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Multispectral and hyperspectral remote sensing

Multispectral and hyperspectral remote sensing
Multispectral and hyperspectral remote sensing

ORIGINAL PAPER

Multispectral and hyperspectral remote sensing

for identi?cation and mapping of wetland vegetation:a review

Elhadi Adam ?Onisimo Mutanga ?Denis Rugege

Received:14October 2008/Accepted:25November 2009/Published online:11December 2009óSpringer Science+Business Media B.V.2009

Abstract Wetland vegetation plays a key role in the ecological functions of wetland environments.Remote sensing techniques offer timely,up-to-date,and relatively accurate information for sustainable and effective management of wetland vegetation.This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation,and estimating some of the biochemical and biophysical parameters of wetland vegetation.Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed.The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation,discriminating wetland vegetation using broad-and narrow-bands,as well as estimating water content,biomass,and leaf area index.It can be

concluded that the remote sensing of wetland vege-tation has some particular challenges that require careful consideration in order to obtain successful results.These include an in-depth understanding of the factors affecting the interaction between electro-magnetic radiation and wetland vegetation in a particular environment,selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation.

Keywords Biomass áLeaf area index áMapping áRemote sensing áWater content áWetland vegetation

Introduction

Wetland vegetation is an important component of wetland ecosystems that plays a vital role in environmental function (Kokaly et al.2003;Lin and Liquan 2006).It is also an excellent indicator for early signs of any physical or chemical degradation in wetland environments (Dennison et al.1993).

Mapping and monitoring vegetation species dis-tribution,quality,and quantity are important techni-cal tasks in sustainable management of wetlands.This task involves a wide range of functions includ-ing natural resource inventory and assessment,?re

E.Adam (&)áO.Mutanga

Discipline of Geography,University of KwaZulu-Natal,P.Bag X01,Scottsville 3209,Pietermaritzburg,South Africa

e-mail:205527619@ukzn.ac.za;emiadam2006@https://www.sodocs.net/doc/9113841119.html,

E.Adam

Geography Department,Elfashir University,P.Bag 125,Elfashir,Sudan

D.Rugege

Centre for Environment,Agriculture &Development (CEAD),University of KwaZulu-Natal,Pietermaritzburg,South Africa

Wetlands Ecol Manage (2010)18:281–296DOI 10.1007/s11273-009-9169-z

control,wildlife feeding,habitat characterization,and water quality monitoring at a given time or over a continuous period(Carpenter et al.1999).Moreover, it is essential to have up-to-date spatial information about the magnitude and the quality of vegetation cover in order to initiate vegetation protection and restoration programme(He et al.2005).

Traditionally,species discrimination for?oristic mapping requires intensive?eld work,including taxonomical information,collateral and ancillary data analysis,and the visual estimation of percentage cover for each species;this is labor intensive and costly and time-consuming and sometimes inappli-cable due to the poor accessibility,and is thus,only practical on relatively small areas(Lee and Lunetta 1996).Remote sensing,on the other hand,offers a practical and economical means to discriminate and estimate the biochemical and biophysical parameters of the wetland species and it can make?eld sampling more focused and ef?cient.Its repeat coverage offers archive data for detection of change over time,and its digital data can be easily integrated into Geographic Information System(GIS)for more analysis(Shaikh et al.2001;Ozesmi and Bauer2002).For this advantage,many researchers have used both multi-spectral data such as Landsat TM and SPOT imagery to identify general vegetation classes or to attempt to discriminate broad vegetation communities(May et al.1997;Harvey and Hill2001;Li et al.2005), and hyperspectral data to discriminate and map wetland vegetation at the species level(Belluco et al.2006;Schmidt and Skidmore2003;Rosso et al. 2005;Pengra et al.2007;Vaiphasa et al.2005). Moreover,the use of remote sensing techniques has been extended into measuring the biophysical and biochemical properties such as leaf area index(LAI), biomass,and water content of wetland vegetation (Rendong and Jiyuan2004;Proisy et al.2007; Penuelas et al.1993a;Kovacs et al.2005).

The rapid growth in the number of studies that have investigated the use of remote sensing in studying wetland species makes it necessary to provide an overview of the techniques that have been used and to identify those aspects that still need further investi-gation.This would be useful practically in wetland management and scienti?cally through highlighting the priorities and challenges for further research.

Previous reviews on remote sensing of wetlands included those by Silva et al.(2008)who discussed the theoretical background and applications of remote sensing techniques in aquatic plants in wetland and coastal ecosystems.Ozesmi and Bauer(2002) reviewed the classi?cation techniques used to map and delineate different wetland types using different remotely sensed data.Lee and Lunetta(1996) reviewed the use and the cost of airborne and satellite sensors in the inventory of and change detection in wetlands.The review by Klemas(2001)addressed the current use of remote sensing and its opportunities pertinent in monitoring the environmental indicators in coastal ecosystems.Hardisky et al.(1986) reviewed different remotely sensed data for coastal wetlands and estimating biomass.

The limitation of the above-mentioned reviews is that no speci?c aspect of the application of remote sensing has been addressed individually and most of the reviews have been focused on the use of remote sensing in mapping and identi?cation of wetland types at a broad level.There has been no speci?c review on the use of both hyperspectral and multi-spectral remote sensing in discriminating wetland vegetation as well as estimating its biophysical and biochemical properties which is essential in wetland management.Hence,this review focuses speci?cally on the application of remote sensing in discriminating and estimating the biophysical and biochemical properties of wetland vegetation.

The speci?c objectives of this study were to review the status of application of both multi-spectral and hyperspectral remotely sensed data in wetland vege-tation with special focus on:(1)discriminating and mapping wetland vegetation,(2)estimating some of the biophysical and biochemical properties of wetland vegetation,and(3)highlighting the major challenges and further research needed for a successful applica-tion of remote sensing in wetland vegetation. Challenges in mapping wetland vegetation

Wetland plants and their properties are not as easily detectable as terrestrial plants,which occur in large strati?cation.This is because of two reasons.First, herbaceous wetland vegetation exhibits high spectral and spatial variability because of the steep environ-mental gradients which produce short ecotones and sharp demarcation between the vegetation units

(Adam and Mutanga 2009;Zomer et al.2008;Schmidt and Skidmore 2003).Hence,is often dif?-cult to identify the boundaries between vegetation community types (Fig.1).Second,the re?ectance spectra of wetland vegetation canopies are often very similar and are combined with re?ectance spectra of the underlying soil,hydrologic regime,and atmo-spheric vapour (Guyot 1990;Malthus and George 1997;Lin and Liquan 2006)(Fig.1).This combina-tion usually complicate the optical classi?cation and results in a decrease in the spectral re?ectance,especially in the near-to mid-infrared regions where water absorption is stronger (Fyfe 2003;Silva et al.2008).Therefore,the current efforts which have been successful at mapping terrestrial vegetation using optical remote sensing,may not be able,either spatially or spectrally,to effectively distinguish the ?ooded wetland vegetations because the performance of near to mid-infrared bands are attenuated by the occurrences of underlying water and wet soil (Hestir et al.2008;Zomer et al.2008).However,hyperspec-tral narrow spectral channels offer the potential to detect and map the spatial heterogeneity of wetland vegetation (Hestir et al.2008;Vaiphasa et al.2007;Schmidt and Skidmore 2003).

Factors affecting spectral characteristics of wetland vegetation

When light solar radiation interacts with leaves,it may be re?ected,absorbed,and/or transmitted.All vegeta-tion species contain the same basic components that contribute to its spectral re?ectance,including chlo-rophyll and other light-absorbing pigments,water,proteins,starches,waxes,and structural biochemical molecules,such as lignin and cellulose (Kokaly et al.2003;Price 1992).Hence,the spectral separability of vegetation species is challenging due to those limiting factors affecting the spectral response of vegetation species (Price 1992;Rosso et al.2005).In general,the spectral differences among vegetation species are normally derived from leaf optical properties related to the biochemical and biophysical status of the plants.Leaf optical properties depend on leaf surface and internal structure,leaf thickness,water content,bio-chemical composition,and pigment concentration (Kumar et al.2001;Rosso et al.2005).The spectral re?ectance of wetland vegetation is normally subdi-vided into four domains.Vegetation types generally have a high re?ectance and transmittance in the near-infrared region and strong water absorption in the mid-infrared region (Table 1,Fig.1).

The most important factors affecting the spectral re?ectance among wetland vegetation are the bio-chemical and biophysical parameters of the plants’leaves and canopy such as chlorophyll a and b,carotene,and xanthophylls (Kumar et al.2001;Guyot 1990).Wetland species appear to vary greatly in chlorophyll and biomass re?ectance as a function of plant species and hydrologic regime (Anderson 1995).Spectral behaviour of wetland vegetation is also in?uenced by leaf water content which deter-mines the absorption of the mid-infrared region (Datt 1999).Red re?ectance increases with leaf water stress through an association with a reduction in chlorophyll concentration (Fillella and Penuelas 1994).The relationship between increasing of near-infrared leaf re?ectance and decrease of leaf water content has also been reported (Aldakheel and Danson 1997).For example,Lin and Liquan (2006)compared the laboratory and ?eld spectral character-istics of the submerged plant (Vallisneria spiralis )in the constructed wetland at Shanghai in China.They found that the spectral re?ectance measured by the ground-based spectroradiometer sensor was

a

Fig.1Mean canopy re?ectance spectra of Cyperus papyrus L.in swamp wetland with the dominating factor in?uencing each interval of the curve.Most of short wave infrared wavelengths (water content wavelength)are affected by atmospheric noise

combination of plant spectra,segmental water,and fundus spectrum.

Leaf area index(LAI)is also a key variable in the canopy re?ectance of the wetland vegetation.The canopies with a high LAI re?ect more than the canopies with medium or low LAI.However,higher LAI canopies allow only little light radiation to reach to the mature leaves under vegetation canopies and the soil background(Abdel-Rahman and Ahmed2008; Darvishzadeh et al.2008).Studies show that the spectral signature of the tropical wetland canopies is also affected by the different seasons,plant architec-ture,and illumination angle(Cochrane2000;Artigas and Yang2005;Darvishzadeh et al.2008). Mapping wetland vegetation using

multi-spectral data

Historically,aerial photography was the?rst remote sensing method to be employed for mapping wetland vegetation(e.g.Seher and Tueller1973;Shima et al. 1976;Lehmann and Lachavanne1997;Howland 1980).Theses studies concluded that aerial photogra-phy is most useful for detailed wetland mapping because of its minimum mapping unit(MMU). However,aerial photography is not feasible for mapping and monitoring wetland vegetation on a regional scale or for monitoring that requires contin-ual validation of information because it is costly and time-consuming to process.

Currently,a variety of remotely sensed images are available for mapping wetland vegetation at different levels by a range of airborne and space-borne sensors from multi-spectral sensors to hyperspectral sensors which operate within the different optical spectrum, with different spatial resolutions ranging from sub-metre to kilometres and with different temporal frequencies ranging from30minutes to weeks or months.Among them,aerial photography,Landsat TM,and SPOT images were commonly investigated in mapping vegetation types in wetlands.The com-mon image analysis techniques used in mapping wetland vegetation include digital image classi?ca-tion(i.e.unsupervised and supervised classi?cation) (May et al.1997;McCarthy et al.2005;Harvey and Hill2001)and vegetation index clustering(Nagler et al.2001;Yang2007).May et al.(1997)compared Landsat TM and SPOT multi-spectral data in map-ping shrub and meadow vegetation in northern California.They concluded that Landsat TM data were more effective than SPOT data in separating shrubs from meadows.But neither Landsat TM nor SPOT data were effective to distinguish meadow sub-types.McCarthy et al.(2005)in Botswana found that the high spatial and temporal variation in vegetation in the Okavango Delta makes ecoregion classi?cation from Landsat TM data unsatisfactory for achieving land cover classi?cation.In Australian wetlands, Landsat TM has proven to be a potential source of de?ning vegetation density,vigour,and moisture status,but not ef?cient in de?ning the species

Table1The spectral re?ectance of green vegetation on the four regions of electromagnetic spectrum de?ned by de?ned by Kumar et al.(2001)

Wavelengths

region(nm)

Description Spectral re?ectance of vegetation References

400–700Visible Low re?ectance and transmittance due to

chlorophyll and carotene absorption Kumar et al.(2001) Rosso et al.(2005)

680–750Red-edge The re?ectance is strongly correlated with

plant biochemical and biophysical

parameters Mutanga and Skidmore(2007) Clevers(1999)

700–1,300Near-infrared High re?ectance and transmittance,very

low absorption.The physical control

is internal leaf structures Kumar et al.(2001) Rosso et al.(2005)

1,300–2,500Mid-infrared Lower re?ectance than other spectrum

regions due to strong water absorption

and minor absorption of biochemical

content

Kumar et al.(2001)

composition(Johnston and Barson1993).Harvey and Hill(2001)in the Northern Territory,Australia, compared aerial photographs,SPOT XS,and Landsat TM image data to determine the accuracy and applicability of each data source for the spectral discrimination of vegetation types.Their results demonstrated that aerial photography was clearly superior to SPOT XS and Landsat TM imagery for detailed mapping of vegetation communities in the tropical wetland.They also found that the sensitivity of Landsat band2(green),band3(red),band4(near-infrared,NIR),and band5(MIR)provided a more accurate classi?cation than SPOT.Ringrose et al. (2003)used NOAA-AVHRR and SPOT to map the ecological conditions at the Okavango delta in Botswana.It was dif?cult to discriminate grassed ?oodplain from wooded peripheral drylands.Sawaya et al.(2003)at Minnesota in USA were able to map the vegetation groups at a local scale using IKONOS imagery with a high level of classi?cation accuracy (80%).

Imagery from the Landsat TM and SPOT satellite instruments have proven insuf?cient for discriminat-ing vegetation species in detailed wetland environ-ments(Harvey and Hill2001;McCarthy et al.2005; May et al.1997).This is due to:(1)the dif?culties faced in distinguishing?ne,ecological divisions between certain vegetation species,(2)the broad nature of the spectral wavebands with respect to the sharp ecological gradient with narrow vegetation units in wetland ecosystems,and(3)the lack of high spectral and spatial resolution of optical multi-spectral imagery which restricts the detection and mapping of vegetation types beneath a canopy of vegetation,in densely vegetated wetlands.

Although these studies produced reasonable results on mapping wetland vegetation at a regional scale and vegetation communities,more research is needed to explore the bene?ts of incorporating bathymetric and other auxiliary data to improve the accuracy of mapping wetland vegetation at the species level. Improving the accuracy of wetland vegetation classi?cation

Spectral discrimination between vegetation types in complex environments is a challenging task,because commonly different vegetation types may possess the same spectral signature in remotely sensed images (Sha et al.2008;Xie et al.2008).Traditional digital imagery from multi-spectral scanners is subject to limitations of spatial,spectral,and temporal resolu-tion.Moreover,applications of per-pixel classi?ers to images dominated by mixed pixels are often incapa-ble of performing satisfactorily and produce inaccu-rate classi?cation(Zhang and Foody1998).Due to the complexities involved,more powerful techniques have been developed to improve the accuracy of discriminating vegetation types in remotely sensed data.

Domacx and Suzen(2006)in the Amanos Moun-tains region of southern-central Turkey used knowl-edge-based classi?cations in which they combined Landsat TM images with environmental variables and forest management maps to produce regional scale vegetation maps.They were able to produce an overall high accuracy when compared with the traditional maximum likelihood classi?cation method.Another example for improving classi?ca-tion accuracy by incorporating vegetation-related environmental variables using GIS with remotely sensed data was the work of that of Yang(2007)at Hunter Region in Australia.He used digital aerial photographs,SPOT-4,and Landsat-7ETM?images for riparian vegetation delineation and mapping.The overall vegetation classi?cation accuracy was81% for digital aerial photography,63%for SPOT-4,and 53%for Landsat-7ETM?.The study revealed that the lack of spectral resolution of aerial photograph and the coarse spatial resolution of the current satellite images is the major limiting factor for their application in wetland vegetation mapping, Arti?cial neural network(ANN)and fuzzy logic approaches were also investigated to improve the accuracy of mapping vegetation types in complex environments.ANN proved to be valuable in map-ping vegetation types in wetland environments.One disadvantage of ANN,however,is that ANN can be computationally demanding to train the network when large datasets are dealt with(Filippi and Jensen 2006;Xie et al.2008).Berberoglu et al.(2000)at the Cukurova Deltas in Turkey combined ANN and texture analysis on a per-?eld basis to classify land cover from Landsat TM.They were able to increase the accuracy achieved with maximum likelihood classi?cation by15%.Carpenter et al.(1999)com-pared conventional expert methods and the ARTMAP

neural network method in mapping vegetation types at the Sierra National Forest in Northern California using Landsat TM data.Their research illustrated that the accuracy was improved from78%in conventional expert methods to83%when the ARTMAP neural network method was used.The ARTMAP neural network method was found to be less time-consuming and its production to be easily updated with any new observation.

A fuzzy classi?cation technique,which is a kind of probability-based classi?cation rather than a crisp classi?cation,is also useful in mixed-class areas and was investigated for solving the problem of mapping complex vegetation.Sha et al.(2008)at the Xilinhe River Basin in China employed a hybrid fuzzy classi?er(HFC)for mapping vegetation on typical grassland using Landsat ETM?imagery.It was concluded that HFC was much better than conven-tional supervised classi?cation(CSC)with an accu-racy percentage of80.2%as compared to69.0%for the CSC.Promising results have also been achieved in using fuzzy classi?cation for suburban land cover classi?cation from Landsat TM and SPOT HRV data by Zhang and Foody(1998)at Edinburgh in Scotland.They concluded that fuzzy classi?cation not only has advantages over conventional hard methods and partially fuzzy approaches,but also is more feasible in integrating remotely sensed data and ancillary data.

Decision tree(DT)classi?cation has also shown promising results in mapping vegetation in wetlands and complex environments.DT is a simple and ?exible non-parametric rule-based classi?er and it can handle data that are represented on different measurement scales.This is useful especially when there is a need to integrate the environmental variables(e.g.slope,soil type,and rainfall)in the mapping process(Xu et al.2005;Xie et al.2008).Xu et al.(2005)at Syracuse in New York employed a decision tree and regression(DTR)algorithm to determine class proportions within a pixel so as to produce soft land cover classes from Landsat ETM. Their results clearly demonstrate that DTR produces considerably higher soft classi?cation accuracy (74.45%)as compared to the conventional maximum likelihood classi?er(MLC)(55.25%)and the super-vised FCM(54.40%).

It has been revealed from the present review that no single classi?cation algorithm can be considered as an optimal methodology for improving vegetation discriminating and mapping.Hence,the use of advanced classi?er algorithms must be based on their suitability to achieve certain objectives in speci?c areas.

Spectral discrimination of wetland species

using hyperspectral data

In remote sensing,the term‘imaging spectroscopy’is synonymous with some other terms such as‘imaging spectrometery’and‘hyperspectral’or‘ultraspectral imaging’(Clark1999).In general,hyperspectral remote sensing has hundreds of narrow continuous spectral bands between400and2,500nm,through-out the visible(0.4–0.7nm),near-infrared(0.7–1nm),and short wave infrared(1–2.5nm)portions of the electromagnetic spectrum(Govender et al. 2006;Vaiphasa et al.2005).This greater spectral dimensionality of hyperspectral remote sensing allows in-depth examination and discrimination of vegetation types which would be lost with other broad band multi-spectral scanners(Cochrane2000; Schmidt and Skidmore2003;Mutanga et al.2003; Govender et al.2006).Hyperspectral remote sensing data is mostly acquired using a hand-held spectrom-eter or airborne sensors.A hand-held spectrometer is an optical instrument used for measuring the spec-trum emanating from a target in one or more?xed wavelengths in the laboratory and the?eld(Kumar et al.2001).The accurate measurements of the spectral re?ectance in the?eld were established in the 1960s as result of the rapid growth in airborne multi-spectral scanners(Milton et al.2007).Historically, the application focused on the structure of matter. Recently,however,the application has been broad-ened,including other aspects of electromagnetic and non-electromagnetic radiation.

In the last twenty years,?eld spectrometery has been playing vital roles in characterizing the re?ec-tance of vegetation types in situ,and providing a means of scaling-up measurement at both of?eld (canopy and leaves)and laboratory levels(Milton et al.2007).Many attempts have been successfully made to discriminate and classify wetland species based on their fresh leaf re?ectance at laboratory levels with the view to scaling it up to airborne remote sensing(e.g.Vaiphasa et al.2005,2007)and

?eld re?ectance at canopy scale(e.g.Best et al.1981; Penuelas et al.1993b;Schmidt and Skidmore2003; Rosso et al.2005).

The earliest effort on spectral discrimination of wetland species was that of Anderson(1970)who attempted to evaluate the discrimination of ten marsh-plant species which dominated a wetland in Chesa-peake Bay using ISCO Model SR Spectroradiometer. He concluded that the spectral difference between the species is minor in the visible spectrum,but signif-icant in the near-infrared.The variation in the spectral re?ectance with the changing seasons was also reported in the study.Best et al.(1981)investigated the use of four bands of Exotech radiometer to discriminate between the vegetation types which dominated the Prairie Pothole in the Dakotas.The spectral measurements were taken from ten common species during the periods of early-emergent,?ower-ing,early-seed,and senescent phenological stages. Their?ndings showed that the best period to discrim-inate among the eight species studied was during the ?owering and early-seed stages.However,it was dif?cult to differentiate reed(Sparganium euryea-pum)from the other species.It was also concluded that a single species,in different phenological stages, showed signi?cant variation in its spectral re?ectance. Schmidt and Skidmore(2003)used the spectral re?ectance measured at canopy level with A GER 3700spectrometer from27wetland species to eval-uate the potential of mapping coastal saltmarsh vegetation associations(mainly consisting of grass and herbaceous species)in the Dutch Waddenzee wetland.It was found that the re?ectance in six bands distributed in the visible,near-infrared,and shortwave infrared were the optimal bands for mapping salt-marsh vegetation(Table2).Fyfe(2003)attempted to discriminate three coastal wetland species(Zostera capricorni,Posidonia australis,and Halophila ovalis) in https://www.sodocs.net/doc/9113841119.html,ing a single-factor analysis of variance and multivariate techniques,it was possible to distin-guish among the three species by their re?ectance in the wavelengths between530–580,520–530,and 580–600nm.However,the differences were more signi?cant between570and590nm.Rosso et al. (2005)in California,USA,collected spectral re?ec-tance data from?ve species(Salicornia,S.foliosa, S.foliosa,S.alterni?ora,and Scirpus)using an Analytical Spectral Device(ASD)full-range(0.35–2.5nm)PS II spectrometer to assess the separability of the marsh species under controlled conditions. Spectral Mixture Analysis(SMA)and Multiple End-member Spectral Mixture Analysis(MESMA)were used on the AVIRIS https://www.sodocs.net/doc/9113841119.html,ing both SMA and MESMA,it was possible to distinguish between the species with higher classi?cation accuracies.How-ever,the MESMA technique appeared to be more appropriate because it could incorporate more than one endmember per class.Similar work was also conducted by Li et al.(2005)who were able to use AVIRIS imagery to discriminate three salt marsh species(Salicornia,Grindelia,and Spartina)in China and in San Pablo Bay of California,USA.They developed a model that mixed the spectral angle together with physically meaningful fraction and the rms.The results were satisfactory considering the success in discriminating the two marsh vegetation species(Spartina and Salicornia),which covered 93.8%of the marsh area.However,it was dif?cult to discriminate Grindelia from Spartina and Salicornia due to the spectral similarity between the species. Becker et al.(2005)were able to use a modi?ed version of the slope-based derivative analysis method to identify the optimal spectral bands for the differ-entiation of coastal wetland vegetation.They trans-formed hyperspectral data measured by the SE-590 spectroradiometer at canopy level into a second-derivative analysis.Six bands were found across the visible and near-infrared region to be powerful for discriminating the coastal wetland species.

In Thailand,Vaiphasa et al.(2005)were able to identify and distinguish16vegetation types in a mangrove wetland in Chumporn province.Their research was conducted by collecting hyperspectral re?ectance data using a spectroradiometer(FieldSpec Pro FR,Analytical Spectral Device,Inc.),under laboratory conditions.The results of one-way ANOVA with a95%con?dence level(P\0.05),and Jeffries–Matusita(JM)distance indicated that the best discrim-ination of the16species is possible with four bands located in the red-edge and near-infrared and mid-infrared regions of the electromagnetic spectrum (Table2)Vaiphasa et al.(2007)also used the same spectral data set to compare the performance of genetic algorithms(GA)and random selection using t-tests in selecting key wavelengths that are most sensitive in discriminating between the16species.The JM distance was used as an evaluation tool.The results showed that the separability of band combinations

selected by GA was signi?cantly higher than the class separability of randomly selected band combinations with a95%level of con?dence(a=0.05).Mangrove wetland species were also discriminated and mapped in Malaysia by Kamaruzaman and Kasawani(2007)who were able to use ASD Viewspec Pro-Analysis to collect the spectral re?ectance data from?ve species at Kelantan and Terengganu,namely Rhizophora apicu-lata,Bruguiera cylindrica,Avicennia alba,Heritiera littoralis,and Hibiscus tiliaceus.The canonical step-wise discriminant analysis revealed that the?ve species were spectrally separable at?ve wavelengths (693,700,703,730,and731nm)located in the red-edge and near-infrared region.

Wang et al.(2007)attempted to map highly mixed vegetation in salt marshes in the lagoon at Venice in Italy.Six signi?cant bands of Compact Airborne Spectral Imager(CASI)were selected using Spectral Reconstruction(SR).The results showed that accu-racy of Vegetation Community based Neural Net-work Classi?er(VCNNC)can be used effectively in the situation of mixed pixels,thus,it yielded an accuracy higher(91%)than the Neural Network Classi?er(84%).Another attempt in discriminating marsh species was that by Artigas and Yang(2005)in the Meadowlands District in north-eastern New Jersey,USA.They conducted a study to characterize the plant vigour gradient using hyperspectral remote sensing with?eld-collected seasonal re?ectance spectra of marsh species in a fragmented coastal wetland.Their results indicated that near-infrared and narrow wavelengths(670–690nm)in the visible region can be used to discriminate between the most marsh species.However,it was dif?cult to discrim-inate between the two Spartina species because they belong to the same genus.It was concluded that these mixed pixels could be minimized using pixel unmix-ing techniques to discover the linear combinations of spectra associated with the pixels.

In summary,Most of the previous studies have stated that wetland vegetation have greatest variation in the near infrared and red-edge regions(Asner 1998;Cochrane2000;Thenkabail et al.2004; Daughtry and Walthall1998;Vaiphasa et al.2005; Schmidt and Skidmore2003).Hence,most of the wavelengths selected to map wetland vegetation were mainly allocated in near infrared and red-edge regions of the electromagnet spectrum(Table2).

Table2Frequency of wavelengths selected in some studies for mapping wetland vegetation adapted into the four spectral domains de?ned by Kumar et al.(2001)

Wavelengths regions(nm)References Selected bands(nm)

Visible(400–700)Daughtry and Walthall(1998)

Schmidt and Skidmore(2003)

Thenkabail et al.(2002)

Thenkabail et al.(2004)550,670

404,628

490,520,550,575,660,675 495,555,655,675

Red-edge(680–750)Daughtry and Walthall(1998)

Vaiphasa et al.(2005)

Thenkabail et al.(2002)

Thenkabail et al.(2004)

Adam and Mutanga(2009)720 720 700,720 705,735 745,746

Near-infrared(700–1,300)Daughtry and Walthall(1998)

Schmidt and Skidmore(2003)

Vaiphasa et al.(2005)

Thenkabail et al.(2002)

Thenkabail et al.(2004)

Adam and Mutanga(2009)800

771

1,277

845,905,920,975

885,915,985,1,085,1,135,1,215,1,245,1,285 892,932,934,958,961,989

Mid-infrared(1,300–2,500)Schmidt and Skidmore(2003)

Vaiphasa et al.(2005)

Thenkabail et al.(2004)1,398,1,803,2,183

1,415,1,644

1,445,1,675,1,725,2,005,2,035,2,235,2,295,2,345

More work is needed to build comprehensive spectral libraries for different wetland plants.Hyper-spectral imagery proved to be useful in discriminat-ing wetlands species with higher accuracy.However, hyperspectral imagery is expensive to acquire,time-consuming to process,even when small areas are covered.Innovative new methods which take advan-tage of the relatively large coverage and high spatial resolution of the?ne sensors and the high spectral resolution of hyperspectral sensors could result in more accurate discrimination models of wetland species with a reasonable cost.

Estimating biophysical and biochemical parameters of wetland species

The main biochemical constituents found in vegetation are nitrogen,plant pigment,and water.Whereas biophysical properties of the plant include LAI,canopy architecture and density,and biomass(Govender et al. 2006),estimating the biochemical and biophysical properties of wetland vegetation is a critical factor for monitoring the dynamics of the vegetation productiv-ity,vegetation stress,or nutrient cycles within wetland ecosystems(Asner1998;Mutanga and Skidmore 2004).The most important biochemical and biophys-ical properties that characterize the wetland species are:chlorophyll and biomass concentration,and leaf water content(Anderson1995).Few studies,however, have been conducted to study these properties that affect wetland plant canopies using both multi-spectral and hyperspectral remote sensing.

Mapping wetland biomass

Estimating wetland biomass is necessary for studying productivity,carbon cycles,and nutrient allocation (Zheng et al.2004;Mutanga and Skidmore2004). Many studies of?eld biomass have used vegetation indices based on the ratio of broadband red and near-infrared re?ectance.Ramsey and Jensen(1996)in the USA used a helicopter platform to measure spectra of the canopies of four species which dominated in south-west Florida to describe the spectral and structural change within and between the species and community types.Re?ectance values were gen-erated from the canopy spectral data to correspond with AVHRR(bands1and2),Landsat TM(bands 1–4),and XMS SPOT(bands1–3)sensors.The relationship between canopy structure and re?ectance showed the dif?culties of discrimination of mangrove species based on optical properties alone.Moreover, species composition was not correlated to any com-bination of re?ectance bands or vegetation index. However,the study revealed the possibility of estimation of vegetation biomass such as LAI using red and near-infrared bands on various sensors.

Tan et al.(2003)used Landsat ETM bands4,3, and2false colour,and?eld biomass data to estimate wetland vegetation biomass in the Poyang natural wetland,China.Linear regression and statistical analyses were performed to determine the relation-ship among the?eld biomass data and some trans-formed data derived from the ETM data.Their results indicated that sampling biomass data has the best positive correlation to Difference Vegetation Index (DVI)data.The authors developed a linear regression model to estimate the total biomass of the whole Poyang Lake natural conservation area.Similarly, Rendong and Jiyuan(2004)at Poyang in China, attempted to estimate the vegetation biomass in a large freshwater wetland using the combination of Landsat ETM data,GIS(for analyses and projecting both the sampling and Landsat ETM data),and GPS for(?eld biomass data).The results showed that the sampling of biomass data was best relative to the ETM4data with the highest coef?cient of0.86,at the signi?cance level of0.05.The study revealed that the near-infrared band could be used to estimate the wetland vegetation biomass.

The use of coarser spatial resolution sensors e.g. (VHR)IKONOS and AVHRR images has also been investigated in estimating wetland biomass.Proisy et al.(2007)created a new textural analysis method in which they applied Fourier-based Textural Ordi-nation(FOTO)in1m panchromatic and4m infrared IKONOS images to estimate and map high biomass in forest wetland in French Guiana in the Amazon. Their work yielded accurate predictions of mangrove total aboveground biomass from both1m and4m IKONOS images.However,the best results were obtained from1m panchromatic with the maximum coef?cient determination(R2)above0.87.

Moreau et al.(2003)investigated the potential and limits of two methods to estimate the biomass production of Andean wetland grasses in the Bolivian

Northern Altiplano from NOAA/AVHRR.The?rst method was based on monthly?eld biomass mea-surement and the second one was based on Bidirec-tional Re?ectance Distribution Function(BRDF) normalized difference vegetation index(NDVI). Their results showed that BRDF normalized NDVI was sensitive to the green leaf or photosynthetically active biomass.The study also revealed that the optimal time for estimating the biomass with remotely sensed data in wetland species is during the growing season.

The limitations of using vegetation indices such as NDVI for estimation of biomass,especially where the soil is completely covered by the vegetation,have been reported in the literature.This is due mainly to the saturation problem(Thenkabail et al.2000; Mutanga and Skidmore2004).Nevertheless,Mutan-ga and Skidmore(2004)developed a new technique to resolve this saturation problem.They compared the use of band depth indices calculated from continuum-removed spectra with two narrow band NDVIs calculated using near-infrared and red bands to estimate Cenchrus ciliaris biomass in dense vegeta-tion under laboratory conditions.The results clearly showed that band depth analysis approach proved to be ef?cient with a high coef?cient in estimating biomass in densely vegetated areas where NDVI values are restricted by the saturation problem. Estimation of leaf and canopy water

content in wetland vegetation

Water availability is a critical factor in wetland plants’survival.There has been a rapid growth in remote sensing research to assess the vegetation water content as an indicator for the physiological status of plants,?re potential,and ecosystem dynam-ics at both laboratory and?eld level using very high resolution spectrometers such as the ASD spectral device with spectral sampling intervals of less than 2nm(Toomey and Vierling2006;Liu et al.2004; Stimson et al.2005).However,no signi?cant research has been carried out on estimating water content in wetland plants especially.This is because the studies using remote sensing on wetland plants have been aimed mainly at discriminating and mapping,rather than estimating plant physiology such as water content and water stress.

Quite a number of different indices and techniques have been developed for estimating plant water content using the absorption features throughout the mid-infrared region(1,300–2,500nm)of the electro-magnetic spectrum e.g.in Netherlands(Clevers and Kooistra2006),Canada(Davidson et al.2006),and USA(Gao1996).The authors determined the canopy water content by scaling the foliar water content (FWC,%)with the speci?c leaf area(SLA,LAI),and the percent canopy cover for a speci?c forest canopy. However,Ceccato et al.(2001)noted that this technique relies on estimation of SLA,which varies according to species and phenological status.

Work by Penuelas et al.(1993a)found the water band index(WI),which has been developed based on the ratio between the water band970nm and re?ectance at900nm,to be strongly correlated with relative plant water https://www.sodocs.net/doc/9113841119.html,ing re?ectance at857 and1,241nm,Gao(1996)developed the normalized difference water index(NDWI)in California in USA to estimate the vegetation water.The results showed that the NDWI is less sensitive to atmospheric scattering effects than NDVI and it is useful in predicting water stress in canopies and assessing plant productivity.It was recommended that further inves-tigation is needed in order to understand this index better by testing it with the new generation of satellite instruments such as MODIS and SPOT-VEGETA-TION.Less sensitive semi-empirical indices for atmospheric scattering have also been developed by Datt(1999)to determine the relationship between spectral re?ectance of several Eucalyptus species and both the gravimetric water content and equivalent water thickness(EWT).The results showed that EWT was signi?cantly correlated with re?ectance in several wavelength regions.However,no signi?cant correla-tions could be obtained between re?ectance and gravimetric water content.

The use of remote sensing in estimating plant water content is challenging because it is dif?cult to distinguish the contribution made by foliar liquid water and atmospheric vapour on the water-related absorption spectrum.This is because the absorption band related to water content is also affected by atmospheric vapour(Liu et al.2004)(Fig.1). Attempts have been made to minimize the atmo-spheric interference by using red-edge position which is located outside the water absorption bands.In China,Liu et al.(2004)found a signi?cant

correlation between plant water content with the red-edge width in six different growth stages of wheat plants.The correlation coef?cients were between 0.62and0.72at0.999con?dence level.The results were more reliable than those obtained using the WI and the NDWI.Similar results were reported in the USA by Stimson et al.(2005)who correlated foliar water content with the red-edge position to evaluate the relationship between foliar water content and spectral signals in two coniferous species:Pinus edulis and Juniperus monosperma.The results showed signi?cant correlations of R2=0.45and R2=0.65respectively.

As there has been no signi?cant research on estimating water content and water stress of wetland vegetation speci?cally,additional studies on these aspects are needed to better understand the spectral response of wetland plants.The results of such research could help the researcher to develop accu-rate models for describing,for example,the separa-bility of wetland plant varieties and for estimating foliar nutrients and developing indicators that can quantify the integrated condition of wetland plants and can identify their primary stressors across a range of scales.

Estimating leaf area index of wetland vegetation Leaf area index(LAI)is de?ned as the total one-sided area of all leaves in the canopy per unit ground surface area(m2/m2)(Gong et al.2003).Information on LAI is valuable for quantifying the energy and mass exchange characteristics of terrestrial ecosystems such as pho-tosynthesis,respiration,evapotranspiration,primary productivity,and crop yield(Kumar et al.2001;Gong et al.1995).Research efforts on estimating LAI from spectral re?ectance measurements have been focused mainly on forests(e.g.Schlerf et al.2005;Gong et al. 1995,2003;Pu et al.2005;Davi et al.2006)and crops (e.g.Thenkabail et al.2000;Hansen and Schjoerring, 2003;Pay et al.2006).However,regardless of the work that has been done at Majella National Park,in Italy by Darvishzadeh et al.(2008),the estimation of LAI for heterogeneous grass canopies has not been done. Moreover,a few studies dealing speci?cally with estimating LAI of wetland species have been con-ducted only in forest wetlands and mangrove wetlands (Green et al.1997;Kovacs et al.2004,2005).

In general,the above-mentioned studies have investigated several analytical techniques to estimate LAI from re?ectance data.This can be grouped into two main techniques:the stochastic canopy radiation model and the empirical model.The empirical model has been more widely investigated than the stochastic canopy radiation model.The univariate regression analysis with vegetation indices such as NDVI and simple ratio,derived from visible and near-infrared wavelengths,is the most widely used empirical model and has been used in estimating LAI(Then-kabail et al.2000;Gong et al.1995,2003;Green et al.1997;Kovacs et al.2004;Schlerf et al.2005; Kovacs et al.2005).

Green et al.(1997)in UK developed a model based on gap-fraction analysis and NDVI derived from Landsat TM and SPOT XS to estimate LAI from three species:Rhizophora mangle,Laguncularia racemosa,and Avicennia germinans in mangrove wetland in West Indies.The model produced a thematic map of LAI with a high accuracy(88%) and low mean difference between predicted and measured LAI(13%).

Vegetation indices derived from high spatial resolution data were shown to be effective in monitoring LAI in mangrove forests.Kovacs et al. (2004)tested the relationship between in situ esti-mates of LAI and vegetation indices derived from IKONOS imagery in a degraded mangrove forest at Nayarit,Mexico.Regression analysis of the in situ estimates showed strong linear relationships between LAI and NDVI and simple ratio.Moreover,no signi?cant differences were found between the simple ratio and NDVI models in estimating LAI at both plot sizes.In the same area,Kovacs et al.(2005) examined the potential of IKONOS in mapping mangrove LAI at the species level.A hand-held LAI-2000sensor was also evaluated for the collection of data on the in situ mangrove LAI as a non-destructive alternative for the?eld data collection procedure.A strong signi?cant relationship was found between NDVI,derived from IKONOS data, and in situ LAI collected with a LAI-2000sensor.It was concluded that IKONOS satellite data and the LAI-2000could be an ideal method for mapping mangrove LAI at the species level.

Researchers have shown that vegetation indices (VIs)derived from the narrow-band could be vital for providing additional information for quantifying the

biophysical characteristics of vegetation such as LAI (Blackburn and Pitman1999;Mutanga and Skid-more2004).In wetland environments speci?cally, however,only one work,that by Darvishzadeh et al.(2008)at Majella National Park in Italy,has investigated the use of hyperspectral data in estimating and predicting LAI for heterogeneous grass canopies,in Italy.The study investigated the effects of dark and light soil and plant architecture on the retrieval of LAI red and near-infrared re?https://www.sodocs.net/doc/9113841119.html,ing A GER3700spectroradiometer, the spectral re?ectances were measured from four different plant species(Asplenium nidus,Halimium umbellatum,Schef?era arboricola Nora,and Chrys-alidocarpus decipiens)with different leaf shapes and sizes under laboratory conditions;then many VIs were calculated and tested.A stronger relation-ship was found between LAI and narrow-band VIs in light soil than in dark soil.However,the narrow-band simple ratio vegetation index(RVI)and second soil-adjusted vegetation index(SAVI2)were found to be the best overall choices in estimating LAI.

Although reasonable results were obtained from narrow-band VIs in estimating LAI(Darvishzadeh et al.2008;Pay et al.2006;Thenkabail et al. 2000),some authors noted that the strengths of a large number of hyperspectral bands have not yet been exploited by these methods because only two bands from red and near-infrared regions are used to formulate the indices(Schlerf et al.2005; Hansen and Schjoerring2003).A technique such as multiple linear regression(MLR)which uses the advantages of the high dimensionality of the hyperspectral data to select optimal band combina-tions to formulate VIs,was shown to be effective at estimating the biophysical and biochemical proper-ties of vegetation such as LAI(Schlerf et al.2005; Thenkabail et al.2000).

Despite some success in estimating the biochem-ical and biophysical parameters in some ecosystems, estimation remains challenging in wetland environ-ments where visible and near-infrared canopy re?ec-tance has been revealed to be strongly affected by the background of soil and water,and atmospheric conditions.Further research is needed to develop indices that can reduce the effects of background and atmospheric quality.Overall challenges and future research

Over the last few decades,considerable progress has been made in applying sensor techniques and data processing in discriminating,mapping,and monitor-ing wetland species.However,there are still chal-lenges to be addressed in many aspects.First, traditional digital imagery from multi-spectral scan-ners is subject to limitations of spatial and spectral resolution compared to narrow vegetation units that characterize wetland ecosystems.

Second,despite the agreement on the effective performance of hyperspectral data in discriminating wetland species,the re?ectances from different vegetation species are highly correlated because of their similar biochemical and biophysical properties. Furthermore,these properties are directly in?uenced by environmental factors and therefore the unique spectral signature of the plant species has become questionable(Price1994).In addition,spectral vari-ations can also occur within a species because of age differences,micro-climate,soil and water back-ground,precipitation,topography,and stresses.

Third,measurement of the biophysical and bio-chemical properties of vegetation using VIs derived from broadband sensors can be unstable due to the underlying soil types,canopy and leaf properties,and atmospheric conditions.For example,NDVI asymp-totically saturate after a certain biomass density and for a certain range of LAI(Mutanga and Skidmore 2004).Hence,the measurement accuracy drops considerably(Thenkabail et al.2000;Gao et al. 2000).

A fourth research challenge is that in most African countries(e.g.South Africa)there are only a handful of studies that have used hyperspectral data to characterize savanna vegetation due to high cost and poor accessibility(e.g.Mutanga et al.2004; Mutanga and Kumar2007;Mutanga and Skidmore 2004)Also,no research has yet been carried out on discriminating wetland vegetation and estimating its biophysical and biochemical parameters using pro-cess-based models that use remotely sensed data as input parameters.

Despite these shortcomings,there is no doubt that remote sensing technology could play a vital role in discriminating and monitoring wetland species effec-tively by selecting appropriate spatial and spectral

resolution as well as suitable processing techniques for extracting species spectral information.

From a research perspective,however,there are several major challenges in the application of remote sensing in wetland species that need to be addressed.

First,the most current remote sensing techniques in mapping vegetation have been undertaken in arid and semi-arid regions with low vegetation cover and less complexity within the vegetation unit.These tech-niques are therefore of little use for narrow vegetation units that characterize wetland ecosystems.Addi-tional research effort is needed to adopt more classif?cation techniques to improve the accuracy of the spatial resolution of the current sensors which varies from20to30m.Hyperspectral radiometers are considered to be the sensors of choice in the future for mapping and monitoring wetland species.This has increased the need to build comprehensive spectral libraries for different wetland plant species under different plant conditions and environmental factors. Additionally,the fundamental understanding of the relationship between the re?ectance measurements, wetland species’canopy density,and bottom re?ec-tance parameters should be studied further.The spectral libraries of wetland species will help in discriminating not only between wetland species,but also between wetland species and upland species as there has been no speci?c research dealing with the difference in spectral response of canopies of wetland species and upland species.

Second,in the southern African region,more research is needed to enhance ability in discriminating wetland vegetation and estimating its biophysical and biochemical properties which have been overlooked in the scienti?c research.For example,papyrus swamp (Cyperus papyrus L.)(which characterizes most of the tropical Africa wetlands,with a high rate of biomass production,a tremendous amount of combined nitro-gen,that play vital roles in hosting habitats for wildlife and birds)is omitted in the application of remote sensing in discriminating wetland vegetation.

Third,although some studies have been under-taken on estimating the vegetation biophysical and biochemical parameters(https://www.sodocs.net/doc/9113841119.html,I,water content, biomass,pigment concentration,and nitrogen)in different ecosystems,these is paucity of research on wetland species.After the progress in the?eld of spectrometry,researchers began to measure vegeta-tion properties in complex ecosystems using new narrow-band indices(Mutanga and Skidmore2004) and red-edge position(Mutanga and Skidmore2007). These efforts should be further extended and devel-oped so as to cope with wetland species environments where the saturation and the atmospheric vapour affect the near-infrared region.A fourth research prospect is the availability of hyperspectral sensors which could allow mapping of both species and quality in wetland ecosystems.This will enhance a fundamental understanding of the spatial distribution of wetland species-quality which could lead to the development of early warning systems to detect any subtle changes in wetland systems such as signs of stress,and to develop techniques to classify wetland area conditions(e.g.healthy or disturbed)based on their species quality and quantity.

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