Object-based classification ofsemi-arid wetlands...Object-based classification ofsemi-arid wetlands Meghan Halabisky, a L. Monika Moskal, a and Sonia A. Hall b a University of Washington,

Object-based classificationof semi-arid wetlands

Meghan HalabiskyL. Monika MoskalSonia A. Hall

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Object-based classification of semi-arid wetlands

Meghan Halabisky,a L. Monika Moskal,a and Sonia A. Hallb

a University of Washington, Remote Sensing and Geospatial Analysis Laboratory,School of Forest Resources, Seattle, Washington 98195

[email protected]; [email protected] The Nature Conservancy, 6 Yakima Street, Suite 1A,

Wenatchee, Washington [email protected]

Abstract. Wetlands are valuable ecosystems that benefit society. However, throughout historywetlands have been converted to other land uses. For this reason, timely wetland maps arenecessary for developing strategies to protect wetland habitat. The goal of this research was todevelop a time-efficient, automated, low-cost method to map wetlands in a semi-arid landscapethat could be scaled up for use at a county or state level, and could lay the groundwork forexpanding to forested areas. Therefore, it was critical that the research project contain two com-ponents: accurate automated feature extraction and the use of low-cost imagery. For that reason,we tested the effectiveness of geographic object-based image analysis (GEOBIA) to delineateand classify wetlands using freely available true color aerial photographs provided through theNational Agriculture Inventory Program. The GEOBIA method produced an overall accuracy of89% (khat = 0.81), despite the absence of infrared spectral data. GEOBIA provides the automa-tion that can save significant resources when scaled up while still providing sufficient spatialresolution and accuracy to be useful to state and local resource managers and policymakers.C© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3563569]

Keywords: semi-arid wetlands; geographic object-based image analysis; segmentation;GEOBIA/OBIA; feature extraction; aerial photography.

Paper 10160R received Oct. 19, 2010; revised manuscript received Dec. 22, 2010; accepted forpublication Jan. 19, 2011; published online Mar. 21, 2011.

1 Introduction

Wetlands provide a wide range of critical ecosystem services. This includes: gradual rechargeof groundwater, critical habitat for plants, fish and wildlife, erosion control, water pollutionmitigation, food and recreational bases for people, and healthy water cycles and lake levels.1,2

However, throughout modern history wetlands have been converted to other land uses.3 From1890 to 1980 the United States lost over 50% of the nation’s wetlands.4 Many wetland specieshave declined significantly in numbers and are at risk of extinction.3 Policies have been devel-oped at all levels of government to protect against wetland losses, and government agenciesand nonprofit organizations invest millions of dollars annually to manage and restore wetlandecosystems.

Comprehensive, up-to-date wetland maps are a necessary first step to monitor wetlandpolicy, develop strategies to conserve wetland habitat and biodiversity, and to prioritize areasfor conservation. However, many regions throughout the United States lack adequate wetlandmaps to effectively assess current wetland habitat conditions. Remote sensing has provided auseful tool for identifying and mapping wetlands. However, in order for remote sensing to be apractical tool it must occur at a scale that is useful for decision makers. The goal of this researchwas to develop a time-efficient, low-cost method to map wetlands at a high spatial resolution

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Journal of Applied Remote Sensing 053511-1 Vol. 5, 2011


Halabisky, Moskal, and Hall: Object-based classification of semi-arid wetlands

that could be scaled up for use at a county or state level. Therefore, it was critical that theresearch project contain two components: automated feature extraction and the use of low-cost,high resolution imagery.

1.1 Manual Image Interpretation

Beginning as early as the 1930s, manual interpretation of aerial photography was used to maplandscape features such as wetlands, and is still preferred for detailed mapping of wetlands.5–9

Manual photo interpretation produces high quality maps, however, it is a time consumingprocess and is limited to visible distinctions between objects. The investment necessary formanual analysis is often prohibitive for many state and local agencies interested in creating awetland inventory or for nonprofit organizations interested in creating baseline maps for usein conserving wetland habitat and biodiversity. Consequently, revisions and updates to manualanalysis are only undertaken at very infrequent intervals, making the timeliness of the dataunsuitable for monitoring and decision making processes.

1.1.1 NWI wetland maps

The most readily available wetland maps in the United States are generated by the NationalWetlands Inventory (NWI) program, administered by the U.S. Fish and Wildlife Service. Cur-rently, the NWI database is available for 61% of the country.8 The NWI database is createdthrough manual photo interpretation with a most common minimum mapping unit set at 0.2hectares.8 Although the NWI database is freely available to the public it is often inadequatefor many users as most regions throughout the United States are now more than 20 yearsout-of-date. Although rates of wetland loss have declined since the 1980s, wetland loss is stillsignificant with an estimated 58500 acres of wetlands loss per year in the conterminous UnitedStates between 1986 and 1997.10 Likely due to the lower quality imagery and methods available20 years ago, many of the mapped areas have low accuracy, with an error bias toward errorsof omission.11,12 Aerial imagery as well as photo interpretation methods, such as the use ofbetter ancillary data, has greatly improved since the inception of the NWI program. Despitethese improvements manual photo interpretation is still very costly to update for large spatialextents. Furthermore, funding restrictions have significantly reduced the NWI program and theNWI database is updated at a rate of only 1% to 2% per year.8 Therefore, the responsibility ofupdating wetland maps has shifted to state, county, and local agencies.

1.2 Pixel-Based Remote Sensing Methods

Because manual photo interpretation is cost prohibitive for many agencies and nonprofits toundertake, automated image segmentation and object delineation is an attractive option. Satelliteremote sensing is commonly used to detect and monitor wetlands6,9 as the imagery typicallycovers large spatial extents, offers repeat coverage, and generally provides a higher spectralresolution than aerial photos.

Traditionally, wetland remote sensing has used a pixel-based classification approach. Thisapproach uses the spectral signatures of homogeneous classes of interest to classify each pixelwithin an image scene.13 It relies heavily on spectral information and therefore requires imagerywith spectral bands that extend beyond the visible spectrum. NASA Landsat satellite imageryis the most commonly used multispectral imagery for remote sensing of wetlands.8 However,the 30-m pixel resolution of Landsat 7 and prior satellites limits the ability to delineate smallwetlands. Initially, satellite remote sensing was examined as a possible approach for creatingthe NWI, but was rejected as it could not provide the spatial resolution necessary to monitortrends.5 In many regions of the world small wetlands compose much of the landscape andcannot be ignored if complete coverage is desirable. For example, in the prairie pothole region




of North Dakota between 73% and 88% of wetlands are less than 0.4 hectares.14 Inventorying andmonitoring small wetlands is important as small wetlands are considered valuable ecosystemsfor maintaining biodiversity,15 and are particularly vulnerable to climate change16 and land useconversion. On the other end of the cost spectrum, there are high spatial resolution satelliteimages, such as IKONOS and Quickbird, that are available, but high costs often limits their use.Regardless, a per pixel approach is not the most appropriate approach for hyperspatial imageanalysis where additional object features, such as the oval or rounded shapes of wetlands, andthe landscape context of these features cannot be used for classifying objects.17

1.3 Geographic Object-Based Image Analysis

With geographic object-based image analysis (GEOBIA) it is possible to combine the analyst’spersonal image interpretation skills with the power of computer processing. GEOBIA differsfrom the pixel-based approach by first grouping similar pixels into segments which can then beclassified using features such as color (spectral properties), texture, shape, and context, whichare built into a classification algorithm (Fig. 1).

Like with other automated classification methods, once a classification algorithm is devel-oped and tested, the GEOBIA method can increase efficiency through automation and batchprocessing. The comprehensive review by Blaschke demonstrates that GEOBIA improves theaccuracy classification of hyperspatial imagery.18 Frohn et al. successfully used GEOBIA to

Fig. 1 Example of GEOBIA process: (a) Oblique photo of wetland; (b) aerial photo of wetland;(c) Segmentation; (d) classification.




map wetlands using Landsat satellite imagery.12 They were able to detect wetlands as small as0.02 hectares. However, it is important to note that there is a difference between detection anddelineation of wetlands. Landsat satellites have a pixel resolution of 30 m; therefore for smallwetlands the shape of the pixel influences the shape of the wetland polygon. A wetland thesize of 0.02 hectares is only comprised of approximately two Landsat pixels. For this reason,wetland maps derived from Landsat satellite imagery are not appropriate for most natural re-source managers. Although imagery containing infrared spectral information is preferable formost vegetation mapping, it is not always freely available and is not as widespread as true color(RGB) imagery. We are only aware of one other study that has tested the GEOBIA methodusing true color (RGB) imagery to detect and classify objects.19

2 Methods

2.1 Study Area

Our study area covers 50000 acres in northern Douglas County, in central Washington State(Fig. 2). The study area is part of the Columbia Plateau ecoregion and is a semi-arid environmentwith an average of 11.41 in. of precipitation annually.20 Precipitation occurs primarily in thewintertime in the form of snowfall. The wetlands in the Columbia Plateau ecoregion are dynamicsystems where evaporation exceeds precipitation in the spring and summer months20 and thewater levels in most wetlands decline, with many completely drying out by the end of thesummer.

The Columbia Plateau consists of shrub steppe habitat which contains many rare and endemicplant and animal species. Because the Columbia Plateau is a semi-arid environment, waterresources, mostly in the form of wetlands, are critical resources for all species. The wetlands ofthe Columbia Plateau face direct threats from land conversion from practices such as drainingand filling as well as indirect threats through habitat degradation from surrounding land practices,primarily in the form of overgrazing and agricultural runoff, as well as climate change. Wetlandsin this area are protected under the Washington State Water Pollution Control Act and throughvarious incentive programs, such as the Wetland Conservation Provisions of the U.S. Farm Bill(also known as “Swampbusters”).

Fig. 2 Study area location: Douglas County, Washington State.




2.2 Data

We used georeferenced, aerial photography supplied by the U.S. Department of Agriculture’sNational Agriculture Imagery Program (NAIP). NAIP imagery is flown primarily to support theFarm Services Agency programs and is often available to other users for no or low cost. NAIPis flown every 1 to 4 years, which is useful for annual change analysis, but cannot be used forseasonal change analysis. The aerial photography was flown in early summer of 2006 and wasused in an uncompressed format as a series of 32 image scenes. The 2006 NAIP imagery has apixel resolution of 1 m and the coordinate system is defined as NAD 1983, Washington StatePlane South.

We collected training data in the field in November 2008 to aid in the classification processby randomly selecting four parcels owned by the Washington State Department of NaturalResources (DNR) within the study area for comprehensive survey. By using line of sighttransects we were able to locate, identify, and GPS all wetlands contained within each sectionfor a total of 66 samples. We used a Trimble Geo XT unit to collect GPS points with a datadictionary containing all field notes and class descriptions.

2.3 Definitions: Target Wetland Types

We were interested in identifying and mapping three general types of wetlands: open waterwetlands, emergent wetlands, and dry beds (Fig. 3). Open water wetlands contain visible waterat the time the photos were taken, emergent wetlands contain wetland vegetation, and dry bedsare dried out ponds with a mud, sandy, or salt-crusted bottom. The unclassified matrix consistsof agricultural fields, roads, rocky outcrops, and upland shrub/scrub vegetation.

We based our definition of wetlands on the Cowardin classification system used for theNational Wetland Inventory.21 Under the Cowardin definition, for an ecosystem to be considereda wetland, one of three factors needs to be present: hydric soils, wetland vegetation, andappropriate hydrology.21 We did not attempt to classify jurisdictional wetlands as regulated bythe Clean Water Act and defined by the U.S. Army Corps of Engineers.

2.4 Classification

We used Definiens (eCognition) Developer 7.0, a GEOBIA software program to identifyand map the three types of semi-arid wetlands defined above. The process is iterative, with

Fig. 3 Wetland classes: (a) open water wetland; (b) emergent wetland; (c) dry bed.




Fig. 4 Hierarchical decision tree approach with two segmentations at different scales to capturevariability of object size used for final classification. The numbers refer to features used forclassification identified in Table 1.

classification and segmentation following each other, until the final methodology is selected.We describe the iterative steps below.

Before developing a classification algorithm we drafted a hierarchical decision tree. Thedecision tree was an iterative process and was modified as features were tested for effectiveness.The features were tested in the chronological order provided by the feature descriptor list inDefinines, starting with color/spectral characteristics, shape second, and texture third. Uncom-pressed high resolution images have a large file size; therefore, running various componentsof the classification algorithm can take significant computer processing time. Certain features,such as object texture, can be computationally intensive. Therefore, we focused on using simplefeatures first to mask out dry areas such as agricultural fields and upland, and then ran morecomplex features only on the remaining areas of interest. Figure 4 displays the final approachwe used to create the algorithm structure used for classification.

The first step in GEOBIA is to group similar pixels into objects or segments. DefiniensDeveloper 7.0 uses a proprietary algorithm to run segmentation based on the approach introducedby Baatz and Schape.22 Users can weigh certain factors such as shape and compactness andchoose the appropriate scale to segment the image. Increasing the shape factor de-emphasizesuse of color in segmentation, while increasing the compactness factor creates more compactsegments. We tested different values from 0.0 to 1.0 for each factor beginning with the defaultshape (0.01) and compactness (0.5) parameters and determined from visual assessment that itdid not improve segmentation results; this was an iterative process. Therefore, the default shapeand compactness parameters were used. Adjusting the scale parameter, however, greatly affectsthe segmentation results. The scale parameter determines the size of the segmented objects. Theoptimal scale parameter depends on the size of the objects of interest and the resolution of theimagery. A large scale parameter (e.g., 500) groups pixels into large objects while a small scaleparameter (e.g., 50) groups pixels into smaller objects. Different scale parameters are usefulto segment different objects of interest. Wetlands vary significantly in size. For this reason wechose to use a multiresolution segmentation with an initial scale parameter of 600 to delineatelarge objects, and then a second scale parameter of 100, run only on objects which had a lowbrightness value, suggesting that the object may contain emergent or open water wetland. Eachof these scale parameters were chosen through an iterative process with visual assessment ofthe results. Segmenting the image at two scales allowed us to effectively capture the variabilityof different objects.




After pixels were segmented into objects, we tested features to determine which featuresbest separated objects into distinct classes, defined using the training data collected in thefield. Because there are hundreds of features to choose from and the testing of features takessignificant processing time, we examined features in three groups: color, shape, and texture.Next, we compared the most significant features from each group to each other. Adding a newfeature can improve results, but it can also add confusion to the classification. Therefore, werepeated the process multiple times to determine which combination of features produces thebest results. An important thing to note is that this process only tests the sample training data ofone location as it can only be tested on one image scene at one time. Building a classificationalgorithm that is too complex with narrow definitions risks over-fitting the data to one particularimage scene. We strove for a parsimonious solution. For example, rather than setting narrowthreshold values for each feature we built-in hierarchically embedded definitions that allowedus to capture the range of gray tones in the mosaiced NAIP imagery. Thus, this approach makesthe algorithm transferable to other NAIP imagery within the same ecoregion.

After we identified important features, we set threshold values for each of these featuresthat best separated out classes from one another. These were chosen based on sample editingtools in Definiens. These features were built into the classification algorithm which was thentested on image scenes representing the diversity of objects found within the study area. Afterwe completed testing we ran the finalized classification algorithm on all of the image scenes.The final classification algorithm (Fig. 4) uses a hierarchical approach to classify image objects,first separating out “wet areas” from “dry areas.”

After initial segmentation we classified objects into the following categories: temp–dryareas, shadows and trees, large open water wetlands, and temp–wet areas. We further classifiedtemp–dry areas into agriculture, dry beds, and roads.

Next, we ran a second segmentation on the unclassified wet areas to segment them intosmaller objects. We classified the open water and emergent wetlands within these wet areas.Next, we added rules to clean up the classes of interest; open water, emergent wetlands, drybeds, and upland that included misclassified objects. We used a variety of color, shape andtexture features to separate out classes (Table 1). The homogeneity texture was applied due toits simplicity; the texture was not derived from the imagery directly but from the image segmentsinstead. It has been demonstrated that image-based texture can be useful in the classification ofhyperspatial resolution imagery.23 We did not implement this approach because it would haverequired producing image-based texture layers outside of the Definiens software. Althoughspectral features of objects were the most commonly used features, additional features such assize, shape, and landscape context provided refinement of class separation. Finally, we manuallycleaned up the classification for each image scene removing obvious errors. Manual correctionswere performed for only large misclassified features, such as the delineation of the ColumbiaRiver in the Northwest section of the study area. This issue could have been addressed through arefinement of the algorithm, for example, by using a drainage map for the study region, however,our approach was more time efficient in this case. We exported only the classes of interest to anESRI ArcGIS geodatabase.

2.5 Accuracy Assessment

The objective of this accuracy assessment was to identify how well the classification comparesto manual photo interpretation by comparing our classified data to reference data classified by atrained photo interpreter. We used traditional remote sensing classification methods as describedby Congalton and Green.24 Open water wetlands are easy to classify, yet make up only 19% ofthe wetlands within our study area. To avoid oversampling open water wetlands and inflatingthe accuracy assessment we chose to use a stratified random sampling approach. We randomlygenerated 150 reference points within the wetland classification and weighted these samplepoints by area for the three classes: open water wetlands, emergent wetlands, and dry beds.




Table 1 Features of objects used for separating out classes (in order ofimportance).

1. Brightness2. Red ratio (ratio of mean red reflectance to total mean color reflectance)3. Green ratio (ratio of mean green reflectance to total mean color reflectance)4. Green mean reflectance5. Red mean reflectance6. Maximum difference of all colors7. Area8. Perimeter to area ratio9. Length of object border

10. Length of shared border with adjacent class11. Proximity (distance) to other classes

There are very few dry beds within the study area limiting the number of sample points for drybeds. This resulted in 29 open water points, 116 emergent wetland points, and 5 dry bed points.An additional 50 points were randomly distributed throughout the unclassified area. Next, thepoints were visually assessed by a trained photo interpreter. We compared the classified pointsto the assessed reference points to form the accuracy assessment.

2.6 Error Attribution

As stated above, open water wetlands are easy to assess using high resolution aerial photography,while emergent wetlands and upland areas are more difficult to separate. Therefore, additionalgroundtruthed data was collected in March 2009 to supplement the accuracy assessment foremergent wetlands and determine the sources of error. Because groundtruthing occurred beforethe completion of a revised classification we used a preliminary classification to generate arandom sample of 122 points on 25 Washington State DNR parcels, which were accessible tous. We randomly sorted the parcels and visited them in the order they were listed. We usedresource grade GPS units (Garmin eTrex and Trimble Geo XT/XR) to locate all of the pointswithin the parcels we visited and classified each point. In traveling to the reference wetlandpoint we mapped and classified any wetlands that we encountered. The reasoning behind this isto identify any errors of omission. Of the 122 points randomly selected 36 were assessed on theground and 33 additional reference points were collected. A total of 69 reference points werecollected and groundtruthed to supplement the visual assessment using aerial photography.

3 Results and Discussion

Overall, the GEOBIA method produced useful results using freely available true color NAIPaerial photography. The overall accuracy for the wetland classification using the GEOBIAmethod was 89% (khat = 0.81; Table 2). Dry beds were easily classified and had a 100% user’sand producer’s accuracy. Open water wetlands had a user’s accuracy of 75.9% and a producer’saccuracy of 100%. The classification algorithm overclassified open water wetlands with sevenemergent wetlands misclassified as open water. All of the points that showed error occurred inwetlands less than 0.2 hectares with the majority of the wetlands less than 0.1 hectares.

Emergent wetlands had a user’s accuracy of 88.8% and a producer’s accuracy of 92.0%. Allof the error for emergent wetlands occurred in polygons smaller than 0.4 hectares, with most erroroccurring in polygons smaller than 0.08 hectares. Seven points that should have been classified asupland were misclassified as emergent wetlands. This error was again due to the issue of wetlandsize. For example, if a segmented polygon included a rock, tree canopy, or other dark objectsit would affect the overall color of the polygon and cause misclassification. Also, underlyingsoil color affected the overall color of the segmented polygon, causing misclassification of verysmall wetlands.




Table 2 Accuracy assessment.

Reference Data

Classification Open User’sdata water Emergent Dry bed Upland Total accuracy

Open Water 22 7 29 75.86%Emergent 103 13 116 88.79%Dry bed 5 5 100.00%Upland 2 48 50 96.00%Total 22 112 5 61 200Producer’s Accuracy 100.00% 91.96% 100.00% 78.69%

Overall accuracy 89.00%Khat 0.81

The additional groundtruth data collected for error attribution (Table 3) helped us to deter-mine additional sources of error in the classification of emergent wetlands. The groundtrutheddata is useful because it identifies types of error that cannot be detected by visual assessmentof aerial photographs. We created an accuracy assessment of the groundtruthed data; however,it is error-biased and should not be used to replace the official accuracy assessment of theclassification.

Further examination of the erroneous points provided useful information regarding the typesof error influencing the classification. The misclassification error occurred in three distinctinstances: 1. error related to size of segmented polygon; 2. error related to disturbance causedby farming; 3. error related to spectral confusion.

1. Error related to size of segmented polygon: Examination of the error of omission showsthat all of the misclassified wetlands were very small in size (∼ 0.05 hectares) and were juston the borderline for hydrological, vegetation, and soil indicators. We visually assessed eacherror of omission and could not identify these wetlands using the NAIP 2006 aerial photography.Wetlands are difficult to delineate on the ground using the Cowardin definition.21 This is becausethe three wetland indicators (hydrology, vegetation, and soils) do not have clear boundaries, butrather fall along a gradient. There is not always agreement among wetland ecologists, even onsite, in a wetland determination, demonstrating how difficult it is to classify an ecosystem thatdoes not fit a narrow definition, but rather falls along a gradient from wet to dry.

2. Error related to disturbance caused by farming: Although the agricultural fields appeardry in the 2006 NAIP photograph there are certain green spots intermittently dispersed withinthe agricultural fields that were classified as wetlands. Groundtruthing of these areas provedthat these wetlands do not actually fit the Cowardin definition of wetlands.21 Potential causes

Table 3 Error attribution table.

Reference Data

Classification Emergent User’sdata wetland Other Total accuracy

Emergent Wetland 28 7 35 80.00%Other 7 27 34 79.41%Total 35 34 69Producer’s accuracy 80.00% 79.40%

Overall accuracy 79.74%Khat 0.59




Fig. 5 Comparison of classification to USFWS NWI.

for this misclassification error could be that continuous plowing of the field disrupts soil andvegetation indicators and disturbs hydrology and/or that agricultural fields create spots of greenvegetation that mimic the look of wetland vegetation. However, upon further examination ofhistoric aerial photos many of these green areas were once flooded or are surrounded by areas ofdense wetlands, suggesting that these areas may have been flooded at one time and were eitherdrained or plowed under.

3. Error related to spectral confusion: Many wetlands that were correctly classified containeda band around the wetland that was not mapped. This part of the wetland was dominated bysalt grass (Distichlis spicata), which has a similar spectral reflectance to upland vegetation. Theamount of spectral confusion depends heavily on the date of the imagery. The imagery used forthis research project was flown early in the summer when the wetlands still contained water andactively growing wetland vegetation. Imagery flown later in the season would likely increaseerror of omission caused by spectral confusion from dried up wetlands.

The misclassification issues related to wetland size could be fixed by setting a minimummapping unit at 0.08 hectares, further manual clean-up focused on small wetlands, or appendingthe algorithm to include rules focused only on small wetlands. A minimum mapping unit of0.08 hectares is well below the Federal Geographic Data Committee (FGDC) requirement of0.2 hectares (FGDC, 2009). The addition of infrared imagery could also remove errors causedby dark non-photosynthesizing objects such as rocks.

We compared our results to the U.S. Fish and Wildlife’s (USFWS) NWI. In Douglas County,NWI wetlands were manually delineated and classified in 1982 using lower resolution black andwhite aerial photography and only received minimal groundtruthing. They are the best existingwetland classification for Douglas County. The results of this project provide an improved




dataset identifying significantly more wetlands, especially emergent wetlands, than the NWIwetland dataset (Fig. 5). With a minimum mapping unit set at 0.02 hectares our classificationcontains a total of 2778 wetlands comprising 807.87 hectares. In comparison, the USFWSidentified a total of 671 wetlands within the pilot study area comprised of 400.57 hectares. Thisdifference is unlikely to be due to formation of new wetlands as precipitation has been wellbelow average within the last decade and there is very little irrigation in Douglas County beyondthe margins of the Columbia River that could provide input to create new wetlands.

The strength of the GEOBIA method is the ability to combine the skill of a photo interpreterwith the power of computer processing. Although software programs such as Definiens offeruseful tools to assess features it is also necessary that the analyst identify the features thathumans naturally use to recognize objects. For example, to determine which features can beused to detect open water wetlands from emergent wetlands a person may first recognize thedistinct dark color of an open water wetland, next the typically round or elliptical shape of thepool, and then finally the smooth texture of the water. Sometimes the results from the softwaretools disagree with this logic and can mislead the analyst into overlooking critical features. Forthe most part the tools available in the software package are useful, but only if the analyst’sknowledge and experience is kept in the forefront.

4 Conclusions

Using the GEOBIA method with freely available true color aerial imagery, policy and naturalresource decision makers will be able to economically monitor the size, location, and type ofwetlands on a regular basis to aid in planning and policy decisions that can protect wetlandsfrom further deterioration.

4.1 Future Research

This method can be adapted to historical imagery to track annual and decadal changes towetlands over time. While it may be more difficult to detect emergent wetlands using black andwhite imagery, open water wetlands could be easily detected. Although aerial photography doesnot contain the temporal resolution to track seasonal changes it can be combined with lowerspatial resolution imagery such as Landsat satellite data to monitor changes in hydrology andvegetation. This can be done by examining the pixels contained within the delineated wetlandfor changes to the spectral signature. In addition, this method can be translated to other semi-aridregions of the world where aerial or high spatial resolution satellite imagery is available to mapand monitor wetlands.

The GEOBIA is simplified in arid systems where wetlands are more easily distinguishablefrom upland vegetation and are not heavily forested. Use of GEOBIA to map wetlands in forestedareas would be a much larger challenge, however, the addition of high density aerial LIDARto the GEOBIA method would likely mitigate this issue. LIDAR used in conjunction with highresolution aerial imagery has been used to improve wetland identification and mapping usingmanual photo interpretation.25 This suggests that the GEOBIA method could successfully mapand classify wetlands in forested areas while providing automation necessary for large spatialextents.

Large spatial extents would best be automated through batch processing; this is an additionaloption in the Definiens suite. The cost of GEOBIA algorithm development is fixed for smallspatial extents, but can be reduced by the automation process on larger spatial extents. Thealgorithm presented here will perform best on semi-arid wetlands. Automation beyond this typeof Ecoregion with the same data sources would require additional development. Furthermore,forested wetlands would not benefit from the method we have outlined in this research unless anadditional data source such as LiDAR, or multispectral imagery are implemented. Employing anew data source would require an additional revision of the feature set and a segmentation thatis scaled to the new data.




Acknowledgments

The authors would like to thank the field assistance and training from Charlie Repath, projectassistance and manual photo interpretation conducted by Chris Vondrasek, and additionalgroundtruthing from Joe Rocchio and Rex Crawford at the Washington Department of Nat-ural Resources’ Natural Heritage Program. Funding and resources for this research project camefrom The Nature Conservancy’s Priscilla Bullitt Collins Trust Northwest Conservation Fund,and from the University of Washington, School of Forest Resources, Precision Forestry Co-operative. This research was conducted at the University of Washington Remote Sensing andGeospatial Analysis Laboratory. We thank the anonymous reviewers whose comments helpedto improve this manuscript.

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Meghan Halabisky is a concurrent Master’s student at the University ofWashington where she is pursuing a MS at the School of Forest Resourcesand a MPA at the Daniel J. Evans School of Public Affairs. She is focusedon bridging the gap between policy, management, and science throughthe development of affordable environmental monitoring tools for naturalresource managers and policymakers. Her research interests include devel-opment of new remote sensing techniques for spatiotemporal analysis ofecosystem dynamics, landscape change, and climate change impacts.

L. Monika Moskal received her PhD in remote sensing and GIS from theUniversity of Kansas where she also worked for the Kansas Applied Re-mote Sensing Program (KARS). She is the principal investigator at the UWRemote Sensing and Geospatial Analysis Laboratory (RSGAL). RSGAL’sresearch goal is to understand multiscale dynamics of landscape changethrough the application of remote sensing, GIS, and geospatial tools. Thelab develops tools necessary to analyze hyper-resolution remotely senseddata by exploiting spatial temporal, and spectral capabilities of the data.RSGAL work focuses on the application of high spatial resolution remote

sensing (passive and active) to investigate landscape and vegetation structure. Her researchhas been applied to the following themes: ecosystem services and function, bioenergy/biomass,forest inventories, forest health, change analysis, biodiversity, habitat mapping, spatiotemporalwetland assessment, geostatistical analysis of prairie vegetation communities, urban growth,and forest fragmentation.

Sonia A. Hall is the arid lands ecologist with The Nature Conservancy inWashington State. She received her BS in agronomy from the University ofBuenos Aires (Buenos Aires, Argentina) in 2000, and her PhD in ecologyfrom Colorado State University (Fort Collins, Colorado) in 2005. Herresearch interests focus on methods, techniques, and information that areneeded to inform management and policy decisions that influence theconservation and restoration of arid and semi-arid landscapes, includingshrub steppe, grasslands, and associated freshwater systems.



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Object-based classification ofsemi-arid wetlands...Object-based classification ofsemi-arid wetlands Meghan Halabisky, a L. Monika Moskal, a and Sonia A. Hall b a University of Washington,