management information systems, c.a. brebbia & p. pascolo ...€¦ · sensed data is merging...

Use of remote sensing data for generating a

digital terrain model aimed at hydrologic and

hydraulic modelling

A. Sole, A. Crisci & G. Spadino PippaDepartment of Environmental Engineering and Physics, BasilicataUniversity, Italy

Abstract

Many factors condition extensive use of satellite images. This is due to the rawsatellite imagery containing geometric distortions so significant that it cannot bedirectly superimposed to base maps. Other problems occur when remotelysensed data is merging into Geographic Information System (GIS). Recently,many authors have developed integration of Remote Sensing techniques and GISto study hydrologic processes. Applications of integration of remotely senseddata with GIS for hydrologic studies concern the determination of land use, landcover classification, precipitation, soil moisture, evapotranspiration, waterextend, ground water, water quality and runoff. In this paper a methodology tocreate a Digital Terrain Model (DTM), starting from JERS-1 satellite data , andits application to a river basin in southern Italy are presented. The aim of study isto verify accuracy and precision of such DTM, and the correct scale forhydrologic and hydraulic application.

1 Introduction

Real time continuous data acquisition regarding evolutionary land dynamics is offundamental importance in prediction and control of environmental risk. It isonly possible to pursue these objectives through the identification of the diversespatial distributions of physical and chemical parameters which intervene in

Management Information Systems, C.A. Brebbia & P. Pascolo (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-815-5

368 Management Information Systems

climatic and environmental processes. Knowledge of these parameters, inrelation to the land extension under observation, is possible by means ofdifferent types of survey instruments. Thus, given a more or less vast stretch ofland, it is possible to acquire data at a distance using aerial sensors by remotesensing or, on site, using point sensors interconnected by a telemeasurenetwork. The typically high precision measurement of observed parameters bypoint sensors is offset by the high spatial resolution of aerial sensors andtherefore of satellite data. Also, the direct measurement of observed parametersthrough the use of point sensors reduces the complexity of data elaboration. Theuse of aerial sensors, on the other hand, subordinates data availability to the useof simulation models calibrated by information derived from appropriatelylocated point sensors. The systematic integration of both point and aerial datarelative to the same portion of land, represents, therefore, the optimal solution tothe objectives fixed at the outset. Within the field of planning and landmanagement, the technical characteristics of land observation satellite datafacilitate regional application through the production of derived information orthematic cartography.The present work focuses on the possibility of integrating satellite images in aGIS to extrapolate data useful for the application to simulation models ofenvironmental dynamics. In the case under examination, steroscopic imagesfrom the JERS-1 satellite (fig. la) were elaborated for the drawing up of a DTM.This model provided useful data for application of rainfall-runoff model used forflood estimation and of a hydrodynamic model used for flood risk zonedelimitation, (fig.lb).The area under study is a river in southern Italy; the Basento Basin covering an

a) JERS-1b) Proposed procedure

Figure 1

area of some 1500 km. The area was divided into 2 zones of a control sectionwith the upstream portion being chosen for simulation of flood generationbecause of its orographic features. The dowstream section, extending 85 km tothe sea and historically subject to problems of flooding, was utilised for floodwave propagation generated after the rainfall events examined, (fig.2).

1.1. JERS-1 Characteristics - The JERS-1 ( Japanese Earth Resources Satellite)(fig.lb) in orbit since 1992 distinguishes itself from other earth resourcesmonitoring satellites through its capacity to obtain a vast range of data utilizing apair of sensors (OPS-optic and SAR-Synthetic Aperture Radar) which


Management Information Systems 369

Flood runoffgeneration

Control section"Campomaggiore"

Flood vawepropagation

Figure 2: Study Area

simultaneously acquire data co.recorded in a broad interval of theelectromagnetic spectrum (visible, infrared and microwave).Thus to the list ofapplications obtained with optic sensor satellites (land use, agriculture, forestry,environmental impact, thematic cartography etc) it is possible to add thoseobtained through the use of radar sensors (oceanography, hydrology, geology,glaciology). A further characteristic is the possibility of producing DTM andortho-images from stereo pairs acquired simultaneously; the stereoscopic shotsare obtained using 2 optic sensors positioned on a satellite platform, one with aforward off-nadir of 15.3 degrees in the same direction as the orbit. The model,as produced, presents an average vertical error of 40m. which can be reduced to20m. with the support of a global position system (GPS) and a land resolution of24m.. For their characteristics, JERS-1 satellite data, allowing the production ofthematic cartography in scale 1:50,000. Tab.l reports some satellite features.(JemmaF.[l]).

Principal!caratteristichesatellite JERS-1

Orbital heigth 568Orbital Incl: 97,7°Rivisit. time: 44 dRec. width:75x75Ground Resolu18 x 24m

del

km

kmtion

Sensore ottico (OPS)

Sensor: VNIR (visible ainfrared)N. spectral band: 4Spectral Range: 0,52-0,86mRec. stereo: 15,3°

Sensore radar (SARSyntetic Aperture Rada

Frequecy: 1,275 GHzband)Polarization: horizontalAngle: 38,5°

r)

(L-

Table 1: JERS-1 Characteristics (Jemma F.[l])



2 DTM elaboration from JERS-1 images and extraction of

useful data for application to the proposed methodology

DTM was created with the aid of 2 stereoscopic views taken by the JERSsatellite in 1998,using IPS software developed by ACS company (Frascati, Italy),installed in a workstation "Silycon Graphics".The procedure under examination was organized in 2 phases. In the first phase,the original images were converted to orthographically correct images throughpre-treatment operations, with the aim of bringing them to a system ofterrestrial co-ordinates .The result is an image inclined according to the angle oforbit and lacking the characteristic noise on the left of the picture. In the secondphase it was necessary to create a file containing Ground Control Points(GCP).That is, points surveyable on both the images (band3 and band4) and onexisting cartography to which should be associated planimetric co-ordinates xand y and the quota z which allows for the calibration of the altimetric modelwith respect to real data. To these ends, 66 orthophoto map in scale 1:5000 andscale 1:10000, reproducing the area under study, were utilised. The choice ofGCP obviously influences the quality of the model in terms of accuracyunderstood as a measure of how much an estimated value differs from the truevalue, while the equipment used to collect data influences the precision, that, instatistical terminology, is a measure of the dispersion of observations about amean value (Sole & Valanzano, [2]).Once the GCP have been identified, they are given as input to the softwarethrough the creation of a file containing geographic information about the GCPwith respect to image (pixel, line band) and to cartagraphic data (quota in metres,terrestrial co-ordinates x and y ). Further data is provided for the system throughthe creation of a mask which identifies flat surfaces seas, basins, lakes,reservoirs, etc). Masks are created on the third band and a polygon issuperimposed on each area to be masked, with the corresponding quota. Thus thesoftware effects the sterorestitution and the interpolation of the 2 images underconsideration. Fig.3 reports a Grey Scale DTM view.

Figure 3: Grey scale DTM view Basento river basin



The separate elaboration of the 2 images brings about different quota values inthe area of overlap such that it is necessary, in the union phase, to correctinevitable errors. Through the use of Arc/Info (ESRI) software, a mosaicoperation generating quota values congruent with those of the surrounding area,was undertaken. Fig.4 reports the results of this operation.

Overlapped areas

Figure 4: Images union

This model, still within ARC/INFO environment, was again subject tointerpolation using the Inverse Weighted Distance Method and errors such as pitsand sinks were corrected .(Jenson and Domingue, [3]) obtaining a matrix of 4475x 2819 pixels, comprehensive of nodata points, fig.5.In order to check the quality of the DTM a drainage network was generated from

Fig.ure 5: DTM Basento river basin


it following a hydrological method (Jenson and Domingue [3], Mark [4] ). Thisapproach is based on the assumption that "the drainage represents those points atwhich runoff is sufficiently concentrated that fluvial processes dominate overslope processes. If the spatial concentration of surface runoff is simulated, thenthose points at which this runoff exceeds some threshold can be considered to bethe drainage network" (Mark, [5]). The same author (Mark, [4]) delineatesseveral phases: calculation of drainage direction matrix, given the elevationmatrix; pit identification and removal; definition of weight matrix; calculation ofdrainage accumulation matrix; application of a threshold criterion in order toproduce a channel network. A drainage direction matrix is a matrix which storesthe flow direction for each cell. The method assumes that each cell can drainonly to exactly one neighbouring cell. The optimal threshold value Tarboton etal. [6]) is that which generates a digital network for which, laws or networkproperties derived for river are still valid. In particular for the case underexamination a threshold value was chosen so as to respect the classification ofthe drainage network following the method of Horton-Strahler, already in usefor the basin under examination (Sole et al. [7]). Fig.6a reports a comparisonbetween the generated network and the digitalized network belonging to theEnvironmental Engineering and Physics Department geography data archive(Sole et al. [7]). Fig.6b shows the classification of the generated networkaccording to the Horton -Strahler method. Thus, the delimitation of the sub-basins took place according to the method proposed by Marks et al [8]), fig.6b.

a)

Figure 6: a) Generated and digitalized drainage network comparison,b) Horton-Strahler classification, c) example of sub-basin generation



The following were also evaluated: length of link, average slope, sub basin area,total drainage area, drainage density.The application of a hydrodynamic model of flood wave propagation needs thedefinition of river bed cross section. For the areas under consideration, startingfrom DTM, a contour lines coverage with an equidistance of five meters havebeen generated. In order to extract the sections from DTM, it is necessary tocreate a cover with the location of sections and with its trace on contour linescoverage. This operation is possible by means of the Intersectarcs addcommand. Therefore using the last coverage and the DTM as an input, theSurfacexsection command interpolates and writes surface profile coordinates in afile. This georeferenced data become the input for the Data Base Cross Sectionof hydrodynamic module. The geometrical characterisation of cross riversections is basic information for the mathematical modelling of wavepropagation. Hits level of detail influences the accuracy of the results. For thisreason, the choice of the sections on DTM must be carried out with care in orderto ensure the maximum detail in sections drawn especially for the correctdefinition of riverbed bottom. In fact the chosen sections must intersect, ifpossible, the bottom river bed in correspondence with a contour line.

3 Flood runoff generation

A flood event is commonly defined as an excessive flow discharge or increase inwater level due to an unusual rainfall input or the rapid emptying of a reservoiras would be the case with a dam break or instantaneous opening of spillway andbottom outlet The present work looks at the problem of flood production causedby rainfall phenomena from the perspectives of both a lumped model and adistributed model.A common feature of lumped models is the omission of spatial variation ofprecipitation, water flow and other related processes and the consideration ofinput, output and parameter averaged through the basin scale. These modelsrequire a long parameter calibration period which depends on the availability of ahigh number of events (rainfall-runoff). Considerable improvement can beobtained through the use of physically based distributed models which take thelack of rainfall uniformity and non heterogeneity of topography, soil andvegetation, into account. While it is known that surface downflow depends onvarious factors (Seven and Kirby,[9]), topography is one of the factors whichmost influence the physical phenomenon of rainfall-runoff transformation.Indeed, it plays a primary role in the effects produced by the gravity on watermovement inside a basin.The present study has utilised the determinist model NAM (DHI [10]) based onphysical structures and equation used together with semiempirical ones. Modelparameters were calibrated using historical data recorded in the control section(fig.2). As a parallel, we also adopted a modified version of Clarks model(Maidment et al [11]) implemented in the TAHR system (Mendicino, [12]).Thismodel, based on a spatially distributed geomorphological approach, allows forthe determination of hydrological response on the basis of DTM alone.



Given the dimensions of the input matrix, it was necessary to aggregate the cellsby a 3x3 factor. Fig.7 reports an example, obtained during the calibration phase,of the simulation and measurement of hydrograph, using the two proposedmethods.Once the models were calibrated, a flood event occurring in the area under study

400350-| —300J .,250-| -4-A-2004150 j

24/2 25/2 26/2 27/2 28/2 29/2 1/3

a) NAM b) TAHR

Figure 7: Example of models calibration (Event 24/2/1956)

was simulated. Georeferenced esondation data, observed during the same event,was introduced into the opportunely predisposed GIS. Fig.8 reports the measuredpluviograph and consequent hydrograph determined in the control section by theNAM model.

Figure 8: Simulated event

4 Flood Wave Propogation

The wave propagation is evaluated by using the hydrodynamic module of Mike11 model, developed by Danish Hydraulic Institute (DM [10]). These results,coupled with GIS technology, allow the definition of flooding areas. Thesimulation was carried out using the cross river section extracted by DTM data.This georeferenced data become the input for the Data Base Cross Section



module of MIKE 11. The definition and positioning of the fluvial sections istherefore the most important task of the modelling process (Sole & Crisci [13]).As stated above, it has been possible using ARC/INFO software, to both choose88 sections and determine their geometry, starting from DTM (fig.9).Fig. 10 reports the longitudinal profile of the water course and fig. 11 shows anexample of the extracted sections, as it was memorised in module HD of model

Figure 9: Selected cross section along the Basento River

Mike 11.

The model requires the definition of boundary and initial condition. As upstreamboundary condition the hydrograph of the considered event has been fixed. The

10000.0 20000.0 30000.0 40000.0 50000.0 60000.0 700000 800000

Figure 10 Longitudinal Profile or river course



downstream boundary condition is determined by the sea level, with its constantvalue. As initial condition a baseflow in the river as been fixed.The bed resistance is relevant for flood wave propagation, and is variable alongthe river course. In the present work a constant value of Manning number hasbeen assumed. This value, equal to 0.030 m "̂ s, has been chosen consideringthe rough characteristics of river bed.The fully dynamic flow model has been chosen for the simulation with time andintegration step in respect of the Courant condition.Included in the output furnished by the program was the discharge , water levelsand maximum top width values for any time step in every selected cross section.

River name: TopolD: Chainage: ~jbwertte ' ' ' Jbwento . }1*34##%* . _ m*&*%* , . . BawJQpen jj JRcsistanceRadkjs JjJ JO 000• • •jil̂ HfeJnjfciri &WW*-— •- • • — ' " - — • — •— •— - ••••-• ••- . •• •" r Dfvide Section 'e. basenJo %j

- basento jo47? J14342360390147045198555058416132641867607187753579038329 jj

Insert Oc»$«cboo... |17 4wrhmMmmmrw«M*A

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_̂ _.,,̂ ,_., !Level of DMde {̂ m IX | 2 { Re«*Bt«nce 1 1*** |^0000! 520300] .000̂ 1; i2 OOO: 51 9 900 ! 000 i ! |4 000 [" 519' 400^" 'OOO" ̂4300l 519.400' .0004.300} 519400 .000 iB.XOi 518.900! .000 ]8.300! 518.400; .00010300 518000 .00012300 517500, .000 !14300 517000! .000:16 300 ! 51 S 500! 00018300! 51 S 000; 000:20 300 1 515500! 000 j ,22.300! 515000 .00024.300; 514500 .000;26300! 51 4 200: .OOO!op qnn i ^1 ? mnn i nnn JUJUpdate Market* | View Proce«rt Date.. |—& tt™t̂ r¥nr«M««rifWrtJ«jtnnv*rfl»j ZJ

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555.0 -

550 0 -

545.0-

540.0 -

S3S 0 -

530.0 -

525.0-

520.0-

515.0 -

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basento - basento - 1434. DOCO

W00 500 1000 1500 2000

Figure 11: Examples of cross section extracted by DTM

This last georeferenced data facilitates the tracing of a polygonal of the floodedarea, obtained by simulation and its comparison with those measured on site forthe same event. Fig. 12 reports this comparison.



Fig. 12 gives rise to two considerations. The first regards the underlined box areawhere there is a lack of correspondence between the observed floodable areaand the calculated floodable area which results as being grossly overestimated.This is because of the lack of DTM precision in this area, largely due todifficulties in the definition of an adequate number of Ground Control Points.The simulated valley flooded areas define a flood risk fluvial zone in relation toflows with the same return times as those under consideration, as numeroushistorical observations in the area have confirmed.

m Simulated flooded areas

HH| Observed flooded areas

Figure 12: Comparison between simulated and observed flooded area

5 Conclusions

In the light of the obtained results it is possible to draw some conclusionsregarding the utilisation of a DTM generated from satellite images for theidentification of flood risk perifluvial areas. It can be observed that thegeneration and consequent utilisation of a DTM as a data base for the studyundertaken has brought about inevitable advantages and disadvantages withrespect to the use of ground acquired data. One clear advantage of the formerover the latter lies in the possibility of examining vast areas with a highresolution. One only has to remember that the territory under study has an aerialextension of 1500 km̂ . with a ground level resolution of 24 m, a surfacedescribed by approx.6 million effective data units which would be completelyimpossible to measure using a point system. The DTM is also preferable from aneconomic point of view. Although it utilises information systems with a highcapacity data elaboration, both the acquisition of steroscopic images and therealisation of the digital model have relatively low costs in relation to thequantity of data obtained. A the same time, however, two fundamental problemswere encountered during the DTM realisation phase. The first concerns thedifficulty in supplying sufficient suitable data for the interpolation of the digital



model in prevalently flat areas where it was difficult to identify an adequatenumber of control points (GCP). The second is closely connected to the averagevertical error of DTM in the overlap zone of the two images where it provednecessary to carry out a further data elaboration to guarantee a correctrepresentation of the territory. As far as the applicability of hydrological flowmodels utilised (NAM and modified Clark) are concerned, it is possible to statethat the data supplied by DTM integrated with GIS data were valid for a correctschematisation and calibration of such models. In the case of the hydrodynamicmodel, HD, where it is necessary to represent the phenomenon to the fluvial bedscale, the inevitable errors present in DTM, due to the small number of GCP andthe low resolution for such a scale of study, produce an overestimation of theflooded area in some places. A systematic integration of diverse information isadvisable, in this case, in order to obtain a valid representation of the simulatedevents. Altimetric data measured on the ground through GPS or remote sensing ,in relation to the same portion of territory, represent the optimal solution to theinitial objective.

Acknowledgments

The authors would like to tank GEOCART company, for his contribution to therealisation of the present study and Eng. Mendicino for the possibility of usingTAHR software.

References

[1] Jemma F.,. 'JERS-1 Japanese Earth Resources Satellite', MondoGIS n.4Dicembre 1996[2] Sole A. & Valanzano A., 1996. 'Digital terrain modelling'. Chapter 7, GIS inHydrology, eds. V.P. Singh & M. Florentine, Kluwer Academic Publishers, pp.77J-7P4.[3] Jenson S. K., Domingue J. O., 'Extracting Topographic Structure from DigitalElevation Data for Geographic Information System Analysis', PhotogrammetricEngineering and Remote Sensing, vol.54, 1988.[4] Mark D. M., ' Network models in geomorphology'. In Anderson M.G.,Modelling Geomorphological Systems, John Wiley&Sons Ltd., 1988.[5] Mark D. M., 1984, 'Automated detection of drainage network from digitalelevation models', Cartographica, vol.21,.[6] Tarboton D.G., Bras R. L., Rodriguez-Iturbe I., 'On the extraction of channelnetwork from digital elevation data', Hydrological Processes, vol.5, 1991.Mechanics Publications, Southampton, pp. 121-130.[7] Sole A., Valanzano A., Crisci A., Scuccimarra V., 1999. A GeographicInformation System for a Decision Support System aimed at Water ResourceManagement, Flood Forecasting and Risk Zoning. Proc. of the InternationalConference on Water, Environmental, Ecology, Socio-Economics, and HealthEngineering, Seoul, Korea, in press[8] Marks D., Dozier J., Frew J., 1984. 'Automated basin delineation from digitalelevation data', Geoprocessing, vol.12,.



[9] Beven, K.J. and Kirkby, M.J., 1979. A Physically Based VariableContributing Area Model of Basin Hydrology. Hydrol. Sci. Bull., 24(1), pp. 43-69.[10] Dffl, Mike 11 User and Reference Manual, 1995[11] Maidment, D.R., Olivera, F., Calver, A., Eatherall, A. and Fraczek, W.,1996. Unit Hydrograph Derived from Spatially Distributed Velocity Field.Hydrological Processes, 10, pp. 831-844.[12] Mendicino, G., 1998. A CIS-Based Program for the Analysis of AreasSubjected to Hydrological Risk. In: GIS Technologies and their EnvironmentalApplications, C.A. BrebbiaandP. Pascolo (eds.), Computational[13] Sole A. & Crisci A., 1997a. Dam break flood forecasting and risk zoning,Proc. Of II International DHI Software User Conference, Helsingor (DK)


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