpretation of MSS and TM/ETM+ Landsat Tasseled Cap imagery worked well for capturing annual change in wetland area for broader wetland categories.
•
Little spatial detail was sacrificed by using MSS data and 12 years of important annual data was gained through its use.
•
Temporal context through annual imagery, ancillary data layers, and Google Earth were valuable and necessary for manual interpretation of wetland change in a spectrally and temporally dynamic agricultural setting.
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•
Future studies could utilize high density (annual, seasonal, or every clear pixel)
Landsat imagery for more sophisticated, automated methodologies of wetland change detection.
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