Digital analysis of the hydrologie components of...

Hydrologie Applications of Space Technology (Proceedings of the Cocoa Beach Workshop, Florida, August 1985). IAHS Publ. no. 160, 1986.

Digital analysis of the hydrologie components of watersheds using simulated SPOT imagery

KATHRYN F. CONNORS, THOMAS W. GARDNER & GARY W, PETERSEN The Pennsylvania State University, University Park, Pennsylvania, USA

Abstract Système Probatoire d'Observation de la Terre (SPOT), a French satellite scheduled for launch in 1985, will have three multispectral bands and a panchromatic band with 20 m and 10 m ground resolution, respectively. In this study, simulated SPOT data discriminate among surfaces with different infiltration and runoff characteristics. Reflectances of features with different hydrologie characteristics are classified using cluster, non-parametric linear discriminant, Bayesian maximum likelihood, and edge enhancement techniques. Simulated SPOT data from a semiarid site in New Mexico discriminate among surfaces with different stabilities that result from differing rates of fluvial and eolian erosion. Simulated SPOT data distinguish components of the hydrologie system disturbed by surface coal mining in a humid temperate site in Pennsylvania. Spectral reflectance and edge characteristics discriminate groundwater recharge and some discharge sites. The digital format of the data allows for ready input into hydrological model data bases.

Introduction Numeric hydrologie models which predict sediment and water discharge on watersheds require, among other things, data bases containing the areal extent of surface characteristics such as infiltration rate, runoff rate, soil texture, vegetation cover, and surface roughness. Many of the specific components of the hydrologie system of watersheds can be characterized with remotely sensed data (Figure 1). The digital nature of remotely sensed data provides an element by element format that could easily be incorporated into hydrologie models. The first generation of remotely sensed data (LANDSAT-1,2, and 3) has been used to view regional physiography and land use patterns and to grossly classify surface features to an Anderson (1976) Level I. This level of classification, however, is generally not useful in hydrologie modeling. The second generation of satellite remote sensing systems has improved spatial resolution (SPOT), or improved spectral and spatial resolution (LANDSAT-Thematic Mapper) (Table 1) that allow Level II, and III and possibly Level IV classification (Anderson, J. R. et al., 1976). These improvements in resolution may allow for significant improvements in remote sensing input to numeric hydrologie models.

This study evaluates the utility of digital and analog simulated SPOT data to characterize the hydrologie components of watersheds in both semiarid and humid regions. The semiarid data set is located in McKinley County, northwestern New Mexico, northeast of the town of Crownpoint on U.S. Geological Survey 7.5 minute quadrangles of Antelope Lookout Mesa, Becenti Lake, Milk Lake, and Nose Rock. It is situated in the San Juan Basin of the Colorado Plateau Physiographic Province and is centered around Kim-me-ni-oli Wash, an emphemeral, discontinuous arroyo and southern tributary of the Chaco River. Bedrock consists of gently north-dipping

355

356 Kathryn F.Connors et al.

WATER ROCK CHEMISTRY CHEMISTRY*

PHYSICAL PROPERTIES OF SUBSTRATE*

/ D E E P GROUNDWATER 1 STORAGE

/GROUNDWATER DISCHARGE AND / (QUALITY!

\ springs effluent streams*

REMOTE SENSING OF THE HYDROLOGIC COMPONENTS OF WATERSHEDS

INPUT,OUTPUT ? DOMINANT CONTROL PARAMETERS

POTENTIAL REMOTE SENSING CHARACTERIZATION

FIG.l Flow diagram showing watershed components that can be remotely sensed.

upper Cretaceous, continental sandstone, mudstone, shale, and coal of the Menëfee Formation (Hackman and Olson, 1977). Bedrock is discontinuously mantled by Quaternary alluvium and sandy eolian deposits. The climate is high desert, semiarid to arid. Summer precipitation is in the form of high intensity, local thunderstorms. Vegetation consists of rangeland grasses, forbes, weeds, cacti and shrubbery.

Geomorphic processes within Kim-me-ni-oli Wash drainage basin include eolian erosion and deposition, sheetwash, fluvial headward extension, lateral stream migration, piping, pedogenesis, soil creep and rock fall. Detailed field studies (Wells, 1982, 1983; Wells et al., 1983a, 1983b) have produced extensive values for the rates of dominant geomorphic processes in the study area (Figure 2). The upland is characterized by Holocene eolian mantles deposited during high rates of eolian transport and deposition. Radiocarbon dates of the eolian sediment range from 5970+105 yrs BP to modern (Wells et al., 1983a). Present eolian transport rates are low (Wells, 1982; Wells et al., 1983a). Soils are coarse-loamy, mixed, mesic Typic Haplargids. According to Soil Taxonomy (U.S.D.A. Soil Conservation Service, 1977), " argids that have an argillic horizon but do not have a natric horizon are formed on late-Pleistocene or older erosion surfaces or sediments." This is consistent with Wells' data which dates the uplands at early to mid-Holocene. These late Pleistocene and early Holocene uplands also show little evidence of erosion and hence, are considered stable. The valley floor component is characterized by locally very rapid tributary headward extension and drainage integration, lateral stream migration, and sheetwash (Mills and Gardner, 1983). Radiocarbon dates of the alluvium range from 3320+60 yrs BP to 390+75 yrs BP (Wells et al., 1983a). Soils are formed on mildly alkaline alluvium and are fine-loamy, mixed, mesic Typic Torrifluvents and fine, mixed, mesic Typic Camborthids. The valley floor of Kim-me-ni-oli Wash has abundant evidence of present day erosion. Work by Mills and Gardner (1983) document a maximum tributary

Hydrologie components of watersheds 357

headcutting rate of 12.3 m/yr and a maximum lateral channel migration rate of 5.1 m/yr. Because valley floor soils are young or weakly developed and there is active erosion and deposition; they are considered unstable.

MAGNITUDE OF GEOMORPHIC PROCESS

process rate (m/yr)

0.5 1.0 5.0 10.0

DRAINAGE HETWOBKS

- rejuvenat ion

alley floor deposition

-vertical downwasting

• - v e r t i c a l accretion

ER0S1OM

-aggradation

vertical deflation

-*-extension

piping/gullying

— * - headcutting — * - bank erosion

I -bank accretion

*-meander growtn

FIG.2 Geomorphic process rates for northwestern New Mexico (from Wells S Gardner,1984).

The humid temperate data set is located in central Pennsylvania in the Allegheny Plateau Physiographic Province on U.S. Geological Survey 7.5 minute quadrangles of Frenchville, Karthaus, Pottersdale, Snow Shoe and Snow Shoe Southeast. The site possesses reclaimed and unreclaimed. bituminous coal surface mines, active mines, and unmined watersheds. The surficial bedrock consists of Mississippian age shale, siltstone, sandstone and some conglomerate of the Mauch Chunk Formation and Burgoon Sandstone, and Pennsylvania age sandstone and conglomerate of the Pottsville Group and cyclic sequences of sandstone, shale, limestone, and clay of the Allegheny Group. The area is extensively mined for the Freeport, Kittanning, Clarion and Mercer coals of the Allegheny Group (Berg, 1980 and Williams, 1960). The climate is humid temperature. Natural vegetation consists of second growth mixed deciduous and coniferous forest. Some portions of the study area have been converted to cropland. Mined land is vegetated with grasses and mixed tree cover.

The SPOT Program SPOT (Système Probatoire d'Observation de la Terre) is a French observational satellite. SPOT will orbit circular sun-synchronously at altitudes between 600 and 1200 km with an orbital repeat period of 26 days, but with the capability to observe mid-latitude regions up to 12 times in a 26 day cycle (Chevrel et al., 1981). It will sense in a multispectral scanning (MSS) and a panchromatic mode with high resolution, visible (HRV) range instruments (Table 1). The HRV are composed of 3000 charge-coupled device (CCD) detectors per MSS band and 6000 detectors for the panchromatic band. These detectors are in a linear array and scan in a pushbroom mode as the satellite moves forward along its orbit (Chevrel et al., 1981). Stereoscopic capabilities of the sensor are available because of across track pointability of the HRV. There will be an increase in spatial resolution


Comparison of First Generation and Second Generation Satellite Systems: Spectral Bands and Spatial Resolution

SPECTRAL RESOLUTION (wavelength)

FIRST GENERATION (Landsat 1, 2, and 3)

Band 4 0.5-0.6 u Band 5 0.6-0.7 u Band 6 0.7-0.8 u Band 7 0.8-1.1 u

SECOND (SPOT)

Band 1 0.51-0.59 u Band 2 0.61-0.68 u Band 3 0.79-0.89 u Band 4(P) 0.51-0.73 u

GENERATION (Landsat-Thematic Mapper)

Band 1 Band 2 Band 3 Band 4 Band 5 Band 6

0.45-0.52 u 0.52-0.60 u 0.63-0.69 u 0.76-0.90 u 1.55-1.75 u 2.08-2.35 p

Band 7 10.40-12.50p

SPATIAL RESOLUTION (ground cell)

79 by 57 m HSS bands 20 m panchromatic

band 10 m

bands 1-6 30 m band 7 120 m

of more than an order of magnitude from LANDSAT-1,2, and 3 to SPOT MSS data and of nearly an order of magnitude difference in resolution between the LANDSAT-Thematic Mapper and the SPOT panchromatic data.

In the summer of 1983, SPOT IMAGE Corporation of Washington, D.C., the marketing company for SPOT data sets, sponsored a simulation program to evaluate the potential utility of SPOT. The simulation campaign included 52 test sites throughout the United States. Each site was scanned using a Daedalus AADS 1268 Digital Multispectral Scanner System flown in a Learjet 25-C. Two site types were used to simulate the higher resolution (10m) panchromatic band (P site type) and the lower resolution (20 m) multispectral bands (S site type). P sites were scanned from an altitude of approximately 6.35 km and S sites from 12.50 km (SPOT IMAGE Corporation, 1983). The semiarid data is acquired from a P site and the humid temperate data from an S site. Because there are considerable differences between aircraft-acquired and satellite-acquired data, several corrections were applied in order to simulate SPOT data (SPOT IMAGE Corporation, 1983).

Computer Classification Methodology Digital classification of the simulated SPOT data include hybrid, unsupervised-supervised Euclidean distance analysis (New Mexico data), supervised Euclidean distance analysis (Pennsylvania data), Bayesian maximum likelihood classification (Pennsylvania data), and non-parametric linear discriminant analysis (New Mexico data). Comparison of unsupervised Euclidean distance classification of LANDSAT-3 and simulated SPOT data is made for the New Mexico data set. Feature extraction using edge enhancement is also investigated. Accuracy of classification results is evaluated by comparing digitized planimetric area of features on the classification map with a zoom-transferscope-superimposed ground truth map for a small strip of the classified maps.

All computer programs except where otherwise noted are from the ORSER (Office for Remote Sensing of Earth Resources) image processing system. Details on the methods of analysis can be obtained from the ORSER User's Manual (Turner et al., 1982).

Unsupervised Euclidean distance classification is initiated with cluster analysis of portions of the raw, digital, simulated SPOT data using the CLUS program. Final cluster mean relectances in n-dimensional space are termed "feature signatures". Ground surface features that correspond to cluster feature signatures can be determined by comparing the spatial


distributions of ground features with the spatial distribution of clustered data.

Supervised methods are used to derive signatures for areas unclassified by CLUS for the New Mexico data set and all of the signatures for the Pennsylvania data set. Blocks of pixels are selected as training fields for each ground feature. Spectral signatures are determined by the STATS program for each training field.

Feature signatures that are similar to one another, have relatively low standard deviations, and representative of ground truth features are used in subsequent classification. Signatures for similar ground features define a feature class. The CLASS program uses feature class signatures and an Euclidean distance classifier for final scene classification. Pixels are assigned to feature classes by signature similarity to input feature signatures. In order to avoid large spectral overlaps between feature classes, signatures with very large standard deviations are not used. The resulting classified data set is cleaned with the DISPLAY program to smooth the appearance of the output map (LMAP program) by retaining only contiguous blocks of a given feature. LANDSAT-3 data analysis is identical to the simulated SPOT Euclidean distance analysis.

The Bayesian maximum likelihood classifier (MAXCLASS program) classifies according to weighted distances of separation between categories. This method is used because although reflectances are theoretically normally distributed, variance is not the same for each band of data. MAXCLASS assumes a multivariate normal distribution and defines hyperellipsoids which describe the distribution of feature classes. Input to MAXCLASS is a covariance matrix for each signature (output from the STATS program). Because some reflectances for the humid temperate data set saturate the sensor on bands 1 and 2, there is no variance for these bands. This classifier cannot use near zero or zero covariances. Feature signatures without variance are not input to this analysis. The DISPLAY and LMAP programs are used to clean and output the classified map as- described for the Euclidean distance classification.

The NEIGHBOR procedure in SAS (Statistical Analysis System) (Goodnight and Sarle, 1982) uses nonparametric linear discriminant analysis. Two sets of signatures are used in this procedure: a training set and a test set. A nearest neighbor discriminant analysis defines functions to separate training signatures into user-defined feature classes representative of ground surface features. Test feature signatures are classified by the discriminant functions defined from the training signatures.

Edge enhancement is investigated as a means of feature extraction. Edge enhancement programs were written by J. Ronald Eyton of the Department of Geography of The Pennsylvania State University and include DERIVE, ZHIST, and VTONE. DERIVE determines the first and second spatial derivatives of the data which correspond to the spectral edges, i.e., the rate of change along the ground of pixel reflectance (slope) and the rate of change of the slopes, respectively. The program ZHIST produces a histogram of these derivatives so that tails in the distribution can be deleted and the data can be linearly stretched to expand the number of grey levels over which the data could be displayed with the program VTONE.

Remote Sensing of Watershed Components Because surface features commonly have unique spectral reflectances, remotely sensed data can be used to distinguish watershed components (Figure 1). Rainfall input to a watershed will result in surface runoff or groundwater recharge depending on the infiltration rate of the surface. If the infiltration rate is exceeded, runoff occurs, interacts with surface morphology, and can result in channelized flow. If the infiltration rate


is not exceeded, groundwater recharge can occur, the water interacts with the substrate, and is either stored as deep groundwater or is discharged to the surface.

Simulated SPOT data allow characterization of surfaces with different infiltration rates and several of the parameters that control these infiltration rates including vegetation cover, soil texture, surface morphology and antecedent moisture. This capability is demonstrated with the semiarid data set which separates high infiltration rate, 27 cm/hr (Duffy and Gardner, 1983) moderately well-vegetated, coarse-grained, relatively smooth, dry upland surfaces from moderate infiltration rate, moderately vegetated, medium-grained, hummocky, dry, intermediate surfaces from low infiltration rate, 9.7 cm/hr (Duffy and Gardner, 1983), poorly vegetated, fine-grained, sodium-rich, level, dry surfaces, and from low infiltration rate, well vegetated, fine-grained, level, wet surfaces (Table 2).

Table

Teat

Coar:

Court

Med a

Med a

Med a

Med a

Med a

soda

; 2 . Nonparametr ic L i n

F e a t u n

i e -g ra l r

: Class

l e d , Eol ian-mant

l e - g r a l n e d , S o l i air-man t

m-gra.it

im-gra l r

. n - g r a l ,

im-gra i i

im-gra i i

las-r lch, i t e r i a l

Sod ium- r i ch

Sod i i

Sod i i

H e l l

H e l l

We l l

Wel l

Wel l

i m - r i c h i t e r i a l

i t e r i a l Vegetal

Vegetal

led l a t e r l a l

ied m a t e r i a l

led m a t e r i a l

ied m a t e r i a l

ied m a t e r i a l

, f i n e - g r a i n e d




Led Areas

:ed A r e a ,

Vegetated Area3

Ve8etai

Y . s . t a l

ted Areas

Led Areas

.ear D i s c r i m i n a n t Ana

Coar Eol

• l e

• l e

Percent Cl

se -g ramed ian Mantle

83.33

95.2*1

35.71

— 7 . 1 "

6.25

— ™

~~

1.35

17.62

— ___ 22.86

. l y s l a , Semlar

a s s t f i e d i n t o Medium-gra ined M a t e r i a l

16.66

1.76

52.38

83.31

85.71

81.25

92.86

1.00

16.67

15-71

11.90

16.66

33.33

— ~" 11.13

i d S i t

T T a ^ sSd Fin

e Resu l ts

iisg F i e l d -lu.-n-ncn e -g ra ined

M a t e r i a l

— — 16.66

2.38

12.50

7.11

88.00

93-33

51.29

80.96

11.91

16.67

™

'--___

- Def ined Claa: W e l l -

vegetated Areas

— ... — — — — — .__ — ... — 59.26

2.33

83.09

100.00

60.00

Other

...

... 10.91

... 1.77

... —

3.00

— ...

7.14

7.12

...

...

... 5.71

Areas that contribute runoff to the hydrologie system can be detected from simulated SPOT data because the spectral characteristics of these surfaces are influenced by their surface morphology, especially roughness and vegetative cover. The humid temperate disturbed land data set edge enhancement vividly displays this (Figure 3)- The bituminous coal strip mine displayed in Figure 2 shows two major surfaces: a mottled surface and a dark surface. The former represents the ungraded, rough microtopography of old unreclaimed mine spoil. The latter represents the regraded, smooth, convex-up slopes of newly reclaimed mine spoil. These smooth slopes are relatively impermeable (Jorgensen and Gardner, in press) and can contribute large volumes of runoff that excessively gully the surface and deleteriously affect adjacent streams (Touysinthiphonexay and Gardner, 1984). Large unstable channels with high sediment transport rates resulting from high runoff volumes (Gryta and Gardner, 1983, 1984) are also detected on edge enhancements (Figure 4). These 7 - 9 m deep, 5 - 10 m wide gullies have transported mine spoil to a 50 by 100 m alluvial fan just beyond the mine edge. The spectral contrast between the mine spoil and surrounding forest (spectral edge) terminates at these gullies. The alluvial fan spectral reflectance is so similar to the mine that no edge appears between them.

m-gra.it


FIG.3 first derivative edge enhancement, channel 2 showing the microtopography of the mine spoil, Snow Shoe Mine, PA. Mottled areas are generally groundwater recharge sites,dark areas have high rates of runoff.

;* « V

0-5 k«

FIG.4 First derivative edge enhancement, channel 1. Arrow shows location of gullies at the Pine Glen Mine, PA.

Relative runoff rates can be discriminated with the semiarid data set with some knowledge of average slope gradients, landscape position, and local geomorphology and remotely sensed information on vegetation cover,


soil texture, salt content and microtopography (Figure 5). High runoff rates occur on steep, smooth slopes, surfaces with low infiltration rates, and/or poorly vegetated areas. Low runoff rates occur on level surfaces, areas with high infiltration rates and/or well-vegetated areas.

Groundwater recharge can occur when rainfall rate does not exceed infiltration rate or where local topography detains surface water long enough to allow infiltration. Areas of potential groundwater recharge are located by edge enhancements for the humid temperate data set (Figure 3). The hummocky, ungraded spoil piles, displayed in a mottled tonal pattern in the figure, allow surface water to collect in topographic lows and are thereby internally drained. Infiltrating groundwater interacts with the substrate which in this case is fragmented pyritic shales and changes the water chemistry. The high sulfur content of the shales results in highly acidic groundwater (Williams et al., 1982).

At the toe of many reclaimed surface coal mines, groundwater is discharged as acid mine drainage seeps. Manual interpretation of the simulated SPOT imagery, especially enhanced imagery, located several seeps. Digital classification was less successful presumably because the seeps were generally only one or two pixels in size.

HIGH POTENTIAL RUNOFF RATE i K~

MODERATE POTENTI VL RI \OFF R \TE

FIG.5 Potential runoff rate Euclidean distance classification map, New Mexico site.

Discussion Methods of analysis vary in their ability to characterize the landscape surface (Table 3). Four methods of digital analysis of the simulated SPOT


imagery are investigated as techniques for extracting hydrologie data: supervised and unsupervised Euclidean distance, edge enhancement, nonpara-metric linear discriminant, and Bayesian maximum likelihood classification. Details in the results from each of these techniques is discussed in Connors, 1985; summaries of results (Tables 3 and 4) show an Anderson (1976) Level III classification of surface features. All of the methods except edge enhancement successfully discriminate surfaces with different infiltration and runoff rates for the semiarid data set. Infiltration rates are not strongly correlated with spectral reflectance for the humid temperate data set however, and are thus not distinguished well by any of the methods. Sensor saturation due to miscalibratlon for the very high reflectances of the mine surface severely limits data extraction for this site. Relative runoff rate, on the other hand, can be discriminated by nearly all methods from this data set. Landscape stability classes of the semiarid data set are obvious on enhanced images by manual interpretation. Eolian erosion is readily distinguished by nearly all methods, whereas fluvial erosion is best discriminated with the 10 m resolution of the panchromatic band. Fluvial erosion at the humid temperate site is best discriminated with edge enhancement. Areas of groundwater recharge are also best discerned on edge enhancements of the humid temperate site because recharge areas there have rough microtopography reflected in numerous spectral edges. Groundwater discharge sites are distinguished for some locations on enhanced imagery but are not distinguished well by any method of digital classification.

Conclusions The high spatial resolution of simulated SPOT data allow surfaces of small areal extent with differing hydrologie properties to be discriminated. Soil and vegetation characteristics and geomorphic features are commonly spectrally distinct and can be determined from this remotely sensed data. The high resolution of simulated SPOT permits the distinction of infiltration rates, runoff rates, groundwater recharge sites, some groundwater discharge sites, landscape stability classes, eolian erosion, and fluvial erosion. Previous remotely sensed data (LANDSAT-1,2, and 3)

Table 3. Evaluation or Digital Classification Methods for the Interpretation or Humid Region Disturbed Land and Arid Land £ SPOT Imagery.

GEOMORrHIC AMD HÏDR0L0GIC CHARACTERISTIC

Infiltration rate

Runorf rate

Areas of Groundwater Recharge

Ares of Groundwater Discharge

Fluvial Erosion

M F llnoupervise Supervised Euclidean Distance

N.A./N.A.

good/good

good/good

poor/fair

N.A./N.A.

T il

II 0 D 0 L 0 0

Edge Enhancement

»... good

excellent

U.A.

fair

Ï

Maximum Likelihood*

fair

fair

faii-

poor

poo,-

GEOMORPHIC AND HÏDR0L0GIC CHARACTERISTIC

Infiltration Rate

Runoff Rate

Landscape Stabll

Eolian Erosion

Fluvial Erosion

ty

R

M E T H Unsupervised/ Supervised Euclidean Distance

good

good

good

exceller

poo,

t

0 D 0 L 0 G Ï Ifonpararaetrlc Linear Discriminant Analysis

goo,

goo»

fair

good

». . .

Edge Enhancement

N.A.

If.A.

fair

«.„.

excellent

1 Sensor saturation on two of the t N.A. » Hot applicable.

ely limited this technique.

could not identify all of these geomorphic and hydrologie characteristics (Table 5). Hydrologie parameters such as infiltration rates, soil texture, and vegetation cover can be determined from a combination of field data and remotely sensed data. Field data provide quantitative values for parameters. Where reflectances are high correlated with values of


Levels of Land Cover Classifies

T_______r HiBJd Data Set H Forest Land

6 Wetland

3 Rangeland

7 Barren Land

* Levels HI and

et a l . , 1976)

i*2 Evergreen Forest Land

13 Mixed Forest Land

61 Forested Wetland

62 Non-forested Wetland

75 Strlpraine3, Quarries, and Gravel Pita

31 Herbaceous

33 Mixed

Shrub and Brush

71 Dry Salt Flat:

71 Bare Exposed Rock

IV arc created by 1

LEVEL III»

Acid Seeps

Shaly s t r i p mines

Sandy a t r ip mines

Topaoiled s t r i p

s i l t y areas very «ell vegetated al luvial clay

well vegetated alluv

coppice dunes

LEVEL IV*

Well vi Poorly

Well vi Poorly

Poorly

-«. ;iura

no B-h

eolian mantled uplanda

j sa l t pans gravel lag s l l t y - sa l t y areas

sandstone mudstone saprol l te

:he authors for the att> dy-

vegetated

sgetated vegetated

vegetated

Table 5-

Geomorphic Hydrologie Characters

Inf i l t ra t i< Rate

Runoff Rate

Ground Wat' Recharge Si tes

Ground Wat Discharge Si tes

Landscape Stabi l i ty

orlzon exposed fluvial

Hydrologie Model Pararaeter-ization

B — _ ma em

Utili .ty of Data Hydrologie Chara

and

; t ' c s

« •

i de p a r

Field Data

a t i f i ab le w t i a l l y Iden

ldentif lab.

Types in c t e n s t t

Aerial Photogra

1th data t [ f iab le le with

i the c s .

iphy

type

a«r,

Inves

S i

t iga t i .

muiatei SPOT analog/d

;ype type

on of Ccomorphlc and

d LANDSAT 1,2, and 3

ig i ta l analog/digi tal

hydrologie parameters, remotely sensed data can provide the location and areal extent of these values. The digital format of remotely sensed data allows for ready input into hydrologie model data bases.

Acknowledgments This research was supported under agreement No. DE-AC02-83ER60182 by the U.S. Department of Energy as part of the REFLEX program. Dr. Frank Wobber of the Office for Health and Environment of the U.S. DOE is especially acknowledged. George Baumer of the Office for Remote Sensing of Earth Resources and Dr. J. Ronald Eyton of the Geography Department, The Pennsylvania State University, were very helpful in image processing.

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