Non-Peer ReviewedHeterogeneity, the degree of dissimilarity, is one of the most important and widely applicable concepts in ecology. It is highly related to ecosystem condition and wildlife habitat. In this study, the feasibility of applying Radarsat-1 HH polarization Synthetic Aperture Radar (SAR) image on heterogeneity study is tested on the Grasslands National Park (GNP) and surrounding pastures. GNP is located in southern Saskatchewan along the international boundary of Canada and the United States, which represents northern mixed grasslands. Fieldwork was conducted at GNP and surrounding pastures from June to July,
2003. Biophysical variables including species composition, cover, biomass, and canopy height were collected from 10 sites. Height heterogeneity index, Shannon’s index, and standard deviation were calculated based on field data. One standard mode Radarsat-1 HH image acquired on August 1, 2003 was used for this study. Parameters from texture analysis and standard deviation of the backscatters are correlated with biophysical parameters to measure grasslands heterogeneity. Results showed that different parameters had various abilities to detect field heterogeneity. Correlation showed the highest ability to explain variation of bareground cover (79%). Mean and contrast can also be utilized to explain the variation of grass biomass and standing dead cover (59% and 56% respectively)

Guo, X.

Sissons, R.

Wilmshurst, J.

Zhang, C.

English

eCommons@USASK

Application of Radarsat imagery on grassland heterogeneity assessment

University of Saskatchewan Research Archive

1Application of Radarsat Imagery on Grassland heterogeneity AssessmentChunhua Zhang1, Xulin Guo1∗, John Wilmshurst2, Robert Sissons 31Department of Geography, University of Saskatchewan, Saskatoon, Saskatchewan, CanadaS7N 5A52 Parks Canada, 145 McDermot Ave., Winnipeg, Manitoba, Canada R3B 0R93 Grasslands National Park, Box 150, Val Marie, Saskatchewan, Canada S0N 2T0Heterogeneity, the degree of dissimilarity, is one of the most important and widelyapplicable concepts in ecology. It is highly related to ecosystem condition and wildlifehabitat. In this study, the feasibility of applying Radarsat-1 HH polarization SyntheticAperture Radar (SAR) image on heterogeneity study is tested on the Grasslands NationalPark (GNP) and surrounding pastures. GNP is located in southern Saskatchewan along theinternational boundary of Canada and the United States, which represents northern mixedgrasslands. Fieldwork was conducted at GNP and surrounding pastures from June to July,2003. Biophysical variables including species composition, cover, biomass, and canopyheight were collected from 10 sites. Height heterogeneity index, Shannon’s index, andstandard deviation were calculated based on field data. One standard mode Radarsat-1 HHimage acquired on August 1, 2003 was used for this study. Parameters from texture analysisand standard deviation of the backscatters are correlated with biophysical parameters tomeasure grasslands heterogeneity. Results showed that different parameters had variousabilities to detect field heterogeneity. Correlation showed the highest ability to explainvariation of bareground cover (79%). Mean and contrast can also be utilized to explain thevariation of grass biomass and standing dead cover (59% and 56% respectively).Key words: Heterogeneity, Radarsat, synthetic aperture radar (SAR), Grasslands NationalPark, Texture analysis1. IntroductionHeterogeneity, the degree of dissimilarity, is one of the most important and widelyapplicable concepts in ecology (Armesto et al., 1991). A higher degree of heterogeneity inecological systems is supposed to associate with higher ecosystem stability (Tilman andDowning, 1994). Mixed grassland has been transformed to cultivated land or ranch for longtime periods (Lauenroth et al. 1994). The original mixed grassland plant communitydisturbed by bison and fire has different secondary successions. Grassland has been describedas inherently heterogeneous because composition and productivity are highly variable acrossmultiple scales (Ludwig and Tongway, 1995). To preserve northern mixed grasslandbiodiversity (or heterogeneity), Grasslands National Park of Canada (GNP) was establishedto exclude cattle grazing. The enclosure of ungulate grazing might produce important impactson the northern mixed grassland ecosystem and its heterogeneity. Therefore, it is necessary toinvestigate heterogeneity for the purpose of grazing management and wildlife habitatprotection.                                                          ∗ Corresponding author. Tel: +1-306-966-5663; Fax: +1-306-966-5680; Email: xulin.guo@usask.ca  (X. Guo).2Optical remote sensing have been applied to study heterogeneity (Briggs and Nellis,1991; Lauver, 1997; Zhang et al., in press). However, the lack of its capability to detectvegetation structure (Guo et al., 2003) and the reduced availability during growing seasonbecause of clouds cover make optical remote sensing inappropriate for northern mixedgrassland study. Synthetic Aperture Radar (SAR) image can be used as an alternative foroptical remote sensing in northern mixed grassland for its sensitivity to soil moisture andtopography (Goyal et al., 1999) and its ability to penetrate through cloud cover. Literatureshowed some studies in grassland using SAR products (Goyal et al., 1999; Hill et al., 1999;Buckley, 2004). In a study conducted in the mixed grassland ecossytem, Hill et al. (1999)noted that soil moisture is a significant confounding factor influencing radar backscatter fromthe grassland and changes in the water content of soils and vegetation can cause largevariations in radar backscatter. However, none of the current studies attempted to investigatethe heterogeneity of grassland with SAR data. Therefore, the objective of this study is to testthe feasibility of using a HH polarization Radarsat-1 image to measure grasslandheterogeneity.2. Study AreaThe study area included Grasslands National Park (GNP) (49° N, 107° W) andsurrounding pastures, located in southern Saskatchewan along the Canada - United Statesborder. This area falls within the mixed grassland ecosystem. The park is approximately906.5 km2 in area but in two discontinuous blocks, west and east. The first land was acquiredfor the park in 1984; as a result, some areas of the park have been under protection fromlivestock grazing for almost 20 years. The park area consists of upland grasslands andlowland grasslands. The dominant plant community in the uplands of the mixed grass prairieecosystem is Needle-and-thread—Blue grama (Stipa-Bouteloua), which covers nearly twothirds of the park’s ground area. The dominant species in this community include needle-and-thread grass (Stipa comata Trin. & Rupr), blue grama grass (Bouteloua gracilis (HBK) Lang.ex Steud.), and western wheatgrass (Agropyron smithii Rydb. Selaginella Beauv.) (Fargey etal., 2000). Apart from the Needle-and-thread—Blue grama (Stipa-Bouteloua) community,lowland grasslands contain higher densities of shrubs and occasional trees. The entire areaconsists of northern mixed grassland (Davidson, 2002). The GNP area has a mean annualtemperature of 3.8 ºC (Environment Canada, 2003) and a total annual precipitation of 325mm; approximately half of the precipitation is received as rain during the growing season.3. Materials and methodsField workField work was conducted in June and July of 2003. Ten sites were randomly selectedwithin the park and surrounding pastures. Three 100x100 m plots were set up in each site,and each plot was composed of two 100 m transects placed perpendicularly to each otherwith a north-west orientation. Twenty-one quadrats (20x50 cm) were placed in each plot at 10m intervals. Percent cover of grass, forb, shrub, standing dead, litter, moss, lichen, and bareground as well as species composition was collected at each quadrat. Biomass was collectedat 20 m intervals using the harvesting method. Clipped fresh biomass was sorted into fourgroups: grass, forb, shrub, and dead materials. They were then dried in the oven for 48 hoursat 60_. LAI was measured using a LiCor-LAI-2000 Plant Canopy Analyzer. At each plot, LAI isthe average of four automatically calculated LAI values; each was the result of one above3canopy reading compared with 10 below canopy readings. These measurements werecompleted within two minutes to avoid atmospheric variation. The 10 below canopy readingswere set at five meter intervals. The sensor was shaded when observations were being takento reduce the glare effect from direct sunshine.Biophysical parameters were integrated into each site by averaging quadrat values.Standard deviation (SD) and coefficient of variation (CV) were used to measure the variationof biophysical parameters within sites. Shannon’s index (Rosenzweig, 1995) andHeterogeneity index of height (HIH) (Wiens, 1974) were also calculated to stand for speciesdiversity and the variation of canopy height inside sites respectively (Table 1).Table 1. Grassland heterogeneity indices and their formulas used in the paperIndex Equation NotesShannon’sindex- ∑ )ln(p p ii pi is the proportion of the total number ofindividuals occurring in species iHeterogeneityindex of height ∑∑ −xMinMax )( Max=maximum value of the canopy heightwithin quadrats, Min=minimum value of thecanopy height within quadrats, N=the totalnumber of quadrats, x  is the mean value ofcanopy height in a quadratImage analysisOne standard mode Radarsat -1 image with HH polarization was collected for this study.The image was taken on August 7, 2004 and has a spatial resolution of 12.5 m. DNs wereconverted to backscatter (db) and a Gamma filter was conducted to remove speckles beforethe conversion (PCI Geomatics software, 2004). The image was registered to a UTMprojection. A nearest neighboring method was used in the correction and 35 GCPs werecollected. The RMSE was 0.35 pixel.GLCM Texture analysis is a commonly used method for describing localized variationof surface features in grey scale. During the process of texture analysis, a grey level co-occurrence matrix or grey level co-occurrence vector is computed to describe the stochasticproperties of spatial distribution of grey level (Hall-Beyer, 2000; He and Wang, 1990).Results of textural analysis can be used to describe the heterogeneity within a landscape(Briggs and Nellis, 1991). Energy (angular second moment), contrast, entropy, have beenused as indicators of heterogeneity or local variance(Anys and He, 1995; Briggs and Nellis,1991; Woodcock and Strahler, 1987).Texture analysis was applied to the HH polarization image with a window size of 3x3. Tenmeasures of texture analysis, i.e., contrast, homogeneity, dissimilarity, Angular SecondMoment, entropy , GLDV entropy, GLDV Angular Second Moment, mean, standarddeviation, correlation were calculated. These parameters can be applied to measure fieldheterogeneity (Baraldi and Parmiggiani, 1995; Briggs and Nellis, 1991). A 7x7 window wasused to correlate textural parameters with field data. Mean values of textural parameters are4used to measure imagery heterogeneity.The relationships between standard deviation of biophysical parameters andcorresponding textural parameters were first highlighted by Pearson correlation. Then linearregression was applied to decide the relationships between image heterogeneity and fieldheterogeneity.4. Results and DiscussionBiophysical parameters and field heterogeneityThe field measured biophysical variables showed greater variation within sites for uplandgrasslands (Table 2), which was corresponded with our field design. The centre of each plotwas placed on the top of hills and transects were placed in north-south and east-westdirections along the slope. Therefore, there was a gradient with changing vegetation species,cover, biomass, and density along each transect. As a result, big variation existed within sites.Different parameters showed various levels of heterogeneity with forb showed the highestvariation because of the small amount of forb biomass in total biomass and small amount islikely resulted in high variation. Among all sites, G1 (a site in grazed prairie) had the lowestdegree of heterogeneity, which can be explained by the low fluctuations of elevation andhomogeneous soil moisture in G1. In northern mixed grassland, flat areas with homogeneoussoil moisture have low level of heterogeneity due to the critical role of soil moisture invegetation growth.Table 2 Descriptive statistics of biophysical variablesGrass Forb Dead materials Total biomassAverage 52.9 22.9 66.7 110.1SDG1 28.8 15.1 30.2 54.0Average 62.1 109.9 65.3 52.0CV(%) G1 37.2 152.2 47.2 35.7Shannon’s index 2.13HIH 1.32SAR image heterogeneityHeterogeneity variables derived from the SAR image with the texture analysis indicated ahigh level of heterogeneity for the study area except one site (G1), which is the same siteshowed the lowest variation based on field measurements (Figure 1). G1 had much lowercontrast, dissimilarity, and entropy values and higher homogeneity value. The gap betweenG1 and other sites is bigger than measured field heterogeneity (Table 2), which could beexplained by the low fluctuations of elevation and soil moisture too. Soil moisture influencesRadarsat backscatter by changing the soil dielectric rate and homogeneous soil moistureresulted in homogeneous backscatters.5024681012HomogeneityContrastDissimilarityEntropyAverage G1Figure 1 Imagery heterogeneity by textural parametersModeling field level heterogeneity with RADARSAT imagery heterogeneityThe relationships between the field level heterogeneity (variation) and the image levelheterogeneity (texture analysis) are significant for several variables (Table 3). About 79%variation of bareground cover can be explained by the correlation parameter derived fromSAR texture analysis. The mean and contrast derived from SAR texture analysis were highlycorrelated with the variation of the standing dead cover negatively (r=-0.64 and -0.75respectively). The variation of biomass was highly correlated with all image heterogeneityvariables either positively or negatively (Table 3). However, there were no significantcorrelations between the image heterogeneity variables and Shannon’s index or HIH.Consequently, these relationships can be simulated with linear regression (Figure 2).Correlation can explain 79% of variation of percent bareground. The proportion decreased to76% when validation was applied.Table 3 Correlation coefficients between field level heterogeneity and image level heterogeneityHomogeneity Contrast Dissimilarity Mean CorrelationGrass biomass -0.63* 0.63* 0.64* 0.76** -0.69*Standing dead cover 0.63* -0.75** -0.68* -0.64* 0.19% of bareground 0.46 -0.36 -0.43 -0.44 0.89**** significant at 0.01 level* significant at 0.05 level6y = 17.769x + 4.9338R2 = 0.790123456-0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0Correlation% of bareground(a)y = 3.781x + 26.352R2 = 0.5901020304050607080900 2 4 6 8 10 12MeanGrass biomass(b)7y = -1.3597x + 19.239R2 = 0.5602468101214163 5 7 9 11ContrastStanding dead cover(c)Figure 2. Field geterogeneity prediction models based on imagery heterogeneity, (a) baregroundcover, (b) grass biomass, (c) standing dead cover.In northern mixed grasslands, soil moisture, the key factor of vegetation growth, isdecided by topography (Zeleke and Si, 2004). Influences of topography on vegetationcommunity is important that Rey-Benayas and Pope (1995) even used topographic index tostand for vegetation richness. Topography and soil moisture are the major sources of variationin SAR backscatter coefficients in high relief areas (Goyal et al., 1999). Therefore, measuredlandscape heterogeneity in the image indicated how vegetation communities vary acrossspace. Accordingly, heterogeneity from Radarsat image indirectly showed vegetationvariation.5. ConclusionsIn a HH polarization SAR image, the information from vegetation canopy is very weak(Bindlish and Barros, 2001). However, textural measurements, like contrast, mean, andcorrelation, can be applied to detect field heterogeneity through measuring the variation ofsoil moisture and topography. They could be used to predict heterogeneity of standing deadcover, grass biomass, and bareground cover. This study indicated that about 40% to 80% offield level grassland heterogeneity can be estimated through RADARSAT-1 SAR datathrough texture analysis. Other studies have concluded that cross-polarized (HV or VH)channel is better for vegetation study (Bindlish and Barros, 2001; Buckley, 2004; Goyal et al.,1999; Hill et al., 1999; Kasischke et al., 1997).Buckley (2004) simulated Radarsat-2 image tostudy prairie landscapes classification and found that VH and HV polarization are good atdetecting vegetation canopy and structure. Therefore, VH and HV polarization Radarsat-2image will be investigated for the same study area.8ReferencesAnys, H. and He, D., 1995. Evaluation of textural and multipolarization radar features forcrop classification. IEEE transactions on geoscience and remote sensing, 33(5): 1170-1181.Armesto, J.J., PIckett, S.T.A. and McDonnell, M.J., 1991. Spatial heterogeneity duringsuccession: a cyclic model of invasion and exclusion. In: J. Kolasa and S.T.A. Pickett(Editors), Ecological heterogeneity. Springer-Verlag, New York, pp. 256-269.Baraldi, A. and Parmiggiani, F., 1995. An investigation of the textural characteristicsassociated with gray level cooccurrence matrix statistical parameters. IEEEtransactions on geoscience and remote sensing, 33(2): 293-304.Bindlish, R. and Barros, A.P., 2001. Parameterization of vegetation backscatter in radar-based, soil moisture estimation. Remote sensing of environment, 76: 130-137.Briggs, J.M. and Nellis, M.D., 1991. Seasonal variation of heterogeneity in the tallgrassprairie: a quantitative measure using remote sensing. Photogrammetric engineering &remote sensing, 57(4): 407-411.Buckley, J.R., 2004. Enhanced classification of prairie landscapes using simulatedRADARSAT-2 imagery. Canadian journal remote sensing, 30(3): 510-516.Canada, E., 2003. Canadian Climate Normals 1971-2000 Val-Marie, Saskatchewan.http://www.climate.weatheroffice.ec.gc.ca/(December 2003).Davidson, A., 2002. Integrating field sampling and remotely sensed data for monitoring thefunction and composition of northern mixed grass prairie. Ph D Thesis, University ofToronto, Toronto, 225 pp.Fargey, K.S., Larson, S.D., Grant, S.J., Fargey, P. and Schmidt, C., 2000. Grasslands NationalPark field guide. Prairie wind & silver sage - friends of grasslands Inc.Goyal, S.K., Seyfried, M.S. and O'Neill, P.E., 1999. Correction of surface roughness andtopographic effects on airborne SAR in mountainous rangeland areas. Remote sensingof environment, 67: 124-136.Hall-Beyer, M., 2000. GLCM texture: A tutorial.http://www.ucalgary.ca/~mhallbey/texture/texture_tutorial.html, 03/11/2003.He, D. and Wang, L., 1990. Texture unit, texture spectrum, and texture analysis. IEEEtransactions on geoscience and remote sensing, 28(4): 509-512.Hill, M.J., Donald, G.E. and Vickery, P.J., 1999. Relating radar backscatter to biophysicalproperties of temperate perennial grassland. Remote sensing of environment, 67: 15-31.Kasischke, E.S., Melack, J.M. and Dobson, M.C., 1997. The use of imaging radars forecological applications--A review. Remote sensing of environment, 59: 141-156.Lauver, C.L., 1997. Mapping species diversity patterns in the Kansas shortgrass region byintegrating remote sensing and vegetation analysis. Journal of Vegetation Science, 8:387-394.Ludwig, J.A. and Tongway, D.J., 1995. Spatial organization of landscapes and its function insemi-arid woodlands. Landscape Ecology, 10: 51-63.Rey-Benayas, J.M. and Pope, K.O., 1995. Landscape ecology and diversity patterns in theseasonal tropics from Landsat TM Imagery. Ecological applications, 5(2): 386-394.Rosenzweig, M.L., 1995. Species diversity in space and time. Cambridge University Press,New York, NY.Wiens, J.A., 1974. Habitat heterogeneity and avian community structure in North AmericanGrasslands. American midland naturalist, 91(1): 195-213.Woodcock, C.E. and Strahler, A.H., 1987. The factor of scale in remote sensing. Remotesensing of environment, 21: 311-332.9Zeleke, T.B. and Si, B.C., 2004. Scaling properties of topographic indices and crop yield:multifractal and joint multifractal approaches. Agronomy Journal, 96: 1082-1090.

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Application of Radarsat imagery on grassland heterogeneity assessment

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