Water volume variations in Lake Izabal (Guatemala) from in situ measurements and ENVISAT Radar...

Journal of Hydrology 382 (2010) 34–48

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Journal of Hydrology

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Water volume variations in Lake Izabal (Guatemala) from in situ measurementsand ENVISAT Radar Altimeter (RA-2) and Advanced Synthetic Aperture Radar(ASAR) data products

Camilo Medina *, Jesus Gomez-Enri 1, Jose Juan Alonso 2, Pilar Villares 2

Applied Physics Department, CASEM, University of Cadiz, SpainAvenida República Saharaui, s/n, 11510 Puerto Real, Cadiz, Spain

a r t i c l e i n f o

Article history:Received 12 June 2008Received in revised form 15 September2009Accepted 12 December 2009

This manuscript was handled byK. Georgakakos, Editor-in-Chief, with theassistance of Emmanouil N. Anagnostou,Associate Editor

Keywords:Volume variationsSARAltimetryShoreline extraction

0022-1694/$ - see front matter � 2009 Elsevier B.V. Adoi:10.1016/j.jhydrol.2009.12.016

* Corresponding author. Address: Applied Physicssity of Cadiz, Spain. Tel.: +34 956016071.

E-mail addresses: [email protected] (C. M(J. Gomez-Enri), [email protected] (J.J. A(P. Villares).

1 Tel.: +34 956016595.2 Tel.: +34 956016070.

s u m m a r y

The water storage variations in lakes affect their physical, chemical and biological processes. Besides, thewater masses of these waterbodies reflect the balance of the rainfall and evaporation with surface andground waters. The lake’s water volume is estimated combining water level variations with accuratebathymetry and shore topography maps. Lake Izabal is the largest waterbody of Guatemala (approxi-mately 673.29 km2). Its water volume has been estimated in the past but the volume variations are stillunknown. The lake water level variations are monitored in situ since 2004, but regrettably accurate infor-mation about the bathymetry and shore topography is not available. The main objective of this study wasto make a first estimate of the Lake Izabal water volume variations. To do this, we combined level vari-ations and inundated area variations. The lack of accurate bathymetry and topography maps was over-came by using inundated area variations in the assumption that every level change reflects aninundated area response, depending on bathymetry and shore topography. The level variations were esti-mated from an in situ moored gauged in the lake and from the ENVISAT Radar Altimeter (RA-2). The inun-dated area variations were obtained using 12 ENVISAT Advanced Synthetic Aperture Radar (ASAR)images. Prior to the area estimates the lake’s shoreline was extracted making use of a chain of existingimage processing algorithms. The correlation analysis between in situ lake levels and inundated areavariations yielded a correlation coefficient of 0.9. The volume variations of the lake were then estimatedon the dates of the acquired SAR images. Then, a group of rating curves relating level, area and volumewere developed. In order to extend the area and volume estimates to the whole study time period (Feb-ruary 2003 to December 2006) the lake levels from the RA-2 dataset were entered to the rating curves.The estimated water volume variations of Lake Izabal range between 8271.2 � 106 m3 (17th December of2005) and 9018.15 � 106 m3 (15th July of 2006) in agreement with the most recent estimation of the LakeIzabal water volume (8300 � 106 m3). Regarding the inundated area variations, they range between672.44 � 106 m2 (17th December of 2005) and 677.2 � 106 m2 (15th July of 2006) in agreement withthe Guatemalan government information (673.29 km2). The water volume, inundated area and waterlevel fluctuations of the Lake Izabal show a strong seasonal signal with high stages during the rainy sea-son (May–November) and low stages during the dry seasons (December–April). These results give valu-able information for the Lake Izabal management and strengthen the idea of using remote sensing as apowerful and cheap complementary tool for hydrologic purposes.

� 2009 Elsevier B.V. All rights reserved.

ll rights reserved.

Department, CASEM, Univer-

edina), [email protected]), [email protected]

Introduction

Fresh inland waters support the terrestrial ecosystems in whichmost of human activities are based. These waters can be globallyfound as ice caps, snow, underground (as soil moisture and under-ground reservoirs) and surface waters (as rivers, lakes, and wet-lands). Natural lakes are an essential fresh waters subjectbecause they show complex relationships between the atmo-sphere, surface and underground waters, responding to weatherconditions. Besides, those relationships are affected by upstream

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C. Medina et al. / Journal of Hydrology 382 (2010) 34–48 35

land/water uses for agriculture, industry and/or human consump-tion (Crétaux and Birkett, 2006). Accurate information about theextent of fresh waterbodies is important for their proper manage-ment (Chiara et al., 2006; French et al., 2006). It is important toknow the relationship between the water mass stored in the lakeand the amount of water feeding the lake because it determinesthe lake water exchange (residence time). Physical, chemical andbiological processes in the lake ecosystem are affected by the watermass stored in the lakes bed, its variations and its residence time(Ambrosetti et al., 2003).

Fluctuations in the water mass stored in lakes reflect the varia-tions of rainfall and evaporation, and could be used to study thecombined impact of climate change and water resource manage-ment (Mercier et al., 2002; Crétaux and Birkett, 2006). The volumevariations of a lake are driven by the water inputs/outputs. Thewater inputs are the sum of direct rainfall, surface runoff of thetributaries and underground water inputs, whereas the outputsare the joint effect of evaporation rates, ground seepage, and sur-face outflow (Crétaux and Birkett, 2006). Besides, the water in-puts/outputs are modulated by anthropogenic activities such asirrigation.

These water mass fluctuations are perceived as level and sur-face extent variations depending on the bathymetry and topogra-phy of the lake shore. According to this, lakes water volumevariations can be measured indirectly by estimating the variationsof the lake level and surface extent. According to Dellepiane et al.(2004), the most common surface estimation method is based onthe use of visual photo-interpretation of high resolution aerialimages. This methodology is composed of four steps: (i) acquisitionof images from airborne sensors; (ii) geometric correction ofsuch data; (iii) in situ comparison using Ground Control Points;and (iv) estimation of the surface extent. Nevertheless, thismethodology needs financial investments, which cannot usuallybe afforded by development countries. However, the use of acheaper data source such as remote sensing data might help tomitigate that limitation.

The lake shoreline detection is one of the main aspects in themonitoring of lake surface extent. According to Dellepiane et al.(2004), the extraction of the shoreline has been an important re-search issue in the last years and many algorithms have beendeveloped on the basis of different image processing methodolo-gies. Optical/radar remote sensing imagery has been successfullyused to evaluate water/land boundaries such as coastal zonesand lakes shorelines (Gupta and Banerji, 1985; Heremans et al.,2003; Tan et al., 2005; Fleming, 2005; Chiara et al., 2006; Frenchet al., 2006). All the aforementioned references agree that the Syn-thetic Aperture Radar (SAR henceforth) is particularly appropriatefor shoreline detection mainly thanks to its capability to acquireimages independently of daylight and regardless the weather con-ditions (cloud covering).

The work presented here is focused on the largest lake in Gua-temala: Lake Izabal. The role of this waterbody in the country isquite important from both ecological and social points of view.However, its physical features are still poorly known (URL, 2002).Recently, a study focused on its water level fluctuations using thecombination of ENVISAT Radar Altimeter (RA-2) and in situ lake le-vel heights has been done (Medina et al., 2008). This study pointedout some of the benefits of using remote sensing data in the mon-itoring of this waterbody. They analyzed the seasonal pattern ofvariations in the lake level and the interaction with regional cli-mate change.

The work presented here continues the research interest for thiswaterbody. The main objective is to estimate the Lake Izabal watervolume variations. The lake water volume variations provide infor-mation related with the lake’s catchment water mass balance,since they reflect water inputs/outputs rates. The limiting factor

to achieve the objective was the absence of accurate informationabout the topography of the surroundings and/or the lakesbathymetry. To overcome this, we combined the level fluctuationsderived from in situ and RA-2 measurements and the inundatedarea variations obtained by extracting the lake shoreline usingENVISAT Advanced Synthetic Aperture Radar (ASAR) images. Toobtain the inundated area variations from SAR images, we applieda chain of image processing algorithms. Then, a validation exercisewas achieved by comparing the estimated inundated area varia-tions with simultaneous in situ level changes (Chiara et al.,2006). Rating curves relating the Lake Izabal volume/area/level fea-tures were developed in order to estimate a time series of volumeand area variations for the study period. To our knowledge, this isthe first time the Lake Izabal water volume variations areestimated.

The paper is organized as follows: Section ‘‘Lake Izabal descrip-tion” gives a brief description of the Lake Izabal settings. Section‘‘Datasets” describes the three datasets used: in situ and RA-2 lakelevel heights and SAR images. The methodology for the Lake Izabalshoreline extraction used to estimate the lake surface extent fromthe set of SAR images is presented in Section ‘‘Lake Izabal shorelineextraction”. The water volume variations estimated through thelake levels and surface extent are outlined in ‘‘Results” section fol-lowed by discussion section by a discussion of the obtained results.The conclusions are presented in last section.

Lake Izabal description

The Lake Izabal lies in the North-eastern side of Guatemala(Fig. 1A), close to the Caribbean Sea shore (�40 km) at 15�300Nand 89�100W (Fig. 1B). It is considered a lowlands waterbody be-cause its surface rises approximately 10 m above the mean Carib-bean Sea level (Basterrechea, 1993; AMASURLI [GuatemalanAuthority for Sustainable Management of Lake Izabal Basin],2006). It has an irregular elongated shape with a West–East orien-tation (Fig. 1B). The lake has 18 permanent tributaries being thePolochic River the most plentiful (more than 70% of the total waterinput). The superficial outlet of the lake is located in the North-eastern part (the Dulce River) (Basterrechea, 1993). Table 1 sum-marizes the main physical characteristics of Lake Izabal. Sincethe limit between Lake Izabal and Dulce River is vague; we consid-ered the limit at the North latitude of 15�38051.140 0 (Fig. 1B). TheLake Izabal ecosystem assembles a wide variety of flora and faunaspecies, including several rare and endangered wildlife species. Italso plays an important role in regulating the hydrologic cycleand the local climate. In addition the lake is involved in manysocioeconomic activities (maritime transport, food security, tour-ism, fishery, mining industry, and surrounding villages’ liveli-hoods) which are of great importance to Guatemala (URL, 2002).

The water inputs into a lake depend on several factors: (i) catch-ment size and geomorphology; (ii) climate conditions over thecatchment; and (iii) human activities affecting the basin hydrology(Birkett, 2000; Mercier et al., 2002). Regarding (i), the Lake Izabalcatchment, its extension yields a relationship of 10:1 with the lakesurface (Table 1) (Basterrechea, 1993; AMASURLI, 2006). Thecatchment has a broken topography with altitudes ranging from10 m to more than 2600 m above the Caribbean Sea mean level.Concerning (ii), according to Thattai et al. (2003) local weatherconditions are influenced by the topography. In the highest alti-tudes of the basin, rainfall reaches 3500 mm/yr being the meantemperature 15 �C. In the lake’s surface the values are 1500 mm/yr(rainfall) and 31 �C (mean temperature) (AMASURLI, 2006).According to AMASURLI (2006) the relationship between the an-nual precipitation and the potential evapotranspiration yield a po-sitive balance of 11015 � 106 m3/yr which is converted whether in

Table 1Physical characteristics of Lake Izabal and its catchment.

Parameter Measure Source

CatchmentCatchment area 6862 km2 Basterrechea (1993)Altitudes max./min. 2600/10 m AMASURLI (2006)Mean annual rainfall 2000 mm URL (2005)Mean annual

temperature25.2 �C URL (2005)

Surface tributaries 18 Permanentcurrents

Basterrechea (1993)

LakeLake maximum depth 17 m AMASURLI (2006)Lake mean depth 12 m AMASURLI (2006)Mean surface area 644.8 km2 MAGA-CATIE-ESPREDE

(2001)Mean water volume 8300 � 106 m3 OTECBIO (2003)Residence time 6 months Brinson (1976)

Fig. 1. (A) Localization of Guatemala in Central America, and situation of the Lake Izabal within the country. (B) Map of the Lake Izabal showing the tributary rivers, the maintributary (Polochic River) and the surface outlet (Dulce River).

36 C. Medina et al. / Journal of Hydrology 382 (2010) 34–48

surface runoff or underground waters. The tropical weather in theregion is characterized by constant rainfall events and cloudy con-ditions. Climate seasonality is found with a rainy season approxi-mately from May to November and a dry season from Decemberto April. The Lake Izabal location makes the lakes surface to be af-fected by winds coming from the Caribbean Sea. With respect tothe anthropogenic activities (iii), the estimated number of inhabit-ants in the catchment is about 10,00,000 giving one of the highestdensities in the country (157 inhabitants/km2: AMASURLI, 2006).This population lives out of the catchment natural resources. Theanthropogenic activities include irrigation, deforestation and set-tlements (among others) and they modify the hydrologic responseof the basin.

The water budget of Lake Izabal is influenced by regional cli-mate changes due to its low depth and large extension (Table 1)(Arrivillaga, 2002). Changes in rainfall, temperature of humidityin the region are forced by El Niño Southern Oscillation and theTropical North Atlantic Anomaly (Giannini et al., 2000; Thattai etal., 2003). According to Restrepo and Kjerfve (2000), those climatechanges also affect the water resources availability.

The water volume of Lake Izabal affects the physical and chem-ical processes in the lake such as the residence time and substancesconcentrations (Dix et al., 1999). Some efforts have been done toestimate the Lake Izabal water volume (Table 1) (OTECBIO,2003), but to our knowledge the volume variations are still un-known. Based on the lake’s bathymetry OTECBIO (2003) deter-mined that 25.5% of the lake is less than 6 m deep. The wetlandformed in the Polochic river mouth works as a physical and ecolog-ical buffer to the lake (Basterrechea, 1993). Thus the water massdynamics are affected by these wetland buffer properties (Dix etal., 1999).

Datasets

The Lake Izabal water volume variations were obtained in threesteps: (i) estimation of Lake Izabal inundated area variations usingSAR images; (ii) development of rating curves between lake levelvariations obtained with in situ measurements taken by the Guate-malan Authority for Sustainable Management of Lake Izabal Basin(AMASURLI), RA-2 time series (European Space Agency: ESA), andinundated area variations from SAR images (ESA); and (iii) estima-tion of water volume variations using the set of rating curves ob-tained. The analyzed time period spans almost 4 years: February2003 to December 2006. Rating curves were needed due to thegeometry of the instrument on-board ENVISAT, since it is not

Fig. 2. Geometry of the RA-2 and SAR measurements on-board ENVISAT. The nadir-point of RA-2 and the SAR incidence angle (23�) inhibit simultaneous data acquisition ofboth instruments over the lake surface.


possible to have RA-2 and SAR measurements over the lake surfaceat the same time (Fig. 2).

The surface extension variations of inland waters have beenestimated in the past with radar or optical imagery (Alsdorf etal., 2000; Prigent et al., 2001; Tan et al., 2005; French et al.,2006). Radar altimeter data have been used in the past to monitorthe inland waters dynamics through the estimation of water stages(Mercier et al., 2002; Frappart et al., 2006; Crétaux and Birkett,2006; among others). Several studies have used separately waterstages (from gauges and/or altimetry) and inundated area varia-tions derived from remote sensing data (Birkett, 2000; Sarch andBirkett, 2000; Chiara et al., 2006). To our knowledge just few worksused the combination of both to derive lake water volume varia-tions (Gupta and Banerji, 1985; Zhang et al., 2006).

In situ lake level heights

The lake level is being routinely monitored with daily in situmeasurements. The daily dataset of Lake Izabal in situ water levelused in this work was provided by AMASURLI spanning 3 years:January 2004 to December 2006. The gauge station is a mooredrule located at 15�310250 0N–89�190460 0W (close to the Polochic Riv-er mouth) and is not referenced to any international reference sys-tem. It measures the lake surface height from the bottom of thelake in the moor location. Regardless the referencing issue we haveused this dataset on the basis of the main objective of this work.Thus we focused on the estimation of the water volume variationsbased on relative level and area variations.

RA-2 derived lake level dataset

The estimation of the lake surface height from radar altimetermeasurements is made thanks to a basic principle: the altimetermeasures the distance (travel time) between the satellite’s centreof mass and the illuminated lake surface (known as range). A pre-cise satellite’s orbit determination enables the determination of

the satellite altitude over a reference surface (ellipsoid). Finallythe lake surface height over the ellipsoid is obtained by subtractingthe corrected range to the altitude (ESA, 2002). Some authors haveproved the suitability of radar altimetry to monitor continentalwater stages (Mercier et al., 2002; Crétaux et al., 2005; Berry etal., 2006; Crétaux and Birkett, 2006; Frappart et al., 2006). The sa-tellite altimeter multimission (Topex/Poseidon, Jason-1, ENVISATand GFO) provides a broad number of continental waterbodiespotentially seen from the space. The Lake Izabal surface is over-flown by the RA-2 descending orbit number 269 along a 23 km-long ground segment. This length is long enough to obtain validheight level estimates (Mercier et al., 2002). The temporal resolu-tion of the RA-2 measurements is 35 days. The Lake Izabal waterlevel fluctuations were obtained following the methodology pro-posed in Medina et al. (2008). The timeseries goes from February2003 to December 2006.

The product used was the Geophysical Data Records (GDR) pro-vided by ESA. The range measurement used to estimate the lake le-vel fluctuations was derived from the Ice-1 retracking algorithm(Medina et al., 2008), which gave the lower rms. from the set ofretrackers available in the GDR product: Ocean, Ice-1, Ice-2 andSea-Ice. The geophysical corrections applied to the range to com-pensate the interaction of the radar signal with the atmosphereand the lake’s surface included (corrected range): Dry troposphere,wet troposphere, ionospheric correction, solid earth tide, and poletide. A detailed description on the RA-2 data processing for theLake Izabal surface can be found in Medina et al. (2008).

SAR imagery data

The SAR sensor is an imaging active microwave instrument(C-band) which measures the microwave return from the earthsurface. Land and water surfaces give different backscatter re-sponses to the SAR antenna. The land–water separation is basedon the hypothesis that water surfaces are much smoother thansurrounding dry land. Thus water surfaces behave as a specular


reflector of the microwave, yielding low backscatter responses(Horritt et al., 2001; Xiaoliang et al., 2005). The Lake Izabal inun-dated area variations were estimated making use of 12 descendingENVISAT ASAR 4-look-amplitude Image Mode Precision Images(from 15th June 2003 to 19th February 2006), provided by ESA.The amplitude images are 30 m resolution with a pixel spacing of12.5 � 12.5 m and a swath width of 105 km. Table 2 shows the im-age characteristics (ESA, 2007). Note the unavailability of images ata constant interval of 35 days. However, the predominant cloudyconditions in the study area precluded the use of conventionaloptical imagery. According to this, the RA-2 and SAR products aremore appropriate for routine monitoring of the lake conditions,thanks to its capability to operate under all weather conditions(Birkett, 2000; Horritt et al., 2001; Dellepiane et al., 2004; Fleming,

Table 2ENVISAT ASAR images used with some specifications.

No. Acquisition date Orbit track (183)

1 15–06–2003 Orbit 67502 02–11–2003 Orbit 87543 07–12–2003 Orbit 92554 11–01–2004 Orbit 97565 21–03–2004 Orbit 107586 30–05–2004 Orbit 117607 04–07–2004 Orbit 122618 08–08–2004 Orbit 127629 17–10–2004 Orbit 13764

10 19–06–2005 Orbit 1727111 02–10–2005 Orbit 1877412 19–02–2006 Orbit 20778

Fig. 3. Block diagram of the lake’s shoreline detection from SAR images. Rectangles stgeographic based software.

2005; ESA, 2007). In addition the temporal resolution (P35 days)and spatial resolution (12.5 � 12.5 m) and the possibility to iden-tify water-filled pixels in the SAR images were the main consider-ations taken into account in the selection criteria of this product.

Lake Izabal shoreline extraction

Our objective is a single pixel boundary between land and waterin order to estimate the lake extent. We propose a series of image-processing steps on a pixel by pixel basis (Fig. 3) to detect the LakeIzabal shoreline. A short summary of the methodologies for auto-matic coastline detection using SAR images is given here.

Imhoff et al. (1987) used a simple thresholding technique forautomatic classification of flooded areas. Lee and Jurkevich(1990) developed an algorithm for the global detection of coast-lines based on image-processing steps. Niedermeier et al. (2000)proposed a coastline detection methodology using wavelet and ac-tive contour methods. Horritt et al. (2001) developed an automaticsegmentation algorithm for flood boundary delineation. Heremanset al. (2003) compared two techniques (pixel based classificationand object-oriented classification techniques) to detect flooded re-gions in SAR images. Dellepiane et al. (2004) used a methodologytaking textural and multi-temporal aspects into considerationand Yu and Acton (2004) proposed a diffusion-based method fordelineation coastline. Ahtonen and Hallikainen (2005) applied atwo phase system based on thresholding and an active contourmodel.

High backscatter produced by wind-roughness increases theSAR noise level (Lee, 1981, 1986; Lee and Jurkevich, 1990; Horritt

eps were achieved using image processing software while ovals were done using


et al., 2001). The windy effects on ocean surface roughness and ra-dar backscatter are well known (Ulaby et al., 1986), but the effecton continental waters is more complex due to the local topography(Horritt et al., 2001). The SAR images used in this study were af-fected by winds coming basically from the North–West and fromthe North–East with the latter having a higher effect on lake’s sur-face roughness due to the topography surrounding the lake. Thisaffects the pixel-to-pixel variety in the SAR images analyzed. Windeffects cannot be easily removed because wind-forced noise is localand its grey level value in the images can be even higher than pixelvalues over land.

Image pre-processing: image subset and orthorectification

Fig. 4A shows an ENVISAT SAR image (11th January of 2004)over the study area. The image subset is a spatial resize which re-

Fig. 4. Pre-processing results of the SAR image. (A) Raw image acquired on 11–01–2004.line is the lake shoreline from geographic information. White points are the Ground Co

Fig. 5. Orthorectified SAR images acquired on 11–01–2004 (softly wind-roughened: Fig.the images after the histogram equalization applied to enhance the land/water boundar

duces the raw image size (105 � 105 km) to the Lake Izabal size(�55 � 35 km). The SAR product used was not georeferenced. Thus,in order to compare the results with geographic information, anorthorectification was made. The images were firstly georefer-enced to the geographic coordinates system (Fig. 4B) and thenorthorectified using eight Ground Control Points (Fig. 4C).

Speckle reduction

SAR images are generated by coherent processing of scatteredsignals, and these images are susceptible to speckle noise becauseof the coherent interference of waves reflected from many elemen-tary scatters (Porcello et al., 1976; Lee et al., 1994; Oliver and Que-gan, 2004). Speckle noise in SAR images makes difficult theinterpretation of the images. Many speckle filters have been devel-oped and successfully applied to SAR images: Lee (Lee, 1981); Frost

(B) Image after speckle reduction (lee filter twice) and georeferenciation; the whitentrol Points used for orthorectification. (C) Orthorectified image.

5A) and 21–03–2004 (highly wind-roughened: Fig. 5B) respectively. (C) and (D) arey. The white arrows indicate the wind direction.

Fig. 6. (A) and (B) are the equalized images after the application of the Gaussian filter (Fig. 6A: 11–01–2004 and Fig. 6B: 21–03–2004). (C) and (D) are the binary images afterthresholding.

Table 3Lake Izabal inundated area at different dates from SAR and in situ lake level.

No. Date Threshold value used Area (km2) Lake level (m)

1 15–06–2003 159 674.93924422 02–11–2003 160 674.45413083 07–12–2003 160 674.74791174 11–01–2004 160 674.1800985 0.585 21–03–2004 162 674.1422903 0.576 30–05–2004 161 675.3203361 0.767 04–07–2004 160 676.1757707 0.888 08–08–2004 158 674.4872373 0.79 17–10–2004 160 674.733867 0.75

10 19–06–2005 160 674.3843634 0.6511 02–10–2005 161 675.4668633 0.912 19–02–2006 161 673.172776 0.45


(Frost et al., 1982) and Gamma-MAP (Lopes et al., 1993), amongothers. The images were de-speckled, by the application of theLee filter (twice) with a 3 � 3 filter size, a 1.0 multiplicative noisemean and a 0.25 noise variance. For the purposes of the presentwork, the Lee filter is enough suited for the image de-specklingsince preserve image sharpness and detail remaining the coastlineedges unaffected (Lee and Jurkevich, 1990). Fig. 5 shows two de-speckled SAR images: one with low surface roughness due to windcoming from N–W (5A) and one with high surface roughness dueto wind coming from N–E (5B).

Land/water edge detection

Histogram equalizationThe flattening aims at increasing the contrast between dark

(lake surface) and bright (land) pixels (Fig. 5C and D). After thisthe radiometric resolution of the images was reduced from 16 to8 bits (256 grey levels). The equalization procedure was based inthe de-speckled image histogram. In high wind conditions theequalization increased the contrast inside the lake between windnoisy bright pixels and their darker neighbours. A 3 � 3 median fil-ter was then applied to smooth the images. After the histogramequalization some single pixels whose values are far out of rangewith neighbouring pixels are produced. With the median filterthose single points are removed smoothing the image.

Land/water edge detectionA preliminary edge map was obtained applying the Sobel edge

detector (Pratt, 1978) following (Lee and Jurkevich, 1990). Theland–water edge was then enhanced by combining 30% of the pixelvalue from the Sobel edge detector with 70% of the pixel valuefrom the filtered image (Fleming, 2005). After this, a 5 � 5 Gauss-ian filter was applied providing less difference between neighbour-ing pixels. The resulting images are presented in Fig. 6A (low wind)and B (high wind).

ThresholdingThe histograms of the resulting images were computed and the

basic statistics obtained (mean pixel value for land and water

surfaces, standard deviation, maximum and minimum value). Theland/water separation (segmentation) was achieved using thethreshold value based on the image histograms. The thresholdswere set at a pixel value of 160, however some images with highwind noise presented many mistaken surfaces with that value.By changing the value from 160 to 158 or 162 (sometimes changesto 159 or 161), those mistaken surfaces were significantly avoided.The threshold value changes were decided after visual analysis (Ta-ble 3 shows the threshold value used for each image). After thres-holding the segmentation set all the pixels lower/higher than thethreshold to zero/one. The binary images obtained are presentedin Fig. 6C (low winds) and D (high winds). Finally, we appliedthe Robert’s edge operator (Pratt, 1978) producing 1-pixel wideedges.

Grid conversion and vectorization

The resulting images were exported to Geotiff format for the fi-nal processing chain.

Grid conversionSome of the images have false edges and islands caused mainly

by windy weather conditions coming from N–E. In order to reduce

Fig. 7. Two examples of pixel misclassification: land pixels declared as water (Fig. 7A) and water pixels declared as land (Fig. 7C) (image acquired on 21–03–2004). (B) and (D)show the results of the manual tune-up. The white line is the extracted lake’s shoreline.


this, we transformed the images into grids (i.e. x and y values) butwithout loosing spatial features. The cell size was 156.25 m2

(12.5 � 12.5 m, see Table. 2). The resulting grids were then gener-alized by removing regions that were smaller than 1000 cells andreplace their values with those of their nearest neighbours.

VectorizationThis consists of the conversion of the cell-based grid into an ob-

ject-oriented (polygon) graphic. The bright response of ashorewaters and low backscattering of some land pixels affected someshoreline suspicious areas. In order to solve this problem, a manualrefinement was applied (Ahtonen and Hallikainen, 2005; Fleming,2005; Chiara et al., 2006). Two examples of areas of the lake’s shoreshowing land/water pixels wrongly declared as water/land are pre-sented in Fig. 7A and C. The final 1-pixel wide shoreline are pre-sented in Fig. 7B and D. At the end, the Lake Izabal polygonswere used to calculate their geometric features (area andperimeter).

Results

Once the Lake Izabal shoreline was extracted, the area andwater volume variations were estimated for the study period.Firstly, we estimated the area of the lake from the polygons ob-tained in the shoreline extraction on the dates of the SAR images.Then the volume variations were estimated combining the level(from in situ measurements) with the area changes (from SARpolygons). A group of rating curves relating level, area and volumewere developed. In order to extend the area and volume estimatesto the whole study time period the lake levels from the RA-2 data-set were entered in the rating curves.

Lake Izabal area variations

The literature focused on Lake Izabal shows a disagreement inthe lake surface extension. Basterrechea (1993) and Arrivillaga(2002) indicate a surface extent of 717 km2; Michot et al. (2002)and URL (2002) indicate an area of only 590 km2, whereas the Gua-

temalan geographic database (MAGA-CATIE-ESPREDE, 2001) statesthat the Lake Izabal surfaces has an extension of 678 km2. The mostrecent, AMASURLI (2006), reports 757 km2. The main reason forthese differences might be in the fact that some of the authors in-cluded in their estimation part or even the whole outlet: Dulce Riv-er. For the sake of this study, the Lake Izabal was delimited up toNorth latitude of 15�38051.140 0 (�2.5 km downstream in the DulceRiver) (Fig. 1B). Taken into account this limit, the Lake Izabal areafrom the Guatemalan geographic database (MAGA-CATIE-ESPRE-DE, 2001) is now 673.29 km2. The estimates of the area from theSAR images are presented in Table 3. They are in agreement withthe modified area from MAGA-CATIE-ESPREDE (2001).

The shift of the coastline does not have the same magnitudealong the whole shore depending on its topography. Differentstages of inundated lake areas using three different zoom viewsat three different dates are presented in Fig. 8.

The estimated lake surface areas were validated through the useof in situ lake level measurements. There is no availability ofground surveys or high resolution aerial photos (taken simulta-neously to the SAR images), needed to a more accurate validationexercise. In addition, information about the Lake Izabal bathymetryor shore topography would provides means to relate lake levelvariations with inundated area variations (Zhang et al., 2006).The time series of lake area variations (derived from SAR images)and lake levels (from in situ measurements) were compared andthe results presented in Fig. 9. In situ data are presented at 35 daysintervals corresponding to the RA-2 repeat cycle dates, whereas theSAR derived lake surface does not have a constant time intervaldue to data unavailability (Table 3). The Lake Izabal water level fol-lows the climate seasonality, with high water stages occurring dur-ing the rainy seasons (May–November), and low water stagesduring the dry seasons (December–April) (Medina et al., 2008). Itis observed that the Lake Izabal area variations also follow this pat-tern. Note that given the geometry of the lake (large area, lowdepth) the y-axes in Fig. 9 were set at different magnitude orders(0.2 m for level and 1 � 106 m2 for inundated area).

The soundness of the Lake Izabal area estimations from SARimages was analyzed. The simultaneous acquired paired samplesof lake level and area were isolated, plotted and statistically

Fig. 8. Examples of Lake Izabal area variations. Three dates of different lake stages are presented together with the corresponding lake’s area (in km2) and level (m2).

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Fig. 9. Lake Izabal in situ water level timeseries (solid line). Corresponding SAR derived lake surface (black circles).


analyzed (Fig. 10). The lake area and level line plots, regardless thetimescale, have a similar behaviour (Fig. 10A). Increases/decreasesof the inundated area (from SAR data) coincide with lake levelrises/drops (from in situ measurements). The regression analysisbetween Lake Izabal inundated area and water level yielded a cor-relation coefficient of 0.9 indicating a strong statistical relationshipbetween the two variables (Fig. 10B). The high correlation ob-served is strengthened by the agreement among the average ofthe estimated areas from SAR (674.67 km2) and the GIS-based esti-mated area (673.29 km2).

Volume variations from level/area changes: rating curves

The overall water volume of a lake is a function of surface areaand lake level, and it is a critical parameter in the water mass bud-get of a lake catchment (Crétaux and Birkett, 2006). Manmade res-ervoirs use hypsometric curves or bathymetry maps to transformlevel into volume, because the inundated area depends on that fea-tures. The remotely sensed estimation of the inundated area vari-ations can be used as an alternative powerful tool. Therefore, thewater volume variations can be calculated despite the lack of the

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1 2 3 4 5 6 7 8 9Paired samples

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)

Area Level

y = 5.6699x + 670.74R2 = 0.9036

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0.3 0.4 0.5 0.6 0.7 0.8 0.9Lake Level (m)

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B

Fig. 10. (A) Line plots of Lake Izabal area in 106 m2 (solid line) and its corresponding paired sample of in situ measured lake level in m (dashed line). (B) Regression analysisbetween lake levels and areas. Also shown the regression equation and correlation coefficient.


shore topography information. This section is focused on the rela-tive volume variations. For that reason the lake surface increases/decreases and level rises/drops were used. The water volumechanges in the lake were computed using the following equation.

DVðtÞ ¼ SðtÞ þ Sðt � 1Þ2

� �� ½LðtÞ � Lðt � 1Þ� ð1Þ

where DV(t) is the lake volume change as a function of time (t) inm3, per each area/level change; S(t), the lake surface as a functionof time (t) in m2, per each level variation and L(t) is the lake levelas a function of time (t) in m

Eq. (1) estimates the lake water volume changes, assuming a per-fect triangle formed by level changes, area changes, and the lake’sbottom. Thus it is rough volume change information which doesnot take into account small distortions, which otherwise are actu-ally found in the lake’s bottom. For this reason, the water volumevariations estimated here are considered as a first approximationconsidering that a precise knowledge of the inundated shorelinetopography should be needed for a more precise estimation.

The Lake Izabal volume changes obtained in Eq. (1) were thencompared with the other two datasets (lake level and lake surface

variations). After this, the rating curves were obtained and pre-sented in Fig. 11. Different kinds of relationships were found inthe paired datasets. The volume changes as a function of areachanges presented a third-grade polynomial relationship with ahigh correlation coefficient of 0.99 (Fig. 11A). The volume changesdriven by the lake level variations showed a linear relation with acorrelation coefficient of 0.91 (Fig. 11B). Finally, the area changesas a function of level changes yielded a linear relation with a cor-relation coefficient of 0.94 (Fig. 11C). The rating curves reveal therelationships between volume/surface/level changes of Lake Izabal,which are closely connected with the lake’s shore topography andbathymetry. Furthermore, the remotely sensed inundated areaestimation could provide us, besides the volume changes informa-tion, a way of improving our knowledge about the Lake Izabaltopography and bathymetry, because the slope of the lake’s bottomcan be inferred from the inundated area change per unit of level.

Lake Izabal water volume timeseries

According to Medina et al. (2008), the altimeter RA-2 is highlyaccurate in deriving the Lake Izabal water level fluctuations.

y = 62486x3 + 40783x2 - 14402x - 3976.3R2 = 0.9929

-600000

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Volu

me

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ges

(m3 )

y = 2E+06x3 - 458668x2 + 533766x + 430.81R2 = 0.9891

-600000

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0

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200000

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

Level changes (m)

Volu

me

chan

ges

(m3 )

y = 0.93x2 + 5.6578x - 0.0759R2 = 0.941

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-2

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-1

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0

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Area

cha

nges

(106 m

2 )

A

B

C

Fig. 11. Rating curves and correlation coefficients for: (A) area changes vs. volume changes; (B) level changes vs. volume changes; and (C) level changes vs. area changes.


Although the in situ level measurements and RA-2 derived levelare not referenced to the same system, the RA-2 dataset is suitableto be used in this analysis because the rating curves are based onrelative level changes. The RA-2 level rises/drops can be used as in-put in the rating curves as the in situ relative level variations arestatistically similar to the RA-2 level variations: linear correlationcoefficient of 0.83 and the rms of the differences error of 0.09 m(Medina et al., 2008). The volume of the water stored in a lake can-not be measured but it depends on the surface area and height.Hence remotely sensed water surface area or level can provide agood approach to estimate that (Zhang et al., 2006). Lake level datawere converted into water volume using the rating curves. The

mean Lake Izabal depth as well as the absolute inundated areas(measured and estimated) was considered to estimate the overallwater volume variations. The Lake Izabal estimated volume time-series for the study period is presented in Fig. 12A, including a vol-ume estimate from the inundated area values (using theappropriate rating curve). There is a good agreement between bothdatasets (RA-2 and SAR time series). The average of the estimatedwater volume stored in the Lake Izabal during the study period was8506.52 � 106 m3 (using RA-2 dataset) and 8565.57 � 106 m3 (SARdataset). The results are also in agreement with the most recentestimation of the Lake Izabal water volume found in the literature:8300 � 106 m3 (OTECBIO, 2003).

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RA-2 estimated volumeASAR estimated volume A

B

Fig. 12. (A) Timeseries of Lake Izabal water volume (in 106 m3) estimated from level variations by RA-2 lake levels (solid line) and from area variations by SAR images(circles). (B) Same as (A) but for Lake Izabal surface timeseries (in 106 m2).

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06 m3 )

RainfallEvaporationWater Volume

Fig. 13. Timeseries of Lake Izabal water volume (in 106 m3) estimated from level variations by RA-2 lake levels (dashed thin line) compared with rainfall (solid line) andevaporation (dashed thick line).



Discussion

The volume estimation of Lake Izabal has been obtained with-out error ranges due to the unavailability of a timeseries ofin situ volume estimates. For this reason the water volume valuesshould be considered with care only as a first approximation to thelake water storage variations. Even though this is a rough ap-proach, it unearths valuable information regarding the Lake Izabalhydrologic dynamics such as the lake’s water mass budget. Theknowledge on water volume changes of a lake is important forhydrology and limnology related fields. For operational and socialpurposes, it is important the knowledge about inundated area vari-ations (estimation of flooded areas). The inundated area timeseriesof Lake Izabal were calculated using the RA-2 level dataset andproper rating curve (Fig. 12B). The estimates were comparedagainst those obtained with SAR images. Both inundated areas esti-mations are in agreement. The lake level variations reflect inun-dated area and water volume variations (Birkett, 2000; Mercieret al., 2002; Crétaux and Birkett, 2006). Thus the fluctuations foundin the lake inundated area and water storage are expected to followthe same pattern as the fluctuations found in the lake level shownin Figs.10 and 13. There were some strong lake level variationswhich were out of the rating curves range: the lake level rise inJune 2006 and the lake level drop in December 2005. The estimatedwater volume and inundated area in those dates have anothersource of error caused by unknown bathymetry changes. However,the analysis of the timeseries assumed homogenous bathymetry.The range of the water volume stored in Lake Izabal estimated dur-ing the study period ranges between 8271.2 (17th December of2005) and 9018.15 � 106 m3 (15th July of 2006) whereas the inun-dated area variations from RA-2 levels ranges from 672.44 (17thDecember of 2005) to 677.2 � 106 m2 (15th July of 2006). Thetimeseries obtained from the rating curves present a strong sea-sonal signal forced by the weather seasonality. An important inter-annual variation was also found with an unclear pattern (Figs. 10and 13). A detailed description of the Lake Izabal seasonal andinterannual lake level fluctuations can be found in Medina et al.(2008).

The water volume variations obtained reflect the balance be-tween water inputs and outputs (Crétaux and Birkett, 2006). Ulti-mately, all the inputs depend on the weather conditions since thesurface and underground tributaries are driven by the rainfall/evapotranspiration over the catchment. It was mentioned thatthe region showed climate seasonality (rainy and dry seasons),but there is also a strong interannual variability (Medina et al.,2008). Within the catchment there are located four meteorologicalstations recording data about daily rainfall (mm), temperature (�C),and evaporation (mm), among others. Those datasets were used toobtain monthly timeseries (average of the four stations) of rainfalland evaporation/transpiration over the lake/catchment. Thetimeseries of rainfall/evaporation of the study period and the watervolume variations are presented in Fig. 12. The timeseries of thewater stored in Lake Izabal have the same variation pattern ofthe climatic conditions (rainfall and evapotranspiration) over thelake surface and its catchment area. Note that the strong watervolume rise in July 2006 was preceded by high amounts of rainfall1 month before. The water volume drop during the first months of2005 coincides with a 3 month period when the evaporation wasbigger than the rainfall (Fig. 12). During the study period, the start-ing/ending dates of the rainy and dry seasons vary every year aswell as the magnitude of the amount of rainfall during the rainyseason. Those interannual variations also affect the water volumeof the lake. According to Giannini et al. (2000), this time/magni-tude variations of the rainfall are partially explained by the heatcontent of the Tropical North Atlantic and Easter Pacific oceanic re-gions. Further studies in the catchment area would include cli-

mate-driven runoff models and climatic analyses in order to findout more information about the water mass balance of the LakeIzabal basin.

The physical properties of the Lake Izabal are poorly known andfield data are generally scarce and sporadic. Based on this, the re-mote sensing techniques have arisen as the ideal way for comple-menting the scarce ground measurements in the area. Thegeometry of the Lake Izabal bed reveals that the horizontal scale(inundated area) is 7 orders of magnitude greater than the verticalscale (level). Thus it is expected that small changes in the lake levelwould reflect big changes in the lake inundated area.

The results obtained in this study would improve our knowl-edge on the Lake Izabal water mass balance. In addition, the meth-odology applied here to estimate the Lake Izabal water volumevariations is exportable to other lakes or wetlands located in otherregions of the world. The water levels could be estimated in situ orderived from altimetry and the inundated areas could be deter-mined using different sensors (optical or radar imagery).

Conclusions

The main objective of this study was to give a first estimate ofthe water volume variations in Lake Izabal by combining leveland inundated area variations. To our knowledge, this is the firsttime that this kind of study has been developed in this particularlocation of the world. The water volume cannot be directly mea-sured, but as a first guess it can be estimated by analyzing the lakelevel and inundated area variations. Those variations were ob-tained using in situ measurements, RA-2 lake levels and SARimages. Active remote radars were used because they are not af-fected by the cloudy conditions in the region. In addition, the loca-tion of the Lake Izabal near the Caribbean Sea shoreline makes thelake to be influenced by strong winds coming from the Caribbean,especially during the hurricanes season (September–November).

In the SAR images, smooth water surfaces yield low returns tothe antenna (dark areas) whereas rough water surfaces return ra-dar signals of varying strengths (bright areas). In addition land pix-els with low radar values can be confused by water. Thus wind-roughened water surfaces might produce misclassification of thewater, and flat land surfaces near the shore might produce mis-classification of the land. In order to overcome this, it has beendemonstrated that the image-processing steps applied to the SARproducts are suitable for the Lake Izabal shoreline extraction andthus for obtaining reliable inundated area estimations. We mustnote that completely automatic shoreline extraction could not beachieved and some critical areas were manually sharpen. The esti-mated inundated area variations were compared with ground lakelevel measurements, yielding a linear correlation coefficient of 0.9.Once the inundated area variations were estimated rating curveswere developed (volume/area/level). Almost 4 years of RA-2 de-rived Lake Izabal water levels were used to compute the timeseriesof volume and area variations. The geometric characteristics ofLake Izabal (large area and low depth) make the lake level estima-tion critical and needs to be as accurate as possible. Thus errors inthe lake level can lead to large errors in the area or volume compu-tation. With the lack of simultaneous aerial photos or ground sur-veys, it was not possible to provide absolute errors to validate theresults obtained. However, the inundated area estimates agreewith the Guatemalan Geographic database. We also found a stronglinear correlation between SAR derived inundated areas and in situlevel variations (0.9). We conclude that Lake Izabal area increases/decreases coincide with lake level rises/drops.

The combination of lake level (from in situ measurements) andinundated area variations (from SAR images) make it feasible toderive Lake Izabal water volume variations. The relationships


found between the three parameters (volume/surface/levelchanges) were used to develop rating curves. The correlation coef-ficients between the lake’s bed parameters were: 0.99 (area/vol-ume), 0.92 (level/volume) and 0.94 (level/area). The obtainedcorrelation coefficients are good enough to apply the rating curvesto derive the timeseries to the whole study time period. Ratingcurves could not be extended to the extreme level events found(December 2005 and June 2006) because of the unavailability ofSAR images on those dates. Thus we assumed that the lake’s shorewould follow the same behaviour. The rating curves allowed us totransform the RA-2 derived lake level timeseries into lake inun-dated area and water volume timeseries. Since the errors budgetcould not be estimated due to a lack of ground estimates of volumevariations, the estimated timeseries should only be considered as arough approach. However, the results obtained should contributefilling a gap in the general knowledge regarding the physical pro-cesses of the Lake Izabal. The information obtained here is valuablefor hydrologic, ecologic, operational and social purposes.

This study shows the potential on the jointly use of remotesensing and in situ data to assess and monitor a subtropical shal-low waterbody: Lake Izabal (Guatemala). The scarcity of field dataor ground surveys in the area, mainly due to a lack of financial re-sources, enhances the importance of remote sensing as a cheaperand accurate data source. The combination of remote sensing tech-niques with hydrological and climatic models will bring to lightinformation about other parameters involved in the Lake Izabalwater mass balance such as the surface and groundwater input,the surface outlet and seepage.

Acknowledgments

The corresponding author thanks to the Spanish Agency ofInternational Cooperation (AECID) for supporting his research. Thiswork was partially funded by the Spanish Research and Develop-ment Programme (Project code: CGL2004-01473/CLI). The altime-try data was provided by the European Space Agency (ESA)under the project 4245. The SAR images used were provided byESA under the project 4420. The in situ level dataset was deliveredby AMASURLI. The climate records from the meteorological sta-tions were provided by the Guatemalan Institute of Meteorology(INSIVUMEH). Special thanks to Barbara Corsale and Franco Manto-vani (Department di Scienza della Terra, of the University of Ferr-ara, Italy) who helped us with the processing of the SAR images.

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