• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Abstract Resumen

Since there are no mathematical models that can calculate the Laguna

Ante la inexistencia de modelos matemáticos que calculan el

de Bustillos’ water storage levels, water balance requires this data to almacenamiento de agua de Laguna de Bustillos, el balance hídrico

understand the connectivity between this water body and the

requiere este dato para comprender la conectividad entre este cuerpo

Cuauhtemoc aquifer. This article presents a new three-dimensional de agua y el acuífero Cuauhtémoc. Este artículo presenta una nueva

reconstruction technique based on a time series of multispectral

técnica de reconstrucción tridimensional basada en series de tiempo

remote sensing images, bathymetry, a topographic survey with high

de imágenes de sensores remotos multiespectrales, batimetría,

precision GPS, and regional contours. With the images of Landsat

levantamiento topográfico con GPS de alta precisión y curvas de nivel

ETM+/OLI and Sentinel 2A from 2012 to 2013, 2016, and 2017, the regionales. Con las imágenes de Landsat ETM +/OLI y Sentinel 2A de

contours of the water surface were extracted using the MNDWI and 2012 a 2013, 2016 y 2017, se extrajeron los contornos de la superficie

were associated with an elevation received from GPS. An Autonomous del agua utilizando el MNDWI y se asociaron con una elevación obtenida

Surface Vehicle was also used to obtain the bathymetry of the lake. A a través del GPS. Se utilizó un Vehículo Autónomo de Superficie para

topographic survey was carried out using GPS in populated areas, and obtener la batimetría del lago. Se realizó un levantamiento topográfico

the contour lines extracted from the INEGI Continuous Elevations

usando GPS en áreas pobladas y se usaron las curvas de nivel extraídas

Model 3.0. A DEM was constructed using ArcGIS 10.5.1, and surfaces del Modelo 3.0 de Elevaciones Continuas del INEGI. Se construyó un

and volumes were calculated at different elevations and compared with MDE, las superficies y volúmenes se calcularon a diferentes elevaciones

16 Landsat TM/ETM+/OLI multispectral images from 1999 to 2018.

y se compararon con 16 imágenes multiespectrales Landsat TM/

The results showed that the mean of the average intersection area

ETM+/OLI de 1999 al 2018. Los resultados mostraron que la media del

between the test images and the area extracted from the 3D model is área promedio de intersección del modelo y las imágenes tiene una

above 90.9% according to the confidence interval, kappa overall

eficiencia superior al 90,9% de acuerdo con el IC, precisión general

accuracy 95.2–99.7%, and a coefficient 89.9–99.3%. This model

kappa 95.2-99.7% y un coeficiente 89.9-99.3%. Este modelo demostró

proved to be very accurate on a regional scale when the water level ser muy preciso en escala regional, sobre todo cuando el espejo del

exceeded 1971.32 meters above mean sea level and useful to evaluate agua supera los 1971.32 metros sobre el nivel medio del mar, y a la vez

and administer water resources. útil para la evaluación y administración de los recursos hídricos.

Palabras clave:

DEM, storage, lake, MNDWI, RTK, sound.

Keywords:

MDE, almacenamiento, lago, MNDWI, RTK, sonar.

Topobathymetric 3D model reconstruction

of shallow water bodies through remote

sensing, GPS, and bathymetry

Reconstrucción de modelo 3D topobatimétrico de cuerpos de agua

someros mediante teledetección, GPS y batimetría

UGO

UIS

OJAS

-V

ILLALOBOS

1,6

, L

UIS

ARLOS

LATORRE

-C

EJUDO

, B

LAIR

TRINGAM

ZOHRAB SAMANI4 AND CHRISTOPHER BROWN5

Recibido: Enero 6, 2018 Aceptado: Noviembre 11, 2018

_________________________________

1 NEW MEXICO STATE UNIVERSITY. Water Science and Management Program. Department of Geography, 1525 Stewart St. Breland Hall, Las

Cruces, NM. USA. 88003. Tel. (+1 575) 646-5755.

2 UNIVERSIDAD AUTÓNOMA DE CIUDAD JUÁREZ, Sede Cuauhtémoc. Programa de Geoinformática, Departamento de Arquitectura. Carretera

Anáhuac S/N, Cd. Cuauhtemoc, Chih, México. 31600. (+52 625) 128-1700.

3 NEW MEXICO STATE UNIVERSITY. Plant and Environmental Sciences. College of Agriculture, Consumer and Environmental Sciences, 945

College Drive. Skeen Hall, Las Cruces, NM. USA. 88003. (+1 575) 646-7665

4 NEW MEXICO STATE UNIVERSITY. Department of Civil Engineering, 3035 S. Espina St. Hernandez Hall, Las Cruces, NM. USA. 88003.

(+1 575) 646-2904

5 NEW MEXICO STATE UNIVERSITY. Department of Geography, 1525 Stewart St. Breland Hall, Las Cruces, NM. USA. 88003. Tel (+1 575)

646-1892.

6 Dirección electrónica del autor de correspondencia: hlrojas@nmsu.edu

Ingeniería y Tecnología Artículo arbitrado

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

Introduction

he Laguna de Bustillos is in a region that has a high demand for groundwater for the

agricultural industry, making the Cuauhtemoc aquifer the largest over-exploited aquifer

in northwest Mexico (Comisión Nacional del Agua, 2016). It is necessary to provide updated

data to the water balance of the basin to improve water management in the region.

Because there are no known mathematical

models that calculate water storage, it is imperative

to develop a new technique or method that allows us

to estimate the water volume contained in water

bodies. The calculation of water storage of shallow

water bodies requires the construction of 3D models

of the terrain including the surrounding areas.

Integrating techniques based on sound, spectral

analysis of satellite imagery, and GPS allow

researchers to increase the accuracy of the existing

3D models and expand them from the reservoir

representation to a topobathymetric integrated model.

Topobathymetry is a geospatial concept that

integrates bathymetric and topographic data from

different spatial scales, time, and sensors. The terrain

model is applied to monitor coastal erosion, sea level

rise, flood impact reduction programs, and coral

barrier studies (Gesch et al., 2016). Digital terrain

models, topography, bathymetry, and the use of water

body contours are essential sources for integrating

this model. Some research tried to get 3D models, but

only one or two data sources were used in comparison

with those applied in this research. The delimitation

of water bodies is an indirect way of getting contour

lines through differentiating the spectral response

between the green band (G) and the bands near infra-

red (NIR) or the infra-red short-wave band (SWIR).

The Normalized Difference Water Index (NDWI)

(McFeeters, 1996) and the Modification of Normalized

Difference Water Index (MNDWI) (Xu, 2006) have

been used to monitor (Lu et al., 2013) changes in the

extent of the lakes (Ma et al., 2007), and the location

of water bodies (Rana and Neeru, 2017). Sonar is a

technique that uses sound waves to calculate water

depth (Knott and Hersey, 1957) and has advantages

such as high accuracy (± 0.1 m), low cost, and the

device can be mounted on any boat. Several types of

research have used sound for mapping water bodies

(McPherson et al., 2011; Popielarczyk and Templin,

2014; Giordano et al., 2015). Leon and Cohen (2012)

modeled the volume of Lake Eyre in Australia using

bathymetry and remote sensing. The authors used

surveys realized in 1974 and 1976 with the precision

of ± 0.3 m in the vertical component and up to ± 500

m in the horizontal component, which proved to be a

very limited and inaccurate method.

Water storage has two components: groundwater

and surface water (lakes, ponds, or reservoirs) (Brooks

et al., 2012). Some variations in water storage in the

reservoirs are due to an underground hydraulic

connection between aquifers and water bodies (Isiorho

et al., 1996; Winter, 1999). These variations in the

volume of water can be so drastic that large reservoirs

dry up in a short time like Laguna de Bustillos had in

years 2002 to 2006 and 2013 (NASA, 2017). Although

there is a geohydrological study that supports recharge

deficit in the aquifer, there is no information about the

storage capacity of Laguna de Bustillos. The lack of

information encourages the main objective of this

research to generate a new technique to generate a 3D

topobathymetric model that contributes to the

generation of updated data, which allows the deduction

of variables, such as underground infiltration from the

catchment area of Laguna de Bustillos. Despite these

models of volumetric estimation of water bodies, the

combination of more than two different topobathy-

metric measurement techniques had not been explored.

This document proposes a unpublish new method

integrating three methodologies to generate a more

robust and accurate three-dimensional model.

Materials and methods

This study was conducted between 2016 and the

first semester of 2018 in the Spatial Applications and

Research Center at the New Mexico State University.

The study area of Laguna de Bustillos is in the quadrant

between the coordinates 28° 38' 51'’ N – 28° 28' 27'’ N

and 106° 57' 3'’ W – 106° 38' 50'’ W in the municipality

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Figure 1. The study area of Laguna de Bustillos, Chihuahua. Source: LandsatLook Viewer (USGS, 2017a).

of Cuauhtemoc, in the state of Chihuahua (Figure 1).

This region’s climate is warm and semi-arid since it is

in a transition zone between the semi-humid climate

of the mountains and the Chihuahua desert (Kottek et

al., 2006). The average annual temperature ranges

from 6.9 to 21°C, with an average annual rainfall of

about 528 mm per year (Servicio Meteorológico

Nacional, 2017).

The authors designed a new four-stage method

to develop a 3-D topobathymetric model for the

purpose of determining water storage: i) extract

contour lines through a time series of remote sensing;

ii) determine bathymetry; iii) perform a topographic

survey (GPS-RTK); and iv) extract contours from the

regional terrain digital model. Also, it was included a

regression analysis in determining the two equations

that provide the volume and surface area using water

height. The flowchart below (Figure 2) shows the

modeling process.

Bathymetry

The New Mexico Water Resources Research

Institute (WRRI) funded a project to build an

Autonomous Surface Vehicle (ASV) to generate

bathymetric data for shallow water bodies. A PVC

center frame was attached to a two-hulled catamaran

boat, propelled by two motors, and equipped with a

GPS on the top to receive signals via satellite to provide

the direction and location. An Ardupilot® system

automated the catamaran navigation through an

Arduino® MEGA 2560 board to receive the GPS signal

while the sonar data bus decoded and recorded the

information on an SD card. Subsequently, the recorded

points were downloaded to a computer for

processing. The transducer was a Garmin® Intelliducer

Thru-Hull NMEA-0183, which does not require the

previous calibration and can measure from 60 cm to

200 m with a 0.1 m accuracy (Rojas-Villalobos, 2016).

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Figure 2. Schematic of the workflow to generate the 3D model.

Figure 3. Components to calculate the height of the lake bottom above sea level.

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

To construct a 3D model of the region including

the bottom of the lake, the bathymetry data (depth)

was transformed into topographic data (height).

Figure 3 shows the schematic of the surveying

process to transform to the correct topographic

points.

The following equation ( 1 ) was developed to

calculate the altitude above sea level for each

bathymetric point:

(1)

Here, ABP is the height of the bathymetric point,

ASNM is the altitude above sea level of the reference

level, PS is the depth of the sonar, and PR is the

recorded depth. The bathymetry consisted of 5

trajectories, and the data were adjusted through the

above equation using the reference levels of the

survey days. A GPS-RTK was used to establish the

fixed reference point corresponding to the height of

the lake contour and was linked to the bathymetry

obtained that day.

Contour extraction from remote sensors

Since the spatial resolution of remote sensing is

the most important factor for delineating the

contours of water bodies, Landsat ETM +, Landsat

OLI (Operational Land and Imager), and Sentinel 2A

(Table 1) were chosen to build the MNDWI.

These images are available for free on the

LandsatLook Viewer websites of the United States

Geological Survey (USGS, 2017a) and the Copernicus

Open Access Hub of the European Space Agency (ESA,

2017). Seven images were selected with the lowest

possible cloudiness over the study area during the time

the lake had gradually dried (March 2012 – August

2013). Also, six recent images were downloaded to

establish the maximum lagoon extent and baseline

curves for the bathymetry data (June 2016 –

September 2017). Using the Semiautomatic

Classification extension (Congedo, 2013) in QGIS®,

atmospheric correction was applied to the images

using the method of Subtraction of Dark Objects 1

(Chavez, 1996). Then, a fusion of images was

performed with the panchromatic band (ETM + and

OLI) using the Brovey transformation (Johnson et

al., 2012) to increase the spatial resolution to 15 m

before the MNDWI construction.

The Normalized Difference Water Index (NDWI)

was created to identify Landsat water bodies. The

high relative reflectance of green (G) in the electro-

magnetic spectrum contrasts with the high absorption

of the NIR in clear water (McFeeters, 1996). Excessive

suspended matter in the water increases reflectance

measurements in the NIR band (Ruddick et al., 2006),

thus dramatically reducing the difference between

the G-NIR bands, which makes it difficult to

distinguish between water and non-water surfaces.

Table 1. Collection of remote sensing data used in this article.

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Therefore, the NDWI method is not fit for Laguna de

Bustillos due to the turbidity of water. The MNDWI

suppresses this problem by replacing the NIR band

with an infrared shortwave band (SWIR) because the

water absorbs energy and the reflectance is low. The

equation that determines the MNDWI (Xu, 2006) is:

(2)

Where G is the green band of the electromagnetic

spectrum and SWIR is the short-wave band of the

infrared spectrum. The possible MNDWI values are

from -1 to 1.

In ArcGIS®, the raster calculator was used to

apply the MNDWI equation to Landsat and Sentinel

images. According to the MNDWI method, positive

values represent water and negative values the

surface without water. Therefore, the resulting raster

was reclassified by assigning 1 to those values greater

than 0 and 0 to values less than or equal to 0. From

the reclassified images, the contours were extracted

and examined through visual interpretation. This

procedure ensures that the extracted contours

correspond to the edge of the lake using false infrared

color composite images and avoids errors due to the

influence of the vegetation.

A failure of the SLC (Scan Line Corrector)

introduced strips with missing data in the Landsat

ETM+ images captured on May 31st, 2003 (USGS,

2017b). Due to this error in the sensor, only segments

were vectorized corresponding to the edge of the

water surface.

An orthometric height was assigned to the

contours using the closest ABP to the contour line (<

0.5 meters). When there were no bathymetry points

near the line, points were selected in a buffer of 1 to 2

m on each side of each contour. The contour took the

mean height following the Classic Central Limit

Theorem (Erdös and Rényi, 1959; Dowdy et al., 2011).

According to this theorem, when the sample size

increases, the average sample will approximate a

normal distribution. This procedure reduces the

uncertainty and variability of bathymetric data due to

boat sway and sonar accuracy (Krause and Menard,

1965; Eltert and Molyneux, 1972; Schmitt et al., 2008).

Topography

The GPS points were measured using two

SOKKIA GRX2 GNSS devices with a horizontal

accuracy of 5 mm and 10 mm on the vertical axis. A

GPS was established as base at the coordinate 28° 27'

25.1532" N and 106° 47' 24.9432" O at the height of

2069.08 on the WGS ellipsoid of 1984. 1006

topographic points were collected and transformed

to the Mexican Gravimetric Geoid 2010 (GGM10) to

generate altitude above the mean sea level (INEGI,

2015).

Digital elevation model

A contour was extracted at every meter from the

Mexican Elevation Continuation 3.0 (CEM 3.0) of the

National Institute of Statistics and Geography (INEGI,

2016). On September 5th, 2017, the water level of

the lake was 1975.56 m above sea level (masl). For

this reason, contour lines below 1976 m were

eliminated from the regional DEM.

Topobathymetric 3D model and volume estimation

An MDE with a spatial resolution of 2 m was

created using the four sources of elevation data using

the Topo-to-Raster tool contained in the 3D analysis

module of ArcGIS. This tool allows the creation of

hydrologically correct lifting meshes based on the

ANUDEM program (Hutchinson et al., 2011). Since

the triangulated irregular network (TIN) generates

more accurate volumetric calculations (Mi et al., 2007;

Hanjianga et al., 2008), the DEM was converted into

a TIN. The volume and water surface were calculated

from 1970.50 m to 1978.9 masl every 1 mm using

the ArcGIS Polygon Volume tool.

Statistical Evaluation

Since there is no previous model to evaluate the

lake storage, 16 areas of water coverage of different

scenes were extracted through remote sensing (RS)

when the lake was drying (real area) (Table 2).

The area of each scene was used to extract the

corresponding contour line from the 3D model and

generate the area. Using ArcGIS, the intersection of

the two layers was the area of a coincidence that was

statistically evaluated (Figure 4).

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Table 2. List of multispectral images used to compare 3D model

contours.

Figure 4. Demonstration of matching areas between water

surface extracted from a multispectral satellite image and the

3D model at the same reference level.

Some Landsat ETM + and OLI images were

replaced with recent Sentinel 2 images (early 2018)

to distribute the extracted contours along the height

through the 3D model (Table 3). This procedure is

used to evaluate the model accuracy (reality vs. model).

Table 3. List of multispectral images used to compare areas

between reality and 3D model. Added images are identified with *.

Because of the surface area changes according to

the elevation of the water surface, it is not possible to

evaluate the efficiency of the model directly. For this

reason, the relationship between the coincidence surface

and the reference area of the satellite image were used.

The maximum possible relation between both

areas is 100% because the level curves obtained from

the 3D model are directly related to the waterbody

contours. The water/non-water coverage maps of

the model and the satellite images of each year (Table

3) were analyzed using the Kappa statistic (K-hat)

through QGIS (QGIS, 2018) and Semi-Automated

Classification Plugin (Congedo, 2013). The Kappa

coefficient and overall accuracy allows us to know

the degree of agreement between the 3D model and

the water body surface (Card, 1982; Jensen, 2007;

Congalton and Green, 2008; Lillesand et al., 2014) .

Also, the t-statistical distribution was applied to find

the lower limit of the 95% Confidence Interval and

estimated the range of acceptable match surface

values (from Table 2) according to the sample mean

(Dowdy et al., 2011) ( 3 ).

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

(3)

Where is the mean of the sample,  is the level

of significance, v is the degrees of freedom (n -1), s is

the standard deviation, and n is the sample size.

Finally, two equations were generated repre-

senting the area of the water surface and the volume

contained in the lake according to the elevation of

the water surface.

Results and discussion

Figure 5 shows the sources of data used for the

reconstruction of the topobathymetric model: 13

contours from remote sensors, 29,715 bathymetry

points, 1,006 GPS points, and INEGI contours.

Figure 5. Map showing bathymetry, GPS points, derived curves

from multispectral RS, and regional contours (INEGI).

Figure 6. Triangulated Irregular Network is representing the topobathymetric 3D model of Laguna de Bustillos.

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

As a result of the reconstruction data process,

Figures 6 and 7 show the 3D topobathymetric model

and a 3D perspective of the Laguna de Bustillos. The

results show that the deepest point of the lake is

at 1970.215 masl, the maximum depth is 3.785 m

when the water level reaches the 1974 masl, the

water storage is 324.4 Mm3, and the average depth

is 1.37 m.

Figure 7. 3D perspective of Laguna de Bustillos (5 times height

exaggeration for better visualization).

Since the Kappa statistic shows the difference

between classified values of the satellite image

(reference data) and the surface of water body

generated by the 3D model, the coincidence is

expected to be high. Typically, Kappa values greater

than 0.80 represent a strong match between the

compared data. The result of the comparison shows

an overall accuracy higher than 95.21% and the K-

hat coefficients above 0.899. Table 4 shows the

increase of the values of overall accuracy and the

Kappa coefficient when the water level is higher.

It is observed that the values of elevation that

are between 1971.168 and 1971.284 have a value of

K-hat less than 0.9289 and are associated with water

coverage less than 80 km2. When the water level rises

above 1971.284 m, the Kappa indicator increases its

value above 0.93, reaching levels of 0.99. Also, low K-

hat values (0.8993 – 0.9289) are associated with low

depth averages (<0.41 m) in contrast to those K-hat

values above 0.96 that are in depth averages greater

than 0.71 m.

Table 4. Kappa coefficient values and overall accuracy between imagery (reality) and simulation (3d model).

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Conversely, with a confidence level of 95%, the

mean of the percentage of matching areas between

the satellite images and the 3D model is greater than

90.9% (Table 5).

Table 5. Confidence Interval analysis for the percentage of the

matching area between the three-dimensional model and

the sample images.

Below the contour 1971.325 m, four of the six

comparisons are below the lower limit of the

confidence interval (Figure 8).

Figure 8. Graph showing the behavior of the intersection

percentage between the surfaces of the 3D model and the

areas of RS images along elevation.

The mean area intersected below the reference level

is only 88.91%, while in the upper range, it is 97.01%.

Two equations were generated that estimated the

area of water coverage according to the depth of the

lake. The first equation calculated the volume below

the 1971.325 masl and the second equation calculated

the remaining volume above it. Similarly, two other

equations were generated estimating the amount of

water in the lake. The determination coefficients (R2)

for the estimated equations are greater than 0.9882;

this indicates that the equations obtained are suitable

for the topobathymetric model within the extent limits

of the lake (Figure 9).

Figure 9. Graphs of the surface and volume equations adjusted

to the 3D model.

Conclusions

Four different techniques, such as bathymetry,

GPS-RTK points, and contour lines extracted from the

remote sensors, were decisive in creating this new

three-dimensional modeling methodology for water

bodies. Its efficiency is demonstrated after the statistical

analysis applied. According to the results obtained in

the Kappa analysis and the confidence interval, the 3D

model is a robust and precise model (Kappa>0.80).

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Three processes were important in the cons-

truction of the model:

· The use of high precision GPS helped in fixing

the reference height points of the contours of the most

recent satellite images (2015 – 2018) with great

precision and accuracy.

· The bathymetric points linked to the current

height of the water level of the lake were instrumental

in establishing the height above sea level at the

bottom of the lagoon.

· The related height between the bathymetric

points and the levels closest to the bottom of the lake

was extracted from the satellite images (1999 – 2002).

Additionally, it was observed that the segments

of the contours extracted from the Landsat ETM+

images with an error in the SLC (USGS, 2017b)

influenced the relative low efficiency (0.8993 < Kappa

<0.9079) of the model below 1971.325 masl. On the

other hand, effectiveness in the top height ranges

from 1971.5 to 1974 masl was as a result of the spatial

resolution of the satellite images of Landsat OLI (15

m panchromatic) and Sentinel 2 (10 m) (Figure 10).

Although this 3D hydrological model is very

robust to be used in the administration of water in

the basin, special care must be taken in forecasting

floods in rural-urban areas. The model simulates much

of the flooded areas of Mennonite farmers, but the

3D model should not be used to prevent flood risks

due to the topographic complexity with dams and

ditches.

In future work, researchers should continue the

bathymetric survey with greater data density using a

sonar with increased accuracy to further the model’s

efficiency. The acquisition of more bathymetric data

will allow replacing contours extracted from the oldest

images such as Landsat ET and ETM +. Additionally,

the photogrammetric triangulation could be of great

benefit in urban and agricultural zones to delineate

more accurate topography. This development is the

first step to estimate the volume of water in the

Laguna de Bustillos as this work produces estimates

that approximate the actual values and such research

is relevant to water management in the region.

Figure 10. RS time series contours. The dark contour delimits the outer areas with greater 3D model performance and the internal

area with less accuracy.

HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

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HUGO LUIS ROJAS-VILLALOBOS, LUIS CARLOS ALATORRE-CEJUDO, BLAIR STRINGAM, ZOHRAB SAMANI, CHRISTOPHER BROWN: Topobathymetric 3D

model reconstruction of shallow water bodies through remote sensing, GPS, and bathymetry

• Vol. XII, Núm. 1 • Enero-Abril 2018 •

Este artículo es citado así:

Rojas-Villalobos, H. L., L. C. Alatorre-Cejudo, B. Stringam, Z. Samani, C. Brown. 2018. Topobathymetric 3D model reconstruction of

shallow water bodies through remote sensing, GPS, and bathymetry. TECNOCIENCIA Chihuahua 12(1):42-54.

DOI: https://doi.org/10.54167/tch.v12i1.129