Application of Remote Sensing and Geo Grap

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ELSEVIER Aquatic Botany 58 (1997) 393-409

Application of remote sensing and geographicinformation systems to the delineation andanalysis of riparian buffer zonesSunil Narumalani>* , Yingchun Zhou, John R. JensenbaDepattment of Geography, Uni versit y f Nebraska, Li ncoln: N E 68588-0135, USA

bDepart ment of Geography, Uni versit y f South Caroli na, Columbi a, SC 29208, USA

Accepted 18 November 199 6

Abstract

Non-point source pollution has a significant impac t on the quality of wa ter resourc es.Studies have revealed that agricultural activities are often major contributors to non-pointsource pollution of aquatic environments. A common means o f reducing the threat ofnon-point source pollution is throug h the establishment of riparian vegetation strips (orbuffers) along those areas o f stream channels that would be most susceptible to the threat.Rem ote sensing and geographic information systems (GIS) offer a means by which criticalareas can be identified, so that subsequent action towa rd the establishment of riparian zonescan be taken. T his resear ch focuse s on the development and analysis of riparian bufferzones for a portion of the Iowa River basin. Landsat Thematic Ma pper (TM ) data were usedto characterize the land cover for the study area. An updated hydrology data layer wasdeveloped by integrating the United States Geological Survey (USG S) Digital Line Graph(DLG ) da ta base with the TM -derived classification of surface water bodies. Spatial distancesearch tools were applied to develop the buffer zones around all surface hydrologic features.The buffer zones were integrated with the remotely sensed classification data to identifycritical areas for the establishment of riparian v egetation strips. Results indicated thatwhile most of the main channel of the Iowa River was protected by natural vegetation, morethan 4 4% (or 1008 ha) of the area along its tributaries lack any protective cover fromnon-point source pollution. As these critical areas are adjacent to agricultural fields it isimportant that water resources m anagement strategies focus on the establishment of

*Corresponding author: Tel.: + 1 402 472984 2; fax: + 1 402 4721 185; e-mail: sn@ unlinfo.unl.edu

0304-3 770/97 / 17.00 0 1997 Elsevier Science B.V. All rights reserved.PZZ SO304-3770(97)00048-X


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riparian zones in orde r to minimize the impa ct of non-point source pollution. 0 1997Elsevier Science B.V.Keywords: Rem ote sensing; Geographic information systems (GIS); Riparian buffer zones;Vege tation strips; Non-point source pollution

1. Introduction

Over the last several decades it has been recognized that non-point sourcepollution is a major problem that can have a considerable effect in reducing waterquality. A US Environm ental Protection Agency (1990) study estimated thatnon-point source pollution contributes over 65% of the total pollution load to theUS inland surface waters (US Environm ental Protection Agency, 1989). Non-po intsource pollutants include nitrogen (N), phosphorus (P), heavy metals, and otherchemicals from fertilizers, pesticides, herbicides, anima l wastes , overland flowwastew ater treatment systems, urban stormwater, and other sources (Muscutt etal., 1993). Federal, state, and local government agencies work cooperatively indesigning the best manag ement practices to control non-point source pollution.The establishment of riparian buffer zones is an important component of theirintegrated management plans. Riparian buffer zones, which are also referred to asvegetated filter strips, are permanently vegetated areas located between the pollu-tant sources and water bodies (e.g. rivers, streams, lakes, etc.). A filter strip allowsrunoff and associated po llutants to be attenuated before reaching surface andunderground water sources via infiltration, absorption, uptake, filtering, and depo-sition.

The effectiveness of riparian buffer zones in decreasing non-point source pollu-tion has long been recognized and utilized in management practices (Lowrance etal., 1984 ; Peterjohn and Correll, 1984 ; Jacobs and Gilliam, 1985 ; Dillaha et al.,1989). Lowrance et al. (1985) measured N and P inputs and outputs for the riparianecosystem in a 3873-acre drainag e area of the Little R iver watershed near Tifton,Georgia during 1979 and 1980. They found that N removal by denitrification andstorage by woody vegetation was six times as much as N output to streamflow. Inaddition, as much a s half of the P outflow was taken up by the vegetation and theremainder was exported in streamflow. According to investigations by the UnitedStates Department of Agriculture (USD A) Forest Service (USD A, 19911, P issignificantly reduced by the filtering action of riparian vegetation because about85% of available P is transported with the sm all soil particles comprising thesediment.The United States Departm ent of Agriculture soil erosion estimates for 1992showed that Iowa had the highest quantity of soil loss on cropland in the US A bywater erosion (USD A, 1995). The sediment and other po llutants, carried by surfaceand sub-surface runoff, impact water quality for hum ans as well as the aquatic


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environment. The objectives of this research were to develop a nd analyze riparianbuffer zones for a section of the Iowa River watershed in Iowa, USA, using remotesensing and geographic information system (GIS) techniques, and to quantify theland cover that would fall within these buffer zones. Landsat Thematic Mapper(TM) imagery w as used to characterize the land cover of the Iowa River landscape.The surface water features from this land cover image-m ap were subsequently usedto update the hydrology map derived from the Digital Line Graph data base for thecreation of buffer zones, with the aid of ancillary data sources such as the countysoil surveys.2. Functions of riparian buffers for removing agricultural pollution

Surface an d sub-surface runoff are the major mechanisms of transportingsediment, fertilizer, and pesticides to ground or surface water. Neely and Baker(1989) found that NO ,-N concentrations of lo-20 mg 1-l were common insub-surface drainage in the United States. Surface runoff occurs when surface soilsbecome saturated as the result of a high water table, or when rainfall intensityexceeds the infiltration rate of soils. The runoff not only carries so luble pollutantsbut also sediment, whose prime source is the eroded soil material. Soil erosion isgreatest during the early part of the growing season when crop foliage is not denseenough to intercept precipitation, and after harvesting has occurred, when the soilsare bare and exposed to heavy rains. This movement of both organic and inorganicsediment may be a major pathway of pollutant transport in agricultural catchments,while sediment P may constitute a major portion of total P export (M uscutt et al.,1993). Organic forms of N are also transported with sediments and debris.The effects of riparian buffers in removing non-point source pollution can beexamined from their mechan ical, chemical, or biological functions. From themechanical perspective, the increased hydraulic roughness of vegetation cover inbuffer zones decreases the velocity of surface flow and consequently reduces itssediment carrying capacity which in turn results in sediment deposition (USD A,1991). In addition, the filtering effect through infiltration is enhanced because ofchanged soil structure and longer time span required for surface flows to moveacross buffer zones as a result of reduced velocities. Besides physically interceptingsedimen ts and debris, dense vegetation also helps stabilize stream bank s thusreducing s treambank erosion. Buffer zones also block the direct contamination ofhydrolog ic resources by the off-spray from fertilizers and pestic ides.The chemical and biological functions of riparian buffer zones pertain to theprocesses that are activated in the riparian ecosystem for transforming pollutantsinto different compounds. For example, bacteria and fungi in the filter zonesconvert N in runoff and decaying organic debris into mineral forms (NO,), whichcan then be synthesized into proteins by plants or bacteria. In other cases,denitrifying bacteria convert dissolved N into its gaseous form, thus returning itinto the atmosphere (USD A, 1991). Studies reveal that N concentrations weresignificantly reduced in surface runoff flowing from agricultural fields through a19-m buffer of riparian forest (Peterjohn and Correll, 1984).


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Riparian vegetation can also convert some forms of toxic chemicals such aspesticides into non-toxic forms through microbial decom position, oxidation, hydro-lysis, and other biodegrading processes occurring in the soil and water. Ano therimportant chemical and biological process of riparian vegetation is its ability to actas a nutrient sink if trees or grasses are harvested periodically to maintain a netuptake of nutrients. Because certain types of vegetation have a very high rate of Nuptake, the nutrients stored in the litter can be converted into peat an d stored fora long time by the ecosystem.The effectiveness of a riparian zone in removing non-point source pollution isinfluenced by many factors including the dimensions of the buffer zone, composi-tion of vegetation species w ithin the zone, land use, soil types, topography,hydrology, microclimate, and other characteristics of the agrosystem. D esigning ariparian buffer zone is a very complex p rocess and this research describes thedevelopment of riparian buffer zones along many of the tributaries flowing into themain stream channel of the Iowa River.3. Development of riparian buffer zones

A riparian buffer zone reduces the connection between potential pollutionsources and aquatic resources. Buffer w idths ranging from 3 to 200 m have beenfound to be effective, depending on site-specific conditions (Castelle et al., 1994).The methods used by water resources scientists, researchers, and various USgovernment agencies can be broadly classified into three categories. These include:(1) application of a constant buffer w idth for the entire area under consideration;(2) determination of a minim um buffer width based on soil capability, extent ofsource area, or slope (Trimble and Sartz, 1957 ; USD A, 1991); and (3) spatialmodeling methods which take into consideration the regional variations in physical,ecological, and socio-economic conditions (Delong and Brusven, 1991 ; Xiang,1993).The first strategy facilitates the delineation of wetland zones and makes it moremanageable to implemen t. However, it does not take into consideration regionaldifferences, which define the uniqueness of a given area. The second is moreapplicable than a complicated model, and takes into account the regional variationof soils or slope, which are two important parameters in determining pollutantyield. The third method deals with a set of variables related to pollutanttransportation and provides a systematic and scientific foundation for the establish-ment and maintenance of buffer zones. However, it is often less feasible because itis dependent on data availability and is computationally rigorous. Also, it is difficultto implemen t because of the spatially dynam ic, variable buffer w idths.The United States Department of Agriculture Forest Service (1991) has recom-mended three m ethods of determining the minim um w idth of riparian forestbuffers based on soil hydrologic group, source area, a nd soil capability class. As tothe vegetation composition within these buffer zones, it was concluded that thenatural vegetation pertinent to the geographic area should be preserved. Thebuffered areas would be comprised of three zones that include a combination of


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S. Narumalani et al. /Aquutic Botany 58 1997) 393-409 397forest and grassland . Vegetation composition in zones 1 and 2 should be stream-side tree species w ith a minim um width of approximately 23 m, while zone 3 whichwou ld be located between zone 2 and cropland (or pasture), should consist ofperennial grasses and forbs.From the above discussion it is evident that several m ethods have been sug-gested for implem enting wetland buffer zones to minimize the effects of non-pointsource pollution on aquatic resources. Because of the complexity of some of thesemethods, the application of remote sensing and GIS techniques can aid in facilitat-ing and analyzing the selection and implemen tation of an appropriate method ofbuffering by examining multiple data sources, and using appropriate modelingprocedures.

4. Integration of remote sensing and geographic information systems GIS ) fornon-point source pollutionRemote sensing provides a synoptic view of the terrestrial landscape and is usedfor inventorying, monitoring, and change detection analysis of environmental andnatural resources. For example, H ewitt (1990) used Landsat TM data to mapriparian classes associated with the river, lakes, and wetlands, along the Yak imaRiver of central Wa shington and achieved a classification accuracy of 80% in thedetection of such land cover types. In another study, Jensen et al. (1995), utilizedmulti-sensor remotely sensed data including Landsat Multispectral Scanner (MSS)and SPOT H igh Resolution Visible (HRV ) im ages for a change detection study ofaquatic macrophyte distribution and composition within the Florida EvergladesWa ter Conservation Area from 1973 to 1991 . The authors concluded that theexpansion of specific aquatic m acrophyte species such as cattails (Typha domingen-sis) and sawgrass (Cludium jumaicense) in the area might be attributed to increasedP loading due to the agricultural activities surrounding the water conservationarea. Several other studies have also demonstrated the utility of remote sensing forexamining non-point source pollution (e.g. Pelletier, 1985 ; Hewitt and Mace, 19881.A major draw back of current remote sensing systems has been their relatively

coarse sp atial resolution (e.g. TM = 30 x 30 m; SPOT HRV multispectral = 20 x 20ml. Often, such resolution may be inadequate for the detection and analysis ofriparian zones since it wou ld exceed the physical dim ensions of the zones. How-ever, it is expected that with the improved spatial resolution on future satellitesensor systems (i.e. 3 X 3 m or 1 X 1 ml, remote sensing will be an invaluable datasource for detailed and temporally frequent studies of the impact of non-pointsource pollution on water resources.GIS are useful for the analyses of spatial and temporal biophysical parametersdetected by remote sensing techniques. When in situ spatial data (e.g. soil,topography, rainfall, pollution load measu res, etc.) are compiled in a GIS, they canbe used in conjunction with remotely sensed data for the development of a varietyof water resource manag ement models (e.g. modeling soil loss, pollutant yield,designing non-point source pollution filter strips, etc.). Researchers have used


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several m odels within a GIS environment for the estimation of soil loss andsediment yield, including USLE (Universal Soil Loss Equation), CRE AM S (Chem-ical, Runoff, and Erosion from Agricultural Managem ent Systems), and AN -SWE RS (Area1 Non-point-Source Watershed Environment Response Simulation).Information on land cover characteristics derived from remotely sensed data hasbeen synergistically used in these models to enable a robust analysis of aquaticconditions (Pelletier, 1985 ; Sivertun et al., 1988).5. Study area

The study area is a section of the Iowa River basin from the edge of theCoralville Reservoir in the east and extending 25 km to the west (Fig. 1). The IowaRiver is one of the two largest rivers in the state, m eandering over the gentlyrolling plains, with num erous oxbow lakes and abandoned channels on the flood-plain. Much of the surrounding land is agricultural, with corn, soybeans, and otherfeed grains being the major crops. The intensity of these a gricultural activities andtheir considerable spatial extent creates a serious pollution problem to the surfacewaters. The forest resource of the basin is limited to riparian trees and shrubswhich form effective filter belts to reduce non-point source pollution, however,many parts of the river system (especially the tributaries) are not protected bynatural vegetation buffers. In this study, the Landsat TM data were used tocharacterize the landscape and identify critical areas of non-point source pollutionto aid in the development of non-point source pollution control strategies (i.e. thedevelopment of riparian buffer zones).6. Methodology

The development and analysis of riparian buffer zones required the implem enta-tion of several steps . First, a contemporary land cover map wa s generated from aLandsat TM subscene using spectral pattern recognition techniques. Second,stream network data were extracted from the classified image-m ap for updating thehydrologic information of the USG S Digital Line Graph data. Third, the dimen-sions of the buffer zones were determined based on the analysis of the soilscapability classes of the study area. Finally, buffer zones were delineated and thoseareas which were unprotected against potential non-point source pollution identi-fied.6. I. Image c lass i f i ca t i on

The Landsat TM image used in this study was acquired on 8 September 1992.The image had been rectified and georeferenced to the Universal TransverseMercator (UTM ) projection, and resampled to a 30 X 30 m pixel size by the EROSData Center, Sioux Falls, SD. To determine the optimum band s for characterizingthe land cover, a correlation matrix was developed for the six visible and infraredband s. Because of the lower correlation between TM bands 3 (red), 4 (near-in-


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/ 4 Kmmatao \ a)100 0 100

Landsat TM Scene o f Study Area 0)Band 4 N ear Infrared). 08 September 1992h - - - I Ki l ometars5 5

Fig. 1. Location of the Iowa River basin study area.

frared), 5 mid-infrared), and 6 mid-infrared), they were selected for classifyingland cover using statistical pattern recognition algorithms.

the


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A total of 100 clusters w ere generated by applying the Iterative Self-OrganizingData Analysis (ISODATA ) algorithm. The means and covariance matrices of theseclusters were u sed in a maxim um likelihood classifier to assign each pixel to thecluster it had the greatest probability of being a mem ber. This refined im age-mapwas overlaid onto a false color com posite image (RGB = TM ba nds 4, 3, 2) of thecorresponding area to assess the information quality of each cluster. A scatterplotof band 4 and band 3 wa s used to assist in the thematic information interpretationof each cluster based on its radiometric reflectance in the red and near-infraredportions of the spectrum (Fig. 2). Each cluster w as assigned to one of the followinginformation categories including water, wetland, forest, agriculture, grassland ,barren, and urban/roads . A few clusters could not be assigned to specific landcover categories because they contained pixels from more than one informationclass. The scatterplot show s these m ixed clusters to be located between agriculturaland forest clusters, or between agricultural and grassland clusters. To resolve thisconfusion the cluster-busting m ethod described by Narum alani et al. (1993) andJensen (1996) was used to extract an additional 50 clusters for those areasbelonging to the mixed clusters. The labeling procedure described above for thefirst level classification was also used to assign each cluster to one of the informa-tion classes. Subsequently, the two classified image-m aps were merged to produce acomposite land cover map of the study area (Fig. 3).6.2. D evel o pm en t o f t h e h yd r o g r a ph y da t a l a ye r

The hydrography of the study area was derived from 1:lOO OO O cale Digital L ineGraph data produced by the US Geological Survey. Digital Line Graph map s arestored in 15 or 7.5 blocks depending on the density of the mapped features. Thehydrography map is a digital representation of surface water bodies, an d the mapof the study area was comprised of four 1 5 blocks. These data were integrated withthe remotely sensed information to produce an updated composite hydrographydata layer. The utility of such integration is illustrated by the contribution thateach data source makes toward developing the composite. The USGS Digital LineGraph data are necessary for the analysis because streams less than one-pixel w ide(30 X 30 m) may not be detected by the Landsat TM sensor system. Conversely, thecurrent information on the distribution of surface water bodies derived from TMdata can be incorporated into the Digital Line Graph data to produce an up-to-datehydrography data layer.Because terrestrial features are in a constant state of flux due to naturalevolution or hum an alteration, the utility of such integration cannot be overstated.Several sm all water bodies in the south-central portion of the study area are a clearillustration of this because while they do not appear in the Digital Line Graph dataset, they are clearly visible and interpreted from the TM image. On the other hand,much of the dense network of small streams that drain into the Iowa River arebarely detected on the image. Most other lakes and the Iowa River channel in theDigital Line Graph data closely match the im age classification. An overlay of the


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Fig. 2. Scatterplot of 100 clusters derived from applying statistical pattern recognition techniques. Notethe confused clusters between forest/agriculture/grassland.


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Landsat TM Image of S tudy Area

Fig. 3. (a) False color composite of the Landsat TM im age (Bands 4,3 ,2 = RGB ). The different shadesof red indicate vegetation reflectance in the near-infrared. (b) Land cover classification of the IowaRiver basin study area derived from the Landsat TM image.

water features from the two sources produces a complete and updated map of thehydrology of the study area (Fig. 4).6.3. D el i n ea t i o n o f r i p a r i a n buf f er s

The United States Departm ent of Agriculture Forest Service has developed aSo i l Capab i l i t y C l a s s method for classifying soil units into specific categories basedon their utility in agricultural use (Table 1). The classes indicate progressivelygreater limitations and narrower choices for practical use (USD A, 1983). Classe sI-III provide the best soil for agricultural activity without too many restrictions,while unsuitable soils are included in Classe s V-VIII. An intermediate capabilityclass (Class IV> specifically pertains to those soils that have severe limitations andrequire careful managem ent. This study utilized the So i l Ca p a b i l i t y criterion sinceit has often been used to determine the minim um riparian buffer zone width forthe protection and enhancem ent of water resources.According to the county soil survey reports, the soils along the Iowa Riverchannel belong mainly to Capability classes VIIe, IVe, and VIIw, while those alongthe tributaries are classified into C apability Classe s I, IIw, IIe, and VW (USD A,1983). Based on this, the buffer widths would range from 30 m for Capability


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Features erived from USES DLO DsI**-- -

m Surface water feature derived from DL G0 Surface water feature derived from Landast TM

tktmstrrr5 0 5

Fig. 4. Updated hydrologic feature map derived from the integration of USG S Digital Line Graph da tabase and TM classification of surface water fea tures. The hydrology is overlaid on band 4 (near-infrared)of the TM. Image appears darker because its contrast has been reduced in order to enhance the overlayof the hydrologic features.

classes I, II, and V to 40 m for Capab ility classes II and IV; and 53 m for C apabilityclasses VI and VII for water quality protection. Because the TM data are 30 m2,the width of buffer zones applied to the study area were determined as two-pixels


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Table 1Soil capability classes and their uses

Class DescriptionI

IIIIIIVV

VIVII

VIII

Few limitations that restrict useMod erate limitations that reduce the choice of plantsSevere limitations that reduce the choice of plantsVery severe limitations and require very careful managementImpractic al for most crop usesUnsuited for cultivationUnsuited for cultivationSuited only for recreation or wildlife

for the main Iowa River channel and one-pixel for its tributaries (USD A, 1991 ;Mu scutt et al., 1993 ; Castelle et al., 1994). The buffer zones were delineated usinga spatial distance search for the Iowa River channel and its tributaries using widthsof 60 m and 30 m, respectively (Fig. 5).6.4. I d en t i f i c a t i o n o f c r i t i ca l a r ea s

Once the riparian buffer zones were delineated, the land cover informationwithin these zones was extracted and used to identify those areas where theestablishmen t of filter strips wou ld be recommended. The land cover along much ofthe main channel of the Iowa River comprised of well-developed streamside forestswhich served as filter strips. However, an analysis of the upland tributaries, whichusually flow across productive and well-drained land, revealed that few riparianforests were distributed along them, and much of the network of small streams wasleft unprotected. In such cases, the riparian filters were either absent or discontinu-ous.7 Results and discussion

The integration of remote sensing and GIS provides a framework for determin-ing critical areas where the presence of permanen t riparian vegetation would beuseful for water resources management (Fig. 6). An assessment of these bufferzones with relation to their land cover reveals tha t m uch of the Iowa River channel(two pixel buffer) is protected by streamside forest (Fig. 7). Unfortunately, manyareas along the tributaries that flow into the river have agricultural land, which isthe prime source of non-point source pollution, adjacent to them. There are alsoseveral sm all lakes which do not have any protective buffers, thus impacting theiraquatic environment.If riparian buffer zones were to be developed in the areas identified by thisstudy, a total of 2277 ha of land would be required. Of these, approximately 26% or


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one ixel bufferK d o m r rw r

3 0 5

Fig. 5. Delineation of buffer zones along the hydrologic features based on the USDA Soil Capabilityclasses. The buffer zones are two-pixels wide for the Iowa River main channel and one-pixel wide for itstributaries.

598 ha are along the Iowa River channel corridor. Because non-point sourcepollution cannot be attributed to a specific source, it is well recognized that thepollutants in main stream channels (e.g. the Iowa River) may have originated fromremote sources and have flowed in via the tributaries. Therefore, the distributionof critical areas along the tributaries highligh ts their importance in contributing tonon-point source pollution.


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----is - K J a n u t r~5 0 5

Fig. 6. Identification of critical areas for establishment of riparian vegetation strips.

The final stage in this analysis is the determination of the spatial distribution ofthe critical areas based on the land cover. It is evident from Fig. 7 that all types ofland cover interpreted from satellites wou ld be impacted by the implemen tation ofriparian buffer zones. How ever, those land cover types such as forest, grasslan d,and wetland are natural vegetation and provide protection to water quality. In thecase of urban areas and road networks, it wou ld either be difficult or economically


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i .- -_._-__ ..__ . __ -... . . ----.- _Land Cover Impac ted by Rlparian Buffer Zones

One Pixel Buffer Two Pixel Buffer

1000800

g 600g 400

200

Land Cover.

AgricultureWetland

Forest Grasslandm Barren Urban/Roads

Fig. 7. Land cov er impacted by the riparian buffer zones for the critical areas. N ote the extremely highamo unt of agricu ltural land (903 ha) in the one-pixel buffer zones (i.e. the tributaries).

inefficient to develop buffer zones (e.g. moving buildings or changing a highwayroute). Therefore, it is in agricultural areas where the riparian buffer zones can bedeveloped. More than 44% of the area that lacks riparian buffers alon gside streamchannels is comprised of agricultural and barren land (1008 ha) and is thuscharacterized as critical. Barren land is being included for two reasons. First,because of the lack of vegetation cover, it wou ld be more susceptible to erosionand thus contribute significant amou nts of sediment and pollutants into the streamnetwork. Second, much of the barren land consists of fields that are fallow andwhich would normally be used for agricultural activity.Remote sensing and GIS provide an opportunity to study large areas usingmultiple data sources. In this study, critical areas that lack the filtering function toattenuate non-point source pollution have been identified. Further investigations ofsuch areas (e.g. using in situ teams) could yield additional information for justifyingthe development of riparian buffer zones along the tributaries, thus leading to thepreservation and wise use of our most valuable natural resource.8 Conclusion

The role of riparian buffers in mitigating or controlling non-point sourcepollution has been recognized by water resources managers. This research provides


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a rapid and easy-to-use methodology for identifying critical areas where establish-ment of riparian filter strips wou ld be required. However, a major consideration isthe size of these buffers, which remains an environmentally and politically explo-sive issue. Several US government agencies provide specific guidelines on therequired minim um widths that wou ld have a satisfactory filtering effect. Buffersthat are undersized may place aquatic resources at risk. Conversely, those bufferswhich are larger than needed may deny the land owners their righ ts for appropriateland use. In this study, the United States Departm ent of Agriculture Forest Serviceguidelines for buffer w idths based on the Soil Capability class were used . Thoughwidely used, this method does not consider other local physical conditions and theeffectiveness of the buffers for the given environment. Several models for develop-ment of buffer zones exist which consider several physical variables. However,these models often are computationally intensive or require data that are notreadily available. Efforts should be made whereby many biophysical characteristicsrequired for the models can be derived from remotely sensed data and analyzed ina GIS environment, where large volumes of data can be manipulated. Xiang (1993)integrated a model into a GIS that considered several physical conditions andultimately produced buffers of variable widths based on these conditions. Suchefforts and those described in this study can provide the means by which scientifi-cally acceptable buffer zones can be implemented and the quality of aquaticresources improved.

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