ELSEVIER

Aquatic Botany 58 (1997) 393-409

Application of remote sensing and geographic information systems to the delineation and analysis of riparian buffer zones Sunil Narumalani”>* , Yingchun Zhou”, John R. Jensenb aDepattment of Geography, University of Nebraska, Lincoln: NE 68588-0135, USA bDepartment of Geography, University of South Carolina, Columbia, SC 29208, USA

Accepted 18 November 1996

Abstract

Non-point source pollution has a significant impact on the quality of water resources. Studies have revealed that agricultural activities are often major contributors to non-point source pollution of aquatic environments. A common means of reducing the threat of non-point source pollution is through the establishment of riparian vegetation strips (or buffers) along those areas of stream channels that would be most susceptible to the threat. Remote sensing and geographic information systems (GIS) offer a means by which ‘critical’ areas can be identified, so that subsequent action toward the establishment of riparian zones can be taken. This research focuses on the development and analysis of riparian buffer zones for a portion of the Iowa River basin. Landsat Thematic Mapper (TM) data were used to characterize the land cover for the study area. An updated hydrology data layer was developed by integrating the United States Geological Survey (USGS) Digital Line Graph (DLG) data base with the TM-derived classification of surface water bodies. Spatial distance search 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 identify ‘critical’ areas for the establishment of riparian vegetation strips. Results indicated that while most of the main channel of the Iowa River was protected by natural vegetation, more than 44% (or 1008 ha) of the area along its tributaries lack any protective cover from non-point source pollution. As these ‘critical’ areas are adjacent to agricultural fields it is important that water resources management strategies focus on the establishment of *Corresponding

author: Tel.: + 1 402 4729842; fax: + 1 402 4721185; e-mail: [email protected]

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

394

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

riparian zones in order to minimize the impact of non-point Elsevier Science B.V. Keywords:

Vegetation

Remote sensing; Geographic information strips; Non-point source pollution

source pollution.

0 1997

systems (GIS); Riparian buffer zones;

1. Introduction Over the last several decades it has been recognized that non-point source pollution is a major problem that can have a considerable effect in reducing water quality. A US Environmental Protection Agency (1990) study estimated that non-point source pollution contributes over 65% of the total pollution load to the US inland surface waters (US Environmental Protection Agency, 1989). Non-point source pollutants include nitrogen (N), phosphorus (P), heavy metals, and other chemicals from fertilizers, pesticides, herbicides, animal wastes, overland flow wastewater treatment systems, urban stormwater, and other sources (Muscutt et al., 1993). Federal, state, and local government agencies work cooperatively in designing the best management practices to control non-point source pollution. The establishment of riparian buffer zones is an important component of their integrated management plans. Riparian buffer zones, which are also referred to as vegetated filter strips, are permanently vegetated areas located between the pollutant sources and water bodies (e.g. rivers, streams, lakes, etc.). A filter strip allows runoff and associated pollutants to be attenuated before reaching surface and underground water sources via infiltration, absorption, uptake, filtering, and deposition. The effectiveness of riparian buffer zones in decreasing non-point source pollution has long been recognized and utilized in management practices (Lowrance et al., 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 riparian ecosystem in a 3873-acre drainage area of the Little River watershed near Tifton, Georgia during 1979 and 1980. They found that N removal by denitrification and storage by woody vegetation was six times as much as N output to streamflow. In addition, as much as half of the P outflow was taken up by the vegetation and the remainder was exported in streamflow. According to investigations by the United States Department of Agriculture (USDA) Forest Service (USDA, 19911, P is significantly reduced by the filtering action of riparian vegetation because about 85% of available P is transported with the small soil particles comprising the sediment. The United States Department of Agriculture soil erosion estimates for 1992 showed that Iowa had the highest quantity of soil loss on cropland in the USA by water erosion (USDA, 1995). The sediment and other pollutants, carried by surface and sub-surface runoff, impact water quality for humans as well as the aquatic

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

395

environment. The objectives of this research were to develop and analyze riparian buffer zones for a section of the Iowa River watershed in Iowa, USA, using remote sensing and geographic information system (GIS) techniques, and to quantify the land cover that would fall within these buffer zones. Landsat Thematic Mapper (TM) imagery was used to characterize the land cover of the Iowa River landscape. The surface water features from this land cover image-map were subsequently used to update the hydrology map derived from the Digital Line Graph data base for the creation of buffer zones, with the aid of ancillary data sources such as the county soil surveys. 2. Functions of riparian buffers for removing agricultural pollution Surface and sub-surface runoff are the major mechanisms of transporting sediment, 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 in sub-surface drainage in the United States. Surface runoff occurs when surface soils become saturated as the result of a high water table, or when rainfall intensity exceeds the infiltration rate of soils. The runoff not only carries soluble pollutants but also sediment, whose prime source is the eroded soil material. Soil erosion is greatest during the early part of the growing season when crop foliage is not dense enough to intercept precipitation, and after harvesting has occurred, when the soils are bare and exposed to heavy rains. This movement of both organic and inorganic sediment may be a major pathway of pollutant transport in agricultural catchments, while sediment P may constitute a major portion of total P export (Muscutt 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 be examined from their mechanical, chemical, or biological functions. From the mechanical perspective, the increased hydraulic roughness of vegetation cover in buffer zones decreases the velocity of surface flow and consequently reduces its sediment carrying capacity which in turn results in sediment deposition (USDA, 1991). In addition, the filtering effect through infiltration is enhanced because of changed soil structure and longer time span required for surface flows to move across buffer zones as a result of reduced velocities. Besides physically intercepting sediments and debris, dense vegetation also helps stabilize stream banks thus reducing streambank erosion. Buffer zones also block the direct contamination of hydrologic resources by the ‘off-spray’ from fertilizers and pesticides. The chemical and biological functions of riparian buffer zones pertain to the processes that are activated in the riparian ecosystem for transforming pollutants into different compounds. For example, bacteria and fungi in the filter zones convert N in runoff and decaying organic debris into mineral forms (NO,), which can then be synthesized into proteins by plants or bacteria. In other cases, denitrifying bacteria convert dissolved N into its gaseous form, thus returning it into the atmosphere (USDA, 1991). Studies reveal that N concentrations were significantly reduced in surface runoff flowing from agricultural fields through a 19-m buffer of riparian forest (Peterjohn and Correll, 1984).

396

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

Riparian vegetation can also convert some forms of toxic chemicals such as pesticides into non-toxic forms through microbial decomposition, oxidation, hydrolysis, and other biodegrading processes occurring in the soil and water. Another important chemical and biological process of riparian vegetation is its ability to act as a nutrient sink if trees or grasses are harvested periodically to maintain a net uptake of nutrients. Because certain types of vegetation have a very high rate of N uptake, the nutrients stored in the litter can be converted into peat and stored for a long time by the ecosystem. The effectiveness of a riparian zone in removing non-point source pollution is influenced by many factors including the dimensions of the buffer zone, composition of vegetation species within the zone, land use, soil types, topography, hydrology, microclimate, and other characteristics of the agrosystem. Designing a riparian buffer zone is a very complex process and this research describes the development of riparian buffer zones along many of the tributaries flowing into the main stream channel of the Iowa River. 3. Development of riparian buffer zones A riparian buffer zone reduces the connection between potential pollution sources and aquatic resources. Buffer widths ranging from 3 to 200 m have been found to be effective, depending on site-specific conditions (Castelle et al., 1994). The methods used by water resources scientists, researchers, and various US government agencies can be broadly classified into three categories. These include: (1) application of a constant buffer width for the entire area under consideration; (2) determination of a minimum buffer width based on soil capability, extent of source area, or slope (Trimble and Sartz, 1957; USDA, 1991); and (3) spatial modeling 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 more manageable to implement. However, it does not take into consideration regional differences, which define the uniqueness of a given area. The second is more applicable than a complicated model, and takes into account the regional variation of soils or slope, which are two important parameters in determining pollutant yield. The third method deals with a set of variables related to pollutant transportation and provides a systematic and scientific foundation for the establishment and maintenance of buffer zones. However, it is often less feasible because it is dependent on data availability and is computationally rigorous. Also, it is difficult to implement because of the spatially dynamic, variable buffer widths. The United States Department of Agriculture Forest Service (1991) has recommended three methods of determining the minimum width of riparian forest buffers based on soil hydrologic group, source area, and soil capability class. As to the vegetation composition within these buffer zones, it was concluded that the natural vegetation pertinent to the geographic area should be preserved. The buffered areas would be comprised of three zones that include a combination of

S. Narumalani et al. /Aquutic Botany 58 (1997) 393-409

397

forest and grassland. Vegetation composition in zones 1 and 2 should be streamside tree species with a minimum width of approximately 23 m, while zone 3 which would be located between zone 2 and cropland (or pasture), should consist of perennial grasses and forbs. From the above discussion it is evident that several methods have been suggested for implementing wetland buffer zones to minimize the effects of non-point source pollution on aquatic resources. Because of the complexity of some of these methods, the application of remote sensing and GIS techniques can aid in facilitating and analyzing the selection and implementation of an appropriate method of buffering by examining multiple data sources, and using appropriate modeling procedures. 4. Integration non-point

of remote sensing source pollution

and geographic

information

systems

(GIS) for

Remote sensing provides a synoptic view of the terrestrial landscape and is used for inventorying, monitoring, and change detection analysis of environmental and natural resources. For example, Hewitt (1990) used Landsat TM data to map riparian classes associated with the river, lakes, and wetlands, along the Yakima River of central Washington and achieved a classification accuracy of 80% in the detection of such land cover types. In another study, Jensen et al. (1995), utilized multi-sensor remotely sensed data including Landsat Multispectral Scanner (MSS) and SPOT High Resolution Visible (HRV) images for a change detection study of aquatic macrophyte distribution and composition within the Florida Everglades Water Conservation Area from 1973 to 1991. The authors concluded that the expansion of specific aquatic macrophyte species such as cattails (Typha domingensis) and sawgrass (Cludiumjumaicense) in the area might be attributed to increased P loading due to the agricultural activities surrounding the water conservation area. Several other studies have also demonstrated the utility of remote sensing for examining non-point source pollution (e.g. Pelletier, 1985; Hewitt and Mace, 19881. A major drawback of current remote sensing systems has been their relatively coarse spatial resolution (e.g. TM = 30 x 30 m; SPOT HRV multispectral = 20 x 20 ml. Often, such resolution may be inadequate for the detection and analysis of riparian zones since it would exceed the physical dimensions of the zones. However, it is expected that with the improved spatial resolution on future satellite sensor systems (i.e. 3 X 3 m or 1 X 1 ml, remote sensing will be an invaluable data source for detailed and temporally frequent studies of the impact of non-point source pollution on water resources. GIS are useful for the analyses of spatial and temporal biophysical parameters detected by remote sensing techniques. When in situ spatial data (e.g. soil, topography, rainfall, pollution load measures, etc.) are compiled in a GIS, they can be used in conjunction with remotely sensed data for the development of a variety of water resource management models (e.g. modeling soil loss, pollutant yield, designing non-point source pollution filter strips, etc.). Researchers have used

398

S. Narumaluni et al. /Aquatic Botany 58 (1997) 393-409

several models within a GIS environment for the estimation of soil loss and sediment yield, including USLE (Universal Soil Loss Equation), CREAMS (Chemical, Runoff, and Erosion from Agricultural Management Systems), and ANSWERS (Area1 Non-point-Source Watershed Environment Response Simulation). Information on land cover characteristics derived from remotely sensed data has been synergistically used in these models to enable a robust analysis of aquatic conditions (Pelletier, 1985; Sivertun et al., 1988). 5. Study area The study area is a section of the Iowa River basin from the edge of the Coralville Reservoir in the east and extending 25 km to the west (Fig. 1). The Iowa River is one of the two largest rivers in the state, meandering over the gently rolling plains, with numerous oxbow lakes and abandoned channels on the floodplain. Much of the surrounding land is agricultural, with corn, soybeans, and other feed grains being the major crops. The intensity of these agricultural activities and their considerable spatial extent creates a serious pollution problem to the surface waters. The forest resource of the basin is limited to riparian trees and shrubs which form effective filter belts to reduce non-point source pollution, however, many parts of the river system (especially the tributaries) are not protected by natural vegetation buffers. In this study, the Landsat TM data were used to characterize the landscape and identify critical areas of non-point source pollution to aid in the development of non-point source pollution control strategies (i.e. the development of riparian buffer zones). 6. Methodology The development and analysis of riparian buffer zones required the implementation of several steps. First, a contemporary land cover map was generated from a Landsat TM subscene using spectral pattern recognition techniques. Second, stream network data were extracted from the classified image-map for updating the hydrologic information of the USGS Digital Line Graph data. Third, the dimensions of the buffer zones were determined based on the analysis of the soils capability classes of the study area. Finally, buffer zones were delineated and those areas which were unprotected against potential non-point source pollution identified. 6. I. Image classification The Landsat TM image used in this study was acquired on 8 September 1992. The image had been rectified and georeferenced to the Universal Transverse Mercator (UTM) projection, and resampled to a 30 X 30 m pixel size by the EROS Data Center, Sioux Falls, SD. To determine the optimum bands for characterizing the land cover, a correlation matrix was developed for the six visible and infrared bands. Because of the lower correlation between TM bands 3 (red), 4 (near-in-

S. Narumalani et al. /Aquatic Botany 5X (1997) 393-409

/‘--

100

h”

-

(a)

Kmmatao

100

\

Landsat TM Scene of Study Area Band 4 (Near Infrared). 08 September 5

4

0

399

-

0

(0) 1992 I Kilometars 5

Fig. 1. Location of the Iowa River basin studyarea.

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

for classifying

the

400

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

A total of 100 clusters were generated by applying the Iterative Self-Organizing Data Analysis (ISODATA) algorithm. The means and covariance matrices of these clusters were used in a maximum likelihood classifier to assign each pixel to the cluster it had the greatest probability of being a member. This refined image-map was overlaid onto a false color composite image (RGB = TM bands 4, 3, 2) of the corresponding area to assess the information quality of each cluster. A scatterplot of band 4 and band 3 was used to assist in the thematic information interpretation of each cluster based on its radiometric reflectance in the red and near-infrared portions of the spectrum (Fig. 2). Each cluster was assigned to one of the following information categories including water, wetland, forest, agriculture, grassland, barren, and urban/roads. A few clusters could not be assigned to specific land cover categories because they contained pixels from more than one information class. The scatterplot shows these mixed clusters to be located between agricultural and forest clusters, or between agricultural and grassland clusters. To resolve this confusion the cluster-busting method described by Narumalani et al. (1993) and Jensen (1996) was used to extract an additional 50 clusters for those areas belonging to the mixed clusters. The labeling procedure described above for the first level classification was also used to assign each cluster to one of the information classes. Subsequently, the two classified image-maps were merged to produce a composite land cover map of the study area (Fig. 3). 6.2. Development of the hydrography data layer The hydrography of the study area was derived from 1:lOOOOOscale Digital Line Graph data produced by the US Geological Survey. Digital Line Graph maps are stored in 15’ or 7.5’ blocks depending on the density of the mapped features. The hydrography map is a digital representation of surface water bodies, and the map of the study area was comprised of four 15’blocks. These data were integrated with the remotely sensed information to produce an updated composite hydrography data layer. The utility of such integration is illustrated by the contribution that each data source makes toward developing the composite. The USGS Digital Line Graph data are necessary for the analysis because streams less than one-pixel wide (30 X 30 m) may not be detected by the Landsat TM sensor system. Conversely, the current information on the distribution of surface water bodies derived from TM data can be incorporated into the Digital Line Graph data to produce an up-to-date hydrography data layer. Because terrestrial features are in a constant state of flux due to natural evolution or human alteration, the utility of such integration cannot be overstated. Several small water bodies in the south-central portion of the study area are a clear illustration of this because while they do not appear in the Digital Line Graph data set, 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 are barely detected on the image. Most other lakes and the Iowa River channel in the Digital Line Graph data closely match the image classification. An overlay of the

S. Narumalani

et al. /Aquatic

Botany 58 (1997) 393-409

Fig. 2. Scatterplot of 100 clusters derived from applying the confused clusters between forest/agriculture/grassland.

statistical

pattern

recognition

401

techniques.

Note

402

S. Narumalaniet al. /Aquatic Botany 58 (1997) 393-409

Landsat TM Image of Study Area

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

water features from the two sources produces a complete and updated map of the hydrology of the study area (Fig. 4). 6.3. Delineation of riparian buffers The United States Department

of Agriculture

Forest Service has developed a

Soil Capability Class method for classifying soil units into specific categories based

on their utility in agricultural use (Table 1). The classes indicate ‘progressively greater limitations and narrower choices for practical use’ (USDA, 1983). Classes I-III provide the best soil for agricultural activity without too many restrictions, while ‘unsuitable’ soils are included in Classes V-VIII. An intermediate capability class (Class IV> specifically pertains to those soils that have severe limitations and require careful management. This study utilized the Soil Capability criterion since it has often been used to determine the minimum riparian buffer zone width for the protection and enhancement of water resources. According to the county soil survey reports, the soils along the Iowa River channel belong mainly to Capability classes VIIe, IVe, and VIIw, while those along the tributaries are classified into Capability Classes I, IIw, IIe, and VW (USDA, 1983). Based on this, the buffer widths would range from 30 m for Capability

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

403

Features Derived from I**-USES DLO- Ds

m

Surface water feature derived from DLG

0

Surface water feature derived from Landast TM Ktktmstrrr

5

0

5

Fig. 4. Updated hydrologic feature map derived from the integration of USGS Digital Line Graph data base and TM classification of surface water features. 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 overlay of the hydrologic features.

classes I, II, and V to 40 m for Capability classes II and IV; and 53 m for Capability classes 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

404

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

Table 1 Soil capability classes and their uses Class

Description

I II III IV V VI VII VIII

Few limitations that restrict use Moderate limitations that reduce the choice of plants Severe limitations that reduce the choice of plants Very severe limitations and require very careful management Impractical for most crop uses Unsuited for cultivation Unsuited for cultivation Suited only for recreation or wildlife

for the main Iowa River channel and one-pixel for its tributaries (USDA, 1991; Muscutt et al., 1993; Castelle et al., 1994). The buffer zones were delineated using a spatial distance search for the Iowa River channel and its tributaries using widths of 60 m and 30 m, respectively (Fig. 5). 6.4. Identification of critical areas Once the riparian buffer zones were delineated, the land cover information within these zones was extracted and used to identify those areas where the establishment of filter strips would be recommended. The land cover along much of the main channel of the Iowa River comprised of well-developed streamside forests which served as filter strips. However, an analysis of the upland tributaries, which usually flow across productive and well-drained land, revealed that few riparian forests were distributed along them, and much of the network of small streams was left unprotected. In such cases, the riparian filters were either absent or discontinuous. 7. Results and discussion

The integration of remote sensing and GIS provides a framework for determining critical areas where the presence of permanent riparian vegetation would be useful for water resources management (Fig. 6). An assessment of these buffer zones with relation to their land cover reveals that much of the Iowa River channel (two pixel buffer) is protected by streamside forest (Fig. 7). Unfortunately, many areas along the tributaries that flow into the river have agricultural land, which is the prime source of non-point source pollution, adjacent to them. There are also several small lakes which do not have any protective buffers, thus impacting their aquatic environment. If riparian buffer zones were to be developed in the areas identified by this study, a total of 2277 ha of land would be required. Of these, approximately 26% or

305

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

onepixel

buffer Kdomrrwr

3

0

5

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

598 ha are along the Iowa River channel corridor. Because non-point source pollution cannot be attributed to a specific source, it is well recognized that the pollutants in main stream channels (e.g. the Iowa River) may have originated from remote sources and have flowed in via the tributaries. Therefore, the distribution of critical areas along the tributaries highlights their importance in contributing to non-point source pollution.

406

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

----is

0

5

Fig. 6. Identification

of ‘critical’ areas for establishment

-

KJanut;r~

5

of riparian

vegetation

strips.

The final stage in this analysis is the determination of the spatial distribution of the critical areas based on the land cover. It is evident from Fig. 7 that all types of land cover interpreted from satellites would be impacted by the implementation of riparian buffer zones. However, those land cover types such as forest, grassland, and wetland are natural vegetation and provide protection to water quality. In the case of urban areas and road networks, it would either be difficult or economically

S. Narumalani et al. /Aquatic

i

Botany 58 (1997) 393-409

.- -_._-__ ..__. . __ -... “. ” . Land Cover Impacted by Rlparian

407

----.- _ Buffer Zones

Two Pixel Buffer

One Pixel Buffer

1000 800 g 600 g 400 200 0 Land Cover

. Agriculture

??Wetland

m

Forest

Grassland

Barren

Urban/Roads

Fig. 7. Land cover impacted by the riparian buffer zones for the ‘critical’ areas. Note the extremely high amount of agricultural 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 highway route). Therefore, it is in agricultural areas where the riparian buffer zones can be developed. More than 44% of the area that lacks riparian buffers alongside stream channels is comprised of agricultural and barren land (1008 ha) and is thus characterized as ‘critical’. Barren land is being included for two reasons. First, because of the lack of vegetation cover, it would be more susceptible to erosion and thus contribute significant amounts of sediment and pollutants into the stream network. Second, much of the barren land consists of fields that are fallow and which would normally be used for agricultural activity. Remote sensing and GIS provide an opportunity to study large areas using multiple data sources. In this study, critical areas that lack the filtering function to attenuate non-point source pollution have been identified. Further investigations of such areas (e.g. using in situ teams) could yield additional information for justifying the development of riparian buffer zones along the tributaries, thus leading to the preservation and wise use of our most valuable natural resource. 8. Conclusion

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

408

S. Narumalani et al. /Aquatic Botany 58 (1997) 393-409

a rapid and easy-to-use methodology for identifying ‘critical’ areas where establishment of riparian filter strips would be required. However, a major consideration is the size of these buffers, which remains an environmentally and politically explosive issue. Several US government agencies provide specific guidelines on the required minimum widths that would have a satisfactory filtering effect. Buffers that are undersized may place aquatic resources at risk. Conversely, those buffers which are larger than needed may deny the land owners their rights for appropriate land use. In this study, the United States Department of Agriculture Forest Service guidelines for buffer widths based on the Soil Capability class were used. Though widely used, this method does not consider other local physical conditions and the effectiveness of the buffers for the given environment. Several models for development of buffer zones exist which consider several physical variables. However, these models often are computationally intensive or require data that are not readily available. Efforts should be made whereby many biophysical characteristics required for the models can be derived from remotely sensed data and analyzed in a GIS environment, where large volumes of data can be manipulated. Xiang (1993) integrated a model into a GIS that considered several physical conditions and ultimately produced buffers of variable widths based on these conditions. Such efforts and those described in this study can provide the means by which scientifically acceptable buffer zones can be implemented and the quality of aquatic resources improved. References Castelle, A.J., Johnson, A.W., Conolly, C., 1994. Wetland and stream buffer size requirement - a review. J. Environ. Qual. 23, 878-882. Delong, M.D., Brusven, M.A., 1991. Classification and spatial mapping of riparian habitat with applications toward management of streams impacted by nonpoint source pollution. Environ. Manage. 15565-571. Dillaha, T.A., Reneau, R.B., Mostaghimi, S., Lee, D., 1989. Vegetative filter strips for agricultural non-point source pollution control. Trans. ASAE 32, 513-519. Hewitt, M.J., 1990. Synoptic inventory of riparian ecosystems: the utility of Landsat Thematic Mapper data. Forest Eco. Manage. 33/34, 605-620.. Hewitt, M.J., Mace, T.H., 1988. EPA remote sensing resources for lake management. Proc. Nat. Conf. On Enhancing State Lake Mgmt., North American Lake Management Society. Jensen, J.R., Rutchey, K., Koch, MS., Narumalani, S., 1995. Inland wetland change detection in the Everglades Water Conservation Area 2A using a time series of normalized remotely sensed data. Photogramm. Eng. Remote Sensing 61, 199-209. Jensen, J.R., 1996. Introductory digital image processing: a remote sensing perspective, 2nd ed. Prentice Hall, Upper Saddle River, NJ, p. 316. Jacobs, T.C., Gilliam, J.W., 1985. Riparian losses of nitrate from agricultural drainage waters. J. Environ. Qual. 14, 472-478. Lowrance, R., Todd, R., Fail Jr., J., Hendrickson Jr., O., Leonard, R., Asmussen, L., 1984. Riparian forests as nutrient filters in agricultural watersheds. Bioscience 34, 374-377. Lowrance, R., Leonard, R., Sheridan, J., 1985. Managing riparian ecosystems to control nonpoint pollution. J. Soil Water Conserv. January/February: 87-91. Muscutt, A.D., Harris, G.L., Bailey, S.W., Davices, D.B., 1993. Buffer zones to improve water quality: a review of their potential use in UK agriculture. Agri. Ecosystems Environ. 45, 59-77.

S. Narutnalani et al. /Aquatic Botany 58 (1997) 393-409

409

Narumalani, S., Jensen, J.R., Hayes, M., Michel, J., Montello, T., Robinson, J., 1993. Gulf war legacy: using remote sensing to assess habitat in the Saudi Arabian Gulf before the Gulf war oil spill. Geo-Info Systems, June 32-41. Neely, R.K., Baker, J.L., 1989. Nitrogen and phosphorus dynamics and the fate of agricultural runoff. In: Van der Valk, A. (Eds.), Northern Prairie Wetlands. Iowa State University Press, Ames, IA, pp. 92-131. Pelletier, R.E., 1985. Evaluating nonpoint pollution using remotely sensed data in soil erosion models. J. Soil Water Conserv. vol. No. 332-33.5. Peterjohn, W.T., Correll, D.L., 1984. Nutrient dynamics in an agricultural watershed: observations on the role of a riparian forest. Ecology 65, 1466-1475. Sivertun, A., Reinelt, L.E., Castensson, R., 1988. A GIS method to aid in non-point source critical area analysis. Int. J. Geog. Inf. Syst. 2, 365-378. Trimble, G.R., Sartz, R.S., 1957. How far from a stream should a logging road be located? J. For. vol. 339-341. USDA Forest Service, 1991. Riparian forest buffers: function and design for protection and enhancement of water resources. USDA Forest Service, Forest Resources Management, Radnor, PA, p. 20. USDA, Soil Conservation Service, 1983. Soil Survey of Johnson County, Iowa. USDA, 1995. Graphic highlights of natural resource trends in the United States between 1982 and 1992. National Resources Conservation Service, National Resource Inventory, April, 1995. US Environmental Protection Agency, 1989. Focus on nonpoint source pollution. The Information Broker of Water Regulations and Standards. Nonpoint Sources Control Branch, Washington, DC, November, 1989. US Environmental Protection Agency, 1990. National guidance: wetlands and nonpoint source control programs. Office of Water Regulations and Standards and Office of Wetlands Protection, Washington, DC. Xiang, W., 1993. A GIS for riparian water quality buffer generation. Int. J. Geog. Inf. Syst. 7, 57-70.

Application of remote sensing and geographic ...

+ 1 402 4729842; fax: + 1 402 4721185; e-mail: [email protected] ... sensing and geographic information system (GIS) techniques, and to quantify the.

1MB Sizes 0 Downloads 209 Views

Recommend Documents

Application of Remote Sensing in Water Resource ... - Springer Link
Apr 13, 2010 - Application of Remote Sensing in Water Resource ... is also very cost-effective. ... algorithm development for lakes (Doerffer and Schiller 2008a, b) and from ..... used to estimate the agricultural pressure on the Trasimeno lake ...

SATELLITE COMMUNICATION AND REMOTE SENSING (Elective ...
SATELLITE COMMUNICATION AND REMOTE SENSING (Elective).pdf. SATELLITE COMMUNICATION AND REMOTE SENSING (Elective).pdf. Open. Extract.

Remote sensing and GIS.pdf
8. a) Explain the necessity of transformation and projection in GIS database. creation. 8. b) Explain the different types of GIS Outputs. 8. 9. a) Explain the ...

design and implementation of a high spatial resolution remote sensing ...
Aug 4, 2007 - 3College of Resources Science and Technology, Beijing Normal University, Xinjiekou Outer St. 19th, Haidian ..... 02JJBY005), and the Research Foundation of the Education ... Photogrammetric Record 20(110): 162-171.

design and implementation of a high spatial resolution remote sensing ...
Aug 4, 2007 - 3College of Resources Science and Technology, Beijing Normal University, ..... 02JJBY005), and the Research Foundation of the Education.

design and implementation of a high spatial resolution remote sensing ...
Therefore, the object-oriented image analysis for extraction of information from remote sensing ... Data Science Journal, Volume 6, Supplement, 4 August 2007.

Comparison of remote sensing data sources and ...
1 School of Environment and Development, University of Natal, Pietermaritzburg, Private Bag X01, Scottsville 3209, South ... computer and paused on every frame. ..... ARCVIEW 3.0 (1997) ESRI Inc. Redlands, California, United States of.

Comparison of remote sensing data sources and ...
Comparison of remote sensing data sources and techniques for identifying and classifying alien invasive vegetation in riparian zones. Lisa C Rowlinson1*, Mark ...

Potential of remote sensing of cirrus optical thickness by airborne ...
Potential of remote sensing of cirrus optical thickness ... e measurements at different sideward viewing angles.pdf. Potential of remote sensing of cirrus optical ...

Integration of Satellite Remote-Sensing of Subtidal ...
stabilizer, and a x24 digital zoom (Riegl et al 2001). ... These points were then connected to form the outline. ... live and dead corals – live corals having had a coral spectral signature and grouping as “corals”, dead ... Ikonos satellite im

Remote Sensing Image Segmentation By Combining Spectral.pdf ...
Loading… Whoops! There was a problem loading more pages. Whoops! There was a problem previewing this document. Retrying... Download. Connect more apps... Remote Sensin ... Spectral.pdf. Remote Sensing ... g Spectral.pdf. Open. Extract. Open with. S