GIS technologies for aquatic macrophyte studies - CiteSeerX

Landscape Ecology vol. 8 no. 3 pp 163-175 (1993) SPB Academic Publishing bv, The Hague

GIS technologies for aquatic macrophyte studies: Modeling applications Marguerite M. Remillard and Roy A. Welch Centre for Remote Sensing and Mapping Science (CRMS), Department of Geography, University of Georgia, Athens, GA 30602 Keywords: Geographic information system (GIs), remote sensing, aquatic macrophytes, landscape modeling -

Abstract

A GIS database developed for Lake Marion, South Carolina was utilized to assess existing relationships between aquatic macrophyte distributions and environmental parameters affecting plant growth. The significance of water depth, sedimentation, nitrogen, phosphorus, top dissolved oxygen, bottom dissolved oxygen, percent light and absolute light was tested using GIS overlay techniques and the Chi Square test of independence. Specific levels of the eight parameters found to be spatially related to aquatic vegetation were then utilized to develop a provisional cartographic model describing optimum growth conditions for aquatic macrophytes. Model validation by comparing predicted vegetation with actual vegetation distributions indicated only water depth and sedimentation data layers are necessary for predicting more than 90 percent of emergent and submergent distributions. Resource managers can use this model to identify lake areas that are susceptible to excessive macrophyte growth and require special attention.

Introduction

Remote sensing and geographic information systems (GIS) have emerged as important tools in the management and inventory of aquatic macrophyte distributions (Brown, 1978; Bogucki et al. 1980; Gilmer et al. 1980; Ader and Johnston, 1982; Bussom et al. 1982; Carter, 1982; Welch, et al. 1988 and 1991; Jensen, et al. 1992). These technologies provide resource managers with an efficient method for monitoring plant distributions over large geographic areas. Geographic information system analysis procedures permit managers to determine changes in macrophyte distributions over time and to identify critical environmental parameters influencing their growth. Models can be created that describe existing relationships among landscape components, predict future plant distri-

butions and assist in making ecologically sound management decisions. Ecological models are generally defined as representations or abstractions of reality. They are used to represent interactions among ecosystem components in order to describe and ultimately understand systems as a whole. Such models enhance the management of aquatic environments, because system behaviour can be quantified, analyzed and predicted (Straskraba et al. 1988; Ulanowicz, 1988; Turner and Gardner, 1991). The majority of existing models for aquatic vegetation represent wetland conditions at individual points in space, usually at the locations of sampling stations (Wiegert et al. 1975; Hopkinson and Day, 1980; Mitsch et al. 1982). An understanding of changes occurring over space as well as time is necessary for models to be useful for management purposes (Risser et al.

168 Table 1. Contingency table of observed polygon frequencies for aquatic vegetation and bathymetric classes Aquatic vegetation classes (Veg-type) Water depth classes (m)

ow

EM

SM

0 - 1.2 1.2 - 2.4 2.4 - 3.6 3.6 - 4.9 >4.9

29 190 1279 154 38

151 108 2 2 0

21 122 46 0 4

plants and parameters potentially affecting their growth (Bhattacharyya and Johnson, 1977). It must be noted, however, that significant Chi Square results do not necessarily conclude cause and effect relationships, but are rather an indication for further investigation of the two related factors. The Chi Square test of independence is based on the selection of random samples for observation. A method of randomly sampling GIS data sets similar to that used by Dicks and Lo (1989) was used to generate contingency tables of frequencies (i.e., number of polygons) classified as both a particular aquatic vegetation type and an environmental parameter range. This method involved the use of ARC/INFO to first generate a 50 by 40 cell grid (0.25 ha resolution) for each of the four 500 ha map segments in the Buoy 109 area. These grids were registered to the vegetation/environmental parameter coverages to create raster-like composite coverages containing over 2,000 polygons per map segment. The shapes and sizes of some polygons (as small as 0.001 ha) precluded the inclusion of an entire grid cell (0.25 ha) within their boundaries. In order to minimize the discrepancy between polygon size and equalize the relative importance of each polygon available for random selection, polygons less than 0.1 ha were omitted from the gridhegetation/environmental parameter composite coverages. A random example of 1,000 polygons per map segment was established by randomly generating polygon identification numbers or User-Ids between 1 and the maximum number of polygons in

the composite coverage. These User-Ids were saved to an ASCII file, input to INFO and related to the attribute file of each composite coverage. Only those polygons having matching User-Ids were saved for the statistical analyses. The ARC/INFO command FREQUENCY was then used to summarize the number of polygons classified as both a particular vegetation type and environmental parameter range. These data were used to create contingency tables of frequency values. For example, Table 1 depicts a contingency table of polygon frequencies for an overlay of September 1985 aquatic vegetation types with bathymetry . Polygon frequency data arranged in contingency tables were used to perform the Chi Square test of independence between aquatic vegetation types (emergents, submergents) and eight environmental parameters (water depth, sedimentation, nitrogen, phosphorus, TDO, BDO, percent light and absolute light). The relative magnitude of computed Chi Square values (X2) suggests that bathymetry, nitrogen and TDO are most strongly related to aquatic vegetation distributions (Table 2). The null hypothesis of the Chi Square test of independence in this study is that aquatic vegetation classes cross-classified with environmental parameter levels are independent of one another and differences between observed frequencies and computed expected frequencies based on the probability of the cross classifications are due to random sampling error. A rejection of the null hypothesis, therefore, indicates that the differences between the observed and expected frequencies are unlikely to be the result of errors due to random sampling but rather the two nominal variables may be related to each other in some unspecified way. The calculated X2 of all eight environmental parameters were in fact greater than the critical X2 values and enabled rejection of the null hypothesis. While the X2 test verifies the existence of a significant statistical association between two variables, other tests such as nominal scale measures of association must be used to determine the strength of the association. According to Barber (1988), two such measures often used for contingency tables are Pearson’s C and Cramer’s V. These measures

169 Table 2. Chi Square (X2) values and nominal measures of association for aquatic vegetation and environmental parameters

Parameter

N’

d.f.2

Bathymetry Nitrogen TDO Phosphorus Absolute Lt. Percent Lt. BDO Sediment

2146 2188 2383 2541 2570 2525 2314 2591

14 30 23 17 11 14 30 11

Calc.3 X2

X2

Crit

Pearson’s4 C

V

Cramer’ss

1363.4 1241.3 619.9 251.7 233.4 202.9 194.9 170.8

36.1 59.7 49.7 40.8 31.3 36.1 59.7 31.3

0.62 0.60 0.45 0.30 0.29 0.27 0.28 0.25

0.56 0.53 0.36 0.22 0.21 0.20 0.21 0.18

-

Pearson’s C = dX2 / (X2 + N) where N = total number of observations Cramer’s V = d X 2 / (N * min (r - 1, c- 1) where N = total number of observations r = number of rows c = number of columns

N = total number of observations degrees of freedom (d.f.) = (r * c) - 1 where r = number of rows c = number of columns 3 Chi Square (X2) = Xi=; Ej=y (f.. 4 -F..)~/F.. 11 11 where fij = observed frequency in cell.. U Fij = expected frequency in cell.. U

Table 3. Contingency table of contributions to total X2 Aquatic vegetation classes (Veg-type) Water depth classes (m)

ow

EM

SM

0 - 1.2 1.2 - 2.4 2.4 - 3.6 3.6 - 4.9 B4.9

- 105.6 - 59.9 52.4 7.9 0.7

648.3 62.1 - 158.7 - 15.3 -5.1

0.5 187.8 -45.1 - 14.0 0.0

* Note that the sign of the difference between the observed and expected frequencies is retained. attempt to standardize X2 with the resulting coefficients ranging between 0 and 1. Coefficients of 0 indicate no correlation between the variables, and coefficients of 1 indicate a perfect correlation. Calculated values of both Pearson’s C and Cramer’s V in this study show the relatively strong relationship between aquatic vegetation and water depth (C = 0.62 and V = 0.56) and between vegetation and nitrogen (C = 0.60 and V = 0.53) (Table 2). TDO follows with C = 0.45 and V = 0.36. The other environmental parameters all have low association coefficients of 0.30 or less. Along with establishing which environmental parameters have strong spatial relationships with

aquatic vegetation, it is helpful to determine the levels of the parameters that are related to vegetation distributions. Following the procedure of Lowell and Astroth (1989), the difference squared contingency tables used to calculate X2 indicate which cross classifications contribute most to the total X2. If the computed value is greater than the critical X2 value, then the variables of the cross classification are significantly related. In our example, emergents are significantly related to depths of 0-1.2 m and 1.2-2.4 m, since the differences squared (648.3 and 62.1, respectively) are greater than the critical value of 36.1 (Tables 2 and 3). In this way, the levels of the eight environmental parameters significantly related to vegetation distributions were determined and used to create a provisional cartographic model.

Cartographic model of aquatic macrophyte distributions A schematic diagram of the provisional cartographic model of aquatic macrophyte distributions is shown in Fig. 8. This model depicts a flowchart of GIS analysis procedures that will result in predicted aquatic vegetation coverages. Following the model,

170

Ernergenu

v

Reselect

EmlSm + Bath

H I F

EmlSm+Sed EmlSm+Nit

H U

I

I

EmlSm + Phos

Veg. with Environ.

EmlSm +TDO

a Perk

4

Bath Predicted EmlSm

I

i

Sed Predicted Em

4

I

Nit Predicted EmlSm

I

i

Phos Predicted Em

I

1

TDO Predicted EmlSm

I

-

EmlSm + BDO

I

BDO Predicted Em

I

I

EmlSm + Perk

4

Perk Predicted Em

I

EmlSm + Abslt

i

Abslt Predicted Em

I

Fig. 8. Provisional cartographic model of aquatic macrophtye distributions.

overlays of aquatic macrophyte distributions and each environmental parameter are performed to produce composite data sets. The Chi Square test of independence determines which ranges of the environmental parameters are statistically related to aquatic vegetation distributions. Applying this information to a new area of the lake, polygons in the environmental parameter coverages that meet the Chi Square derived criteria (e.g., only polygons classified as depth levels 0 to 3.6 m that were found to be significantly related to macrophyte distributions) are isolated using the ARCANFO RESELECT command. The reselected polygons represent areas in which aquatic vegetation is expected to presently exist or grow in the future. Emergent distributions thus are predicted based on optimum bathymetric, sedimentation, nitrogen, phosphorus, TDO, BDO, percent light and absolute light levels, and submergent distributions are

predicted based on optimum bathymetric, nitrogen and TDO levels. Examples of predicted emergent distributions based on this model are shown in Fig. 9. The validity of the cartographic model was tested in four randomly selected map segments (12, 14, 15 and 25) withheld for model validation. To determine the accuracy of the emergent and submergent predictions, an overlay of predicted vegetation and actual vegetation distributions was performed (Fig. 9d). Statistics summarizing the areas of polygons containing both actual and predicted macrophytes were tallied and used to compute the percent of actual vegetation correctly predicted for each parameter or combination of parameters using Equation 2.

172 Percent Actual Vegetation Correctly Predicted

Depth

s8dime"t

NIrcge"

1 12

Pwcem Lt AMmeU

0

10

20

30

50

40

80

70

60

1W

90

Percent

Fig. 10. Percent of actual aquatic vegetation that was correctly predicted by the vegetation model using a single environmental parameter. Percent Actual Vegetation Correctly Predicted

Depth+TDO+PhOS DeplhiNniTDOiPhos

o

m

2

0

m

w

5

o

e

a

m

e

o

Percent

Fig. 11. Percent of actual aquatic vegetation that was correctly predicted by the vegetation model using multiple environmental parameters.

it was not appropriate for sedimentation. Validation is thus very important when models are developed f o r one location and applied to another. Nitrogen followed as third in importance for correctly predicting emergent (21 percent) and submergent (7 percent) distributions. This agrees with results of the Chi Square test of independence in which the Chi Square value for nitrogen was second only to water depth. The remaining environmental parameters, phosphorus, TDO, BDO, percent light and absolute light, were not accurate (less than 13 percent) in their prediction of aquatic vegetation

~

distributions. These results follow the relatively low Chi Square values and association coefficients listed in Table 2. The combination of environmental parameters did not improve the accuracy of predicted vegetation distributions over that of individual parameters. For example, while water depth and sedimentation were 94 percent and 93 percent correct, respectively, in predicting emergent distributions, depth and sedimentation together were only 87 percent correct in their prediction. (Fig. 11). All other parameter combinations were less than 21 percent correct. Based on the results of the Chi Square tests of independence, nominal coefficients of association and GIS overlays of predicted and actual macrophyte distributions, the provisional cartographic model was revised. This validated model includes only water depth and sedimentation and is adequate for predicting approximately 90 percent of the aquatic plant growth in Lake Marion. Other investigations also have indicated that water depth is one of the most important factors influencing macrophyte distributions. This is because the physical structure of emergent vegetation restricts their growth to shallow water areas and submergents are dependent upon light availability in the water column (Spence, 1967; Spence and Chrystal, 1970a, 1970b; Duarte et al. 1986; Machena, 1987). Future refinements of the model may benefit by the addition of data layers such as subsurface slope or roughness, substrate composition, relative distance from shorelines (fetch factor) and improved measures of water clarity. These factors have been demonstrated by other investigators to affect aquatic plant distributions (Barko and Smart, 1983; Duarte and Kalff, 1986; Chambers, 1987; Chambers and Kalff, 1987; Harvey et al. 1987).

Conclusions Remote sensing and geographic information systems are suitable tools for resource managers and ecologists to investigate aquatic environments on a landscape scale. This study has demonstrated the use of these technologies for assessing aquatic mac-

173 rophyte distributions in upper Lake Marion, South Carolina. Analysis of the integrated database containing remote sensing, map and field data revealed spatial correlations between aquatic vegetation and environmental parameters influencing their growth. This information, in turn, was utilized to develop a cartographic model describing optimum conditions for aquatic plant growth. Model validation by comparing predicted vegetation with actual vegetation distributions indicated water depth and sedimentation to be most important in predicting vegetative growth. These results will be used by managers to target areas of the lake that are most susceptible to dense macrophyte growth and require special attention. The creation of a cartographic model of macrophyte distributions demonstrates the power of GIS analysis capabilities to link georeferenced data with models of ecological processes. Such models not only describe existing relationships among landscape components, but also can be utilized to predict and visually display future configurations of landscapes based on model output. Emergent and submergent macrophyte distributions in Lake Marion, for example, were predicted and mapped by reselecting polygons meeting optimum growth criteria. If information on environmental conditions in a larger, encompassing area is known, the model can be applied to extrapolate and predict aquatic macrophyte distributions in new areas. Care must be taken, however, to test or validate the model to ensure accurate predictions. The remote sensing and GIS database procedures employed in this study are applicable to many landscape-scale ecological investigations and are expected to enhance cooperative efforts between ecologists and resource managers. Increased use of GIS mapping, analysis and modeling techniques will lead to a better exchange of theoretical and application-oriented ideas. This, in turn, will result in the implementation of ecologically sound decisions for natural resource management.

Acknowledgements This work was sponsored by the South Carolina Department of Health and Environmental Con-

trol (Contract # EQ-5-427 and # EQ-7-414), the United States Environmental Protection Agency (Contract # 5R-1301-NAEX and # 5B-5304NORR) , Lockheed Engineering and Management Services Company, Inc. (Contract # 68-03-3245), and the United States Army Corps of Engineers (Contract # DACW39-90-K-0005). -

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