Mapping salt-marsh land-cover vegetation using high-spatial and hyperspectral satellite data to assist wetland inventory

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Mapping salt-marsh land-covervegetation using high-spatial andhyperspectral satellite data to assistwetland inventoryLalit Kumara & Priyakant Sinhaaa Ecosystem Management, School of Environment and RuralScience, University of New England, Armidale, NSW 2351,AustraliaPublished online: 18 Aug 2014.

To cite this article: Lalit Kumar & Priyakant Sinha (2014): Mapping salt-marsh land-covervegetation using high-spatial and hyperspectral satellite data to assist wetland inventory, GIScience& Remote Sensing

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Mapping salt-marsh land-cover vegetation using high-spatial andhyperspectral satellite data to assist wetland inventory

Lalit Kumar and Priyakant Sinha*

Ecosystem Management, School of Environment and Rural Science, University of New England,Armidale, NSW 2351, Australia

(Received 11 June 2013; accepted 18 July 2014)

Information on wetland condition can be used for various decision-making processesfor better management of this vital resource. Salt marshes are complex ecosystems thatare not well mapped and understood. This research was conducted to assess thepotential of high-spatial and high-spectral resolution satellite data to map and monitorsalt-marsh vegetation communities of Micalo Island of New South Wales, Australia.The aim of the study was to determine whether different salt-marsh vegetation speciescould be differentiated using high-spectral and high-spatial resolution imagery andwhether these could be linked to wetland condition. To compare sensor capabilities indiscriminating salt-marsh vegetation, high-spatial data sets from Quickbird and high-spectral data sets from Hyperion were used. A hybrid unsupervised and supervisedclassification procedure was used to assess the wetland mapping potential of theQuickbird and Hyperion data. The supervised classification results had greater overalland within-class accuracies and showed greater promise. Most of the vegetationspecies were identified and mapped correctly. One area of concern was the misclassi-fication of Sporobolus into grass categories while using Quickbird imagery, mainlywhere the Sporobolus was tall and dry. They look very similar to the tall reedy grass.The mapping results can be useful in establishing baseline information for subsequentstudies involving change detection of salt-marsh ecosystems.

Keywords: high-spatial resolution; hybrid classification; hyperspectral data; wetland;salt-marsh vegetation; remote sensing

1. Introduction

Coastal intertidal salt marshes are ecologically important habitats that link the marine andterrestrial environments and provide habitat for both marine and terrestrial organisms,including several important biodiversity resource species (Belluco et al. 2006). Coastalsalt marshes provide an important buffer between land and reef as they filter land run-offand improve the quality of water (Corcoran, Knight, and Gallant 2013; Salvia et al. 2009).A key element in intertidal system dynamics is halophytic vegetation, which tends tooccupy the hypersaline soils of the upper intertidal zone (Cronk and Fennessy 2001;Goudkamp and Chin 2006). These communities are generally found growing in areaslocated above the mean sea level but below mean high water level, and thus floodedaccording to local tidal periodicities (Belluco et al. 2006). These vegetations are made upof salt-tolerant flowering plants in the form of low growing shrubs, herbs and grasses. Theareas of bare ground found in and around salt-marsh habitats are known as saltpans orsalt flats, and are generally covered in mats of algae during the wet season (Goudkamp

*Corresponding author. Email: [email protected]

GIScience & Remote Sensing, 2014http://dx.doi.org/10.1080/15481603.2014.947838

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and Chin 2006). Salt-marsh vegetation provides stability in areas by supporting ecologi-cally diverse communities of plants and animals and forms ecosystem links betweenmarine and terrestrial environments. Salt-marsh species have specialized physiologicaladaptations (including the ability to secrete salt from their plant tissue), which allows themto survive and reproduce in these otherwise uninhabitable saline environments (Lugo andSnedaker 1974; Minden et al. 2012). Salt marshes also protect shorelines from erosion bybuffering wave action and trapping sediments. They reduce flooding by slowing andabsorbing rainwater and protect water quality by filtering run-off, and by metabolizingexcess nutrients.

Salt marshes have been subject to extensive exploitation, modification and destruc-tion (Zhang et al. 1997). These intertidal ecosystems are subject to the effects of humanactivities such as coastal development and declining water quality (Mishra 2014). Salt-marsh communities occur in both tropical and temperate regions of Australia, withhigher species diversity found in temperate regions. Both natural- and human-relatedfactors can cause changes in the condition of salt-marsh communities over time(Goudkamp and Chin 2006). In terms of natural factors, climatic variations (especiallyrainfall) have been known to cause changes in local salt-marsh ecosystems (Mayer andLopez 2011). Coastal developments in the form of physical damage or removal andchanges in hydrology and salinity regimes are identified as major anthropogenic pres-sures on salt-marsh ecosystems. Such activities lead to a decline in water quality due toincreased levels of sediments, nutrients and pesticides and also have adverse impacts onaquaculture due to increased siltation, erosion and nutrient loss. A significant area of theeastern coast of Australia has been developed since European settlement, though theactual area of salt-marsh habitat lost since that time is unknown (Goudkamp and Chin2006). Recent trends in salt-marsh ecosystem condition (in terms of their distributionand plant species composition) have shown localized declines in salt-marsh habitats(Adam 2002); however, the overall condition of salt marsh is found relatively stable(Goudkamp and Chin 2006). Nevertheless, in order to detect more subtle changes in thehealth and condition within these ecosystems, there is a need to establish baselineinformation at suitable scales and also for long-term monitoring and retrospectiveremote sensing. Continued research and monitoring is required to provide up-to-dateinformation on mangrove and salt-marsh habitat boundaries, and to improve our abilityto detect subtle changes in the distribution and plant species composition of thesecommunities. Mapping and modelling in salt marshes faces distinct challenges due totidal oscillation and variability, fieldwork logistics and the inherent dynamic nature ofthese environments (Akumu et al. 2010).

Remote sensing has played a very important role in mapping coastal vegetation andhas been applied in wetland and salt-marsh habitat monitoring (e.g. Mayer and Lopez2011; Ozesmi and Bauer 2002; Ritter and Lanzer 1997; Schmidt and Skidmore 2003;Schmidt et al. 2004; Torbick and Becker 2009; Zhang et al. 1997). Mapping of saltmarshes requires repeatable and reliable updates of land-cover maps because of theirdynamic nature. Remote-sensing techniques provide temporal data sets in quick succes-sion and at reasonable costs, and hence are a simple and cost-effective way of informationextraction. In the past, studies on mapping and monitoring coastal wetlands using coarse-scale Landsat or SPOT satellite data have achieved moderate success, probably due moreto intermixing between classes (Johnston and Barson 1993; Kuenzer et al. 2011).However, with SPOT-5 (10 m), a much higher accuracy has been achieved in mappingemergent and submerged wetland vegetation (e.g. Davranche, Lefebvre, and Poulin 2010)and also monitoring their ecological states (e.g. Poulin, Davranche, and Lefebvre 2010).

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Numerous studies used airborne hyperspectral data, particularly, Compact AirborneSpectral Imager (CASI) imagery for mapping and monitoring salt marshes (e.g. Bellucoet al. 2006; Hunter and Power 2002; Thomson et al. 2003); however, the data acquisitionprocess was found to be a time-consuming and expensive activity (Hunter and Power2002). The recent development of high-spatial resolution satellite remote-sensing data hassignificantly improved the capacity for salt-marsh and coastal vegetation mapping. Forexample, Space Imagings IKONOS and Digital Globes QuickBird-2 satellite data,possessing metre to sub-metre spatial resolutions with the advantage of satellite platformfor repeated data acquisition and the relatively low processing costs, facilitated the routinechange detection monitoring of both salt-marsh and terrestrial vegetation. For example,Wang et al. (2007) used high-spatial resolution QuickBird-2 satellite remote-sensing datato map both terrestrial and submerged aquatic vegetation communities of the NationalSeashore Suffolk County, New York, and achieved approximately 82% and 75% overallclassification accuracy for the terrestrial and submerged aquatic vegetation, respectively,and provided an updated vegetation inventory and change analysis results. Ouyang et al.(2011) used Quickbird imagery to efficiently discriminate salt-marsh monospecific vege-tation stands using object-based classification methods in terms of accuracy than pixel-based classification method.

Knowledge of the spatial heterogeneity is also critical to develop a functional analysisof the landscape, where different cover types are identified based on differences inresource dependencies of species or species groups (Fahrig et al. 2011). A large numberof contiguous bands from hyperspectral remote-sensing data carry valuable analyticsignatures for identifying salt-marsh species and for species differentiation (Bachmannet al. 2003; Hirano, Madden, and Welch 2003; Schmidt and Skidmore 2003; Schmidtet al. 2004). Schmidt et al. (2004) showed that the use of hyperspectral data and expertsystems could provide accuracies similar to that obtained using aerial photographs but at amuch lower cost. Zhang et al. (1997) concluded that hyperspectral remote sensing hasshown promising results in mapping salt marshes in San Pablo Bay in California.Manjunath et al. (2013) used hyperspectral in situ data and applied statistics to discrimi-nate mangrove canopies of different species and mudflat classes. Belluco et al. (2006)used several multispectral and hyperspectral data sets to discriminate different salt-marshvegetation species of Venice Lagoon, Italy, and found the classifications of hyperspectraldata to be somewhat superior to those of multispectral data. However, after close analysisof results of the features reduction experiments, they found that spatial resolution affectedclassification accuracy much more than spectral resolution. For the same study, consider-ing the difficulties encountered in mapping mixed halophytic vegetation of severalspecies, Wang et al. (2007) applied vegetation community-based neural network classifieron spectrally sampled CASI image to obtain substantially higher (91%) accuracies.

The current study determines how spectral and spatial resolutions of satellite imagesaffect salt-marsh vegetation discrimination. For wetland inventory, it is essential to have aknowledge of the spatial distribution of salt-marsh vegetation types. This study exploresthe potential of high-spatial and high-spectral resolution satellite data for reliably dis-criminating salt-marsh vegetation species with the help of concurrent detailed fieldobservations. The objectives were (1) to identify a data set suitable for mapping tidalvegetation and (2) to assess the separability of the classes of interest. The results can thenbe used as baseline information for subsequent studies to develop a GIS-based method ofassessing the condition of wetlands in terms of degradation and prioritizing wetlands forrehabilitation.

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2. Material and methods

2.1. Study area

The study area covers the whole of Micalo Island of New South Wales, Australia,including the Clarence Estuary Nature Reserve that forms the southern part of the islandbetween 153 17 50 E to 153 21 03 E longitude and 29 24 45 S to 29 28 25 Slatitude (Figure 1). It covers approximately 950 ha and includes both terrestrial andestuarine habitats. Of this 950 ha, 93% is made up of Micalo and Joss Islands; theremaining 7% is comprised of a portion of Micalo and Joss Channels including thenorth-eastern portion of Wooloweyah Lagoon where they all meet (Figure 1). The studysite, while located close to the coast, is not significantly impacted by coastal tides;however, there was substantial amount of standing water in many parts.

2.2. Remote sensing and field data

Satellite imagery from two sensors were used for this research. High-spatial resolutiondata from Quickbird and high-spectral resolution Hyperion data were used to compare thesensor capabilities in discriminating salt-marsh vegetation. Quickbird images have 0.7 mpixel resolution in the panchromatic mode and 2.4 m resolution in the multispectral mode.The multispectral mode consists of four broad bands in the blue (450520 nm), green(520600 nm), red (630690 nm) and near-infrared (760900 nm) parts of the electro-magnetic spectrum. Hyperion images have 242 narrow bands and a pixel resolution of30 m. The Quickbird satellite data were captured on 12July 2004, and the Hyperionsatellite data were captured on 15July 2004.

Figure 1. Location of Micalo Island in the Clarence Valley, North East New South Wales,Australia. The Quickbird standard false colour composite (FCC) image of 12 July 2004 has beenused to show different vegetation types of study area.


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Extensive fieldwork was conducted in the study area on the 20th and 21st of July2004. Data were collected for a total of 297 random locations, stratified by vegetation andother cover types (pasture, grass, water, etc.). Each of the sample sites were homogenousareas of at least 30 m 30 m so that the data collected could be used for the Hyperion aswell as the Quickbird image training and classification. Ground data included mainvegetation species, percentage occurrence of each species within the selected plots,crown cover and density and their global positioning system (GPS) locations. Duringthe field work, a number of ground control points were also collected for image rectifica-tion using a differential GPS system, and images were rectified to WGS 84 UTM Zone56 S projection system. The entire image-processing task was carried out in ENVI 4.8(ITT Visual Information Solution, USA). The main salt-marsh vegetation species atMicalo Island and their scientific names are given in Table 1.

The main non-salt-marsh vegetation on Micalo Island, other than salt-marsh species,were casuarina (Casuarina glauca), paperbark (Melaleuca quinquenervia), mangroves(Avicennia marina and Aegiceras corniculatum), pasture grass, tall reedy grass and anumber of shrub-type weeds (DEWHA 2010, 60). Sample sites were selected using theaerial photograph of the study area and also the Quickbird image as shown in Figure 1.Sampling sites were selected from both salt-marsh and non-salt-marsh areas, and field datawere collected at these representative sites. The vegetation species at the field sites werefairly diverse, with a number of species in different stages of growth and maturity. Therewere stands of Sarcocornia which exhibited a very reddish colour, while short distancesaway, there were Sarcocornia which were very green. Similar phase differences were alsoobserved for Sporobolus: some were lush and green, while others were tall and dry(Figure 2AD). Given the diverse nature of the vegetation and the large differences in theamount of water in the background, the land-cover types were categorized into 20 groups(Table 2). The per cent occurrence and crown cover of each species were establishedvisually. Permanent standing water constituted between 30% and 80% of the backgroundin many parts, and this complicated vegetation categorization and image processing.

2.3. Image classification

The images were georeferenced to a common coordinate system. The images were masked,and areas of interest were extracted from the remote-sensing data. Salt-marsh land-coverclassification was carried out using the Quickbird bands (B1B4) and all of the bands of theHyperion image. To combine the strengths of supervised and unsupervised approaches, ahybrid classification approach was utilized. Initially, an unsupervised Iterative Self-Organizing Data Analysis clustering into 20 clusters was performed. These 20 clusterswere further regrouped to 13 classes as initial classification indicated similarity in a numberof salt-marsh vegetation types, especially with high water background that masked the

Table 1. Main salt-marsh vegetation species at Micalo Island (DEWHA 2010).

Common name Scientific name

Salt couch Sporobolus virginicusSamphire Sarcocornia quinquefloraCreeping brookweed Samolus repensAustral seablite Suaeda australisSea rush Juncus krausii


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Figure 2. Diverse vegetation species identified at the field sites, with a number of species indifferent stages of growth and maturity from lush and green to tall and dry.

Table 2. Vegetation species groups as per fieldwork data in the study area conducted on the 20thand 21st of July 2004.

Group Explanation

Sporobolus Greater than 90% SporobolusSporobolus dominant Sporobolus was the dominant species (>50%)Sporobolus wet dominant Sporobolus dominant (>50%) with a wet backgroundSarcocornia Greater than 90% SarcocorniaSarcocornia dominant Sarcocornia was the dominant species (>50%)Sarcocornia wet dominant Sarcocornia dominant (>50%) with a wet backgroundMix wet Even mixture of Sporobolus and Sarcocornia with waterSuaeda Suaeda the dominant species (>50%)Samolus dominant Samolus the dominant species (>50%)Juncus type (dry brown) Juncus (type 1) was the dominant species (>50%)Juncus type (wet) Juncus (type 2) was the dominant species (>50%)Casuarina/mangrove/Melaleuca Either of the three present or a mixtureCasuarina dominant Pure casuarina standsGrass Tall dry reedy grassGrass dominant Tall dry reedy grass with shrubby weedsPasture Grazed pasture the only speciesPasture/weeds A mixture of pasture and weedsPasture dominant Grazed pasture the dominant speciesWater dominant Water the dominant feature but with some vegetation


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different vegetation spectra. For example, Sporobolus >90%, Sporobolus dominant andSporobolus with wet background were grouped. Similarly, different types of Sporobolusand other vegetation types were grouped together. Unambiguous signatures were retainedwhile small classes were deleted, and spectrally similar classes of identical land-cover typeswere merged. A comprehensive set of spectral class signatures was generated that was usedin the second stage of training data for a maximum likelihood classification (MLC) through(1) identification of features and selection of training areas based on field sample data, (2)evaluation and analysis of training signature statistics and spectral patterns and (3) classi-fication of the images. Differential GPS-based reference samples collected during the fieldvisit of the sites were first superimposed on Quickbird standard FCCs (2.4 m resolution)using 4 3 2 band combination and checked for class homogeneity around the sample points,and if required, a point was slightly moved to the adjacent pixel to accommodate moresimilar pixels in the surroundings. These sample points were used to make samples of 3 3pixels (9 pixels around each point). Given the fact that the positional accuracy of locationsextracted from high-resolution images can be degraded by off-nadir acquisition and imagedistortion, the 3 3 pixels accounted for any existing positional error. To avoid any classmixing, the 3 3 sample pixels were further refined with respect to class homogeneity byretaining only pure pixels in a given polygon and discarding pixels falling on classboundaries or neighbouring classes. After refinement, a total of 1189 sample pixels wereleft for training and accuracy assessment tasks. From the total sample pixels, 416 trainingpixels were randomly selected for signature generation and image classification, while theremaining samples were used for classification accuracy evaluations. These training siteswere spatially well distributed to capture the signature differences from different parts of thestudy area and also to cover different stages of growth/maturity. The same training siteswere used for Hyperion data sets. This enabled us to compare the effectiveness of the twosystems for mapping salt-marsh vegetation.

To verify how well salt-marsh vegetation could be differentiated from the othercategories, reflectance signatures for similar vegetation species in the group of 20 classeswere merged to make five broad land-cover class signatures, which were used in theclassification of Quickbird and Hyperion images. All salt-marsh species were groupedinto one class called salt-marsh vegetation. Table 3 explains the list of salt-marsh land-cover classes that were grouped together and the resulting classes.

Table 3. Salt-marsh land-cover classes in the study area based on field data collectedon 20 and 21 July 2004 and their final groupings used in supervised classification.

Initial class Final class

Sporobolus Salt-marsh vegetationSuaedaSarcocorniaSamolusSporobolus dominantSarcocornia dominantMixed (sarc, sp., etc.)

Casuarina Cas/man/melMangroveMelaleuca

Pasture PasturePasture dominant

Grass GrassWater Water


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2.4. Accuracy assessment

Classification accuracy assessment was carried out to verify the fitness of classificationproducts and to compare the performances of Quickbird and Hyperion images in salt-marshland-cover classification. Considering the general guidelines for minimum number of samplesrequired for each land-cover category from Congalton and Green (2009) for accuracy assess-ment, 773 sample points were used for classification accuracy assessments. The evaluationwas undertaken by comparing the location and class of each ground-truthed pixel with thecorresponding location and class on the classified images. An error matrix was constructedexpressing the accuracies in terms of producers accuracy (PA), users accuracy (UA) andoverall accuracy (OA) (Congalton and Green 2009; Congalton 1991). This provided a meansof expressing the accuracies of each individual class and their contribution to OA. Kappacoefficient () (Congalton 1991) was also used to quantify how much better a particularclassification was compared to a random classification and to calculate a confidence intervalto statistically compare two or more classifications. A pair-wise test of significance (Z-statistic) (Gong and Howarth 1990) was used to compare obtained from the error matricesof two classifications to determine if they were significantly different.

3. Results

3.1. Classification results

The unsupervised classification resulted in 13 different classes that were in accordance withthe field data collected and helped us in understanding the vegetation grouping based on theirspectral response. Visually, unsupervised classification discriminated some features very well,such as water body, as water has a very different spectral signature to other features. However,very shallow waters and beach heads with relatively high reflectance were confused withother classes. Grasses and pasture were also well separated from wetland vegetation. As agroup,Melaleuca,Mangroves and Casuarina were well separated from the wetland vegetationand grass/pasture categories. However, mixing between mangroves, Casuarina andMelaleucawas evident. The salt-marsh vegetation was also reasonably well separated from non-salt-marsh vegetation; however, there was mixing within the salt-marsh vegetation. The unsuper-vised classification results from the Hyperion data also gave somewhat similar results. Waterand pasture/grasses were well differentiated; however, there was confusion within the salt-marsh vegetation classes. It should be noted that such misclassifications were expected in anunsupervised classification procedure as the variance within classes is not defined withtraining samples (Schmidt et al. 2004). Spectral class signatures generated from the unsuper-vised method were used in the second stage of training data for an MLC.

Table 4 shows the UA, PA and OA for both Quickbird and Hyperion images using thesupervised classification method. The overall classification accuracy obtained fromHyperion image (46.1%) was higher than that from Quickbird image (42.2%), whichsuggests that higher spectral data helps in detecting the spectral variability of the classescompared to high-spatial data which fails in overcoming the homogeneity in land-coverclasses. The Z-value for significance testing shows that Hyperion classification accuracywas significantly higher than Quickbird classification accuracy in terms of OA and kappa at95% confidence level. The supervised classification results also showed high confusionbetween vegetation types. Though similar classes with different background of water weremerged, most of the primary vegetation species were better mapped. Waterbodies were welldelineated by both types of imagery as they both produced 100% PA. Similar studies inwetland areas have also delineated water body from other wetland features (e.g. Davranche,Lefebvre, and Poulin 2010). One area of concern was the misclassification of Sporobolus


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into grasses while using Quickbird imagery. This was mainly the case where the Sporoboluswas tall and dry, looking very similar to the tall reedy grass. Such a scenario is shown inFigure 3. From Figure 3(A), we can see an extensive stretch of Sporobolus. A number ofsamples were taken around this area, and almost all of the open area shown in image 3(B)was Sporobolus. However, in the Quickbird classified image 3(C), most of these areas aremisclassified as pasture and grass. Only a small part of this comes out as Sporobolusdominant. In the Hyperion image 3(D), almost all of the Sporobolus areas are correctlyclassified. The mangroves and Sarcocornia are also generally correctly classified.

3.2. Grouped results

Figure 4 shows the full-extent salt-marsh land-cover classifications for both Quickbird andHyperion images using the 13 grouped classes, and Table 5 shows the corresponding

Table 4. Salt-marsh land-cover classification accuracies from Quickbird and Hyperion images.

Quickbird Hyperion

ClassUsersaccuracy

Producersaccuracy

Usersaccuracy

Producersaccuracy

Cas/man/mel 88.9 42.1 69.6 41.1Juncus + Mixed wetland 4.5 12.5 0 0Grass + Pasture 31.4 88.1 63.3 67.9Sarcocornia 100 35.1 53.2 67.6Sporobolus 45.5 32.9 50.9 34.2Water 14.3 100 29.1 100.0

Overall accuracy = 42.2% Overall accuracy = 46.1%

A B

C

0 0.25 0.5

1 cas/man/mel

Joss Island

Yamba

Micalo Island

N

2 Juncus

3 mix_wetland_sp

4 mix_water_dom

5 pasture_dry

6 pasture_weeds

7 sarc

8 sarc_dom

9 sarc_dom_wet

10 sarc_wet

11 sporobolus

12 sp_dom

13 sp_dom_wet

14 sp_wet

15 water

km

D

Figure 3. Supervised classification results from two images. Photograph (A) is located at xmarked in image B. C gives the classification results for Quickbird image, and D for Hyperionimage.


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classification accuracies. The overall classification accuracy obtained from Hyperionimage (71.1%) was much higher than that from Quickbird image (59.4%). The groupedresults highlighted some interesting differences between the two images. It seemed thatthe Hyperion data grouped the classes much better, and the groups were clearly deli-neated. In the case of Quickbird data, a lot more mixing of groups was observed, resultingin the classification being very speckled. This was not necessarily considered an error ashigh variability can be expected from the very high spatial resolution of Quickbird data

Figure 4. Grouped land-cover classification results for Quickbird (lower left) and Hyperion (lowerright) in the study area.

Table 5. Grouped salt-marsh land-cover classification accuracies from Quickbird and Hyperionimages.

Quickbird (Grouped) Hyperion (Grouped)

ClassUsersaccuracy Producers accuracy

Usersaccuracy Producers accuracy

Cas/man/mel 88.9 41 65.4 44.7Pasture 42.9 27.3 12.5 9.0Dry grass 17.5 91.7 44.4 57.4Salt-marsh vegetation 81.8 64.8 79.3 85.4Water 72.2 81.3 88.4 100.0

Overall accuracy = 59.4% Overall accuracy = 71.1%


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(2.4 m) as compared to 30 m for Hyperion data. This higher resolution of Quickbird dataallowed for the recording of more ground detail on the image, which otherwise was notpossible using the larger pixels of Hyperion.

4. Discussion and conclusion

Salt-marsh vegetation is an important part of wetland ecosystems (Belluco et al. 2006) andalso an excellent indicator for any physical or chemical degradation in wetland environ-ments (Dennison et al. 1993). Salt-marsh vegetation roots stabilize the soil, and theaboveground biomass reduces water flow velocity, thus effectively slowing down sedi-ment resuspension and erosion (e.g. Leonard and Luther 1995). Since mapping andquantifying vegetation species distribution are important technical tasks for sustainablewetland management, it is essential to have accurate and detailed understanding of thespatial distribution of vegetation cover in a given wetland (He et al. 2005). One of theobjectives of this study was to identify a suitable baseline data set for future wetlandvegetation mapping applications and, in terms of accuracy achieved, the classificationresults from hyperspectral data were somewhat superior to those from multispectral data.This implies that Hyperion data achieved a higher level of discrimination than Quickbirddata, especially in discriminating between salt-marsh vegetation such as Sarcocornia andSporobolus. Overall, both types of data showed a lot of promise as a tool for salt-marshmapping. Mapping was affected by the mixed nature of vegetation. There were areas withCasuarina but with Sporobolus in the background. Similarly there were areas withmangroves but with either Sarcocornia or Sporobolus in the background. Most of thesecanopies were fairly open, so the background vegetation contributed significantly to thespectral signature. Though the study site was located close to the coast, it was notsignificantly impacted by coastal tides; however, there was substantial amount of waterin many parts. Classification accuracies were also affected by the amount of water in thebackground. There were large areas in the study site where water covered more than 50%of the background. In such environments, differentiation between various salt-marshspecies was difficult due to the dominance of the water reflectance masking the salt-marsh vegetation signature. The effect was found to be higher with the multispectral databecause the near-infrared band was attenuated by the occurrence of underlying water andwet soil (Hestir et al. 2008; Zomer, Trabucco, and Ustin 2009). Once all salt-marshspecies were grouped together, the OA was found to be much higher with both theimage data types. However, this shows the difficulty in separating individual salt-marshspecies, especially in an area where background water becomes a dominant feature.

The lower effectiveness of multispectral Quickbird data in salt-marsh vegetation dis-crimination can be attributed to its poor spectral resolution as the broad bandwidths areunable to separate narrow vegetation units due to similar biochemical and biophysicalproperties, a characteristic of wetland ecosystems. The spectral variations within a speciescan also be due to age differences, micro-climate, soil and water background, precipitation,topography and stresses (Adam, Mutanga, Rugege 2010). These factors further complicatethe optical reflectance and result in a decrease in the spectral reflectance, especially in thenear-infrared to mid-infrared regions where water absorption is high (e.g. Silva et al. 2008),and hence warrant a more detailed study considering these parameters in the future. Otherstudies have also shown that multispectral data have not been very effective in discriminat-ing vegetation species in wetland environments (e.g. Harvey and Hill 2001; McCarthy,Gumbricht, and McCarthy 2005), mainly due to their broad spectral wavebands that werefound insufficient in distinguishing fine ecological divisions between certain vegetation


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species. The greater spectral dimensionality of hyperspectral data carries in-depth informa-tion and enables discrimination of vegetation types and thus improves classificationaccuracies. Though hyperspectral data used in this study were found effective in discrimi-nating wetland species as compared to multispectral image, the different wetland speciesreflectances were found highly correlated for hyperspectral data, thus getting separatespectral signatures of the plant species was found difficult. In such situations, it might beuseful to run data-reduction algorithms, such as principal component analysis or maximumnoise fraction (MNF), to obtain spectrally uncorrelated data and perform classifications onthese selected bands. For mapping salt-marsh vegetation using multispectral and hyper-spectral remote-sensing data, Belluco et al. (2006) applied different feature extraction/selection algorithms to obtain four bands derived from MNF transformation. Their resultsof the feature reduction experiments showed that spatial resolution affects classificationaccuracy much more than spectral resolution. Nevertheless, the results of this research haveshown that satellite imagery can be used to differentiate salt-marsh wetlands from non-salt-marsh areas. Both the high-spatial resolution Quickbird and the high-spectral resolutionHyperion data were able to achieve acceptable accuracies. The results also show thatsatellite imagery can be used to map different salt-marsh species.

The results from this study can be used as base information on wetland vegetationspecies for monitoring the changes in salt-marsh ecosystems. This is important in under-standing the salt-marsh habitat loss and change in ecosystem condition in terms of theirdistribution and plant species composition. For example, Fries et al. (2012) used five-yearmultispectral imagery for salt-marsh classification and accurately showed subtle changesin vegetation community composition within their boundaries. Though previous reportshave found salt-marsh conditions in the study area to be relatively stable (Goudkamp andChin 2006), there is a need to establish baseline data at suitable scales for long-termmonitoring and retrospective remote sensing. The results from this study have achievedthis task. However, there are a few adjustments that can improve the classification resultsand can be used in subsequent research. For example, to improve the class-separabilityand the classification results, imagery should be obtained during suitable periods ofgrowth of the species of interest (Schill et al. 2004; Sinha, Kumar, and Reid 2012a).This is important as knowledge on temporal heterogeneity/variability helps in differentiat-ing vegetation patterns during development (phenology, senescence) which often differamong species, and therefore combinations of various images taken at different periods ofthe year are important for species differentiation (Sinha, Kumar, and Reid 2012b), alongwith spatial and spectral resolution. For this study, images for only one time period wereused and could be one of the reasons for the low classification accuracies. Ideally, thereshould be consultations with locals and experts in vegetation ecology as to when thespecies present have maximum discrimination and what periods to avoid. In our case,imagery was obtained when both the tall reedy grass and Sporobolus were tall and dry.This led to increased confusion between classes and impacted on the classificationaccuracy. Also, the lack of temporal data for the study site prevented the research teamfrom making any judgements about the condition of the salt marsh. Estimation ofbiophysical parameters of the plants leaves and canopy (Kumar et al. 2001) can alsohelp in discriminating salt-marsh vegetation as these factors affect the spectral reflectanceamong vegetation species, and hence should be included in further studies. Another areathat can improve classification results is through the use of more advanced imageclassification methods such as CART, MEMSA, neural networks, etc., and several studieshave shown improved results in coastal vegetation classifications using these techniques


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(e.g. Becker, Lusch, and Qi 2007; Kokaly et al. 2003; Myint et al. 2008; Reif et al. 2009;Cho et al. 2014; Li, Ustin and Lay 2005).

Nevertheless, the results from this study show that salt-marsh vegetation can bemapped and separated at the species level with reasonable accuracies. With an appropriateselection of image capture timing, these accuracies can be improved and be used formonitoring the change dynamics of salt-marsh ecosystems and future management andrestoration of these dynamic environments.

ReferencesAdam, E., O. Mutanga, and D. Rugege. 2010. Multispectral and Hyperspectral Remote Sensing for

Identification and Mapping of Wetland Vegetation: A Review. Wetlands Ecology andManagement 18: 281296. doi:10.1007/s11273-009-9169-z.

Adam, P. 2002. Saltmarshes in a Time of Change. Environmental Conservation 29 (1): 3961.doi:10.1017/S0376892902000048.

Akumu, C. E., S. Pathirana, S. Baban, and D. Bucher. 2010. Modeling Methane Emission fromWetlands in North-Eastern New South Wales, Australia Using Landsat ETM+. Remote Sensing2: 13781399. doi:10.3390/rs2051378.

Bachmann, C. M., M. H. Bettenhausen, R. A. Fusina, T. F. Donato, L. Russ, J. W. Burke, G. M.Lamela, W. J. Rhea, B. R. Truitt, and J. H. Porter. 2003. A Credit Assignment Approach toFusing Classifiers of Multiseason Hyperspectral Imagery. IEEE Transactions on Geoscienceand Remote Sensing 41 (11): 24882499. doi:10.1109/TGRS.2003.818537.

Becker, B. L., D. P. Lusch, and J. Qi. 2007. A Classification-Based Assessment of the OptimalSpectral and Spatial Resolutions for Great Lakes Coastal Wetland Imagery. Remote Sensing ofEnvironment 108: 111120. doi:10.1016/j.rse.2006.11.005.

Belluco, E., M. Camuffo, S. Ferrari, L. Modenese, S. Silvestri, A. Marani, and M. Marani. 2006.Mapping Salt-Marsh Vegetation by Multispectral and Hyperspectral Remote Sensing. RemoteSensing of Environment 105: 5467. doi:10.1016/j.rse.2006.06.006.

Cho, H. J., I. Ogashawara, D. Mishra, J. White, A. Kamerosky, L. Morris, C. Clarke, A. Simpson,and D. Banisakher. 2014. Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) Datafor Seagrass Mapping in Indian River Lagoon, FL. GIScience & Remote Sensing 51 (2):120138.

Congalton, R. G. 1991. A Review of Assessing the Accuracy of Classifications of RemotelySensed Data. Remote Sensing of Environment 37: 3546. doi:10.1016/0034-4257(91)90048-B.

Congalton, R. G., and K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principlesand Practices. Boca Raton, FL: CRC Press.

Corcoran, J. M., J. F. Knight, and A. L. Gallant. 2013. Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random ForestClassification of Wetlands in Northern Minnesota. Remote Sensing 5: 32123238.doi:10.3390/rs5073212.

Cronk, J. K., and M. S. Fennessy. 2001. Wetland Plants: Biology and Ecology. Boca Raton, FL:CRC Press.

Davranche, A., G. Lefebvre, and B. Poulin. 2010. Wetland Monitoring Using Classification Treesand SPOT-5 Seasonal Time Series. Remote Sensing of Environment 114: 552562.doi:10.1016/j.rse.2009.10.009.

Dennison, W. C., R. J. Orth, K. A. Moore, J. C. Stevenson, V. Carter, S. Kollar, P. W. Bergstrom,and R. A. Batiuk. 1993. Assessing Water Quality with Submersed Aquatic Vegetation.Bioscience 43: 8694. doi:10.2307/1311969.

DEWHA (Department of Environment, Water, Heritage and Arts, NSW). 2010. Wetlands AustraliaNational Wetlands Update. No.18. Accessed October, 2013. www.environment.gov.au/wetlands

Fahrig, L., J. Baudry, L. Brotons, F. G. Burel, T. O. Crist, R. J. Fuller, C. Sirami, G. M. Siriwardena,and J. Martin. 2011. Functional Landscape Heterogeneity and Animal Biodiversity inAgricultural Landscapes. Ecology Letters 14: 101112. doi:10.1111/j.1461-0248.2010.01559.x.

Friess, D. A., T. Spencer, G. M. Smith, I. Mller, S. M. Brooks, and A. G. Thomson. 2012. RemoteSensing of Geomorphological and Ecological Change in Response to Saltmarsh Managed


Dow

nloa

ded

by [

Mem

oria

l Uni

vers

ity o

f N

ewfo

undl

and]

at 1

4:38

07

Oct

ober

201

4

http://dx.doi.org/10.1007/s11273-009-9169-zhttp://dx.doi.org/10.1017/S0376892902000048http://dx.doi.org/10.3390/rs2051378http://dx.doi.org/10.1109/TGRS.2003.818537http://dx.doi.org/10.1016/j.rse.2006.11.005http://dx.doi.org/10.1016/j.rse.2006.06.006http://dx.doi.org/10.1016/0034-4257(91)90048-Bhttp://dx.doi.org/10.3390/rs5073212http://dx.doi.org/10.1016/j.rse.2009.10.009http://dx.doi.org/10.2307/1311969http://www.environment.gov.au/wetlandshttp://www.environment.gov.au/wetlandshttp://dx.doi.org/10.1111/j.1461-0248.2010.01559.x

Realignment, the Wash, UK. International Journal of Applied Earth Observation andGeoinformation 18: 5768. doi:10.1016/j.jag.2012.01.016.

Gong, P., and P. Howarth. 1990. An Assessment of Some Factors Influencing Multispectral Land-Cover Classification. Photogrammetric Engineering and Remote Sensing 56: 597603.

Goudkamp, K., and A. Chin. 2006. Environmental Status: Mangroves and Saltmarshes, The State ofthe Great Barrier Reef On-line, Great Barrier Reef Marine Park Authority, Townsville.Accessed January 20, 2013. http://www.gbrmpa.gov.au/publications/sort/mangroves_saltmarshes

Harvey, K. R., and J. E. Hill. 2001. Vegetation Mapping of a Tropical Freshwater Swamp in theNorthern Territory, Australia: A Comparison of Aerial Photography, Landsat TM and SPOTSatellite Imagery. International Journal of Remote Sensing 22: 29112925. doi:10.1080/01431160119174.

He, C., Q. Zhang, Y. Li, X. Li, and P. Shi. 2005. Zoning Grassland Protection Area Using RemoteSensing and Cellular Automata Modeling A Case Study in Xilingol Steppe Grassland inNorthern China. Journal of Arid Environments 63: 814826. doi:10.1016/j.jaridenv.2005.03.028.

Hestir, E. L., S. Khanna, M. E. Andrew, M. J. Santos, J. H. Viers, J. A. Greenberg, S. S. Rajapakse,and S. L. Ustin. 2008. Identification of Invasive Vegetation Using Hyperspectral RemoteSensing in the California Delta Ecosystem. Remote Sensing of Environment 112: 40344047.doi:10.1016/j.rse.2008.01.022.

Hirano, A., M. Madden, and R. Welch. 2003. Hyperspectral Image Data for Mapping WetlandVegetation. Wetlands 23 (2): 436448. doi:10.1672/18-20.

Hunter, E. L., and C. H. Power. 2002. An Assessment of Two Classification Methods for MappingThames Estuary Intertidal Habitats Using CASI Data. International Journal of Remote Sensing23 (15): 29893008. doi:10.1080/01431160110075596.

Johnston, R., and M. Barson. 1993. Remote Sensing of Australian Wetlands: An Evaluation ofLandsat TM Data for Inventory and Classification. Australian Journal of Marine andFreshwater Research 44: 235252. doi:10.1071/MF9930235.

Kokaly, R. F., D. G. Despain, R. N. Clark, and K. E. Livo. 2003. Mapping Vegetation inYellowstone National Park Using Spectral Feature Analysis of AVIRIS Data. RemoteSensing of Environment 84: 437456. doi:10.1016/S0034-4257(02)00133-5.

Kuenzer, C., A. Bluemel, S. Gebhardt, T. V. Quoc, and S. Dech. 2011. Remote Sensing ofMangrove Ecosystems: A Review. Remote Sensing 3: 878928. doi:10.3390/rs3050878.

Kumar, L., K. S. Schmidt, S. Dury, and A. K. Skidmore. 2001. Review of Hyperspectral RemoteSensing and Vegetation Science. In Imaging Spectrometry: Basic Principles and ProspectiveApplications, edited by F. D. Van Der Meer, and D. Jong SM. Dordrecht: Kluwer.

Leonard, L., and M. Luther. 1995. Flow Hydrodynamics in Tidal Marsh Canopies. Limnology andOceanography 40: 14741484.

Li, L., S. L. Ustin, and M. Lay. 2005. Application of Multiple Endmember Spectral MixtureAnalysis (MESMA) to AVIRIS Imagery for Coastal Salt Marsh Mapping: A Case Study inChina Camp, CA, USA. International Journal of Remote Sensing 26: 51935207. doi:10.1080/01431160500218911.

Lugo, A. E., and S. C. Snedaker. 1974. The Ecology of Mangroves. Annual Review of Ecologyand Systematics 5: 3964. doi:10.1146/annurev.es.05.110174.000351.

Manjunath, K. R., T. Kumar, N. Kundu, and S. Panigrahy. 2013. Discrimination of MangroveSpecies and Mudflat Classes Using in Situ Hyperspectral Data: A Case Study of IndianSunderbans. GIScience & Remote Sensing 50 (4): 400417.

Mayer, A. L., and R. D. Lopez. 2011. Use of Remote Sensing to Support Forest and WetlandsPolicies in the USA. Remote Sensing 3: 12111233. doi:10.3390/rs3061211.

McCarthy, J., T. Gumbricht, and T. S. McCarthy. 2005. Ecoregion Classification in the OkavangoDelta, Botswana from Multitemporal Remote Sensing. International Journal of RemoteSensing 26: 43394357. doi:10.1080/01431160500113583.

Minden, V., S. Andratschke, J. Spalke, H. Timmermann, and M. Kleyer. 2012. Plant Trait-Environment Relationships in Salt Marshes: Deviations from Predictions by EcologicalConcepts. Perspectives in Plant Ecology, Evolution and Systematics 14: 183192.

Mishra, D. R. 2014. Coastal Remote Sensing. GIScience & Remote Sensing 51 (2): 115119.Myint, S. W., C. P. Giri, L. Wang, Z. Zhu, and S. C. Gillette. 2008. Identifying Mangrove Species

and Their Surrounding Land Use and Land Cover Classes Using an Object-Oriented Approach


Dow

nloa

ded

by [

Mem

oria

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and]

at 1

4:38

07

Oct

ober

201

4

http://dx.doi.org/10.1016/j.jag.2012.01.016http://www.gbrmpa.gov.au/publications/sort/mangroves_saltmarsheshttp://dx.doi.org/10.1080/01431160119174http://dx.doi.org/10.1080/01431160119174http://dx.doi.org/10.1016/j.jaridenv.2005.03.028http://dx.doi.org/10.1016/j.jaridenv.2005.03.028http://dx.doi.org/10.1016/j.rse.2008.01.022http://dx.doi.org/10.1672/18-20http://dx.doi.org/10.1080/01431160110075596http://dx.doi.org/10.1071/MF9930235http://dx.doi.org/10.1016/S0034-4257(02)00133-5http://dx.doi.org/10.3390/rs3050878http://dx.doi.org/10.1080/01431160500218911http://dx.doi.org/10.1080/01431160500218911http://dx.doi.org/10.1146/annurev.es.05.110174.000351http://dx.doi.org/10.3390/rs3061211http://dx.doi.org/10.1080/01431160500113583

with a Lacunarity Spatial Measure. GIScience & Remote Sensing 45 (2): 188208.doi:10.2747/1548-1603.45.2.188.

Ouyang, Z. T., M. Q. Zhang, X. Xie, Q. Shen, H. Q. Guo, and B. Zhao. 2011. A Comparison ofPixel-Based and Object-Oriented Approaches to VHR Imagery for Mapping Saltmarsh Plants.Ecological Informatics 6: 136146. doi:10.1016/j.ecoinf.2011.01.002.

Ozesmi, S. L., and M. E. Bauer. 2002. Satellite Remote Sensing of Wetlands. Wetlands Ecologyand Management 10: 381402. doi:10.1023/A:1020908432489.

Poulin, B., A. Davranche, and G. Lefebvre. 2010. Ecological Assessment of Phragmites australisWetlands Using Multi-Season SPOT-5 Scenes. Remote Sensing of Environment 114:16021609. doi:10.1016/j.rse.2010.02.014.

Reif, M., R. C. Frohn, C. R. Lane, and B. Autrey. 2009. Mapping Isolated Wetlands in a KarstLandscape: GIS and Remote Sensing Methods. GIScience & Remote Sensing 46 (2): 187211.doi:10.2747/1548-1603.46.2.187.

Ritter, R., and E. L. Lanzer. 1997. Remote Sensing of Nearshore Vegetation in Washington StatesPuget Sound. Proceedings of 1997 ASPRS Annual Conference, Vol. 3, Seattle, WA, 527536.

Salvia, M., M. Franco, F. Grings, P. Perna, R. Martino, H. Karszenbaum, and P. Ferrazzoli. 2009.Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models. RemoteSensing 1: 9921008. doi:10.3390/rs1040992.

Schill, S., J. Jensen, G. Raber, and D. Porter. 2004. Temporal Modeling of Bidirectional ReflectionDistribution Function (BRDF) in Coastal Vegetation. GIScience & Remote Sensing 41 (2):116135. doi:10.2747/1548-1603.41.2.116.

Schmidt, K. S., and A. K. Skidmore. 2003. Spectral Discrimination of Vegetation Types in aCoastal Wetland. Remote Sensing of Environment 85: 92108. doi:10.1016/S0034-4257(02)00196-7.

Schmidt, K. S., A. K. Skidmore, E. H. Kloosterman, H. Van Oosten, L. Kumar, and J. A. M.Janssen. 2004. Mapping Coastal Vegetation Using an Expert System and HyperspectralImagery. Photogrammetric Engineering & Remote Sensing 70 (6): 703715. doi:10.14358/PERS.70.6.703.

Silva, T. S. F., M. P. F. Costa, J. M. Melack, and E. Novo. 2008. Remote Sensing of AquaticVegetation: Theory and Applications. Environmental Monitoring and Assessment 140:131145. doi:10.1007/s10661-007-9855-3.

Sinha, P., L. Kumar, and N. Reid. 2012a. Seasonal Variation in Land-Cover ClassificationAccuracy in a Diverse Region. Photogrammetric Engineering & Remote Sensing 78 (3):271280. doi:10.14358/PERS.78.3.271.

Sinha, P., L. Kumar, and N. Reid. 2012b. Three-Date Landsat Thematic Mapper Composite inSeasonal Land-Cover Change Identification in a Mid-Latitudinal Region of Diverse Climate andLand Use. Journal of Applied Remote Sensing 6 (1): 063595. doi:10.1117/1.JRS.6.063595.

Thomson, A. G., R. M. Fuller, M. G. Yates, S. L. Brown, R. Cox, and R. A. Wadsworth. 2003. TheUse of Airborne Remote Sensing for Extensive Mapping of Intertidal Sediments andSaltmarshes in Eastern England. International Journal of Remote Sensing 24 (13):27172737. doi:10.1080/0143116031000066918.

Torbick, N., and B. Becker. 2009. Evaluating Principal Components Analysis for IdentifyingOptimal Bands Using Wetland Hyperspectral Measurements from the Great Lakes, USA.Remote Sensing 1: 408417. doi:10.3390/rs1030408.

Wang, Y., M. Traber, B. Milstead, and S. Stevens. 2007. Terrestrial and Submerged AquaticVegetation Mapping in Fire Island National Seashore Using High Spatial Resolution RemoteSensing Data. Marine Geodesy 30 (12): 7795. doi:10.1080/01490410701296226.

Zhang, M., S. L. Ustin, E. Rejmankova, and E. W. Sanderson. 1997. Monitoring Pacific Coast SaltMarshes Using Remote Sensing. Ecological Applications 7 (3): 10391053. doi:10.1890/1051-0761(1997)007[1039:MPCSMU]2.0.CO;2.

Zomer, R. J., A. Trabucco, and S. L. Ustin. 2009. Building Spectral Libraries for Wetlands LandCover Classification and Hyperspectral Remote Sensing. Journal of EnvironmentalManagement 90: 21702177. doi:10.1016/j.jenvman.2007.06.028.


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http://dx.doi.org/10.2747/1548-1603.45.2.188http://dx.doi.org/10.1016/j.ecoinf.2011.01.002http://dx.doi.org/10.1023/A:1020908432489http://dx.doi.org/10.1016/j.rse.2010.02.014http://dx.doi.org/10.2747/1548-1603.46.2.187http://dx.doi.org/10.3390/rs1040992http://dx.doi.org/10.2747/1548-1603.41.2.116http://dx.doi.org/10.1016/S0034-4257(02)00196-7http://dx.doi.org/10.1016/S0034-4257(02)00196-7http://dx.doi.org/10.14358/PERS.70.6.703http://dx.doi.org/10.14358/PERS.70.6.703http://dx.doi.org/10.1007/s10661-007-9855-3http://dx.doi.org/10.14358/PERS.78.3.271http://dx.doi.org/10.1117/1.JRS.6.063595http://dx.doi.org/10.1080/0143116031000066918http://dx.doi.org/10.3390/rs1030408http://dx.doi.org/10.1080/01490410701296226http://dx.doi.org/10.1890/1051-0761(1997)007[1039:MPCSMU]2.0.CO;2http://dx.doi.org/10.1890/1051-0761(1997)007[1039:MPCSMU]2.0.CO;2http://dx.doi.org/10.1016/j.jenvman.2007.06.028

Abstract1. Introduction2. Material and methods2.1. Study area2.2. Remote sensing and field data2.3. Image classification2.4. Accuracy assessment

3. Results3.1. Classification results3.2. Grouped results

4. Discussion and conclusionReferences

Documents

Mapping salt-marsh land-cover vegetation using high-spatial and hyperspectral satellite data to assist wetland inventory