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Meyer, Cynthia A.
Application of remote sensing methods to assess the spatial extent of the seagrass resource in St. Joseph Sound and Clearwater Harbor, Florida, U.S.A.
h [electronic resource] /
by Cynthia A. Meyer.
[Tampa, Fla] :
b University of South Florida,
Title from PDF of title page.
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Thesis (M.A.)--University of South Florida, 2008.
Includes bibliographical references.
Text (Electronic thesis) in PDF format.
ABSTRACT: In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery, 16-20 days, provides a suitable option to detect and assess damage to the seagrass resource. In this study, remote sensing Landsat 5 TM imagery is used to map the spatial extent of the seagrass resource. Various classification techniques are applied to delineate the seagrass beds in Clearwater Harbor and St. Joseph Sound, FL. This study aims to determine the most appropriate seagrass habitat mapping technique by evaluating the accuracy and validity of the resultant classification maps. Field survey data and high resolution aerial photography are available to use as ground truth information.Seagrass habitat in the study area consists of seagrass species and rhizophytic algae; thus, the species assemblage is categorized as submerged aquatic vegetation (SAV). Two supervised classification techniques, Maximum Likelihood and Mahalanobis Distance, are applied to extract the thematic features from the Landsat imagery. The Mahalanobis Distance classification (MDC) method achieves the highest overall accuracy (86%) and validation accuracy (68%) for the delineation of the presence/absence of SAV. The Maximum Likelihood classification (MLC) method achieves the highest overall accuracy (74%) and validation accuracy (70%) for the delineation of the estimated coverage of SAV for the classes of continuous and patchy seagrass habitat. The soft classification techniques, linear spectral unmixing (LSU) and artificial neural network (ANN), did not produce reasonable results for this particular study.The comparison of the MDC and MLC to the current Seagrass Aerial Photointerpretation (AP) project indicates that the classification of SAV from Landsat 5 TM imagery provides a map product with similar accuracy to the AP maps. These results support the application of remote sensing thematic feature extraction methods to analyze the spatial extent of the seagrass resource. While the remote sensing thematic feature extraction methods from Landsat 5 TM imagery are deemed adequate, the use of hyperspectral imagery and better spectral libraries may improve the identification and mapping accuracy of the seagrass resource.
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Advisor: Ruiliang Pu, Ph.D.
t USF Electronic Theses and Dissertations.
Application of Remote Sensing Methods to Assess the Spatial Extent of the Seagrass Resource in St. Joseph Sound an d Clearwater Harbor, Florida, U.S.A. by Cynthia A. Meyer A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts Department of Geography College of Arts and Sciences University of South Florida Major Professor: Ruiliang Pu, Ph.D. Susan S. Bell, Ph.D. Steven Reader, Ph.D. Date of Approval: November 5, 2008 Keywords: Landsat TM, Maximum Likelih ood, Mahalanobis Distance, GIS, Gulf of Mexico Copyright 2008, Cynthia A. Meyer
Dedication This thesis is dedicated to my pack. "So long, and thanks for all the fish." Hitchhikers Guide to the Galaxy
Acknowledgements Pinellas County Waters hed Management Division Kris Kaufman, Southwest Flor ida Water Management District Dr. Bob Muller & Dr. Behzad Mahmoudi, Florida Marine Research Institute Dr. Bell & Dr. Reader, University of South Florida Dr. Ruiliang Pu, University of South Florida,
i Table of Contents List of Tables iii List of Figures iv Abstract vi Chapter One: Introduction 1 1.1 Background 1 1.2 Goal 1 1.3 Objectives 2 1.4 Description of Study Area 3 Chapter Two: Literature Review 7 2.1 Seagrass 7 2.1.1 Seagrass Resource Ecol ogy 7 2.1.2 Seagrass Assessment Met hods 8 2.2 Remote Sensing Applications 9 2.2.1 Landsat Imagery 11 2.2.2 Aerial Photography 13 2.3 Remote Sensing Classificat ion 13 2.3.1 Imagery Classification 13 2.3.2 Hard Classification Methods 14 2.3.3 Soft Classification Methods 15 Chapter Three: Methodology 16 3.1 Methodology Overview 16 3.2 Data Sources 16 3.2.1 Remote Sensing Data Sources 16 188.8.131.52 Aerial Photoi nterpretation SAV Mappi ng 16 184.108.40.206 Satellite Imagery 19 3.2.2 Field Survey Data 21 220.127.116.11 Seagrass Monitoring Data 21 18.104.22.168 Water Quality Monitori ng Data 23 3.3 Landsat 5 TM Imagery Analysis 25 3.3.1 Imagery Preprocessing 25 3.3.2 Imagery Classification 27 3.3.3 Classification Accuracy A nalysis 33
ii 3.4 Analyses 33 3.4.1 Comparison to Existing Maps 33 3.4.2 Ability to Map SAV Variat ion 34 Chapter Four: Results and Discussion 35 4.1 Classification Results 35 4.1.1 Unsupervised Classificati on 35 4.1.2 Supervised Classificatio n 37 22.214.171.124 Hard Classification 37 126.96.36.199.1 Presence/Absence of SAV 37 188.8.131.52.2 Estimated Coverage of SAV 41 184.108.40.206 Soft Classification 46 220.127.116.11.1 Artificial Neural Networks 46 18.104.22.168.2 Linear Spectral Un mixing 48 4.2 Assessment of Classification Methods 50 4.2.1 Accuracy Comparison of SAV M aps 50 4.2.3 Spatial Comparison of SAV M aps 53 4.2.4 Ability to Map SAV Variation 58 Chapter Five: Conclusions 60 References Cited 62
iii List of Tables Table 1: Landsat 5 TM band descriptions. 12 Table 2: Landsat 5 TM image details. 19 Table 3: Radiometric resolution descr iptive statistics calculated for the ROI training data. 30 Table 4: Accuracy estimates for the s upervised classification methods. 39 Table 5: Supervised classifica tion commission and omission errors, and producer and userÂ’s accuracy. 39 Table 6: Validation for classification methods SAV presence/absence. 39 Table 7: Accuracy estimates for the s upervised classification methods. 43 Table 8: Supervised Classificati on commission and omission errors, and producer and userÂ’s accuracy. 43 Table 9: Validation for classification methods SAV estimated coverage 43 Table 10: Comparison of validation for classification methods 52 Table 11: Area calculated for each classification method. 54 Table 12: Aerial Photointerpret ation versus Mahalanobis Distance Classification. 56 Table 13: Aerial Photointerpret ation versus Maximum Likelihood Classification. 57 Table 14: Potential variation associ ated with the estimated accuracies for the classification methods. 58
iv List of Figures Figure 1: Location of the study site. 3 Figure 2: Study area includes St. Jo seph Sound and Clearwater Harbor. 4 Figure 3: Seagrass species f ound in the study area. 5 Figure 4: Rhizophytic algae and Bay Sc allops found in the study area. 5 Figure 5: Aerial photointerpret ation SAV map based on 2006 aerial imagery. 18 Figure 6: Landsat 5 TM satellite image from May 2, 2006. 20 Figure 4: Pinellas County seagrass monitoring program results for Clearwater Harbor and St. Joseph Sound. 22 Figure 8: Observed SAV at the Pinel las County Ambient Water Quality sampling sites for 2005 2007. 24 Figure 9: Landsat 5 TM imagery clipp ed to the study area from 2 May 2006. 26 Figure 10: Mask delineated from band 4 (near infrared). 27 Figure 11: Landsat 5 TM image enhancement using Equalization function. 28 Figure 12: Histograms of t he radiometric resolution of the ROI classes: No SAV, Patchy SAV and Continuous SAV for TM 1 (A), TM 2 (B), and TM 3 (C). 31 Figure 13: Unsupervised ISODATA cl assification of Landsat 5 TM image with environmentally relevant labels. 36 Figure 14: Supervised classificati on of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods. 38
v Figure 15: Differences (red circle) bet ween the supervised classifications of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods. 40 Figure 16: Supervised classificati on of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods. 42 Figure 17: Differences (red circle) bet ween the supervised classifications of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods. 44 Figure 18: Differences (red re ctangle) between the supervised classifications of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods. 45 Figure 19: Artificial neural network cl assification of Landsat 5 TM image. 47 Figure 20: Linear spectral unmix ing of Landsat 5 TM image. 49 Figure 21: Comparison of vali dation data to the SAV Aerial Photointerpretation Map, 2006. 50 Figure 22: Estimated classification accuracies derived from validation analysis for different classification methods. 53 Figure 23: Discrepancies between the AP and MDC for the presence/absence of SAV. 56 Figure 24: Discrepancies between t he AP and MLC for the estimated coverage of SAV. 57
vi Application of Remote Sensing Methods to Assess the Spatial Extent of the Seagrass Resource in St. Joseph Sound an d Clearwater Harbor, Florida, U.S.A. Cynthia A. Meyer ABSTRACT In the event of a natural or ant hropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery, 16-20 days, provides a suitable option to detect and assess damage to the seagrass resource. In this study, remote sensing Landsat 5 TM imagery is used to map the spatial extent of the seagrass resource. Various classificati on techniques are applied to delineate the seagrass beds in Clearwater Harbor and St. Joseph Sound, FL. This study aims to determine the most appropriate seagrass habitat mapping technique by evaluating the accuracy and validity of the resultant classification maps. Field survey data and high resolution aerial photography are available to use as ground truth information. Seagrass habitat in the study area consists of seagrass species and rhizophytic algae; thus, the species assemblage is categorized as submerged aquatic vegetation (SAV). Two supervised classification techniques, Maximum Likelihood and Mahalanobis Distance, are applied to extr act the thematic f eatures from the Landsat imagery. The Mahalanobis Dist ance classification (MDC) method achieves the highest overall accuracy (86%) and validation accuracy (68%) for the delineation of the presence/absenc e of SAV. The Maximum Likelihood classification (MLC) method achieves the highest overall accuracy (74%) and validation accuracy (70%) for the delinea tion of the estimated coverage of SAV
vii for the classes of continuous and patchy s eagrass habitat. The soft classification techniques, linear spectral unmixing (LSU ) and artificial neural network (ANN), did not produce reasonable results for this particular study. The comparison of the MDC and MLC to the current Seagrass Aerial Photointerpretation (AP) pr oject indicates that the classification of SAV from Landsat 5 TM imagery provides a map pr oduct with similar accuracy to the AP maps. These results support the applicatio n of remote sensing thematic feature extraction methods to analyze the spatial extent of the seagrass resource. While the remote sensing thematic feature extraction methods from Landsat 5 TM imagery are deemed adequate, the use of hyperspectral imagery and better spectral libraries may improve the i dentification and mappi ng accuracy of the seagrass resource.
1 Chapter 1 Introduction 1.1 Background As essential nearshore aquatic habitat of the Gulf of Mexico, St. Joseph Sound and Clearwater Harbor require the development and implementation of management plans to protect and sustain the ecosystem. The environmental resources include an extensive seagr ass resource, macroalgae habitat, mangroves, and tidal flats. Understanding t he spatial and temporal scales of the physical substrate is crucial to the asse ssment of the ecosystem resource status, structures and functions. The applicat ion of remote sensing methods may enhance the results from the current fi eld survey monitoring programs and the comprehensive management strategy fo r the resource. The sustainable management requires an understanding of the seagrass spatial distribution and characterization to create accurate habita t maps. Determining the status of the seagrass resource requires a comprehens ive analysis of the geographic extent, composition, health, and abundance of the submerged aquatic vegetation (SAV) in the study area. The current monitoring programs provide data on a limited geographic scale which can not be extrapol ated across the entire resource. In turn, the results of the cu rrent studies can not provi de a comprehensive resource trend analysis or appropriate statistical power. 1.2 Goal The purpose of this research is to det ermine the feasibility of using remote sensing image data to delineate the spatial extent of the seagrass resource. Evaluating the accuracy of the classificati on maps allows the comparison of the study results to the existing aerial phot ointerpretation SAV m aps. The potential
2 to use Landsat 5 TM imagery as a data s ource greatly improves the temporal scale for analyzing spatial changes in the s eagrass. In turn, the analyses provide more frequent information to the environmen tal resource managers and aid in the development of resource preser vation and protection strategies. 1.3 Objectives Objective One : To create hard classification maps delineating the presence/absence and estimated coverage of seagrass resource from Landsat TM imagery using Maximum Likelihood classification (MLC) and Mahalanobis Distance classification (MDC) techniques. Objective Two : To create soft classification maps delineating the presence/absence and estimated coverage of seagrass resource from Landsat TM imagery using a linear spectral unmixi ng (LSU) and non-linear artificial neural network (ANN) algorithms. Objective Three : To determine the most appropr iate classification mapping technique for the seagrass resource by evaluating the accuracy and validity of the resulting classification maps. Objective Four: Determine the ability for ch ange detection by each appropriate classification method.
3 1.4 Description of Study Area Approximately 30 kilometers north of the mouth of Tampa Bay (Figure 1), the area consists of open water regions bounded east and west by the coastal mainland shoreline and the barrier island chain, respectively. The study area for this project, St. Joseph Sound and Clearwater Harbor, occurs along the northwestern coastline of Pinella s County (Figure 2). Of the 95 km2 in the study area, expansive seagrass beds cover nearly 56 km2 providing essential habitat for the marine flora and fauna (Kaufman, 2007) In comparison, the study area has seagrass acreage equivalent to 60% of the total seagrass acreage found in the entire Tampa Bay estuary. Concluded from the resu lts of the seagrass aerial mapping project (Kaufman, 2007), the seagr ass acreage in the study area has increased slightly since the progr am began in 1998 (Meyer and Levy, 2008; Kaufman, 2007). Figure 1. Location of the study site.
4 Figure 2. Study area includes St. Joseph Sound and Clearwater Harbor The ecosystem of the study area prov ides critical bird nesting areas, sessile algal communities, essential fi shery habitats, marine mammal and turtle habitats, and numerous recreational oppor tunities. The prominent seagrass species consist of Syringodium filiforme, Thalassia testudinum, and Halodule wrightii (Figure 3). In addition to the seagr ass species, the SAV includes a variety of rhizophytic algae. Figure 4 shows se ven rhizophytic algae and an invertebrate common in Clearwater Harbor and St. Joseph Sound. The habitat also hosts a plethora of invertebrates in cluding the Bay Scallop ( Argopecten irradians) (Meyer and Levy, 2008)
5 Figure 3. Seagrass species found in the study area. Figure 4. Rhizophytic al gae and Bay Scallops f ound in the study area.
6 The water quality in the study area is relatively good in comparison to the Tampa Bay area (Levy et al., 2008). Tr ansmissivity, measured at 660 nm, is a measurement of the percent age of light that can pass through the water. The mean transmissivity in the study area r anges from 90-95% (Levy et al., 2008). This level of water clarity should be suitable for the use of the sa tellite imagery. Anthropogenic and natural stresses im pact the health, sustainability, and persistence of the aquatic ecosystem (Short et al., 2001). Correlated with urbanization, anthropogenic factors such as stormwater pollution, hardened shorelines, development, eutrophication, and boat propeller scarring cause direct and indirect damages to the nearshore habitats (Meyer and Levy, 2008). Manmade features in the study area include dredge and fill operations, boat channels, spoil islands, finger canal systems, seawalls, and causeways. In turn, natural factors such as water circul ation, beach erosion, climate change, and weather events may also cause changes to occur in the ecosystem. The complexity of the interacting anthropogeni c and natural conditions adds to the intricate dynamics of Clearwater Har bor and St. Joseph Sound. These interacting environmental issues present a challenge for resource managers to develop strategies to protect and sustai n the quality of the ecosystem (Meyer and Levy, 2008).
7 Chapter 2 Literature Review 2.1 Seagrass 2.1.1 Seagrass Resource Ecology Seagrasses are flowering plants, angiosperms, specialized for living in marine nearshore environments (Short et al., 2001). Areas containing dense populations of seagrasses are consider ed a seagrass resource. Ecological functions provided by seagrass resource include structural and physiological characteristics that support species liv ing in the seagrass communities. Functions such as nutrient cycling, detri tus production, sediment formation, and shelter increase the primary productivity of the ecosystem (Dawes et. al., 2004). Seagrass beds grow as continuous meadow s or a mosaic of various size and shape patches (Brooks and Bell, 2001). Alon g the central Flor ida coast of the Gulf of Mexico, the seagrass growi ng season is May-September (Avery and Johansson, 2001) which coincides with t he findings of Robbi ns and Bell (2000) reporting the greater changes in seagrass spat ial extent from the spring to the fall seasons. Other factors such as physiol ogy, growth characteristics, including water depth and salinity gradients may contribut e to the spatial distribution of the seagrass beds (Robbins and Bell, 2000). Seagrass requires available light for photosynthesis (Short et al., 2001), and the depth penetration of the available light is correla ted with seagrass growth and survival (Dennison et al., 1993). Thus good water clarity is crucial to the persistence and growth of the seagrass beds. The health of the seagrass resource may also be an indicator of wa ter clarity and nutrient levels (Dennison et al., 1993). Disturbances in the water qua lity such as nitrification, sediment suspension, and pollution can negatively a ffect water quality and light penetration
8 (Levy et al., 2008). Correlated with urbanization, there is an increase of anthropogenic disturbances to seagrass resources (Tomasko et al., 2005). Environmental managers acknowl edge the relationship between the anthropogenic factors and the degradation of the seagrass resource and realize the importance of sustaining this valuab le ecosystem (Chauvaud et al., 1998). Currently, coastal habitat maps includi ng seagrass areas provide essential information for management and planning deci sions (Mumby et al., 1999). The sustainable management requires an under standing of the seagrass spatial distribution and characterization. 2.1.2 Seagrass Assessment Methods Resource managers and researchers im plement various techniques to assess and monitor the spatial and te mporal changes of the seagrass habitat. Kirkman (1996) describes some of the methods for seagrass monitoring. The most common field survey technique consis ts of permanent transect monitoring. Usually monitored annually, transects are revisited by using spatial coordinates from a Global Positioning System. In mo st cases, the perm anent transects start on or near shore and then c ontinue perpendicular to t he shoreline (Kirkman, 1996). After arriving on site and locating t he transect 0m mark, samplers swim along the transect line with a meter s quare frame collecting data on seagrass species, condition, abundance, and biomass. Other field survey methods include collecting random point dat a, stratified random sampling designs (Meyer and Levy, 2008), and seagrass habitat classifi cation mapping (Kaufman, 2007). The latter is the most intensive method which requires researchers to swim the entire seagrass area (Mumby et al., 1999). In a quest to assess the geographic extent of the seagrass resource, researchers investigate the use of aer ial photography for developing habitat maps. Historical aerial photography provides coarse baselines for the seagrass resource extent making it possible to compare the current geographic extent of the seagrass beds to the previous stat e. Currently, the analysis of aerial
9 photography supplies seagrass acreage maps to track t he spatial and temporal trends for resource management (Kauf man, 2007). Using digital aerial photography for seagrass mapping requires the acquisition of large scale airborne photographs. The resolution of the images typically ranges from 1 meter to 10 meter (Jensen, 2005). Variables such as water clarity and depth can interfere with the ability of the photo-interpreters to accurately delineate the seagrass meadows (Kaufman, 2007). Coastal managers require reliable data to protect and manage ecosystems (Mumby et al., 1999). Ecological management tr aditionally relies on small sample designs and extrapolation of results to larger areas. This practice tends to ignore the spatial dimension and c onnectivity of ecosystems (Schmidt and Skidmore, 2003). Detailed habitat maps ai d in the assessment and monitoring of changes within the seagrass meadows. Seagrass biomass responds quickly to environmental disturbances and alterations (Short et al., 2001). Usually, these changes are large enough for detection by remote sensing techniques. In conjunction with field survey monitoring, remote sensing maps can help provide a better understanding of the extent of spat ial and temporal trends in the seagrass resource based on their synoptic and frequent characteristics. 2.2 Remote Sensing Applications Remote sensing refers to a form of measurement where the observer is not in direct contact with the object of study (Coastal Remote Sensing, 2006). Two main types of remote sensing data collection include active and passive systems. Active systems generate a sour ce of illumination such as sound or light (Jensen, 2005). Passive systems rely on the reflected sunlight and emitted energy from targets to acquire data (Jensen, 2005). Technologies such as aerial photography, multispectral satellite im agery, and hyperspectral imagery also record how the sunlight reflects and re fracts and radiance emits from targets (Jensen, 2005). Multispectral imagery expa nds the classification abilities and mapping of aerial photointerpretation. Mu ltispectral imagery is usually satellite
10 based and collects less than 10 spectr al bands, and requires analysis and characterization to evaluate the features (Mumby et al., 1999). The spectral resolution of the individual channels over the continuous spectrum defines the multi/hyper differentiation (S chmidt and Skidmore, 2003). Researchers commonly use multispectral and/or hyperspectral imagery for ecosystem studies. A basic assumpti on of remote sensing depends on the features of interest uniquely reflecting or emitting light energy; in turn, allowing the delineation and mapping of various f eatures (Fyfe, 2003). As the bandwidths narrow, variation in absorption is detec ted. In applications to the aquatic environment, the specific wavelengths of light absorb and scatter in the water column and benthic substrate (Coastal Re mote Sensing, 2006). Due to the various spectral properties, remote s ensing is applicable for characterizing aquatic vegetation and benthic habitats (Schweizer et al., 2005). The spectral signature of seagrass beds in shallow waters differs significantly from the nonvegetated bottom. Considerat ions for the limitations of passive remote sensing include the water clarity, depth, and wave roughness, and the atmospheric and ionospheric conditions (Phinn et al., 2006) Although the passive remote sensing methods for aquatic benthos are limited to t he visible wavelengths, it provides high spectral and spatial resolution for t he mapping of features (Fyfe, 2003). Remote sensing provides an alter native to the traditional boat or land based surveys required to assess an entir e seagrass habitat (Dekker et al., 2005). Remote sensing is applicable for characterizing aquatic vegetation and benthic habitats due to the various spectral properties for each bottom type (Schweizer et al., 2005). The multispectr al imagery requires several analyses to classify the signatures. In a study cla ssifying the benthic habitat of a shallow estuarine lake, Dekker et al. (2005) addresses five components of the multispectral imagery analysis. The study considers the water and substrate spectral characterization, seagrass and macroalgae spectral characterization, and satellite imagery quality, finally resulting in the benthic substrate classification. Studies by Andrefouet et al. (2003), Schweizer et al.(2005), and
11 Pasqualini et al. (2005) consider similar components during the analysis and classification of various satellite imagery. Beyond the delineation of the SAV, Fyfe (2005) investigates the spectral reflectance of individual seagrass spec ies and determines that seagrass species are indeed spectrally distinct. The proper ties of spectral reflectance depend on the chlorophyll and accessory pigment concentrations and the leaf design characteristics (Thorhaug et al., 2007). Fyfe (2005) includes the considerations of epiphytic coverage, and spatial and tem poral variability in the reflectance determination of each species and record s strong and consistent differences in spectral reflectance between species. The key to mapping species specific seagrass beds is acquiring a reliable spectral library for individual species (Fyfe, 2005). Thorhaug et al. (2007) examines th ree seagrass species and five marine algae to determine the difference in spectr al signatures. The seagrass species, Thalassia testudinum, Halodule wr ightii, and Syringodium filiforme, share a similar spectral signature for the curve; however, they differ in the height of the curve peak. Thorhaug et al. (2007) also fi nds significant differences between the seagrasses and marine algae spectral si gnature. The potential for refining seagrass habitat maps to a species composition level seems possible with the application of remote sensing technologies. 2.2.1 Landsat Imagery The Landsat 5 Thematic Mapper (TM) satellite was launched in March 1984. The TM sensor collects multispectr al imagery by recording the energy in the visible, reflective infrared, middle in frared, and thermal infrared regions of the electromagnetic spectrum (Jensen, 2005). The Landsat 5 TM system is described in detail in EOSAT (1992). Each spectral band of the Landsat TM sensor has specific spectral characteristics (Table 1). For spectral bands 1, 2, 3, 4, 5 and 7, the ground projected resolution is 30m x 30m. B and 6, the thermal band, has a spatial resolution of 120m x 120m (Jensen, 2005). Each band measures the reflectivity
12 at different wavelengths. Band 1, blue, measures 0.45-0.52 m in the visible spectrum. Due to the frequency of the wavelength, band 1 penetrates water. Band 2, green, measures 0.52-0.60 m in the visible spectrum. Studies suggest that band 2 spans the region between t he blue and red chlorophyll absorption making it useful for the analysis of v egetation (Jensen, 2005). Band 3, red, measures 0.63-0.69 m in the visible spectrum and may be used for studies of vegetation for the red ch lorophyll absorption. Band 4 measures 0.76-0.90 m in the near-infrared spectrum. Band 4 is usef ul for the determination of biomass for terrestrial vegetation, and the contrast of land and water. Band 5 measures 1.551.75 m in the mid-infrared spectrum, and is found useful for determining turgidity and the amount of water in plants. Band 6 measures 10.40-12.50 m in the thermal spectrum related to the in frared radiant energy emitted from the surface. Band 7 measures 2.08-2.35 m in the mid-infrared spectrum. Band 7 is mainly used for discriminati ng rock formations (Jensen, 2005). Table 1. Landsat 5 TM band descriptions Band Spectrum Resolution (m) Spectral Resolution (m) Characteristics/Functions 1 blue 30x30 0.45-0.52 Penetration of water and supports vegetation analysis 2 green 30x30 0.52-0.60 Reacts to the green reflectance of vegetation 3 red 30x30 0.63-0.69 Reacts to the red chlorophyll absorption and vegetation 4 near-infrared 30x30 0.76-0.90 Contrast of land and water, and terrestrial vegetation 5 mid-infrared 30x30 1.55-1.75 Useful for turgidity and hydration in plants 6 thermal 120x120 10.40-12.50 Radiant thermal energy 7 mid-infrared 30x30 2.08-2.35 Determining rock formations
13 2.2.2 Aerial Photography Aerial photography is usually collect ed from a plane flying in concentric transects over the study area. Dependi ng on the altitude of the plane and the camera specifications, the swath and reso lution vary. Aerial photography also requires preprocessing such as mo saicing the frames together and georeferencing the imagery prior to spatial analysis (Kaufman, 2007). Agencies use aerial photography to m ap the land surface characteristics and shallow aquatic habitats including S AV. Aerial photography is collected in analog or digital format. The historic aer ial imagery is limited to black and white or color film. The more current aerial phot ography is collected in a digital format. The digital imagery usually focuses on t he three visible spectral bands: red, green, and blue, and may also include the near-infrared band (Kaufman, 2007). True color photography uses the three visibl e bands only. Features of interest are extracted from the images by a photointerpreter and used to produce maps. 2.3 Remote Sensing Classification 2.3.1 Imagery Classification The extraction of thematic informati on from remote sensing data requires a series of processing methods includi ng preprocessing, selecting appropriate logics and algorithms, and assessing the a ccuracy of the resultant product. The preprocessing steps include radiomet ric and geometric correction (Jensen, 2005). The classification of thematic info rmation requires a defined logic and algorithm appropriate for the data. T he image classification method includes parametric, nonparametric, or nonmetric l ogics. Parametric logic assumes that the sample data belongs to a normally distributed population and knowledge of the underlying density function (Jensen, 2005). The nonparametri c logic allows for sample data not from a normally di stributed population. The nonmetric logic may incorporate both ordinal and nomi nal scaled data in the classification method. The algorithms may apply s upervised or unsupervised methods. The
14 supervised classifications use known in formation extracted from training areas concerning the image to label a specific cl ass for every pixel in the image. The unsupervised method allows the algorithm to differentiate between spectrally significant classes automatically. A combination of the supervised and unsupervised methods results in a hybrid approach. 2.3.2 Hard Classification Methods Two supervised parametric methods, also considered hard classification, include the MLC and MDC algorithms. The MLC algorithm is a parametric supervised method. Based on the statistica l probability of a pixel value belonging to a normally distributed population, the al gorithm assigns the pixel to the most likely class. The method assumes that the training data for each class in each band are normally distributed (Jensen, 2005). Calculating the probability for the density functions, the MLC algorithm a ssesses the variance of each training class associated with the pi xel brightness values. The MLC method is not recommended for bimodal or n-modal distri butions. Variations of the maximum likelihood method without probability informat ion assume that each class occurs equally across the landscape of the image. The MDC algorithm is a direction sensitive distance classification similar to the MLC method. The classification method is based on the analysis of correl ation patterns between variables and is a useful way of determining similarity of an unknown pixel to a known one. The MDC assumes that the covariances for all the classes are equal (Richards, 1999). Based on the distance threshold, the algorithm fits pixels to the nearest class. The unsupervised classification method used in this study is the Iterative Self-Organizing Data Analysis Techni que (ISODATA) (Jensen, 2005). The ISODATA requires little input from the anal yst. The ISODATA is based on the kmeans clustering algorithm. The clustering method uses multiple iterations to determine the data grouping (Jensen, 2005) The cluster means are analyzed and pixels are allocated to the most appropr iate cluster. ISODATA is used to for the initial examinati on of data to investigate the number of significant classes.
15 2.3.3 Soft Classification Methods Two supervised nonparametric classifi cation methods, also considered soft classification, include linear spectr al unmixing (LSU) and artificial neural network (ANN) algorithms. Theoretically the pixel-based seagrass abundance is determined by examining the significant spectral signatures of seagrass in individual pixels in the image with a LS U model. The LSU assumes that the spectral signature is the linear sum of the set of pure endmembers which are then weighted by their relative abundance (Hedley and Mumby, 2003). According to Hedley and Mumby (2003) the application of LSU to the aquatic environment is insufficient due to the li ght attenuation properties of the water causing the divergence from the linear mode l. However, if a depth correction can be applied to the pixels, then the LSU may produce reasonable results (Hedley and Mumby, 2003). The ANN is a layered feed-forward classification technique that uses standard back-propagation for supervised learning. Researchers select the number of hidden layers to use and choose between a logistic or hyperbolic activation function. Learning occurs by adjusting the weights in the node to minimize the difference between the out put node activation and the output. One layer between the input and output layers is usually sufficient for most learning purposes (Pu et al., 2008). The learning procedure is controlled by a learning rate, a momentum co efficient, and a number of nodes in the hidden layer that need to be specified empirically based on the results of a limited number of tests. The network training is done by repeatedly presenting training samples (pixels) with known seagrass abundance. Netw ork training is terminated when the network output meets a minimum error cr iterion or optimal test accuracy is achieved. Finally, the trained network can then be used to unmix each mixed pixel. Therefore, ANN classification performs a non-linear classification and spectral unmixing analysis.
16 Chapter 3 Methodology 3.1 Methodology Overview The remote sensing analysis for the study follows the "Remote Sensing Process" as described by Jensen (2005). Th is substantial process consists of image preprocessing, image enhancement, a nd thematic information extraction aiming to map the seagrass resources. The methodology for analysis of remote sensing imagery follows an inductive logi c approach. A deterministic empirical model is applied to analyze the remote sensing data. This study applies unsupervised and supervised classificati on methods to extract thematic information from Landsat 5 TM imagery. 3.2 Data Sources Several types of data are readily available for St. Joseph Sound and Clearwater Harbor. The remote sensi ng data available consists of aerial photography, aerial photointer pretation maps, and Landsat 5 TM imagery. The field survey data include informa tion from the seagrass monitoring and ambient water quality m onitoring programs. 3.2.1 Remote Sensing Data Sources 22.214.171.124 Aerial Photoint erpretation SAV Mapping Available remote sensing data for the study area includes aerial photographs and satellite im agery. The Southwest Florida Water Management District (SWFWMD) collects high resolu tion natural color aerial photography (SWFWMD, 2006). Collected on a 2-year cycl e, the available digital imagery is one-meter resolution.
17 Beginning in 1999, the aerial seagrass mapping project provides data for the extent and spatial va riation of the seagrass resource. The SWFWMD conducts a seagrass mapping program to monitor the changes in seagrass acreages. Using one meter resolution aerial photography, they apply a minimum mapping unit of acre for the photoint erpretation. The images are acquired during the dry season (December-January) when water clarity is good (Secchi disk >2m). The project produces an updat ed seagrass acreage map once every two years. They conduct lim ited field verification to ensure the accuracy of 90% for the final mapping product (Kaufman, 2007). The map classifies submerged aquatic vegetation (SAV) into patchy and continuous grassbeds. The photointerpretation can not discern information on species composition, condition, or biomass. The SAV is inte rpreted from 1:24,000 scale natural color aerial photography using Digi tal Stereo Plotters. The SAV signatures are divided into two estimated cover age categories, pat chy and continuous coverage. The patchy areas represent the delimited polygon consisting of 2575% SAV coverage. The continuous ar eas represent the delimited polygon consisting of 75-100% SAV coverage. The non-vegetated areas contain less than 25% SAV coverage (Kurz, 2002; Toma sko et al., 2005). The most recent photointerpretation map uses data colle cted in February 2006 (Figure 5). The geographic extent of the mapped SAV is comparable to the seagrass bed mapped from the Landsat 5 TM imagery.
18 Figure 5. Aerial Photoi nterpretation SAV Map based on 2006 aerial imagery (Kaufman, 2007).
19 126.96.36.199 Satellite Imagery The Landsat 5 Thematic Mapper (TM) im agery for this study was provided by the Florida Center for Community Design and Research (FCCDR) at the University of South Florida. The image was acquired on 2 May 2006 (Table 2). The image was selected based on the low percentage of cloud cover and the limited budget for the pr oject. The spatial resoluti on of the Landsat 5 TM imagery is 30m x 30m on the ground. The TM bands used in the study include 1 (blue), 2 (green), 3 (red), and 4 (near infrared). Bands 1, 2, and 3 were used for the spectral signature of the SAV associated with water column. Band 4 was only used for creation of masks. The preprocessing steps for the image including geometric and radiometric corrections were completed by the FCCDR prior to this study using the ENVI Version 4.3 software program (ITT, 2006). The specifics of the processes were presented by Andreu et al. (2008). The image was georeferenced to the Univ ersal Transverse Mercat or map projection as WGS1984 Zone 17N. The radiometric calib ration used the Calibration tool to convert the Landsat digital numbers to t he at-sensor reflectance values (Andreu et al., 2008). Andreu et al. (2008) perfo rmed the atmospheric correction by subtracting the atmospheric path radianc e estimated from pseudo-invariant dark water locations. Table 2. Landsat 5 TM image details. Path/Row Acquisition DateScene Identifier 17/41 May 2, 2006 5017041000612210 Processing System: Format: Product Type: LPGS GeoTiff L5 TM SLC-off L1T Single Segmentation
20 Figure 6. Landsat 5 TM satel lite image from May 2, 2006. The natural color composite was made via TM band 3, 2, 1 vs. Red, Green, and Blue.
21 3.2.2 Field Survey Data Available seagrass field survey data consists of information from the Pinellas County Seagrass Monitoring Pr ogram (Meyer and Levy, 2008) and the Pinellas County Ambient Water Quality M onitoring Program (Levy et al., 2008) 188.8.131.52 Seagrass Monitoring Data The Pinellas County Seagrass Monitori ng Program collects information on the status of the seagrass resource. Data parameters include SAV species, shoot density, canopy height, epibiont density, sediment type, and depth information. Data points are collected using a 0.5-meter square quadrat. The sampling occurs at the end of the gro wing season (Oct-Nov). The current seagrass survey sampling design (2006-2008) consists of a combination of stratified-random and permanent transects. The permanent transects intersect the historical permanent transect sites. The random transects are spatially stratified allocating sampling effort to the continuous and patchy grassbeds as delineated from the seagrass aerial mapping project by the Southwest Florida Water Management District (SWFWMD). In the study area, researchers sampled 42 sites in 2006 and 55 sites in 2007 (Figur e 7). To account for variation and inaccuracy in the seagrass mapping, 15% of the sampling effort is allocated to areas that are not classified as pat chy or continuous seagrass beds. The transects are 30 m in length and placed par allel to the shore line. Samplers collect seven data points along each transec t at 5 meter increments (Meyer and Levy, 2008). The mean abundance and density of seagrass was calculated for each transect from the seven observati ons. These means were used in the development of the training data for the thematic data extraction from the remote sensing imagery.
22 Figure 7. Pinellas County seagrass monitoring program results for Clearwater Harbor and St. Jo seph Sound (Meyer and Levy, 2008)
23 184.108.40.206 Water Quality Monitoring Data The Pinellas County Ambient Water Quality Monitoring Program collects water quality and habitat information. The pr ogram samples 72 stratified random sites per year in the study area. Developed in conjunction with Janicki Environmental, Inc, th e stratified-random design is based on a probabilistic sampling scheme used by the Environment al Protection Agency (EPA) in their Environmental Monitoring Assessment Pr ogram (EMAP) (Levy et al., 2008). The EMAP-based design consists of overla ying a hexagonal grid by strata, and randomly selecting a sample location withi n each grid cell. The stratified-random design allows for statistical methods to be applied estimating population means and confidence limits for water q uality metrics (Janicki, 2003). Habitat information collected at eac h site includes the presence/absence of SAV, SAV species, and sediment compositi on. This study only uses the 2005 2007 data to coincide with the sate llite imagery and seagrass information (Figure 8).
24 Figure 8. Observed SAV at the Pine llas County Ambient Water Quality sampling sites for 2005 2007 (Meyer and Levy, 2008).
25 3.3 Landsat 5 TM Imagery Analysis Remote sensing information extracti on techniques are used to estimate the geographic extent and estimated cover age of the seagrass resource in the study area. The goal of the analyses is to determine the feasibility of applying satellite imagery interpretation to delineate the seagrass resource. The following section describes the classification methods applied to the Landsat 5 TM imagery. 3.3.1 Imagery Preprocessing The remote sensing data for the cla ssification maps are based on a digital Landsat 5 TM image. The study uses data consisting of field survey measurements and ancillary datasets to de velop training, testing, and validation data subsets. The field measurements serve as the ground truth data for the model validation as well as biomass and health information for the seagrass. Although this study did not conduct l aboratory analyses data, results adapted from the studies of Fyfe (2005), and T horhaug et al. (2007) provide spectral reflectance information for the Florida s eagrass ecosystem. Additional ancillary data for the analysis includes maps from the Aerial Photoint erpretation (AP) Seagrass Mapping Project produc ed by the SWFWMD. The thematic information extraction from the satellit e imagery requires several processing steps. The preproce ssing includes radiometric, geometric and topographic corrections, image enhancemen t, and initial image clustering analysis. The radiometric and geometric corrections were completed for the Landsat 5 TM imagery prior to this study by the FCCDR (Andreu et al., 2008). The image processing is accomplished us ing the ENVI Version 4.3 software program (ITT, 2006). The first processing st ep saves the raster files for bands 1, 2, 3, 4, 5, 6, and 7 into a single ENVI image. The image is then clipped to the rectangular boundary of the study area (Figur e 9). The clipped image consists of 400 columns and 1050 rows. Due to the strong spectral contrast between the land based features and water, the open water area is masked from the image
26 using the near-infrared band 4 (Figure 10) The frequency distribution of the pixels of the image (i.e., histogram te chnique) allows the segregation of the image based on a threshold for the water versus land spectral properties. This technique does not exclude all of the tidal flat areas in t he study area. Figure 9. Landsat 5 TM imagery clippe d to the study area from 2 May 2006
27 Figure 10. Mask delineated from band 4 (near infrared). 3.3.2 Imagery Cl assification To initially investigate the spectral classes of the im age the Equalization image enhancement is applied to bands 1, 2, and 3 (Figure 11). An image clustering analysis is conducted using an unsupervised classification (ISODATA) prior to the supervised classification The ISODATA classification method is applied to bands 1-3 and categorized the dat a into 10 subclasses. The resultant
28 classification is visually compared to the field survey information to detect spatial correlations and estimated accuracy. The classes are merged into three categories and an environmentally relevant label was applied. The classes are land, SAV, and No SAV. Figure 11. Landsat 5 TM image enhan cement using Equalization function.
29 To map the seagrass resource from the TM imagery, two parametric supervised classifications, Maximu m Likelihood classification (MLC) and Mahalanobis Distance classification (M DC), are performed on the Landsat 5 TM imagery. The first three bands of the Landsat 5 TM imagery are used for these classification methods. These bands hav e centered wavelengths of 485 nm, 560 nm, and 660 nm, respectively. The supervi sed image classifications use field survey seagrass information for the trai ning signature, as well as, testing and validation. The classifications are conduc ted with two levels of SAV delineation. The first analysis focuses on the presence versus absence of SAV. The training and testing data categories for this cl assification include absence (<25% SAV) and presence (25-100% SAV). The second analysis uses three classification categories to delineate the estimated co verage of the SAV. The training and testing data categories include No SAV (<25% SAV), Patchy (25-75% SAV), and Continuous (75-100% SAV). Regions of in terest (ROIs), delineated from the TM imagery for the training and testing areas, are interpreted from a combination of the Pinellas County Seagrass Monitoring fi eld survey data and 6-inch resolution aerial photography. The selected grid ce lls are merged and imported into the ENVI 4.3 software as ROIs (I TT, 2006). Each ROI consis ts of 12 polygons with a minimum of 50 pixels in each polygon. The ROIs are select ed from the areas homogeneous with spatial and spectral pr operties. The ROIs cover a range of water depths, and are spatially distri buted throughout the study area. The estimated percent coverage for SAV is based on the mean abundance of seagrass calculated for each field survey sampling location. Using ArcMap 9.2 software, a 30 m x 30 m grid is created to coincide with the seagrass field survey data (ESRI, 2006). The aeria l photography is used to compare the grid cells surrounding the field survey transect to ensure a homogeneous area for the ROI polygon. The spectral properties of the ROIs det ermine the feasibility of delineating the classes in the classification map. By calculating the radiometric resolution
30 digital number (DN) for the ROIs in each spectral band, the separability of the classes is determined. Descriptive stat istics are calculated for the ROI training data (Table 3). The separability of the RO I categories is examined for the three spectral bands (Figure 12). The ability to accurate separate the categories is relative to the overlap of the histogram curves. As the overlap of the histogram curves increases, the categories become mo re difficult to separate. The patchy and continuous SAV categories are expected to overlap. In the histograms for band 1 and band 2, there is limited overlap between the No SAV and SAV classes. The separability between t he SAV and No SAV categories is greatest for band 2. The categories have the l east separability between categories in band 3. This analysis suggests that it is feasible to delineate the SAV and No SAV classes using the visible bands. The overlap between the Patchy and Continuous SAV classes may limit the abilit y to accurately delineate them during classification. Table 3. Radiometric Resolution d escriptive statistics calculated for the ROI training data. ROI class Pixels Band Minimum DN Maximum DN Mean DN Standard Deviation No SAV 1401 1 75 99 82.51 3.12 2 31 49 35.22 2.17 3 19 37 23.68 2.09 Patchy SAV 1154 1 65 89 73.85 3.51 2 24 37 29.00 1.89 3 16 27 21.48 2.15 Continuous SAV 1493 1 60 79 68.25 3.65 2 21 33 25.40 2.15 3 14 27 18.62 2.04
31 A0 50 100 150 200 250 300 350 400 6065707580859095100 DNFrequency No SAV Patchy SAV Continuous SAV B0 50 100 150 200 250 300 350 400 20253035404550 DNFrequency C0 50 100 150 200 250 300 350 400 10152025303540 DNFrequency Figure 12. Histograms of the radiometric resoluti on of the ROI classes: No SAV, Patchy SAV and Continuous SAV for TM 1 (A), TM 2 (B), and TM 3 (C).
32 The parametric supervised classifica tion methods are calculated with the ENVI 4.3 software program (ITT, 2006). The MLC uses three TM visible bands to map seagrass resource by applying the spectral signatures extracted from the training ROIs. The accuracy assessment of the classification is examined using a confusion matrix based on the testing R OIs. The MDC also uses the three TM visible bands by applying the training ROIs to classify the seagrass resource. The MLC and MDC methods, also considered hard" classifications, are used to classify the presence/absence of seagr ass and the estimated coverage of the SAV. The maps are evaluated using the conf usion matrix with the testing subset ROIs. The assessment includes the av erage accuracy, overall accuracy, producerÂ’s accuracy (omission error), us erÂ’s accuracy (commission error), and Kappa coefficient. The study also applies two supervi sed nonparametric classification methods. Considered soft classifi cation methods, LSU and ANN algorithms provide an alternative approach to the hard classification. The LSU is calculated with the ENVI 4.3 software program (ITT, 2006). The training data is derived from the 1-meter resolution aerial phot ography supplied by the SWFWMD. ESRI ArcMap 9.2 software (ESRI, 2006) is used to examine the MrSID image mosaic and develop ROIs. Due to the small size of the image pixels, a 30m x 30m grid is created using Hawth's Tool (Hawth, 2006) and overlaid on the image. This ensures that the ROIs selected included a minimum of 30-50 (30m x 30m) pixels to coincide with the Landsat TM image. The training ROIs contains a minimum of 30 pixels per polygon and 12 polygons for each ROI category. The LSU can only determine less endmembers than the number of bands us ed in the analysis. Since three bands are used for the classi fication, only two categories, No SAV and SAV are delineated. The ANN analysis is attempted using the ENVI 4.3 software program (ITT, 2006).
33 3.3.3 Classification Accuracy Analysis Post-processing includes several steps to ensure the accuracy of the classification map. The validation of the classifica tion requires a data source independent from the tr aining and testing data. The va lidation ROIs for this study are determined from the s eagrass data collected by t he Pinellas County Ambient Water Quality Monitoring Program. T he validation accuracy assessment is calculated using the ESRI ArcMap 9.2 software program (ESRI, 2006). The classification images are ex ported from the ENVI 4.3 software as ESRI grid files and clipped to the exte nt of the study area using ES RI Spatial Analyst Extension (ESRI, 2006). The validation data includes spatial and temporal information on the presence/absence and species composit ion of SAV. Due to the sampling methods, the validation data point locati on accuracy has a radius of 10 m. HawthÂ’s Analysis Tool (Hawth, 2006) is used in ESRI ArcMap 9.2 (ESRI, 2006) to analyze the correlation between the valid ation data and the classification map. Using the Intersect Point function in Hawt hÂ’s Tools, the vector validation points and the raster classification map are processed. The correlation matrix is developed to assess the accura cy of the classification. 3.4 Analyses The comparison of the classification maps is necessary to assess the most appropriate method for SAV delineation. The estimated accuracy from the validation analysis and spatial variation is used to compare the classification maps. The validation estimated accura cies are compared using descriptive statistics calculated with Microsoft Excel. The spatial comparison is described in the following section. 3.4.1 Comparison to existing maps The AP mapping project conducted by the SWFWMD provides an estimate of the SAV acreage for the study area. Although the project aims for 90% accuracy for the ground-truth points, the geographic extent of the study restricts the validat ion to approximately 10 sites wit hin Clearwater Harbor and St.
34 Joseph Sound. To estimate the accura cy of the AP maps, the validation data from the Pinellas County Ambient Water Quality Monitoring Program is used to develop a correlation matrix. The Intersect Point Function in HawthÂ’s Analysis Tool (Hawth, 2006) is used in ESRI ArcMap 9.2 (ESRI, 2006) to analyze the correlation between the validatio n data and the AP map. The Landsat 5 TM classification maps developed in this study are compared to the results from the AP mapping project conducted by the SWFWMD. To investigate the variati on between the mapping products, a spatial correlation is completed using the ESR I ArcMap 9.2 software program with the Spatial Analyst Extension. The total ar ea is calculated for the classes of SAV, and No SAV. The areas are compared between the two classification methods. The classification maps are converted in to raster grids with 30 m pixel cell dimensions. The grids are overlaid and a comparison analysis is conducted using the Raster Calculator ( ESRI, 2006). The difference in SAV acreage is evaluated to determine the effectiveness of the re mote sensing supervised classification methods in comparison to the AP mapping project. 3.4.2 Ability to Map SAV variation The classification methods are analyzed to assess the minimum amount of variation that may be det ected by the classification. The ability to assess the variation is based on the accuracy of the classification method as determined by the testing ROI confusion matrix and the validation assessment. The detectable variation in the SAV is related to overa ll accuracy of the classification. The ESRI ArcMap 9.2 software program is used to calculate the areas for each class (ESRI, 2006).
35 Chapter 4 Results and Discussion 4.1 Classification Results The unsupervised and supervised met hods produce classification maps with various accuracies. The unsupervised classification method is similar in validation accuracy to the supervis ed hard classification methods. The supervised soft classification methods did not produce reasonable results. Overall the supervised hard classifications are the most appropriate to map the SAV in the study area. 4.1.1 Unsupervised Classification The unsupervised ISODATA classifica tion interprets seven categories from the Landsat 5 TM image. The cat egories are merged into two classes and labeled with environmentally relevant descriptions, SAV and No SAV. The ISODATA classification map (Figure 13) displays the spatial extent of the SAV in the study area. The ISODATA classifica tion reasonably delineates the spectral classes for the SAV features. A valid ation assessment is conducted using an independent data set from t he Pinellas County Ambient Water Quality Monitoring Program (Levy et al, 2008). This poi nt data provides information on the presence/absence and species compositi on of the SAV. The validation dataset (n=216) is compared to the class of the coinciding pixel. The ISODATA validation estimates 76% accuracy for corre ctly classifying the SAV and 51% for No SAV with an overall accuracy es timate of 68% (Table 6).
36 Figure 13. Unsupervised ISODATA cl assification of Landsat 5 TM image with environmentall y relevant labels.
37 4.1.2 Supervised Classification 220.127.116.11 Hard Classification The supervised parametric classifica tion methods, Maximum Likelihood (MLC) and Mahalanobis Distance (MDC), uses ROIs developed from the field survey data to delineate the spectral signatures of the SAV. The methods are first used to delineate the presence/abs ence of SAV. Then, the methods are applied to delineate the estimated coverage of the SAV. Of these applications, the hard classification methods have a hi gher overall accuracy for separating the presence/absence of SAV. 18.104.22.168.1 Presence/Absence of SAV Both the MLC and MDC (Figure 14) depict reasonable maps of the presence/absence of SAV. Calculated with a confusion matrix using the testing ROIs, the overall accuracy of the MLC is 85.54 % with a Kappa coefficient of 0.69 (Table 4). The overall accura cy of the MDC is 86.79% with a Kappa coefficient of 0.70. Both classificati ons produce similar accuracies. The producerÂ’s accuracy is slightly better fo r the classification of SAV in the MDC (Table 5), and for the classification of the No SAV in the MLC. The userÂ’s accuracy is slightly better fo r the classification of SAV in the MLC (Table 5), and for the classification of the No SAV in the MDC. In addition to the accuracy assessment for the classification maps, a validation assessment is conducted using an indepen dent data set from the Pinellas County Ambient Water Quality M onitoring Program (Levy et al, 2008). This point data provides information on the presence/absence and species composition of the SAV. The validation dat aset (n=216) is compared to the class of the corresponding pixel. The MLC validation estimates 66% accuracy for correctly classifying the SAV and 69% for No SAV with an overall accuracy estimate of 67% (Table 6). The MDC validation estimates 74% accuracy for correctly classifying the SAV and 58% for No SAV with an overall accuracy estimate of 68% (Tabl e 6).
38 Figure 14. Supervised classification of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods.
39 Table 4. Accuracy estimates for th e supervised classification methods. Overall Accuracy (%) Kappa Coefficient Maximum Likelihood 85.540.69 Mahalanobis Distance 86.790.70 Table 5. Supervised classificati on commission and omission errors, and producer and userÂ’s accuracy. Commission (%) Omission (%) Commission (Pixels) Omission (Pixels) Producer Accuracy User Accuracy Maximum Likelihood SAV 7.77 14.85 214/2754 443/2983 85.15 92.23 No SAV 24.73 13.70 443/1791 214/1562 86.15 75.27 Mahalanobis Distance SAV 9.26 11.03 271/2925 329/2983 88.97 90.74 No SAV 20.31 17.35 329/1620 271/1562 82.65 79.69 Table 6. Validation for classifi cation methods SAV presence/absence SAV Accuracy % No SAV Accuracy % Overall Accuracy % ISODATA 76.6 51.8 68.4 Maximum Likelihood 66.2 69.4 67.3 Mahalanobis Distance 74.2 58.5 68.9 A comparison of the MLC and MDC m aps presents discrepancies in the classification of SAV in the intertidal areas. Figure 15 illustrates a shallow seagrass bed that is often exposed at low ti de. The MDC correctly classifies the area as SAV; whereas, the MLC classifies the majority of the seagrass bed as No SAV. Overall, the MLC and MDC produce very similar classification maps.
40 Figure 15. Differences (red circle) betw een the supervised classification of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods.
41 22.214.171.124.2 Estimated Coverage of SAV Both the MLC and MDC (Figure 16) depict reasonable maps of the estimated coverage of SAV. Calculated wit h a confusion matrix using the testing ROIs, the overall accuracy of the MLC is 74 % with a Kappa coefficient of 0.61 (Table 7). The overall accuracy of the MDC is 65% with a Kappa coefficient of 0.47. The MLC produces better accuracies than MDC in the classification of the estimated coverage of SAV for this case. The producerÂ’s accu racy is better for the classification of No SAV and the continuous and patchy SAV in the MLC (Table 8). The userÂ’s accuracy is better fo r the classification of continuous and patchy SAV in the MLC (Table 8), and similar in both methods for the classification of No SAV. In addition to the accuracy assessm ent for the classification maps, a validation assessment is conducted using an indepen dent data set from the Pinellas County Ambient Water Quality M onitoring Program (Levy et al, 2008). This point data provides information on the presence/absence and species composition of the SAV. Since the dat a only supports the comparison of SAV and No SAV classes, the patchy and cont inuous classes of the MLC and MDC are combined for the validation. The valid ation dataset (n=216) is compared to the class of the corresponding pixel. The MLC validation estimates 86% accuracy for correctly classifying the SAV and 37% for No SAV with an overall accuracy estimate of 70% (Table 9). The MDC validation estimates 85% accuracy for correctly classifying the SAV and 36% for No SAV with an overall accuracy estimate of 69% (Table 9).
42 Figure 16. Supervised classification of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods.
43 Table 7. Accuracy estimates for th e supervised classification methods. Overall Accuracy (%) Kappa Coefficient Maximum Likelihood 74 0.61 Mahalanobis Distance 65 0.47 Table 8. Supervised classificati on commission and omission errors, and producer and userÂ’s accuracy. Commission (%) Omission (%) Commission (Pixels) Omission (Pixels) Producer Accuracy User Accuracy Maximum Likelihood Continuous 25.93 18.85 202/779 134/711 81.15 74.07 Patchy 44.99 37.10 337/749 243/655 62.90 55.01 No SAV 5.53 22.48 41/741 203/903 77.52 94.47 Mahalanobis Distance Continuous 48.91 24.33 515/1053 173/711 75.67 51.09 Patchy 54.63 45.34 431/789 297/655 54.66 45.37 No SAV 5.89 34.57 64/1086 540/156265.43 94.11 Table 9. Validation for classificat ion methods SAV estimated coverage SAV Accuracy % No SAV Accuracy % Overall Accuracy % Maximum Likelihood 86.5 37.8 70.2 Mahalanobis Distance 85.8 36.5 69.3 A comparison of the MLC and MDC m aps presents discrepancies in the classification of patchy versus conti nuous SAV main areas with deeper water. Figure 17 illustrates a deep (>2m) seagrass bed. The MLC correctly classifies the area as patchy SAV; whereas, the M DC classifies the majority of the seagrass bed as continuous SAV. Figure 18 shows a deeper area classified as mostly continuous SAV by the MDC and pat chy SAV by the MLC. Unfortunately, the field survey data does not have enough sa mpling sites in this area to discern
44 which classification is more accurate Overall, the MLC and MDC produce similar classification maps; however, the MLC is the mo st accurate and reasonable of the tw o methods. Figure 17. Differences (red circle) betw een the supervised classification of Landsat 5 TM image using the Mahalanobis Distance and Maximum Likelihood methods.
45 Figure 18. Differences (red rectangle) between the supervised classification of Landsat 5 TM image using th e Mahalanobis Distance and Maximum Likelihood methods.
46 126.96.36.199 Soft Classification In an attempt to improve the results of the classification technique, the study also applied two supervised nonpar ametric classification methods. Considered soft classification methods linear spectral unmixing and neural network algorithms provide an alternativ e approach to the hard classification. Contrary to the hypothesis the Â“softÂ” classification methods, LSU and ANN did not improve the resolution and accuracy of the hard classification map. However, the application of these methods may provide improved classifications for imagery with more than three useful bands in the aquatic environment. 188.8.131.52.1 Artificial Neural Networks The artificial neural network (A NN) classification does not produce reasonable results (Figure 19). The ANN cl assifies less than 5% of the study area as SAV. Although this method is not su ccessful in this instance, the use of a different software program or algorithm may provide mo re reasonable results. In addition, these may be explained by t he low spectral difference of between the different classes or the number limitation of the spectral dimension of only three visible bands.
47 Figure 19. Artificial Neur al Network Classification of Landsat 5 TM image.
48 184.108.40.206.2 Linear Spectral Unmixing The linear spectral unmixing (LSU) is also applied to the Landsat TM image. The LSU does not pr oduce reasonable results for the classification map (Figure 20). The amount of endmember classes for the LSU must be less than the number of spectral bands used for t he classification. Since only three spectral bands (1, 2, and 3) are appropriate for the classification of SAV, only two endmember classes could be delineated.
49 Figure 20. Linear Spectral Unmi xing of Landsat 5 TM image.
50 4.2 Assessment of Cl assification Methods The assessment of the classificati on methods compares the accuracy and validation estimates, as well as, the spatia l distribution of the variation. The aerial photointerpretation (AP) m ap is used as a baseline for the comparison of the MDC and MLC classifications. The ability of the classification methods to map SAV is estimated from the accuracy and va lidation results for each technique. 4.2.1 Accuracy Comparison of SAV Maps Prior to comparing AP map to the ML C classification map, an accuracy assessment is completed for the AP map. Due to the limited ground truth data collected with the AP project, the 90% accuracy can not be compared to the products from this study. The validati on method for the classification maps is applied to the AP map. Since the validat ion dataset only supports the comparison of SAV and No SAV classes, the patchy and continuous classes of the AP map are combined for the validation. The valid ation dataset (n=216) is compared to the classes of the correspond ing pixels (Figure 21). The AP validation estimates 81% accuracy for correctly classifying the SAV and 51% for No SAV with an overall accuracy estimate of 71% (Table 10).
51 Figure 21. Comparison of validat ion data to the SAV Aerial Photointerpretation Map, 2006.
52 The comparison of the classificati on methods relies on the validation accuracy estimates since it was the only av ailable qualifier for all the methods. Although the overall estimated accuracy va ries slightly between classification methods, the estimated accuracy for the classes of SAV and No SAV varies significantly (Figure 22). To exami ne the variation between the methods the means and standard errors are calcul ated. For the six methods the SAV estimated accuracy from the validation analysis is 78% for SAV (SE= 3.15), 50% for No SAV (SE= 5.10), and 69% for Over all (SE= 0.58). The AP map has the highest overall accuracy (71.4%). T he MDC (69.3%) and MLC (70.2%) maintain a close overall accuracy; however, t he accuracy associated with mapping No SAV is below 40%. Although the overall accuracy for the MLC and MDC is lower for the presence/absence than the estima ted coverage classifications, the No SAV accuracy is much higher. To consider the best classifica tion method, the researcher needs to determine the fo cus of the study and the omission and commission statistics related to each method. Table 10. Comparison of validat ion for classification methods Classification Method SAV Accuracy % No SAV Accuracy % Overall Accuracy % ISODATA 76.6 51.8 68.4 MLC (Presence/Absence) 66.2 69.4 67.3 MDC (Presence/Absence) 74.2 58.5 68.9 MLC (Estimated coverage) 86.5 37.8 70.2 MDC (Estimated coverage) 85.8 36.5 69.3 AP map 81.1 51.2 71.4
53 0 10 20 30 40 50 60 70 80 90 100ISODATA ML C ( P r esence/Absence) M DC (P r e s e n c e / A b s e n c e ) MLC (Estimated Coverage) MDL( E stima t ed Cover a g e ) A PValidation Accuracy % SAV No SAV Overall Figure 22. Estimated cl assification accuracies deri ved from validation analysis for different classification methods. 4.2.2 Spatial Comparison to Existing SAV Maps The classification methods with the highest accuracy, kappa coefficient, and validation accuracy are compared to the existing AP map to determine spatial variation. For the delineation of presence/ absence of SAV, the MDC has 86% overall accuracy with a 0.70 Kappa coef ficient calculated from the testing ROIs. The overall validation accuracy for the MDL is 68%. For the delineation of SAV estimated coverage, the MLC has 74 % overall accuracy with a 0.61 Kappa coefficient calculated from the testing ROIs. The over all validation accuracy for the MLC is 70%. The AP map has an overall validation accuracy of 71% calculated for this study. For each cla ssification method the area per class is
54 calculated (Table 11). The greatest differenc e is between the delineation of the SAV (patchy) class in the MLC (48.76 km2) and the AP (11.50 km2). Table 11. Area calculated fo r each classificat ion method. Number of Pixels km 2 MDC (Presence/Absence) No SAV 67277 60.55 SAV 80623 72.56 Land 29000 26.10 MLC (Estimated coverage) No SAV 46049 41.44 SAV (patchy) 54183 48.76 SAV (continuous) 47668 42.90 Land 29000 26.10 AP (Estimated coverage) No SAV 44413 39.97 SAV (patchy) 12782 11.50 SAV (continuous) 49250 44.33 Land 14248 12.82 The spatial comparison of these classifi cations displays areas of variation between the maps. The compar ison of the AP and MDC for the presence/absence of SAV shows most discrepancies in the deep water areas along the edge of the seagr ass bed (Figure 23). The classes of Land and No SAV are combined to focus on the similarity for the SAV classification. The AP and MDC both map 43.70 km2 of SAV with a discrepancy of 18.74 km2 which is 16% of the study area (Table 12). The comparison of the AP and MLC for the SAV estimated coverage displays the most discrepancies in the deep water areas and along the edges of the seagra ss beds (Figure 24). The AP and MLC map 32.79 km2 of SAV at the same estimat ed coverage class, and 16.30 km2 of SAV with differing estimated coverage classes. The discrepancies cover 21.19 km2 which represents 19% of the study area (Table 13).
55 The spatial variation in the classification may be affected by the water increased water depth along the edges of the seagrass beds. The AP is known to be limited to approximately 2 m water dept h due to the refraction and absorption of the penetrating light wavelengths. The areas with dredged boat channels are consistently misclassified by the MDC and MLC. The width of the boat channels in comparison to the pixel size of the Lands at 5 TM imagery may indicate that the feature is too small to be accurately m apped by these classification methods and resolution. Other areas of discrepancy in clude the intertidal seagrass beds. Depending on the tidal stage at the acquisition time of the Landsat TM imagery, some of the seagrass beds may be expose d with little or no water separating the seagrass blades from the air. This ma y cause a variation in the spectral signature of the seagrass.
56 Table 12. Aerial P hotointerpretation versu s Mahalanobis Distance Classification Number of pixels km 2 Discrepancy 20825 18.74 Land/No SAV 51059 45.95 SAV 48560 43.70 Figure 23. Discrepan cies between the AP and MDC for the presence/absence of SAV.
57 Table 13. Aerial P hotointerpretation ver sus Maximum Likelihood Classification Number of pixels km 2 Discrepancy 23547 21.19 Land/No SAV 42339 38.10 SAV (same estimated coverage) 36442 32.79 SAV (different estimated coverage) 18116 16.30 Figure 24. Discrepancies between th e AP and MLC for the estimated coverage of SAV.
58 4.2.3 Ability to Map SAV Variation The ability to map the seagrass res ource is essential to the management and protection of the resource. The remo te sensing methods used to map and estimate the coverage of seagrass mu st provide reliable information. The detectable amount of the seagrass resource is related to the accuracy of the classification method. To determine the limitations of mapping seagrass, the MDC (presence/absence of SAV) and t he MLC (estimated coverage of SAV) classifications were analyzed. Additiona lly, the AP classification was examined for the ability and confidence of m apping seagrass resource. Based on the calculated accuracies from the confusion matrix analysis, the potential variation for miscl assification ranges from 10.86 km2 41.39 km2 (Table 14). Based on the calculated accura cies from the validation analysis, the potential variation for the miscl assification ranges from 31.06 km2 49.51 km2. These potential variation estimates are based on the entir e study area and not solely on the seagrass resource. Table 14. Potential variat ion associated with the estimated accuracies for the classification methods. Accuracy % Potential Variation (Number of Pixels) Potential Variation (km2) MDC (Presence/Absence) Overall Accuracy 86.79 23368 21.03 Validation Accuracy 68.9 55016 49.51 MLC (Estimated coverage) Overall Accuracy 74 45991 41.39 Validation Accuracy 70.2 52713 47.44 AP (Estimated coverage) Overall Accuracy 90 12069 10.86 Validation Accuracy 71.4 34517 31.06
59 The potential to map and assess the vari ation in the seagrass is important to the development of resource m anagement plans. The AP mapping project currently assesses the change in the resource. The estimated change in the seagrass resource between 2004 and 2006 was 2.92% increase (Kaufman, 2007). According to the analysis in this study, the 1.63 km2 increase in seagrass is below the detectable change threshold. Therefore, the resour ce is most likely within the variance of the classification method rather than truly increasing. Caution should be used when formulating conclusions on the fine scale trends associated with the classification maps.
60 Chapter 5 Conclusions The application of remote sensi ng techniques to map the seagrass resource has been examined in many studies in the past two decades with varying success. The challenges of deli neating habitat classes in the aquatic environment affect the accuracy and re liability of the produced maps. The prominent method of seagra ss mapping in Florida, U. S.A. is the AP mapping method. According to the results from this study, the accuracy of the ranges from the estimated validat ion accuracy of 71.4% to the project's assessed accuracy of 90%. The AP study provides a consistent baseline for the detection of spatial change in the seagrass resource. However, due to the temporal scale of the AP project, a 2-year cycle, t he produced maps may not detect shorter temporal variation in the seagrass re source. The occurrence of natural and anthropogenic events may cause damage to the seagrass resource that would not be detected for up to 2 years. To quickly assess the damage to the resource following the occurrence of a natural or anthropogenic disturbance, such as hurricanes or oil spills, the environment al resource managers require a reliable tool to assess the spatial extent of t he seagrass resource on a finer temporal scale. The temporal availability of the Landsat 5 TM imagery, 16-20 days, provides a suitable option to det ect and assess damage to the seagrass resource. This study provides an overview of thematic information extraction methods applied to the classification of the seagrass resource. The results suggest that the ISODATA and MDC met hods provide the most reliable maps delineating the presence/abs ence of SAV. For the delineation of SAV estimated coverage maps, the MLC method is the mo st appropriate technique according to
61 this study. While these remote sensing methods provide classification maps with similar accuracies to the AP method, additional research is necessary to improve and evaluate the classification techniques. To improve the accuracy for these remote sensing techniques, additional studies may focus on the refinement of t he spectral signatur es of the seagrass habitats. Future studies using the Landsat 5 TM imagery may apply a spectral library for the SAV species in the study ar ea. The time seri es of the Landsat 5 TM imagery beginning in 1984 may pr ovide an opportunity to apply the classification methods from this study to the histor ical Landsat Imagery in an attempt to assess the temporal change ov er the past two decades. Information regarding the spatial and temporal change dynamics assists environmental resource managers in the develop ment of successful management and protection plans for the seagrass resource. In conclusion, the results of this study suggest that the application of remote sensing methods is appropriate to assess the spatial extent of the seagrass resource in Clearwater Harbor and St. Joseph Sound, Florida. The supervised classification methods appli ed to the Landsat 5 TM imagery provide reasonable results that were comparabl e to the existing AP classification methods. While there is always opportunity for improvement, this study offers the option of using satellite imagery as a reliable data source for the mapping of the seagrass resource.
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