Environ Monit AssessDOI 10.1007/s10661-008-0665-z

Exploring the relationship between vegetation spectraand eco-geo-environmental conditions in karstregion, Southwest China

Yuemin Yue · Kelin Wang · Bing Zhang ·Zhengchao Chen · Quanjun Jiao · Bo Liu ·Hongsong Chen

Received: 29 June 2008 / Accepted: 5 November 2008© Springer Science + Business Media B.V. 2008

Abstract Remote sensing of local environmen-tal conditions is not accessible if substrates arecovered with vegetation. This study explored therelationship between vegetation spectra and karsteco-geo-environmental conditions. Hyperspectralremote sensing techniques showed that there weresignificant differences between spectral featuresof vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative

Y. Yue (B) · K. Wang · H. ChenInstitute of Subtropical Agriculture,Chinese Academy of Sciences,Changsha, 410125, Chinae-mail: hnyym829@163.com

B. Zhang · Z. Chen · Q. JiaoCenter for Earth Observation and Digital Earth,Chinese Academy of Sciences, Beijing, 100080, China

B. LiuState Key Laboratory of Remote Sensing Science,Jointly Sponsored by the Institute of Remote SensingApplications of Chinese Academy of Science andBeijing Normal University, Beijing, 100101, China

Y. Yue · K. Wang · H. ChenHuanjiang Experimental Station of Karst Ecosystem,Chinese Academy of Sciences, Huanjiang,Guangxi Province, 547100, China

Y. Yue · B. LiuGraduate University of Chinese Academy of Sciences,Beijing, 100049, China

spectra could delineate karst and non-karst vege-tation groups. Canonical correspondence analysis(CCA) successfully assessed to what extent thevariation of vegetation spectral features can beexplained by associated eco-geo-environmentalvariables, and it was found that soil moisture andcalcium carbonate contents had the most signif-icant effects on vegetation spectral features inkarst region. Our study indicates that vegetationspectra is tightly linked to eco-geo-environmentalconditions and CCA is an effective means ofstudying the relationship between vegetationspectral features and eco-geo-environmental vari-ables. Employing a combination of spectral andspatial analysis, it is anticipated that hyperspectralimagery can be used in interpreting or mappingeco-geo-environmental conditions covered withvegetation in karst region.

Keywords Karst · Reflectance spectra ·Eco-geo-environmental conditions ·Canonical correspondence analysis ·Hyperpsectral remote sensing

Introduction

Hyperspectral imagery is acquired using imag-ing spectrometry, the simultaneous acquisition ofimages in many narrow and contiguous spectralbands (Goetz et al. 1985). As band widths are

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narrow and range of spectra is wide, subtle dif-ferences in spectral features can be extracted andused for mapping physical and chemical proper-ties of bare substrates (van der Meer et al. 2001;Aspinall et al. 2002). The use of spectral charac-teristics of substrates is not accessible if substratesare covered by vegetation. However, as vegeta-tion species and growth condition are sensitiveto standing conditions (Asner 1998; Martin et al.1998; Schmidtlein 2005), spectral characteristics ofvegetation may be used as an indirect measure-ment of substrate properties and local environ-mental conditions. This measurement can be donebecause changes in vegetation standing conditionscan induce modifications in vegetation species’biophysical and biochemical composition (Miltonand Mouat 1989; Asner 1998; Martin et al. 1998).These modifications can result in significantly dif-ferences of vegetation spectra, and thus, spectralfeatures of covered vegetation may be used in-directly to monitor or map local environmentalconditions.

A few studies have demonstrated that vegeta-tion spectra were linked to local environmentalconditions and could be used to indicate geo-chemical stress (Collins et al. 1983) and heavymetals pollution (Mars and Crowley 2003; Chiet al. 2005) or derivate Ellenberg indicator valuesfor assessing water supply, soil pH, and fertility(Schmidtlein 2005). However, these studies justseparately took one or two environmental vari-ables into account in extreme ecological status oronly used indirect environmental measurements,Ellenberg indicator values, which were heavilyinfluenced by vegetation type and usually hadlargely various relationship with environmentalparameters (Wamelink et al. 2002; Smart andScott 2004), but not synthetically and simulta-neously analyzed the relationship between mul-tivariate environmental factors and vegetationspectra. A further exploration of the relationshipbetween vegetation spectra and local environmen-tal conditions is needed.

Karst region is a typical ecological fragilezone constrained by geological setting, with smallenvironmental and anti-interference capability(LeGrand 1973; Yang 1992; Cao et al. 2005).Southwest China is one of the largest karst re-gions in the world (Yuan 1993). Vegetation in

this region has unique adaptation for shortageof soil moisture, rocky standing place, and ex-ceeding calcium (Yuan 1993; Cao et al. 2005). Itis regional and non-zonal vegetation and largelyaffected by eco-geo-environmental conditions ofkarst (Su 1998; Li et al. 2003). Compared to veg-etation distributed in the same district but non-karst region (with acid soil), the flora of karstvegetation and its species are significantly differ-ent (Su 1998; Su and Li 2003; Ou et al. 2004). Inaddition, for karst eco-geo-environmental stand-ings, the eco-physiological traits, nutrient elementcontents, and leaf characters of karst vegetationare also different from non-karst vegetation (Caoet al. 2005; Rong et al. 2005; Yang et al. 2007).Thus, vegetation features reflect well eco-geo-environmental characteristics of karst or non-karst region, and vegetation spectral features maybe linked to eco-geo-environmental conditionsin karst region. However, we found no litera-tures about the vegetation spectral features inkarst region and their relationships with eco-geo-environmental conditions until recently.

The main goal of this paper was to explore therelationship between vegetation spectra and eco-geo-environmental conditions. Specific objectivesincluded: (1) surveying the leaf spectral character-istics of species groups mainly distributed in karstand non-karst regions; (2) examining whetherand how eco-geo-environmental conditions affectvegetation spectral features with a direct ordina-tion method, canonical correspondence analysis(CCA); and (3) investigating whether it is possibleto remotely sense eco-geo-environmental condi-tions through delineating of covered vegetationspectral properties in karst region.

Methods

Study area

The two study sites of this research are lo-cated in the same district but different eco-geo-environmental conditions. These two sites are allin Huanjiang County, Guangxi Province, South-west China (Fig. 1). Mean annual precipitationis 1,389 mm/year, and mean annual temperatureis 19.9◦C (min, −5.2◦C; max, 39.1◦C). The mul-

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Fig. 1 Map showing theexperimental range andlocations of Huanjiangexperimental station ofkarst ecosystem, ChineseEcosystem ResearchNetwork (CERN), andKenfu demonstrationarea

tiannual average relative humidity is 79%. Thefirst one is at Huanjiang experimental stationof karst ecosystem, Chinese Academy of Sci-ences (108◦18′ E, 24◦43′ N), which belongs to theChinese Ecosystem Research Network. It is atypical karst geomorphology with limestone soilcoming from the pedogenesis of limestone. Thevegetation cover is mainly made up of typicalclimbing shrub in carbonate rock region (Table 1and Fig. 2a, b; Guangxi Institute of Botany 1982;Su 1998). The second one, Kenfu demonstrationarea (108◦15′ E, 24◦51′ N), is a non-karst regionwith acid red and yellow soil coming from thepedogenesis of sandstone. The main vegetationcover consists of typical shrub in acid soil (non-karst) region (Table 1 and Fig. 2c, d; Su 1998; Ouet al. 2004).

Data collection and processing

Sampling strategy

For the vulnerable eco-geo-environment and vio-lent human impacts, the most widely distributedvegetation was climbing shrub lochmium in karstregion (Li et al. 2003). Shrub species were cho-sen and identified according to Guangxi Institute

of Botany (1982). A total of 25 distinct shrubspecies or phenotypes were collected, nine mainlydistributed in karst region, six mainly distributedin non-karst region, ten commonly distributed inboth regions (Table 1). Data collection took placeduring May 1–8, 2008, which was in the peak grow-ing season. Within each study site, the samplingplot was random to cover a relative homogeneousvegetation species and contain different eco-geo-environmental gradient. A total of 72 plots werechosen in the two study sites. Each vegetation plotwas 3 × 3 m in area.

Floristic information collection

Vegetation leaves were collected from upper full-sunlight positions in each shrub canopy. About 20to 30 leaves were obtained from each canopy andresealed in the plastic bags. All the samples werestored in a box with ice and transported to the lab-oratory within 2 h. A subsample of five leaves wasused for spectral reflectance collection and five foranalysis of pigment concentrations (chlorophyll-a, b, and carotenoid; Sims and Gamon 2002).The remainder of the samples were weighed andscanned for leaf area. Foliar samples were oven-dried at 70◦C for at least 72 h and weighed for

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Table 1 The sampled vegetation species, the number of sampling sites, and spectra collected

Groups of species Species Number of plots Number of spectra

Mainly distributed in Mallotus repandus 2 100karst region Pyracantha fortuneana 2 110

Paliurus hemsleyanus 3 150Alchornea trewioides 2 80Ficus gibbosa 3 140Desmodium pulchellum 2 100Bauhinia championi 3 150Ficus pumila 2 120Loropetalum chinensis 3 140

Mainly distributed in Eurya ciliata 1 60non-karst region Maesa japonica 2 100

Litsea cubeba 1 60Schefflera minutistellata 2 120Rhodomyrtus tomentosa 2 90Euodia lepta 2 100

Commonly distributed Zanthoxylum armatum 4 200in both regions Pyracantha atalantioides 4 200

Cudrania cochinchinensis 4 190Coriaria nepalensis 4 240Vitex negundo 4 200Mussaenda treutleri 4 180Blechnum orientale 4 220Pithecellobium lucidum 4 200Rubus reflexus Ker var. lanceolobus 4 160

Metc.Styrax faberi 4 200

Fig. 2 Landscape viewsof vegetation mainlydistributed in karst andnon-karst regions: a, bkarst vegetation; c, dnon-karst vegetation

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determination of specific leaf area (SLA, leaf areaper unit dry mass) and equivalent water thickness(EWT, leaf water content per unit; Garnier et al.2001; Ceccato et al. 2001)

Vegetation spectral data collection

The spectral reflectance of leaves was measuredusing an ASD FieldSpecFR spectrometer (Ana-lytical Spectral Devices, Boulder, CO, USA) ina dark room to better control irradiance condi-tions with a 50-W fluorescent lamp. The spectralrange for this instrument is 350–2500 nm with aresolution of 3 nm in the 350- to 1,000-nm rangeand 10 nm in the 1,000- to 2,500-nm range. Beforespectral data collection, a reflectance measure-ment with a standardized white spectralon panelwas made under the same conditions immediatelybefore the target measurements. The measure-ments were taken from a position of 20 cm abovethe surface, thus making sure that the target wasfull of the field-of-view of the spectrometer. Toreduce the noise level, every measurement wasrecorded as the average of ten consecutively ac-quired spectra. Every plot repeated 40–60 times(Table 1). The final conversion to spectral re-flectance was done by dividing the radiance spec-tra of the vegetation samples by the radiancespectra of the spectralon panel.

Eco-geo-environmental factors collection

During collecting each of the 72 plots, eco-geo-environmental factors were record simultane-ously, including slope, aspect, and altitude. Othereco-geo-environmental factors, such as soil mois-ture, organic matter (SOC), pH, and calciumcarbonate content (Ca), were analyzed in the lab-oratory according to Bao (2000). Slope and aspectwere collected by the compass. Since aspect was acircular variable, it was inappropriate to include itin a model without prior transformation. Then theaspect was transformed by creating two variables,“northness” = cos (aspect) and “eastness” = sin(aspect) (Guisan et al. 1999).

Statistical analysis

We first compared biochemical materials andspectral characteristics of the two major groupsof species, mainly distributed in karst region (k-vegetation) and mainly distributed in non-karstregion (nk-vegetation), and then investigated thespectral differences between k-vegetation and nk-vegetation through t tests by wavelength for theabsolute reflectance data as well as the first deriv-ative spectra. The absolute reflectance spectra in-cludes the effects of both scattering (albedo) andabsorption, whereas the derivative spectra use theslope of the spectra and accentuate the individualabsorption features (Tsai and Philpot 1998). Ineach species group, the combined variance wascalculated to account for unequal variances en-countered in the t tests.

Traditional linear-based multivariate methodsfor relating two sets of variables, such as par-tial least squares, canonical correlation analy-sis, and redundancy analysis, are less suitablefor analyzing the relationship between vegeta-tion spectral and eco-geo-environmental variablesbecause floristic information as well as the varia-tion of vegetation spectral features was theoret-ically regarded as having unimodal rather thanlinear distributions along the gradient for giveneco-geo-environmental factors (ter Braak andVerdonschot 1995; Armitage et al. 2004). In ad-dition, vegetation spectral features are usuallysubtle and cannot easily be captured using aband-by-band analysis (Clark et al. 2003; Asneret al. 2008). We used CCA method and ap-propriate vegetation indices (VI) to explore therelationship between vegetation spectra andeco-geo-environmental factors. The method un-derlying CCA is unimodal and can be used to an-alyze multivariate eco-geo-environmental factorssynthetically and simultaneously (ter Braak 1986;ter Braak and Verdonschot 1995; Jan and Peter2003; Zhang 2004), while remotely sensed VI takeadvantage of differences in spectral signatures todiscern vegetation type (Baret and Guyot 1991;Schmidt and Skidmore 2003).

CCA is based on an iterative process of recipro-cal averaging/correspondence analysis ordinationand multiple regressions and is a direct gradientordination that relates two joint sets of variables:

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Table 2 Mean (±SD) leaf biochemical, SLA, and EWT of k-vegetation and nk-vegetation groups from Table 1Groups of species Chlorophyll-a Chlorophyll-b Carotenoids H2O SLA EWT

(mg/g) (mg/g) (mg/g) (cm2/g) (mm)

k-Vegetation 1.24 ± 0.42 0.27 ± 0.13 0.33 ± 0.11 18.63 ± 0.043 80.87 ± 19.85 0.41 ± 0.09nk-Vegetation 1.07 ± 0.37 0.25 ± 0.12 0.28 ± 0.08 28.59 ± 0.051 64.77 ± 11.21 0.58 ± 0.12

first the floristic data and secondly a set of en-vironmental data (ter Braak 1986; Jan and Peter2003). The approach involves the analysis of thetrends present in both sets of variables and theestablishment of the nature of the relationshipbetween them (ter Braak 1986; ter Braak andPrentice 1988; ter Braak and Verdonschot 1995).In CCA, the ordination axes are constrained sothat the distribution of the first set of variablesis explained by a combination of the second setof variables; the variability within the first set ofvariables is summarized by the sampling plot ordi-nation axes. Cause and effect is thus implied, withvariation in the floristic data being determinedand explained by variation in the environmentaldata (Jan and Peter 2003; Armitage et al. 2004).

Results and discussion

Groups of vegetation comparison

The mean and standard deviation of biochemistry-related parameters for the two major groups ofvegetation, mainly distributed in karst region (k-vegetation) and non-karst region (nk-vegetation),was shown in Table 2. It clearly showed thatthe leaf biochemical materials were significantlydifferent between k-vegetation and nk-vegetation.Compared to the nk-vegetation, the photosyn-thetic pigments content and SLA of k-vegetationwere relatively higher, while the mean water con-tent was 18.63 (±0.043), about 10% lower thannk-vegetation (28.59 ± 0.051; t tests; p < 0.05).

Fig. 3 Mean (±SD) leafspectral reflectance ofvegetation mainlydistributed in karst region(k-vegetation) and mainlydistributed in non-karstregion (nk-vegetation)from Table 1

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Leaf EWT, which is leaf water content perunit and the product of leaf water content and(1/SLA), was 0.41 (±0.09) mm for k-vegetationand 0.58 (±0.12) mm for nk-vegetation (t tests;p < 0.05). These results were supported by otherresearches and were mainly due to the poor eco-geo-environmental conditions in the karst region,which was short of soil water, rocky standings, andexceeding calcium content (Yuan 1993; Cao et al.2005; Xie et al. 2007).

As for the spectral data, it was apparent thatthe general spectral curve shape of these twovegetation groups was similar, but with differ-ent absorption depth; the k-vegetation had higherreflectance in the 700- to 1,350-nm wavelengthregion than nk-vegetation (Fig. 3). It might be dueto the fact that the k-vegetation group were mostlydrought-resistant phenotypes and had thickerkeratose and more wax-coating materials thannk-vegetation (Cao et al. 2005), which resulted inhigher leaf radiance. In addition, the k-vegetationgroup also had higher reflectance values in the400- to 680-nm spectrum than the nk-vegetationgroup. Other than these immediate spectral re-flectance characteristics, it was difficult to de-termine whether k-vegetation and nk-vegetationgroups could be discriminated by their spectralreflectance.

Spectral statistical analysis

Since it was hard to assess whether k-vegetationand nk-vegetation groups were spectrally uniquethrough the whole spectral reflectance curveshape, we then employed t-tests to investigatethe spectral separability band-by-band. The re-sults showed a very significant and nearly continu-ous spectral separation in the 1,300- t- 2,500-nmwavelength ranges (Fig. 4a). Spectral variationsin these wavelength ranges are mostly driven bychanges in leaf water content (Ceccato et al. 2001;Asner et al. 2008). The statistically (p < 0.01)higher 1,300- to 2,500-nm reflectance of the k-vegetation group indicates that their leaf watercontent is averagely lower than that of the nk-vegetation group. It was strongly supported bymeasured differences in leaf thickness and wa-ter content between the k-vegetation and nk-vegetation groups (Table 2). The mean water

content and EWT of k-vegetation were lowerthan those of nk-vegetation, whereas SLA of k-vegetation was higher than that of nk-vegetation(t tests, p < 0.05). The leaf reflectance in theshortwave infrared (SWIR) is mostly influencedby EWT and internal structure (Ceccato et al.2001), while internal structure is determined bySLA (Jacquemoud and Baret 1990); thus, SLAand EWT are among the most important deter-minants of SWIR signatures (Asner et al. 2008).Therefore, these measured field leaf biochemicalparameters were well consistent with our spectralresults.

There were scarcely significant differences inthe visible wavelength regions (Fig. 4a). We thencarried out the derivative spectral separation ttests on k-vegetation and nk-vegetation becausevisible portion of the spectrum is dominated byleaf pigment constituents (Asner 1998) and spec-tral derivatives are a good approach to isolatepigment expressions in this wavelength region

Fig. 4 a Reflectance and b first-derivative spectra of k-vegetation vs. nk-vegetation, with band-by-band t testsshowing significant differences in grey bars (p < 0.05)

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(Sims and Gamon 2002). The results showedhighly significant differences in the 400- to 680-nm wavelength region of first-derivative spectrum(Fig. 4b). It was supported by the differences inchlorophyll-a, chlorophyll-b, and carotenoid con-tents of the karst and non-karst vegetation groups(Table 2).

Thus, we conclude that remote sensing of k-vegetation and nk-vegetation is generally possi-ble and may be achieved via a combination ofabsolute reflectance in the 1,300- to 2,500-nm re-gion associated with leaf water content and first-derivative spectra in the 400- to 680-nm rangerelated to photosynthetic pigment. Single bandor spectral region cannot provide universal sep-arability of the k-vegetation and nk-vegetationgroups. Since the underlying mechanism of veg-etation spectral variation is same, our results tosome extent align well with Asner et al. (2008).

Spectral variation responseto eco-geo-environmental conditions

As analyzed above, leaf water content and photo-synthetic pigments were significantly different be-tween the k-vegetation and nk-vegetation groups(Table 2) and thus resulted in different spec-tral features (Fig. 3). All of these indicate thatvegetation spectra are closely linked to its dis-tributed eco-geo-environmental conditions, thatis, eco-geo-environmental conditions have signif-icant effects on reflectance spectra in the karstregion. However, quantifying or semi-quantifyingthe influence of eco-geo-environmental conditionsis challenging because: (1) field-based sampling isinherently limited, and thus, correlation analysesusually lack statistical power once the data areportioned into one groups of vegetation; (2) vari-ation of vegetation spectral features was theoreti-

cally regarded as having unimodal rather than lin-ear distributions along the eco-geo-environmentalgradient (Armitage et al. 2004), whereas it waslinear underlying traditional statistical model (terBraak and Verdonschot 1995); and (3) spectralfeatures of k-vegetation or nk-vegetation cannoteasily be captured by single band or spectralregion. Definitely, our purpose is to explore therelationships between vegetation spectra and eco-geo-environmental conditions, but not to iden-tify relevant wavelength regions and quantifyeco-geo-environmental conditions with them. OurCCA method associated with disease water stressindex (DWSI) overcame several limitations men-tioned above.

The underlying CCA is unimodal, whereas it islinear in traditional statistical methods (ter Braakand Verdonschot 1995). This approach analyzesthe trends presented in both sets of spectral andeco-geo-environmental variables and establishesthe nature of the relationship between them.DWSI was employed for the reason that it wasused for the measurement of internal plant water(Galvao et al. 2005), which was exactly the leafbiochemical parameter that the most significantlydifferent wavelength region of k-vegetation andnk-vegetation was associated with, and its mostformula bands (549, 681, and 1,659 nm) wereincluded in the wavelength range of delineat-ing k-vegetation and nk-vegetation. DWSI thuscan reflect the most spectral features of the k-vegetation and nk-vegetation groups. In addition,DWSI were available for all remote sensing sen-sors (Brown et al. 2008).

The CCA was run with DWSI values as the firstmatrix and the seven eco-geo-environmental vari-ables as the second matrix. For the first two axesof spectral reflectance and eco-geo-environmentalvariables, their correlation coefficients were 0.93

Table 3 Correlation coefficients for the first two axes of spectral reflectance and eco-geo-environmental factors

SPX2 ENX1 ENX2 SOC Moisture Ca Slope Aspect Altitude pH

SPX1 0.03 0.93** 0.00 −0.27 0.90** −0.87** 0.07 0.11 0.20 0.02SPX2 1.00 0.00 0.63* 0.38* 0.07 −0.05 −0.30 −0.04 0.04 −0.15ENX1 0.00 1.00 0.00 −0.29 0.92** 0.89** 0.08 0.12 0.21 0.02SPX1: the first spectral axis, SPX2: the secondary spectral axis; ENX1: the first eco-geo-environmental factors axis, ENX2:the secondary geo-eco-environmental factors axis*p < 0.05, **p < 0.01

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and 0.63, respectively; for the first and second axesof spectral reflectance, their correlation coefficientwas 0.03, which were almost vertical; for the firstand second axes of eco-geo-environmental vari-ables, their correlation coefficient was 0 (Table 3).In addition, the cumulative percentage variancesof spectra–environment relation of the first twoaxes were 85.3% and 98.2%, respectively. All ofthese indicated that the ordination results gener-ated by CCA were creditable (ter Braak 1986; terBraak and Verdonschot 1995; Zhang 2004).

According to CCA, on the ordination diagramof the first two axes, the arrow lines representvarious eco-geo-environmental variables; thelonger the line, the more effects it has onvegetation spectral reflectance (ter Braak 1986;Zhang 2004). Figure 5 showed that the seven eco-geo-environmental factors all affect vegetationspectral reflectance, while soil moisture and cal-cium carbonate content have significant effectsand aspect or soil pH has relative fewer effectson vegetation spectral features. According tothe quadrant of arrowheaded lines of eco-geo-environmental factors within and the angle withthe first spectral axis, it was indicated that soilmoisture, pH, and topographic factors werepositively correlated with the first spectral axis

Fig. 5 Ordination diagrams of the first two axes of CCAfor the 72 sampling plots

and soil moisture >altitude>aspect>slope>pH,whereas soil calcium carbonate and organic mat-ter content had negative correlations with thefirst spectral axis and SOC> calcium carbonatecontent. In combination with the correlation coef-ficients shown in Table 3, it could be deduced thatthe correlations between eco-geo-environmentalfactors and the first spectral axis were: mois-ture>Ca>SOC>altitude> aspect>Slope>pH. Ascompared to the secondary spectral axis, eco-geo-environmental factors had relatively highcorrelation with the first spectral axis (Table 3).It thus indicated that the first spectral axis wellinterpreted the relationships between vegetationspectral reflectance and eco-geo-environmentalfactors. Furthermore, the cumulative percentagevariances of spectra–environment relation of thefirst two axes were 85.3%. We then conclude thatthe extent and degree of influences that eco-geo-environments conditions on vegetation spectralfactors is: moisture>Ca>SOC>altitude >aspect >slope>pH. CCA can assess to what extent thevariation of spectral features can be explained byassociated eco-geo-environmental variation.

Soil moisture and calcium carbonate contentshave the most significant effects on vegetationspectral reflectance. This was supported by thesignificant differences between the karst and non-karst eco-geo-environments. As compared to thenon-karst region, geomorphology of karst is com-plex and fragile. The karst soil is thin and impover-ished and the capacity of water holding and waterretaining is low. In addition, the karst soil containsexceeding calcium and is alkalescent as a resultof the weathering pedogenesis of carbonate rock(Yuan 1993; Cao et al. 2005). All of these eco-geo-environmental conditions resulted in the variationof vegetation biochemical contents (Table 2) andthus the differences of the spectral characteristicsof the karst and non-karst vegetation groups.

Vegetation distributions along CCAordination diagram

All sampling plots in the two study sites wereclearly illuminated by CCA ordination diagram(Fig. 5). In terms of the eco-geo-environmentalconditions of the karst and non-karst regions, themost of sampling plots of vegetation reflectance

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were apparently plotted out with two groups byCCA ordination diagram, along with the gradi-ent for soil water and calcium carbonate con-tent. According to sampling records, group 1was mainly located in relatively high soil wa-ter and low calcium carbonate content regions—non-karst region, while group 2 was mainly dis-tributed in scare water and exceeding calciumcarbonate content areas, which was the typicaleco-geo-environmental characteristics of the karstregion (Cao et al. 2005). CCA ordination diagramsuccessfully distributed the vegetation spectralfeatures along the main eco-geo-environmentalgradients of the karst and non-karst regions. Veg-etation spectra thus well illuminated the eco-geo-environmental features. In conjunction withthe spatial component of hyperspectral imagery,which can be used to interpret the spatial struc-ture of spectral information and the distributionof landscape objects (Aspinall et al. 2002), it isanticipated that eco-geo-environmental featuresof interest covered with vegetation can be indi-rectly monitored or mapped through delineatingof covered vegetation using hyperspectral imageryin karst region. However, our analysis is just atthe leaf level and the influences of canopy andshadows have not been taken into consideration.A further study should be conducted.

Conclusion

Since vegetation spectral reflectance is sensitiveto local environmental conditions, hyperspectralremote sensing makes it possible to monitor theproperties of substrates covered by vegetation.The results of this research presented here can besummarized as follows:

1. The differences in spectral characteristics ofkarst and non-karst vegetation are statisticallysignificant in spectral regions related to leafwater and pigment contents.

2. Remote sensing of the karst vegetation andnon-karst vegetation groups is generally pos-sible via a combination of 1,300- to 2,500-nmreflectance and 400- to 680-nm first-derivativespectra.

3. Vegetation spectra is tightly linked to eco-geo-environmental conditions in the karst re-gion, and CCA method is an effective meansto analyze the relationship between vegeta-tion reflectance spectra and eco-geo-environ-mental variables.

4. The extent of influences of eco-geo-environments conditions on vegetation reflectancespectra is: soil moisture>Ca>SOC>altitude>aspect>slope>soil pH.

Hyperspectral remote sensing can provide apowerful method for eco-geo-environmental mon-itoring and assessment in large scale. Althoughcovered with vegetation, all these findings indicatethat remote sensing of eco-geo-environmentalconditions can be conducted well and studied byexploring and analyzing above-ground vegetationspectra in karst region. With the increasing ofspectral and spatial resolution, it is possible to re-trieve and quantify eco-geo-environmental para-meters from remote sensing imagery.

Acknowledgements This study was carried out with thefinancial assistance of the Major State Basic Research De-velopment Program of China (grant no. 2006CB403208),the Chinese Academy of Sciences action plan for WestDevelopment (grant no. KZCX2-XB2-08), and the West-ern Light Program of Talent Cultivation of the ChineseAcademy of Sciences. The authors would like to thankthe anonymous reviewers for their valuable comments andsuggestions on the improvement of this manuscript.

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Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest ChinaAbstractIntroductionMethodsStudy areaData collection and processingSampling strategyFloristic information collectionVegetation spectral data collectionEco-geo-environmental factors collection

Statistical analysis

Results and discussionGroups of vegetation comparisonSpectral statistical analysisSpectral variation response to eco-geo-environmental conditionsVegetation distributions along CCA ordination diagram

ConclusionReferences

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