Journal of Hydrology 407 (2011) 41–57

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Journal of Hydrology

journal homepage: www.elsevier .com/ locate / jhydrol

Satellite multispectral data for improved floodplain roughness modelling

Giovanni Forzieri a,b,⇑, Massimo Degetto c, Maurizio Righetti c, Fabio Castelli a, Federico Preti d

a Dipartimento di Ingegneria Civile e Ambientale, University of Florence, Italyb Dipartimento di Scienze della Terra, University of Florence, Italyc Centro Universitario per la Difesa Idrogeologica per la Difesa dell’Ambiente Montano, University of Trento, Italyd Dipartimento di Ingegneria Agraria e Forestale, University of Florence, Italy

a r t i c l e i n f o

Article history:Received 8 November 2010Received in revised form 25 March 2011Accepted 2 July 2011Available online 21 July 2011This manuscript was handled byKonstantine P. Georgakakos, Editor-in-Chief,with the assistance of Ehab A. Meselhe,Associate Editor

Keywords:ManningHydrodynamicsRiparian vegetationRemote sensing

0022-1694/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.jhydrol.2011.07.009

⇑ Corresponding author. Address: Dipartimento di Sof Florence, Via E. Fermi 2, Arcetri 50125, Firenze, Italy+39 055 2055317.

E-mail address: giovanni.forzieri@unifi.it (G. Forzie

s u m m a r y

Riparian vegetation plays a crucial role on affecting the floodplain hydraulic roughness, which in turnsignificantly influences the dynamics of flood waves. This paper explores the potential accuracies ofretrieving vegetation hydrodynamic parameters through satellite multispectral data. The method isfocused on estimation of vegetation height (hg) and flexural rigidity (MEI) for herbaceous patterns andof plant density (M), tree height (h), stem diameter (Ds), crown base height (cbh) and crown diameter(Dc) of high-forest (hf) and coppice (cop) consociations for arboreal and shrub patterns. The method isorganized in four sequential steps: (1) classification procedure of riparian corridor; (2) land cover-basedPrincipal Component Analysis of spectral channels; (3) explorative analysis of correlation structurebetween principal components and biomechanical properties and (4) model identification/estimation/validation for floodplain roughness parameterization. To capture the hydrodynamic impacts of stiff/flex-ible vegetation, a GIS hydrodynamic model has been coupled with a flow resistance external routine thatestimates the hydraulic roughness by using simulated water stages and the remote sensing-derivedhydrodynamic parameters. The procedure is tested along a 3-km reach of the Avisio river (Trentino AltoAdige, Italy) by comparing extended field surveys and a synchronous SPOT-5 multispectral imageacquired on 28/08/2004. Results showed significant correlation values between spectral-derivedinformation and hydrodynamic parameters. Predictive models provided high coefficients of determina-tion, especially for mixed arboreal and shrub land covers. The generated structural parameter mapsrepresent spatially explicit data layers that can be used as inputs to hydrodynamic models to analyzeflow resistance effects in different submergence conditions of vegetation. The hydraulic modelling resultsshowed that the new method is able to provide accurate hydraulic output data and to enhance theroughness estimation up to 73% with respect to a traditional look-up table approach, with higherimprovements for low flow conditions and over shrub covers.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

1.1. Hydraulic roughness remote sensing

Understanding flood events and predicting flood prone areasand potential damage have become an important issue in rivermanagement. Considerable efforts have been made in recent yearsto develop 2D and 3D hydrodynamic models that accurately simu-late overbank flow patterns and predict extreme flood water levelsin rivers and floodplains (Bates et al., 1992; Stoesser et al., 2003;Nicholas and McLelland, 2004; Baptist et al., 2005). In addition tothe surface topography, the hydrodynamic roughness of thefloodplain is the key input parameter of modelling flood wave

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ri).

dynamics: an increased floodplain roughness reduces the floodwave celerity and causes water levels to rise during peak dis-charges of the river.

Flow resistance in floodplains depends on many factors such asthe channel bed materials, the cross-sectional area and its varia-tion, obstructions, degree of meandering and vegetation (Chow,1959; Arcement and Schneider, 1989). Riparian vegetation may be-come the dominant factor in high flow conditions, with importanteffects on the dynamics of flood waves. The vegetation-inducedflow resistance is usually characterized by significant spatio-tem-poral variability. This is due both to natural processes such as vary-ing river stages, interplay of river geomorphology and vegetationdynamics and to anthropogenic processes like river maintenance,structural and infrastructural interventions (Darby, 1999; Sellinand van Beesten, 2002).

Conventional approaches to quantify riparian roughness usereference values (Chow, 1959) to select a tabled roughnesscoefficient n in Manning form, including all the sources of flow

42 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

resistance, such as bottom material and irregularity, vegetationand sinuosity. Other visual inspection approaches use photographsof floodplain segments, where n values have been verified, to aid inassigning n values to similar floodplains (Arcement and Schneider,1989). More accurate estimates of hydrodynamic resistanceof riparian vegetation need to consider a momentum balanceapproach, defining the hydrodynamic impacts of vegetation. Sucheffects can be evaluated either by means of laboratory measure-ments or by means of measurements on real plants sampled inthe field (Petryk and Bosmajian, 1975; Armanini et al., 2005;Righetti, 2008). The systematic detection, identification and assess-ment of riparian vegetation using conventional field sampling is of-ten unachievable, as these techniques are time-consuming andexpensive. As in many other environmental monitoring problems,remote sensing may provide unprecedented mapping capabilities,especially for areas poorly monitored or inaccessible (e.g., Forzieriet al., 2008).

Several methods for floodplain roughness remote sensing focuson classification processes of riparian covers (e.g., Bork and Su,2007; Geerling et al., 2007; Antonarakis et al., 2009). Classifica-tion-derived hydraulic roughness maps can be obtained by assign-ing a reference value of the roughness in Manning form by meansof a look-up table to each identified land cover (Forzieri et al.,2010). Despite the fact that classification-derived hydraulic rough-ness maps represent explicit layers useful for hydraulic modelling,the use of a look-up table leads to an undesirable loss of within-class variation showing homogeneous flow resistance within theecotope mapping units (Straatsma and Middelkoop, 2007): thevegetation resistance to flow can often depend on the flow condi-tions itself, such as flow depth, velocity and plant flexibility. Flood-plain roughness modelling based on hydrodynamic modelsrepresents a more accurate method than classification-derivedhydraulic roughness maps for flow resistance parameterization.Although various schemes have been developed to represent veg-etation roughness in two – and three-dimensional hydrodynamicmodels and numerous flume experiments have been reported todetermine vegetation roughness, there is still a considerable lackof quantitative estimates of vegetation patterns and inherentroughness of real floodplains (Darby, 1999; Fischer-Antze et al.,2001; Stoesser et al., 2003; Nicholas and McLelland, 2004). There-fore, a detailed monitoring of riparian vegetation structures isessential for accurately modelling the hydrodynamics of sub-merged floodplains.

In the last years Light Detection and Ranging (LiDAR) data arebecoming a widely used tool in environmental applications thanksto their capacity to capture the 3D structure of monitored surfaces.LiDAR raw data (point cloud) can provide information about verti-cal vegetation structure and biomechanical properties such as veg-etation density, biomass, vegetation height, stem diameter, densityand crown base height, which are not directly recovered by opticalsensors and therefore have been used in floodplain roughnessparameterization (e.g., Cobby et al., 2001; Straatsma and Middel-koop, 2007; Straatsma and Baptist, 2008; Forzieri et al., 2009, inpress-b). The cited studies are strongly dependent on LiDAR acqui-sition opportunities and they can provide only limited informationabout the exposure of vegetation to floods and the subsequent veg-etation dynamics that would require acquisition and processing oflong time series of remote sensing data. This represents a still opencritical issue since it is well known that seasonal variation andmanagement of floodplains lead to a high spatiotemporal variationof vegetation structural characteristics and inherent roughnesspatterns (Baptist et al., 2004; Van Stokkom et al., 2005).

Satellite multi-spectral images, due to their relative low revisit-ing time and their high spatial resolution, are able to capture thespatio-temporal dynamics of riparian vegetation reflectance. Po-tential links between reflectance and biomechanical properties

would suggest the key role of satellite spectral-based methods toexplain hydraulic roughness dynamics (Forzieri et al., in press).Several multispectral-based methodologies have been proposedfor leaf area index (LAI) estimation, most of which focused onvegetation spectral indices and image spectral enhancements (i.e.Colombo et al., 2003; Wulder et al., 1998). Despite the encouragingperformances obtained in the above mentioned studies there is agreater need to progress in hydraulic roughness monitoring to ex-tend the set of remote sensing-derived vegetation hydrodynamicproperties useful in flow resistance models. It is especially essen-tial to calibrate satellite data on the basis of field measurements,in order to build up a fast procedure able to estimate vegetationparameters that are more relevant for a vegetation hydraulic resis-tance evaluation.

This paper presents a new method of automated floodplainroughness parameterization by assessing the vegetation spectralproperties as predictors of hydrodynamic parameters. It deliversa spatially distributed roughness parameterization by using aSPOT-5 multispectral data and by focusing on estimation of a setof biomechanical parameters for herbaceous, arboreal and shrubpatterns. The methodology is calibrated and tested using synchro-nous ground surveys and satellite multispectral data. A GIS basedhydrodynamic model, coupled with an external routine for assess-ing the vegetation hydrodynamic impact, was also applied with theresults of this innovative method that was also compared with atraditional roughness parameterization approach.

1.2. Vegetative roughness

In floodplains vegetation frequently provides most of the flowresistance, as afore-mentioned. As a result, considerable researchhas been carried out to develop resistance laws for channels withrigid and flexible stems (e.g., Petryk and Bosmajian, 1975;Thompson and Roberson, 1976; Pasche and Rouvé, 1985; Kouwenand Fathi-Moghadam, 2000; Righetti and Armanini, 2002; Jarvela,2004). In the following lines, we synthesize some of the most usedroughness formulations for arboreal, shrub and herbaceous vegeta-tion patterns in different submergence conditions.

1.2.1. Arboreal vegetationA majority of research on vegetative flow resistance is based on

theories and experiments with rigid cylindrical elements. Petrykand Bosmajian (1975) presented a model to estimate the Man-ning’s roughness coefficient (n) for emergent vegetation and negli-gible bed resistance as a function of hydraulic radius andvegetation density as:

n ¼ R2=3

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiCd �

PNi¼1Ai

2g �XL

s; ð1Þ

where Cd is the drag coefficient, N is the number of plants along achannel reach having length L (m), Ai is the frontal area of the i-thplant along the reach (m2), X is the cross-sectional area of the flow(m2), R is the hydraulic radius (m) and g is the acceleration of grav-ity (m/s2). If we suppose that all the plants are similar biomechan-ical characteristics in the analyzed ground tile, we can neglect thesubscript i in the equation and

PNi¼1Ai can be calculated using the

Arcement and Schneider (1989) method, that is,PNi¼1Ai

XL¼ hW M � DS; ð2Þ

where M is the relative plant density (#/m2), DS is the tree diameter(m) and hW is the water depth (m). If the crowns are partially orcompletely submerged, additive terms, such as crown leaves andstems, have to be considered in

PNi¼1Ai in order to assess the total

footprint area (Kouwen and Fathi-Moghadam, 2000; Righetti,

G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57 43

2008). For example the total frontal area of a single tree with a sub-merged crown can be estimated by combining a rectangular shape(obtained from the stem projection) and an elliptical or circular area(from the crown projection) reduced by the coefficient d (Degettoand Righetti, 2005). For instance, if an elliptical crown shape is con-sidered, having main diameters DC and (h-cbh), then the referenceplant front area can be estimated as

A ¼ DS � cbhþ dp4

DCðh� cbhÞ; ð3Þ

where h is the tree height (m), cbh is the crown base height (m), Dc

is the crown diameter (m) of an individual plant and d representsthe crown vacuum index of the plant, mainly depending on the veg-etation type and leafy conditions.

1.2.2. Shrub vegetationFreeman et al. (2000) proposed a method to estimate the

hydraulic roughness of flexible shrub as a function of the stiffnessof the plants growing in the channels, the depth, shear velocity andhydraulic radius of the channel, plant density and frontal area ofthe plant obstructing the flow. The authors defined two differentformulations for partially and completely (hw > 0.8h) submergedshrubs, respectively:

n ¼ 3:487 � 10�5 ES � AS

q � A�1 � U2�

!0:15

� M � A�1� �0:166 � t

U�R

� ��0:622

� R2=3S1=2

U�

!; ð4Þ

n ¼ 0:183ES � AS

q � A1 � U2�

!0:183

� hhW

� �0:243

� ðM � A1Þ0:273

� tU�R

� �0:115

� R2=3S1=2

U�

!; ð5Þ

where ES is the modulus of plant stiffness (N/m2), h is the shrubheight (m), q is the fluid density (kg/m2), AS is the total cross-sec-tional area of all of the stems of an individual plant measured ath/4 (m2), A1 is the frontal area of an individual plant blocking flow(m2), A�1 is the net submerged frontal area of the a partially sub-merged plant (m2), U� is the shear velocity (m/s), M is relative plantdensity (#/m2), m is the fluid dynamic viscosity (m2/sec) and S is thebed slope (dimensionless). Research performed in laboratory and infield indicated that the stiffness modulus can be estimated from therelationship (Freeman et al., 2000):

ES ¼ 7:648 � 106ðh=DSÞ þ 2:174 � 104ðh=DSÞ2 þ 1:809 � 103ðh=DSÞ3:ð6Þ

A and A�1 can be approximated by the equivalent rectangulararea of blockage:

A1 ¼ ðh� cbhÞ � DC ; ð7Þ

A�1 ¼ ðhW � cbhÞ � DC ; ð8Þ

while As can be calculated as

As ¼ pDS

2

� �2

� nS; ð9Þ

where nS is the number of stems.

1.2.3. Herbaceous vegetationSeveral researchers have shown that resistance to flow in

channels with herbaceous vegetation can be based on a relativeroughness approach similar to the widely accepted resistance rela-tionships developed for rigid roughness in pipes and open channelswith the roughness height related to stem properties. Following

Kouwen (1988), the significant stem properties are the stem den-sity M and flexural rigidity in bending, given by J = EI, where E isthe stem’s modulus of elasticity, and I is the stem area’s secondmoment of inertia. Kouwen and his co-workers proposed the com-bined effect of the product of M, E, and I as a single quantifiableparameter called MEI, able to completely characterize the hydrody-namic behaviour of aquatic herbaceous plants. Kouwen and Li(1980) showed that the roughness height varies as a function ofthe amount of drag exerted by the flow:

k ¼ 0:14hMEIs

� �0:25

� 1h

� �" #1:59

; ð10Þ

where h is the local height of the strips (m); and s is local boundaryshear stress (N/m2). To assess the vegetation roughness the value ofk so estimated is then substituted in the following equation:

n ¼ h1=6Wffiffiffiffiffiffi

8gp

� aþ b � log10Rk

� �� � !

; ð11Þ

where a and b are two numerical coefficients depend on the ratiobetween the shear velocity and its critical value.

The three above mentioned methods have been used by theauthors to evaluate the hydrodynamic resistance of arboreal,shrub, and herbaceous plants.

2. Methods

2.1. Field and remote sensing data collection

2.1.1. Study areaThe mouth of Avisio river (Trentino Alto Adige, Italy) is used as a

study area (Fig. 1). It consists in a 3-km reach which includes thefloodplain from the clearway bridge (SS12 – Brennero), in themunicipality of Lavis, to the confluence of the Avisio in the AdigeRiver.

The total surface area is �1041,000 m2, whose 54% is made upof vegetated lands, including herbaceous, shrub and arboreal pat-terns. The mouth of the Avisio river is located in the Adige valley,characterized by a semi-continental climate, with rigid and snowywinters and hot sultry summers. The study area can be divided inthree main zones, which present different hydro-geomorphologicfeatures (river cross section, river bed grain size and channel pla-nimetry): the upstream area between the clearway bridge (SS12– Brennero) and the bank lateral openings (Fig. 1A); the centralarea between the bank lateral openings and the gravel caves(Fig. 1B) and the downstream area between the gravel caves andthe confluence in the Adige River (Fig. 1C).

The upstream area presents an average cross section as shownin Fig. 1A. On the right-hand side, near the (hydrographic) banknext to the embankment, there is an inactive reach of the riverbed at ground-level plane completely covered by meadows andsparse shrub-arboreal patterns with piecewise high tree density.This zone is separated from the thalweg by a steep unstable bankscarcely vegetated and subjected to frequent erosion and sliding.In the central part of the cross section the mono-channel planim-etry is prevalent, where gravel mainly represents the bed grainsize.Gravel, accumulated in bars parallel to the longitudinal river direc-tion, allows for the evolution of pioneer vegetation species (shrubvegetation) with high survival, diffusion and growing rate capacity.The left (hydrographic) bank, with a smoother slope, is mainly cov-ered by lush shrub vegetation up to the base of the embankment,where a small vegetation-free segment corresponding to the roadtrack is distinguishable at the ground level.

The central area presents an average cross section as shown inFig. 1B. Two extended meadows are well recognizable near the

Fig. 1. Study area located along a 3 km stretch of the Avisio River (Trentino Alto Adige, Italy). The study area can be divided in three main zones which present differentaverage hydro-geomorphologic features (river cross section, surface grain size and channel planimetry): the upstream area between the clearway bridge (SS12 – Brennero)and the bank lateral openings (1A); the central area between the bank lateral openings and the gravel caves (1B) and the downstream area between the gravel caves and theconfluence in the Adige River (1C). Field surveys over mixed arboreal, shrub and herbaceous patterns are displayed in red, blue and yellow polygons, respectively. (Forinterpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

44 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

banks and specularly placed along the longitudinal direction of theriver. The grain size is prevalently represented by sands and silt.The central part of the cross section (low water bed) shows somestable sub-vertical banks that are subjected to erosion only duringflood events. The prevalent multi-channel path delimits many sed-imentary deposit islands where shrub vegetation can grow undis-turbed from normal flooding. Field survey shows that thecolonization process proceeds quickly from downstream to up-stream and plant species tend to develop quite large shoots andtrunks.

Fig. 1C shows the enlargement of the cross section of the conflu-ence zone and the multi-channel path. In the final reach, sandmainly represents the river bed grainsize. The vegetation is madeup of high-trunk forest species at the evolutionary stage and withhomogeneous density. Herbaceous and shrub fields are exclusivelypresent in sparse marginal areas near to the banks.

2.1.2. DatasetTo characterize the reflectance properties of riparian vegetation

we used a SPOT-5 image acquired on 28 August, 2004. The originalimage is composed of a 20 � 20-m resolution short-wave band(wavelength range from 1.58–1.75 lm) and three 10 � 10�m reso-lution visible/near-infrared bands (wavelength ranges: blue 0.43–0.47 lm, red 0.61–0.68 lm and near infrared 0.78–0.89 lm). Thedata provider applied a pan-sharpening procedure on the originalchannels and downscaled short-wave and visible/near-infraredbands at 10 � 10-m and 5 � 5-m spatial resolutions, respectively.Then, the short-wave band was resampled at the same spatialresolution of the finer visible/near-infrared channels. The SPOT-5

image is geo-referenced in UTM/WGS-84 projection using groundcontrol points (GCPs) with a rational polynomial coefficients(RPC) model and a 1-m orthophoto as base image. The geo-refer-enced SPOT-5 image has been atmospherically corrected throughthe ENVI module FLAASH Model. The parameters for ENVI FLAASHinclude entering information about the sensor and scene andselecting an atmosphere and aerosol model.

The biomechanical properties of the different species of the rec-ognized vegetation, related to hydrodynamic resistance, were mea-sured through an extensive field campaign in August 2004. Thesynchronism between field and remote sensing data represents akey factor for quantifying effective correlations between vegeta-tion spectral properties and hydrodynamic characteristics. Fieldsurveys indicate three main vegetation classes: (1) mixed arboreal;(2) shrub and (3) herbaceous. The dominant species are: willows(salix spp.), false acacia (Rubinia pseudoacacia), black poplar (Populusnigra) ailanthus (altissima) for arboreal plants; elder (Sambucus ni-gra), cornel tree (Cornus mas), alder (Alnus viridis) for shrubs; nettle(urticaceae) for herbaceous plants. Over the study area we selecteda set of sample plots with homogeneous vegetation characteristics.Table 1 lists the number of sample plots and related cumulatedareas for each land cover. The planimetric description of such areasand their vegetation were carried out using GPS and forestryinstrumentation (hypsometer with lens, dendrometric tripod).Over each homogeneous sample plot we measured a set of bio-properties: vegetation height and MEI index obtained through onsite ‘‘board test’’ (Kouwen, 1988) for herbaceous patterns; typesof consociations (high forest or coppice), plant density, vegetationheight, crown diameter, crown base height, stem diameter for

G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57 45

arboreal and shrub patterns (see Table 2 for the notation). Each ob-served parameter is representative of the entire homogeneoussample plot. We collected 22 sample plots for the mixed arborealclass: 12 of them were made up of both high-forest and coppiceconsociations, 5 only with high-forest consociations and 5 only

Table 1Ground-monitored land covers, number of related sample plots for the 5-class set.Reference Manning’s roughness coefficients on reference values (Chow, 1959). Notethat all the 22 sampled areas of mixed arboreal are made up of both high-forest andcoppice consociations with the exception of five sample plot with only high-forestconsociations and further five plots with only coppice consociations.

Land cover ID code # Sampleplot

Area (m2) Reference Manning’sroughnesscoefficient (s/m1/3)

Mixed arboreal 1 22(�) 92,679 0.055Shrub 2 6 9802 0.1Herbaceous 3 4 2508 0.025Bare soil 4 – – 0.03Water 5 – – 0.03

Table 2Ground-measured vegetation hydrodynamic parameters.

Notation Vegetation parameter Unit

Field dataMcop Plant density (coppice) (#/mq)hcop Tree height (coppice) (m)cbhcop Crown base height (coppice) (m)Dscop Cumulated stem diameter (coppice) (m)Dccop Crown diameter (coppice) (m)Mhf Plant density (high forest) (#/mq)hhf Tree height (high forest) (m)cbhhf Crown base height (high forest) (m)Dshf Average stem diameter (high forest) (m)Dchf Crown diameter (high forest) (m)MEI Flexural rigidity of vegetation elements per unit area (Nm2)hg Vegetation height (m)

Fig. 2. Flow diagram of the proposed floo

with coppice consociations. This means that we have 17 sampleplots both for high-forest and coppice consociations.

Topographic information was acquired through mobile GPS(Aschtech�) and double-band frequency stationary GPS (Topcon�).Topographic data was georepherenced and transformed into GISvector format. We used daily-averaged discharge data collectedfrom 1995 to 2004 at the Lavis gauge station, 3 km upstream fromthe outlet, in order to quantify the discharge regime in the studyreach.

2.2. Remote sensing of hydrodynamic properties

The purpose of this study is to assess the potential linkage be-tween spectral-derived information and vegetation hydrodynamicparameters for floodplain roughness parameterization. We focusedon the estimation of vegetation height (hg) and flexural rigidity(MEI) for herbaceous patterns and of plant density (M) and height(h), stem diameter (Ds), crown base height (cbh) and crown diam-eter (Dc) of high forest and coppice consociations for arboreal andshrub patterns. These are the main biomechanical parametersneeded to estimate the hydraulic behaviour of different kinds ofplants subject to a flow. High forest and coppice consociationsare indicated in the next paragraphs with the subscripts hf andcop, respectively.

The researched potential relationships can be synthesized as inthe following equation:

yi ¼ fiðch1; . . . ; chnÞ; ð12Þ

where yi represents the analyzed vegetation biomechanical prop-erty related to hydrodynamic resistance (dependent variable), chj

are the spectral channels (predictor variables) and fi is the unknownanalytical function.

Fig. 2 (see left panel) shows the flow diagram of the proposedapproach. The method is organized in four sequential steps: (1)classification procedure, to remotely discriminate the mainriparian land covers; (2) land cover-based Principal Component

dplain roughness parameterization.

46 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

Analysis of spectral channels, to reduce redundancy in the originalspectral information; (3) explorative analysis to quantify the corre-lation structure between the performed principal components andthe hydrodynamic properties and (4) model identification, estima-tion and validation for the hydrodynamic parameter assessment.Ground measurements were used for the training/testing phasesof the process. In the following sections, the procedure steps areexplained in more detail.

2.2.1. Classification procedureIn order to remotely discriminate the main land cover classes

we performed a riparian vegetation mapping of the monitored bio-tope. We classified the pixel surface reflectance of four SPOT mul-tispectral bands on a 5-class set of land covers including: mixedarboreal, shrub, herbaceous, bare soil and water (Table 1). Thedataset was classified by a pixel-oriented maximum likelihood(ML) classifier with Gaussian class-conditional distributions(Richards and Jia, 2006). The classification was calibrated andtested by using sampled areas and ground control points (GCP),respectively, acquired during the field campaign. The classificationaccuracy was assessed in terms of Overall Classification Accuracy(OCA) and Conditional Kappa Statistics (kcond) defined throughthe following equations:

OCA ¼Pr

i¼1xii

N; ð13Þ

kcond ¼Nxii � xiþ � xþi

Nxiþ � xiþ � xþi; ð14Þ

where r is the number of columns (and rows) in the confusion ma-trix (5 in our case), xii is entry (i,i) of the confusion matrix, xi+ and x+j

are the marginal totals of row i and column j, respectively, and N isthe total number of observations (Richards and Jia, 2006).

2.2.2. Data transformationTo prevent problems of multi-collinearity between the indepen-

dent variables (chi) in Eq. (12), we performed a data transformationbased on Principal Component Analysis (PCA) of the original mul-tispectral bands (Wilks, 2006).

In order to investigate how the mixed vegetation patterns caninfluence the explainable variance, and consequently the remotesensing capacity in capturing the biomechanical properties, weperformed the Principal Component Analysis on four differentschemes of land cover groups. Referring to the riparian vegetationmap obtained in the previous step, the PCA was applied by singu-larly extracting each land cover (ECO transformation), by aggregat-ing all the vegetation classes (ECOVEG transformation), by mergingall the vegetation classes in addition to bare soil (ECOSOIL transfor-mation) and by merging all the land covers (EXECO transforma-tion). The obtained principal components were normalized in the0–1 range (N-PCj). The relationship (12) can be transformed as:

Yi ¼ wi½ðN-PC1Þ; . . . ; ðN-PC4Þ�; ð15Þ

where wi are the new unknown analytical functions.

2.2.3. Explorative analysisTo analyze the potential remote sensing capacity of capturing

biomechanical characteristics in the riverine ecosystem we explorethe correlation between ground-measured vegetation hydro-dynamic properties (yi) and normalized principal components(N-PCj) in terms of Spearman rank coefficient (rk) and significancelevel (p-value). The correlation structure was analyzed by extract-ing over each sample plot ground measurements and the averagevalues of the normalized principal components. Such an explor-ative analysis was conducted by differencing investigated hydro-dynamic parameters, vegetation class (based on the riparian

vegetation map), normalized principal component and remotesensing data transformation. High Spearman ranks drive the iden-tification of the w analytical functions.

2.2.4. Model identification, estimation and verificationTo select the predictor variables (N-PCj) to include in Eq. (15)

we performed a screening procedure of the correlation analysisresults. Potential useful predictor variables have to verify thecriteria (rk > 0.6) \ (p-value < 0.05). The Akaike criteria (AIC), inaddition to the visual interpretation of the obtained scatter plots(yi versus N-PCj), identified for each hydrodynamic parameter thew analytical model among a set of potential predictive models.The Akaike procedure finds the most appropriate model by strikinga balance between goodness of fit, as reflected in log-likelihoods,and a penalty that increases with the number of fitted parameters.

To calibrate/validate the detected predictive models we dividedthe ground-monitored sample plots in independent training andtesting sets. The training set is used to estimate the model param-eters, while the testing set serves to assess the model reliability.Model accuracy was assessed by means of the Generic Analysisof Variance (ANOVA). Then the predictive models we followed,were spatialized through a pixel-approach over the whole flood-plain, by providing spatial maps of the monitored remote sens-ing-derived hydrodynamic parameters (RSHP).

2.3. Hydaulic modelling

In order to assess the effect of the remote sensing-derivedhydrodynamic parameters on water levels, the HEC-RAS (http://www.hec.usace.army) river modelling software was employed.HEC-RAS requires, in addition to surface topography and dischargevalues, a hydraulic roughness map in the form of land use-based‘‘constant’’ Manning values, such as a classification-derivedhydraulic roughness map. This is not the case of vegetation, forwhich the plant drag and related roughness coefficient is stronglydependent on flow depth (see Section 1.2.). This dependence im-plies the implementation of an iterative approach to the estimationof roughness coefficient, with a local flow depth provision and sub-sequent correction.

To capture the hydrodynamic impacts of stiff/flexible vegeta-tion, the hydraulic model was coupled with a flow resistance exter-nal routine (FRER) that iteratively estimates the hydraulicroughness by using water conditions (WS) and aggregated hydro-dynamic parameters (AHP) (Fig. 2, hydraulic modelling).

FRER needs as input a segmented floodplain where each identi-fied object is treated as a different roughness patch with specifichydrodynamic properties. We applied the segmentation watershedalgorithm (Vincent and Soille, 1991) to the first spectral compo-nent. This algorithm allows to detect intra-class regions with sim-ilar spectral properties and consequently expected similarhydrodynamic characteristics. A land cover code was assigned toeach resulting segment through overlap with the riparian vegeta-tion map. The consistency of the segmentation results were as-sessed by visual inspection of the ortophoto image and fieldsurveys. Over each patch we derived the aggregated hydrodynamicparameters (AHP) by spatial average of RSHP. WS (water levels andshear velocity) were directly derived from the HEC-RAS simula-tions through the HEC-geoRAS toolbar that creates the topographyand the hydraulic roughness map as inputs for the river modelling.For the initial hydraulic simulation we used the classification-de-rived hydraulic roughness map (nmod,0), as first tentative flow resis-tance map, obtained by assigning Manning’s roughness coefficientsby means of the look-up Table 1 (based on reference values fromChow, 1959), to each identified land cover. FRER applies for eachsegmented patch the flow resistance equation according to its landcover code by using AHP and WS as inputs and provides an updated

Table 3

Fig. 3. The performed 5-class riparian vegetation map, including mixed arboreal, shrub, herbaceous patterns, bare soil and water bodies.

G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57 47

roughness map related to the expected vegetation hydrodynamicimpacts. HEC-RAS and FRER iteratively run until that the RootMean Square Error satisfies the threshold criteria RMSE(nmod,i �nmod,i�1) 6 10�4 (where i is the number of the iteration). The finalhydraulic roughness maps (nmod,j) and the related floodplain delin-eations (flp) are then provided in GIS format for the 2-years (Q2)and the 500-years (Q500) return period peak flows equal to450 m3/s and 1030 m3/s, respectively. Note that some auxiliaryground-monitored information was also imported in FRER tointegrate the remote sensing-derived vegetation parameters (e.g.,CD and d).

We compared the resulting floodplain roughness parameteriza-tion (nmod,j) and the traditional roughness parameterization ap-proach (nmod,0) with the observed manning (nobs) over the set ofground-monitored sample areas. The observed manning is calcu-lated by using the flow resistance equations with ground measure-ments of vegetation and water levels obtained with the finalhydraulic simulation. Model reliability is quantified, for the twoinvestigated flow conditions and for all the vegetation patterns,in terms of RMSE(nobs � nmod, 0), RMSE(nobs � nmod,j) and DRMSE.The DRMSE gives a percentage value of improvement obtainedwith the proposed approach and it is calculated as:

DRMSE ¼ 100 � 1� RMSEðnobs � nmod;jÞRMSEðnobs � nmod;0Þ

� �: ð16Þ

Classification results in terms Conditional kappa statistics. Areas of the total numberof training sets and number of ground control point (GCP) are also shown in the table.

Land cover Classnumber

Trainingset (m2)

Testing set(num. GCP)

Conditional kappastatistics

Mixed arboreal 1 118,000 86 0.51Shrub 2 6570 27 0.22Herbaceous 3 27,615 65 0.66Bare soil 4 29,950 103 0.78Water 5 13,798 21 0.77

3. Results

3.1. Riparian vegetation mapping

Despite the spectral overlapping between some of the moni-tored land covers, the proposed riparian vegetation mappingclearly distinguishes mixed arboreal, herbaceous, bare soil and

water by providing encouraging conditional kappa statistics values(0.51, 0.66, 0.78, 0.77 respectively) and overall classification accu-racy of OCA = 69.21%. Modest reliability was assessed for the shrubclass (Kappa Conditional Statistics = 0.22) (Table 3). The confusionmatrix (not shown) demonstrates that such errors are mainly dueto similar spectral signatures between mixed arboreal and shrubclasses.

Remote sensing image classification based on hydraulic rough-ness of land covers is a difficult task, due to the strong similaritiesamong some of the considered classes and the related overlappingin the feature space (see shrubs and mixed arboreal classes). Giventhe reduced number of discriminant features (four spectral chan-nels), we retain that the obtained performances are an acceptablecompromise between classification accuracy, data availability (re-mote and field surveys) and computation time. More sophisticatedapproaches for riparian vegetation mapping using remote sensingdata fusion techniques could enhance the classification process ofthe riverine ecosystem (e.g., Forzieri et al., 2010).

The obtained riparian vegetation map allows an accurate delin-eation of the application domains of the flow resistance modelsthat are suitable to different vegetation conditions including flexi-ble/stiff vegetation patterns (Fig. 3). The following results focus on

48 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

the estimation of the hydrodynamic vegetation parameters to beused as inputs into distributed flow resistance models throughobjective computational methods rather than the subjective nvalues (traditional look-up table approaches).

3.2. Principal component analysis

Explained variances (EV) of the performed normalized principalcomponents are listed in table 4 for the explored data transforma-tions (ECO, ECOVEG, ECOSOIL and EXECO). For the ECO transforma-tion the explained variances are calculated separately for each landcover class (the PCA was singularly applied on each land cover),while for the remaining transformations the tabled results referredto the merged vegetation patterns. The first eigenvectors exhibitthe most data variability, especially for the ECOSOIL and EXECOtransformations (EV(N-PC1) = 80.46% and =95.65%, respectively).The ECOVEG transformation spreads most of the variance on

Table 4Explained variances of each normalized principal components (N-PCi) on the fourtested data transformations (ECO, ECOVEG, ECOSOIL and EXECO).

Principalcomponents

Explained variance (%)

ECO ECOVEG ECOSOIL EXECO

Mixedarboreal

Shrub Herbaceous

N-PC1 57.86 88.81 77 62.36 80.46 95.65N-PC2 34.03 6.74 15.46 30.57 16.36 3.77N-PC3 7.61 3.81 6.93 6.69 2.98 0.4N-PC4 0.48 0.62 0.58 0.36 0.18 0.16

Fig. 4. First principal components (N-PC1) of each data transformations: ECO, ECOVEG, ECright color-legend). No data pixels are made up of cells which are excluded in the princshow a zoomed area near to the mouth of the river. (For interpretation of the references to

N-PC1 and N-PC2 (EV(N-PC1) = 62.36% and EV(N-PC2) = 30.57%).Since lots of the explained variances of the four original spectralbands is captured by the first principal component, the use of onlythe first principal component can provide a good approximation.The ECO transformation shows a significant inter-class variabilityof the explained variance values. Multispectral values of mixedarboreal, shrub and herbaceous classes were differently synthe-sized in N-PC1 (EV(N-PC1) = 57.86%, 88.81% and 77%, respectively).The second principal component appears to significantly contrib-ute to the explaination of the original multispectral informationon mixed arboreal patterns and indicates a more complex spectralsignature of high forest plants than shrub and herbaceous patterns(EV(N-PC2) = 34.03% for mixed arboreal class, EV(N-PC2) = 6.74%for shrubs and EV(N-PC2) = 15.46% for herbaceous patterns).

Fig. 4 shows the first principal components of the four datatransformations yielding a set of spatial patterns describing re-gions where vegetation patterns tend to be highly correlated. Asit is evident, the four tested data transformations present signifi-cant differences in synthesizing the multispectral informationand in the related spatial pattern delineation. Such variable perfor-mances are expected to result in different capacity of remotesensing data manipulations in explaining the hydrodynamicsproperties. Note that class-dependent spatial patterns in ECON-PC1, despite the land cover-based normalization, are due to thevariability of the tails in the spectral distributions (not shown).

3.3. Correlation structure

The correlation values between hydrodynamic properties andspectral-derived information are shown separately for land coversand performed data transformations in Table 5. The highest values

OSOIL and EXECO. No data are shown in blue color (corresponding to 0 value on theipal component analysis of ECOVEG and ECOSOIL transformations. The inset boxes

colour in this figure legend, the reader is referred to the web version of this article.)

Table 5Spearman ranks (rk) and related significance level (p-value) for each monitored hydrodynamic vegetation parameter and for each principal component of the explored data transformations. Correlations structure is analyzed on thedifferent riparian land covers. For each hydrodynamic parameter the highest values which verified the (rk > 0.6) \ (p-value < 0.05) screening procedure are shown in bold.

Land cover Hydrodynamicparameter

ECO ECOVEG ECOSOIL EXECO

N-PC1 N-PC2 N-PC3 N-PC4 N-PC1 N-PC2 N-PC3 N-PC4 N-PC1 N-PC2 N-PC3 N-PC4 N-PC1 N-PC2 N-PC3 N-PC4

rk Mixed arboreal Mcop �0.208 �0.48 0.331 �0.227 �0.431 0.032 �0.202 �0.167 �0.401 �0.178 �0.258 �0.007 0.476 0.366 �0.341 0.184hcop 0.009 0.078 0.082 0.073 0.133 0.07 0.068 0.19 0.06 0.068 0.009 0.023 �0.119 �0.047 0.102 �0.002cbhcop �0.106 �0.052 0.239 0.081 �0.077 �0.013 �0.245 0.107 �0.12 �0.114 �0.342 0.122 0.059 0.088 �0.234 0.289Dscop �0.173 �0.086 0.259 0.063 �0.165 0.179 �0.139 0.275 �0.231 0.009 �0.222 0.164 0.136 0.219 �0.077 0.225Dccop �0.158 0.08 0.114 0.009 0.002 0.122 �0.03 0.067 �0.059 0.097 �0.094 �0.043 0.008 0.106 0.017 0.041Mhf 0.367 0.251 �0.235 0.16 0.437 �0.075 0.153 0.274 0.471 0.074 0.248 0.115 �0.398 �0.395 0.174 �0.067hhf 0.537 0.455 �0.317 0.304 0.783 �0.048 0.533 0.519 0.76 0.159 0.567 0.115 �0.714 �0.701 0.578 �0.336cbhhf 0.507 0.365 �0.409 0.257 0.768 �0.051 0.436 0.467 0.755 0.178 0.487 0.065 �0.716 �0.68 0.508 �0.273Dshf 0.495 0.426 �0.26 0.314 0.695 �0.089 0.466 0.496 0.677 0.063 0.468 0.141 �0.627 �0.669 0.494 �0.287Dchf 0.487 0.378 �0.245 0.289 0.671 �0.131 0.435 0.444 0.664 �0.008 0.419 0.114 �0.599 �0.672 0.457 �0.272

Shrub MEI �0.2 �0.028 0.142 �0.085 �0.371 �0.028 0.2 �0.2 �0.6 0.314 0.771 �0.428 0.085 0.542 0.485 �0.942hg �0.202 0.173 0.028 0.115 �0.463 �0.115 0.057 �0.376 �0.753 0.318 0.666 �0.637 0.115 0.608 0.318 �0.927Mcop 0.6 �0.485 0.6 �0.657 0.771 0.2 0.428 0.828 1 �0.6 �0.142 0.942 �0.257 �0.942 0.142 0.542hcop 0.371 �0.314 0.485 �0.6 0.6 0.257 0.371 0.714 0.942 �0.485 �0.257 0.885 �0.2 �0.828 0.028 0.6cbhcop 0.142 0.028 0.485 �0.542 0.257 0.257 0.371 0.485 0.657 �0.371 �0.142 0.6 �0.085 �0.6 �0.028 0.314Dscop 0.771 �0.428 0.314 �0.257 0.6 �0.2 0.028 0.542 0.828 �0.771 �0.314 0.657 0.028 �0.885 �0.142 0.6Dccop 0.371 �0.314 0.485 �0.6 0.6 0.257 0.371 0.714 0.942 �0.485 �0.257 0.885 �0.2 �0.828 0.028 0.6

Herbaceous MEI �0.4 0.8 0.4 0.4 �0.8 �0.8 �0.6 �0.8 �0.4 �0.4 0.4 0.4 0.4 �0.2 �0.8 �0.2hg �0.4 0.8 0.4 0.4 �0.8 �0.8 �0.6 �0.8 �0.4 �0.4 0.4 0.4 0.4 �0.2 �0.8 �0.2

p-Value Mixed arboreal Mcop 0.351 0.023 0.132 0.308 0.045 0.886 0.365 0.456 0.064 0.425 0.245 0.973 0.024 0.093 0.12 0.41hcop 0.965 0.726 0.715 0.745 0.553 0.753 0.761 0.396 0.788 0.761 0.965 0.918 0.595 0.835 0.648 0.989cbhcop 0.638 0.815 0.283 0.719 0.73 0.953 0.27 0.634 0.591 0.613 0.118 0.588 0.792 0.696 0.293 0.192Dscop 0.44 0.7 0.243 0.778 0.461 0.425 0.536 0.214 0.3 0.965 0.32 0.465 0.543 0.326 0.732 0.312Dccop 0.481 0.723 0.613 0.965 0.989 0.588 0.894 0.765 0.792 0.667 0.674 0.846 0.969 0.638 0.938 0.854Mhf 0.092 0.258 0.291 0.474 0.041 0.738 0.494 0.216 0.026 0.742 0.265 0.609 0.066 0.068 0.437 0.765hhf 0.009 0.032 0.149 0.167 0 0.831 0.01 0.013 0 0.478 0.005 0.609 0 0 0.004 0.125cbhhf 0.016 0.094 0.058 0.247 0 0.819 0.042 0.028 0 0.425 0.021 0.772 0 0 0.015 0.218Dshf 0.018 0.048 0.242 0.154 0 0.693 0.028 0.018 0 0.778 0.027 0.53 0.001 0 0.019 0.193Dchf 0.021 0.082 0.27 0.192 0 0.56 0.042 0.038 0 0.969 0.051 0.613 0.003 0 0.032 0.22

Shrub MEI 0.713 1 0.802 0.919 0.497 1 0.713 0.713 0.241 0.563 0.102 0.419 0.919 0.297 0.355 0.016hg 0.7 0.733 0.983 0.844 0.372 0.844 0.933 0.466 0.105 0.544 0.161 0.2 0.844 0.211 0.544 0.022Mcop 0.241 0.355 0.241 0.175 0.102 0.713 0.419 0.058 0.002 0.241 0.802 0.016 0.658 0.016 0.802 0.297hcop 0.497 0.563 0.355 0.241 0.241 0.658 0.497 0.136 0.016 0.355 0.658 0.033 0.713 0.058 1 0.241cbhcop 0.802 1 0.355 0.297 0.658 0.658 0.497 0.355 0.175 0.497 0.802 0.241 0.919 0.241 1 0.563Dscop 0.102 0.419 0.563 0.658 0.241 0.713 1 0.297 0.058 0.102 0.563 0.175 1 0.033 0.802 0.241Dccop 0.497 0.563 0.355 0.241 0.241 0.658 0.497 0.136 0.016 0.355 0.658 0.033 0.713 0.058 1 0.241

Herbaceous MEI 0.75 0.333 0.75 0.75 0.333 0.333 0.416 0.333 0.75 0.75 0.75 0.75 0.75 0.916 0.333 0.916hg 0.75 0.333 0.75 0.75 0.333 0.333 0.416 0.333 0.75 0.75 0.75 0.75 0.75 0.916 0.333 0.916

G.Forzieri

etal./Journal

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ydrology407

(2011)41–

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50 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

for each hydrodynamic parameter that verifies the screening pro-cedure (rk > 0.6) \ (p-value < 0.05) are shown in bold. High correla-tions and low p-values highlight the data transformations, whichmaximize the spectral information in explaining biomechanicalproperties.

The ECO transformation generally shows low correlations andmake evident that a single-class based principal component analy-sis tends to excessively reduce the investigated spectral range byconsequently affecting the capacity of remote sensing in predictingthe spatial heterogeneity of biomechanical properties.

EXECO, by including all the land covers in the multispectralspace transformation, shows higher correlations than ECO. Despitethe encouraging results, the inclusion of no-vegetated areas (espe-cially water bodies) in the Principal Component Analysis repre-sents a potential noise source. Water tends to significantly affectthe spectral transformation of the whole study area due to its spec-tral signature markedly different from vegetated patterns.

The highest correlations were produced with the ECOVEG andECOSOIL transformations, especially on mixed arboreal and shrub

Fig. 5. Explorative analysis between principal component values (on x-axis) and monitoreonly for tree height (hhf), crown base height (cbhhf), stem diameter (Dshf) and crown diasample area.

categories. Mixed arboreal class showed significant correlations(rk) between hhf, cbhhf, Dshf and Dchf and N-PC1 obtained by per-forming the ECOVEG transformation (0.783, 0.768, 0.695 and0.671, respectively). This suggests that the aggregation of all thevegetation classes allows for the extraction of the spectral signa-tures whose spectral ranges (and related linear transformation)have proved to explain the spatial heterogeneity of biomechanicalproperties on mixed arboreal patterns.

Shrub class showed significant correlations between Mcop, hcop,Dscop and Dccop and N-PC1 obtained by performing the ECOSOILtransformation (1, 0.942, 0.828 and 0.942, respectively). Significantcorrelation values were also carried out with N-PC2. Shrub patternsare mainly characterized by lower biomass than mixed arborealareas. The inclusion of the bare soil class in ECOSOIL provides adata transformation that is inevitably affected by the spectral sig-nature of the bare soil, which plays a crucial role in enlarging theinvestigated spectral range and in directing the eigenvector tothe spectral space of scarcely vegetated zones. Such aggregatedPCA shows the enhanced capacity of spectral-derived remote

d vegetation parameters of the mixed arboreal class (on y-axis). Graphics are shownmeter (Dchf) which present more significant results. Each black circle represents a

G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57 51

sensing data of explaining the hydrodynamic properties of shrubpatterns (compare values of ECOSOIL and ECOVEG on shrub class).

Scatter plots between the principal components (on the x-axis)and the selected biomechanical parameters needed to estimate thehydrodynamic properties (on the y-axis) for mixed arboreal andshrub classes are shown in Figs. 5 and 6, respectively. Resultssuggest, especially concerning the mixed arboreal category, anon-linear relationship between biomechanical parameters andN-PC1. A decreasing range of spectral variability with the increas-ing of the number of the principal component is recognizable(N-PC4 values are always below 0.5). This is due to a small bare soilarea near to the bridge on the hydrograph left with very low reflec-tance values. This zone influences the data transformation processby introducing an anomalous stretch of multispectral signaturesevident in the last principal components.

Based on the correlation analysis we selected the hydrodynamicparameters which appear to be well explained by the spectralproperties: regarding the mixed arboreal class we focused on hhf,cbhhf, Dshf and Dchf parameters related to N-PC1 of ECOVEG whereasfor the shrub class on Mcop, hcop, Dscop and Dccop parameters relatedto N-PC1 and N-PC4 of ECOSOIL (values in bold, see Table 5).

Fig. 6. As Fig. 5, for shrub class parameters including: plant density (Mcop)

3.4. Predictive models

A subset of 11 and 4 sample plots (training set) have been usedfor calibration of the wi analytical functions (see Eq. (13)), leavingthe remaining 6 and 2 ground-monitored areas out of the datasetfor subsequent validation (testing set) for mixed arboreal (high-forest) and shrub (coppice) classes, respectively.

The AIC criteria demonstrates that 3-parametric power laws,with a single remote sensing spectral-derived input (representedby N-PC1) captures the investigated vegetation hydrodynamicproperties. The optimization procedure based on the Akaike meth-od finds the best fitting curve by selecting the most discriminantindependent variables. As expected, the selected predictor vari-ables (first principal components) explain most of the data vari-ability. The resulting mathematical relationships are in thefollowing form:

yi ¼ Ci þ Ai � ðNPC1ÞBi ; ð17Þ

where N-PC1 is the normalized principal component using the ECO-VEG and ECOSOIL transformations for mixed arboreal and shrubclasses, respectively, whereas Ai, Bi and Ci are the unknown

, tree height (hcop), stem diameter (Dscop) and crown diameter (Dccop).

52 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

coefficients calibrated through non-linear least square method.Fig. 7 shows observed versus modelled parameters, on x-axis andy-axis, correspondingly. Calibration and validation sample plotsare shown in black and gray circles, respectively and the black lineindicates the regression line 1:1. Significant variability in predictioncapability is recognizable. Table 6 lists the estimated coefficientsand the Generic Analysis of Variance (ANOVA) for the found fittingcurves for each vegetation parameter. Results were encouraging,especially in terms of high coefficient of determination (R2) forhhf, cbhhf, Dshf and Mcop (0.732, 0.738, 0.619 and 0.587, respectively)and mainly in terms of sum of squares of the regression (SSR) androot mean square error (RMSE). The adjusted coefficient of determi-nation (adjR2), given the reduced number of sample plots for thetesting set, showed mainly low reliability values.

Vegetation parameter maps are shown separately in Figs. 8 and9 for mixed arboreal and shrub patterns, respectively. The resultsshowed reasonable floodplain roughness parameterization andhighlighted the capacity of the proposed approach to assess biome-chanical properties useful in hydraulic flow resistance models.

Misclassification derived from the riparian vegetation mappingfor shrub and mixed arboreal classes can affect the floodplainroughness parameterization through: (1) delineation of the spatialdomains of stiff/flexible vegetation hydrodynamic models, and (2)aggregation process of land cover pixels in the principal compo-nent analysis. The first-type effect introduces potential changesin the spatialization procedure of the vegetation hydrodynamic

Fig. 7. Comparison of predicted (on y-axis) and observed parameters (on x-axis). Calibratthe black line indicates the regression line 1:1.

Table 6Estimated model coefficients (A–C) and ANOVA results are listed for the selected hydrodregression (SSR), root mean square error (RMSE), coefficient of determination (R2) and adj

Land cover Hydrodynamic parameter A B

Mixed arboreal hhf 12.476 4.074cbhhf 3.957 3.46Dshf 0.225 3.88Dchf 3.503 5.614

Shrub Mcop 9.996 15.179hcop 19.059 10.763Dscop 0.787 12.027Dccop 5.808 7.782

parameters but does not affect directly the satellite-based estima-tion of hydrodynamic parameters. The second-type effect deter-mines coarse aggregation of pixels for the principal componentanalysis in the ECO transformation and can theoretically affectthe vegetation characterization. However, misclassification due tothe spectral overlapping of land cover classes does not introducesignificant changes in the intra-class variability of the principalcomponents and the consequent correlation structure betweenspectral properties and hydrodynamic parameters. Furthermore,classification errors for shrub and mixed arboreal classes do notinfluence ECOVEG, ECOSOIL and EXECO transformations. AsECOVEG and ECOSOIL are the selected data manipulations for theproposed remote sensing approach, the afore-mentioned mis-classification does not affect the reliability of the vegetation hydro-dynamic parameter estimation.

3.5. Homogeneous biomechanical parameters patterns

Homogeneous vegetation patches, identified through the wa-tershed algorithm, appear to be coherent with ground-observedareas over most of the study region. Fig. 10 shows the providedfloodplain segmentation. Qualitative field surveys mainly con-firmed the segmented vegetation pattern delineation performedvia image processing and demonstrated the homogeneity of theidentified patches based on dominant species and average

ion and validation sample plots are shown in black and grey circles, respectively and

ynamic parameters. Model reliability is quantified in terms of sum of squares of theusted coefficient of determination (adjR2).

C SSR RMSE R2 adjR2

1.784 41.985 1.597 0.732 0.4650.856 3.982 0.485 0.738 0.4760.03 0.01 0.033 0.619 0.2381.486 2.197 0.591 0.511 0.022

0.131 0.017 0.077 0.587 1.8250.999 0.394 0.501 0.439 2.1210.048 0.004 0.052 0.443 2.1120.57 0.086 0.235 0.439 2.121

Fig. 8. Structural parameter maps obtained for the mixed arboreal class with high forest consociation: tree height (hhf), crown base height (cbhhf), average stem diameter(Dshf) and crown diameter (Dchf).

Fig. 9. Structural parameter maps obtained for the shrub class with coppice consociation: plant density (Mcop), tree height (hcop), cumulated stem diameter (Dscop) and crowndiameter (Dccop).

G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57 53

Fig. 10. Segmentation results and some homogeneous patch in situ verified.

54 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

biomechanical parameters. Over-segmented regions are also evi-dent especially near to the mouth of the river, where the multi-lay-ered forest canopies of ecotope units, characterized by a complexplant morphology with overlapping crowns, are more difficult todistinguish from spectral information. However, this does not com-promise the floodplain parameterization but results in a more de-tailed spatial heterogeneity of the AHP maps and, consequently, inlonger computing times in the hydraulic simulations than for po-tential under-segmented scenarios. This approach improves thespatial characterization of the riparian hydraulic roughness (usu-ally defined through land cover-based approaches) and the ob-tained aggregated maps represent functional inputs for the testedhydraulic model.

Fig. 11. Convergence analysis of the flow resistance external routine in quantifiedin terms RMSE(nmod,i � nmod,i�1), on y-axis, versus iterative hydraulic simulations(number of runs), on x-axis, for the reference discharges Q2 (left panel) and Q500

(right panel). Land-cover based convergences for mixed arboreal (CL1), shrubs (CL2)and both vegetation covers (CL1 + CL2) are displayed in dotted, point andcontinuous lines, respectively.

3.6. Hydrodynamic modelling convergence

According to the results of the correlation analysis and the mod-el identification, we implemented FRER by using the flow resis-tance equations for mixed arboreal and shrub classes and bymaintaining for all the other land covers, including herbaceous pat-terns, bare soil and water, the same lookup table values as the clas-sification-derived hydraulic roughness map (nmod,0).

The flow resistance external routine performances aresynthesized in Fig. 11. Fig. 11 shows the convergence ofRMSE(nmod,i � nmod,i�1), on y-axis, versus iterative hydraulic simu-lations (number of runs, i), on x-axis, for the reference dischargesQ2 (left panel) and Q500 (right panel). Land-cover based conver-gences for mixed arboreal (CL1), shrub (CL2) and both vegetationcovers (CL1 + CL2) are displayed in dotted, point and continuouslines, respectively. High RMSE values indicate large discrepanciesbetween the input Manning’s roughness coefficients, used in thehydraulic simulation, and the expected estimated Manning’sroughness coefficients. The high RMSE values at the first run point

out significant approximations on floodplain hydraulic roughnessparameterization based on the classification-derived hydraulicroughness map with respect to the more accurate flow resistancemodels, especially for low water levels (RMSE(CL1 + CL2)|Q2 �0.045 s/m1/3 and RMSE(CL1 + CL2)|Q500 � 0.035 s/m1/3). The refer-ence value of Manning’s roughness coefficient derived from theused look-up table becomes more approximate for low flow condi-tions (Q2). Improvements in hydraulic roughness assessment

G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57 55

through the implemented external routine are particularly evidenton shrub class (RMSE(CL2)|Q2 � 0.07 s/m1/3 and RMSE(CL2)|Q500 �0.055 s/m1/3 at the first run step). The threshold criteria(RMSE(nmod,i � nmod,i�1) < 10�4) is reached after four iterations forboth the explored flow conditions. Then the hydraulic roughnessmaps obtained at the fourth hydraulic simulation are consideredsufficiently representative. The convergence processes present sig-nificant differences between the two investigated flow conditions.In the Q2 case, the process converges very quickly asymptotically.In the Q500 case the process presents a shrub class-induced flexpoint between the iterations 2–3 after which the process goes upwith the expected asymptotic behaviour (just truncated at theforth iteration in the figure). For high flow discharge, variationsin floodplain roughness generally induce a higher variability inwater depths. These oscillations of the water table clearly repre-sent an important discontinuity factor in the manning estimation

Table 7Improvements in hydraulic roughness estimation using the proposed remote sensing ap(nmod,0). Both the floodplain roughness are compared with the observed Manning’s roughdischarges (Q2 and Q500).

Discharge Accuracy Mixed arboreal (Class 1)

Q2 RMSE(nobs � nmod,0) (s/m1/3) 0.0289RMSE(nobs � nmod,4) (s/m1/3) 0.017DRMSE (%) 41

Q500 RMSE(nobs � nmod,0) (s/m1/3) 0.0263RMSE(nobs � nmod,4) (s/m1/3) 0.019DRMSE (%) 27

Fig. 12. Model results for the Avisio floodplain at a 1030 m3/s discharge (Q500). (A) Hdifference in Manning’s roughness coefficient values between nmod,4|Q500 and classificat(flp) obtained by using nmod,4|Q500; (D) difference in water levels between floodplain de

for shrub vegetation by conditioning the partially or completelysubmergence conditions. This probably affects the convergencevelocity of the iterative numerical method on Q500 discharge byresulting in a non-monotonic function of the RMSE gradient forshrub class, made evident by the afore-mentioned flex point.

3.7. Floodplain roughness parameterization

Table 7 synthesizes the obtained improvements on hydraulicroughness estimation using the proposed remote sensing approach(nmod,4) with respect to the classification-derived hydraulic rough-ness map (nmod,0). Both floodplain roughness maps are comparedto the observed Manning’s roughness coefficient (nobs) in termsof RMSE and DRMSE for both the flow discharges. The manningestimation is enhanced by 27–73% with higher improvements forlow flow conditions (Q2) especially over shrub covers. Despite

proach (nmod,4) with respect to the classification-derived hydraulic roughness mapness coefficient (nobs) in terms of RMSE(nmod,j � nmod,0) and DRMSE for both the flow

Shrub (Class 2) Herbaceous (Class 3) Class 1–2–3

0.0611 0.0165 0.03380.0166 0.0165 0.01773 – 500.0491 0.0097 0.02950.0205 0.0097 0.019258 – 35

ydraulic roughness map obtained after four iterations with Q500 (nmod,4|Q500); (B)ion-derived hydraulic roughness map (nmod,0); (C) 500-years floodplain delineationlineations performed by using nmod,4|Q500 and nmod,0.

56 G. Forzieri et al. / Journal of Hydrology 407 (2011) 41–57

the fact that herbaceous roughness was considered with constantmanning (derived from look-up table) in the hydraulic model, asthe MEI was not accurately derived from remote sensing data, wealso listed in the table RMSE values estimated for herbaceouspatterns. As evident, the look-up table approach shows lowerRMSE on herbaceous cover than on mixed and shrub classes(RMSE(nobs � nmod,0)|Q2 = 0.0165 s/m1/3 and RMSE(nobs � nmod,0)|Q500 = 0.097 s/m1/3). Note that the roughness estimation on mead-ows was performed over 2 sample areas (only two of the four mon-itored areas were inundated). More extended field surveys overherbaceous patterns could address a more accurate quantificationof the approximation of reference values over such a vegetationclass.

Fig. 12 shows the floodplain roughness parameterizationobtained after four iterations with Q500 (nmod,4|Q500) (A), thedifference in Manning’s roughness coefficient values betweennmod,4|Q500 and the classification-derived hydraulic roughnessmap (nmod,0) (B), the Q500 floodplain delineation obtained by usingnmod,4|Q500 (C), the difference in water levels between floodplaindelineations performed by using nmod,4|Q500 and nmod,0 (D). Themanning distribution (Fig. 12A) presents a significant spatial heter-ogeneity resulting from the different vegetation hydrodynamicparameters and from the different river bed topography whichaffects the submergence conditions. The classification-derivedhydraulic roughness map tends to overestimate the hydrodynamicimpacts of vegetation especially on shrub class by showing preva-lently negative values on Fig. 12B. Despite high values of Manning’sroughness coefficients (as the used look-up Table 1) are usuallyconsidered more cautionary, the classification-derived hydraulicroughness map can only partially describe the flow resistance.The 500-years floodplain (Fig. 12C) is completely contained withinthe lateral banks. The river cross section enlargement near to themouth of the Adige reduces the water depth and avoids overbankflows. The mouth of the Avisio, in the central zone, results in beingonly partially submerged by leaving a small island entirely coveredby mixed arboreal vegetation. Fig. 12D presents, as expected, sig-nificantly lower water levels than the hydraulic modelling per-formed using tabled Manning’s roughness coefficients (negativevalues). More narrow river cross sections (in the upstream reach)show high variations in water depths (��0.6 m), while those wider(in the downstream reach) present more reduced water stage oscil-lations (up to �0.3 m). Then, in the study case, the use of a hydrau-lic roughness map derived from Table 1 could lead to incorrectinterpretations, as for example the overestimation of water levelsand consequent potential false alert states. Such results high-lighted the importance of an accurate and reliable vegetationparameterization for hydraulic purposes evident. The high variabil-ity of modelled water stages along the river reach shows a variablecontribution of hydrodynamic impact of vegetation on the flowresistance, mainly depending on the stiff/flexible plant type, arealextensions of riparian vegetation patterns and submergenceconditions.

4. Conclusions

Earth observation techniques may contribute significantly togenerate an accurate input for new generations of high-resolutionhydrodynamic models. This paper describes a new method to de-rive hydrodynamically relevant surface characteristics based on sa-tellite multispectral data by focusing on estimation of vegetationheight (hg) and flexural rigidity (MEI) for herbaceous patternsand of plant density (M) and height (h), stem diameter (Ds), crownbase height (cbh) and crown diameter (Dc) of high forest and cop-pice consociations for arboreal and shrub patterns.

The multispectral remote sensing data allowed for the classifi-cation of the investigated floodplain. The selection of the main

hydraulic classes inside the riparian corridor mainly influencesthe representativeness of the provided riparian vegetation map-ping and consequently plays an important role on extracting spec-tral-derived information to estimate hydrodynamic parameters.Despite the complex riparian setting characterized by several landcover classes with similar spectral signature, the classification pro-cedure produced encouraging accuracies, with a low number ofspectrally separate classes, by discriminating five different landcovers: mixed arboreal, shrubs, herbaceous patterns, bare soiland water. As a result, high-resolution satellite data such asSPOT-5 data represent a suitable device to remotely capture theland cover variability inside riparian corridors.

Results point out the correlations between spectral-derivedinformation and biomechanical parameters. In particular, the landcover grouping over which to apply data transformation proce-dures – such as the used Principal Component Analysis – providedsignificantly different correlations. Strong spectral-dependencesare obtained for some of the investigated biomechanical propertiesespecially for high forest consociations in mixed arboreal patternsand for coppice consociations in shrub class. The correlation struc-ture drives the identification of the analytical function for quanti-fying vegetation hydrodynamic parameters.

The proposed predictive models, by providing a significantreduction of the field surveys limiting them only over the calibra-tion/validation sites, are shown to be able to capture structuralvegetation parameters by means of simple tri-parametric powerlaws depending on the first principal component as independentvariable. Such predictive models represent a powerful tool for riv-er-scale modelling, allowing time and cost-efficient vegetationmonitoring over large areas. Despite forest scenes with a very highvegetation density, the proposed remote sensing method explainswith good reliability the biomechanical properties needed for gen-erating an accurate floodplain roughness parameterization forhydraulic purposes. The provided spatial layers of hydrodynamicproperties, (without considering understory vegetation), tend tounderestimate the vegetation resistance. Further improvementscould be carried out quantifying under-wood patterns that can sig-nificantly influence flow resistance estimation, especially on mixedriparian formation.

The flow resistance external routine improves the estimation ofvegetation hydrodynamic impacts. Such an approach overcomesthe undesirable loss of within-class variation that we can certainlyfind in classification-derived hydraulic roughness maps. The vege-tation hydrodynamic maps are also able to well describe the equiv-alent Manning’s roughness coefficient over the tested sample areaswith higher improvements for low flow conditions especially overshrub covers. More accurate predictions of water levels usingfloodplain roughness parameterization, derived from satellite mul-tispectral information suggest the efficiency of the proposed re-mote sensing approach to quantify the flow resistance onvegetated floodplains. Investigations into additional study areas,under different vegetation types and river geo-morphological con-ditions, would be important to fully test the proposed experimen-tal method and to drive operational stage applications.

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