Imperviousness Density Mapping Based on GIS-MCDA and

High-Resolution Worldview-2 Imagery

Lovre Panđa

University of Zadar, Department of Geography, Trg kneza Višeslava 9, Zadar, Croatia

Keywords: WorldView-2, Imperviousness Density, GIS MCDA, LULC, NDVI, Slope, TWI, Urban Area, Zadar, Croatia.

Abstract: Accurate monitoring and extraction of impervious surfaces are essential for urban planning and sustainable

environmental management. Increasing urbanization has led to a significant increase in the extent of

impervious surfaces, which, along with climate change, are the leading cause of increasingly frequent flooding

in urban areas. To prevent flooding disasters in urban areas, flood hazard and risk analyses must be carried

out. An imperviousness density model is one of the most important criteria in such analyses. In this study, an

imperviousness density model of the city of Zadar was created using GIS-MCDA and four criteria (LULC,

NDVI, slope and TWI). The criteria were extracted from WorldView-2 (WV-2) imagery and linearly

standardized using the Fuzzy logic approach. The Analytic Hierarchy Process (AHP) was used to determine

the final model for imperviousness density. The model with a spatial resolution of 0.5 m, based on the WV-2

imagery turned out to be much more detailed than existing publicly available models, such as the Copernicus

imperviousness density model, which is based on Sentinel-2 imagery with a spatial resolution of 10 m.

1 INTRODUCTION

The rapid growth of the world's population and

increasing urbanization have led to a significant

increase in the extent of impervious surfaces in urban

areas (Duan et al., 2022) while decreasing the extent

of forests, wetlands, green areas and bare soil (Xu,

2016). Impervious surfaces are man-made surfaces

covered with materials such as asphalt or concrete

that prevent the infiltration of water into the

underground (Weng, 2012). In urban areas, this can

include asphalted roads, sidewalks, parking lots,

paved yards, airport runways, shipping ports, squares,

cemeteries, etc. Also, a large portion of impervious

surfaces in urban areas are buildings with roofs made

of tile, concrete or sheet metal. Impervious surfaces

have a great impact on the creation of urban heat

islands and flood-prone areas (Fu et al., 2019).

Furthermore, the increasing number of impervious

surfaces can also disrupt the groundwater cycle and

increase the risk of urban flooding (Brun and Band,

2000). To prevent flooding disasters in urban areas,

flood hazard and risk analyses must be carried out. An

imperviousness density model is one of the most

important criteria in such analyses. Impervious

https://orcid.org/0000-0003-4549-4481

surface models are created in different ways, but

generally, they can be divided into four main

categories: (1) machine learning methods (Okujeni et

al., 2018; Zhang et al., 2018), (2) spectral indices (Liu

et al., 2013; Sun et al. 2015), (3) regression model

(Okujeni et al., 2013; Ou et al., 2019), and (4) spectral

mixture analysis (Herold et al., 2004; Yang and He,

2017). However, the methods are being improved

every day and new methods are proposed for

extracting impervious surfaces (Su et al., 2022). The

input data is very important for the creation of a high-

quality model. Data is collected from various

multispectral sensors placed on a specific platform

such as satellites, aircraft or drones. The choice of a

platform and sensor affects the spatial, spectral,

radiometric and temporal resolution, which in turn

affects the output results (Aasen et al., 2018).

Multicriteria GIS decision analysis (GIS-MCDA)

has not yet been used to create imperviousness

density models. Various criteria such as land use –

land cover model (LULC), vegetation indices or

hydrological and geomorphological criteria can

certainly be of great help in the detailed mapping of

imperviousness. Publicly available imperviousness

density models created by Copernicus, based on

222

Pan

da, L.

Imperviousness Density Mapping Based on GIS-MCDA and High-Resolution Worldview-2 Imagery.

DOI: 10.5220/0011988200003473

In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2023), pages 222-229

ISBN: 978-989-758-649-1; ISSN: 2184-500X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Sentinel 2 imagery (URL1) are an excellent input for

hydrological-hydraulic analysis based on models of

lower spatial resolutions. However, for analyses

based on higher spatial resolution models, it is

necessary to create an imperviousness density model

that follows the spatial resolutions of other high-

resolution inputs.

The main goal of this research is to generate a

GIS-MCDA imperviousness density model of Zadar

using high-resolution WorldView-2 (WV-2) satellite

imagery with a spatial resolution of 0.5 m.

1.1 Study Area

The study area is the City of Zadar located in the

middle of the Croatian coast (Figure 1). Zadar is the

fifth largest city in the Republic of Croatia and has

gone through rapid urban growth in recent decades

(Magaš, 1991; Graovac, 2004). This research is

focused only on the mainland part of the City of

Zadar, which in total covers 52.36 km

(Figure 1).

Figure 1: Geographical position of Zadar.

As a result of rapid urbanization and climate

change, pluvial flooding caused by short-peak rainfall

is becoming more frequent (Hallegatte et al., 2013;

Woodruff et al., 2013). Of particular note is the major

flood event on September 11, 2017, when a rain of

great intensity in a short time (only 2 hours) caused

extreme precipitation and flash floods. The rain of a

slightly weaker intensity continued to fall throughout

the morning and early afternoon and further worsened

the situation. In 24 hours, a total of 285 mm of rain

fell in Zadar causing significant material damage

(DHMZ, 2017).

2 METHODOLOGICAL

FRAMEWORK AND DATA

The methodological framework is divided into

several steps that include the acquisition and pre-

processing of satellite imagery, the creation of criteria

for the GIS-MCDA and the GIS-MCDA process itself

(Figure 2).

Figure 2: The methodological framework of the research.

2.1 Acquisition of WV-2 Satellite

Imagery

Launched in October 2009, WV-2 is the first

commercial satellite equipped with a high-resolution

multispectral sensor with 8 spectral bands. The WV-

2 satellite collects data in 8 different spectral bands

and one panchromatic band. The eight multispectral

bands (Coastal, Blue, Green, Yellow, Red, Red Edge,

NIR1 and NIR2) have a spatial resolution of 2 m,

while the panchromatic band has a spatial resolution

of 0.5 m (DigitalGlobe, 2010a; Maxar, 2020).

Since the production of orthorectified

multispectral image (MSI) and high-resolution digital

terrain model (DTM) is planned for the wider area of

the city of Zadar, it was decided to acquire ortho-

ready stereo (OR2A) satellite WV-2 data. OR2A

Imperviousness Density Mapping Based on GIS-MCDA and High-Resolution Worldview-2 Imagery

223

satellite imagery is projected onto a constant

elevation base calculated as the average terrain

elevation within a given polygon. Therefore, OR2A

satellite imagery is suitable for further processing and

orthorectification of a custom high-resolution DTM

(DigitalGlobe, 2010b).

2.2 Pre-processing of WV-2 MSI

Preprocessing of satellite imagery is crucial for the

removal of various deformations and distortions in

satellite images (Campbell and Wynne, 2011).

Therefore, within this manuscript preprocessing has

covered the following four steps:

2.2.1 Creation of a DSM

A digital surface model (DSM) of Zadar was created

from the provided WV stereo images in the

OrthoEngine 2018 extension of Geomatica 2018

software. The entire process of creating a DSM in the

OrthoEngine extension can be divided into the

following steps: (1) Selecting a mathematical model,

(2) adding ground control (GCP) and check points

(CP) required for stereo image orientation, (3) adding

tie points (TP), (4) bundle adjustment, (5) creating of

the epipolar image and (6) the automatic creation of

the DSM.

In the first step, an optimal mathematical model

was chosen for DSM extraction from the WV stereo

imagery, where Optical Satellite modeling based on

rational polynomial coefficients (RPC) and zero-

order polynomial adjustment was chosen, as one the

most commonly used mathematical models (Aguilar

et al., 2013a; Goldbergs et al., 2019).

In the second step, a total of 35 GCPs and 12 CPs,

collected with a real-time kinematic (RTK) GPS,

were added (2) to correct errors caused by the

application of RPC and to improve the overall

georeferencing accuracy (Aguilar et al., 2013a;

Goldbergs et al., 2019).

In the third step, a total of 264 TPs were

automatically collected (3) using stereo imagery pixel

correlation algorithms in Geomatica 2018 software.

In the fourth step, GCPs, TPs and the sensor

geometry generated from the RPC coefficients were

used for bundle adjustment (4) to calculate the exact

position of the satellite at the moment of acquisition.

The fifth step includes the creation of an epipolar

image (5), which represents a stereo pair of images,

where both images are reprojected so that they have a

common orientation and distribution along the

common x-axis (URL2). The created epipolar image

is the basis for the process of automatically creating a

DSM.

The final step of creating a DSM in the

OrthoEngine extension of the Geomatica software is

the automatic generation of a DSM (6) from the

created epipolar image. The method of semi-global

generation (Semi-global matching) of DSM was used

in the process. To achieve the maximum possible

spatial resolution of the created DSM, the pixel

sampling interval was set to the value 1. Thus, the

correlation of the stereo images and the creation of

the DSM was performed with the maximum

resolution of the images, enabling the generation of

the high-resolution model (URL2).

2.2.2 Creation of a DTM

Unlike DSM, which represents the surface of the

relief, natural (e.g. vegetation) and anthropogenic

(e.g. residential buildings, roads, industrial facilities,

etc.) surfaces located on the surface of the relief, a

DTM only represents the surface of the relief (Šiljeg,

2013). Since slope is one of the criteria and an

important criterion for the creation of the topographic

wetness index model (TWI), it was necessary to

create a DTM. The process of a DSM correction and

filtering for the creation of the DTM can be generally

divided into two steps: (1) Automatic correction of

the DSM into the DTM and (2) the manual filtering

of the remaining errors.

The automatic correction of the DSM into the

DTM (1) was performed in the Geomatica 2018

software using the DSM2DTM algorithm, which

enables the automatic filtering of the DSM according

to the given user-defined parameters. The

DSM2DTM algorithm automatically transforms the

DSM into a DTM by applying a series of filtration

steps that progressively remove anthropogenic and

natural elements and smooth the final model by

removing remaining irregularities, such as

depressions and elevations, that do not represent

relief surfaces.

After the automatic conversion of the DSM into a

DTM, it was necessary to manually remove all

remaining errors (2), resulting from the automatic

filtering process. Errors in the created model occurred

mainly in areas covered by water surfaces (e.g. the

sea), where due to the uniformity of the surface, the

software could not perform correlation and

connection of pixels of the satellite imagery. Unlike

artifacts on water surfaces, which include continuous

parts of the model, the errors remaining after

vegetation and anthropogenic objects were removed

were individual, spatially heterogeneous errors that

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

224

could not be removed automatically. Therefore, error

removal was performed manually using the DTM

correction filter from the DEM editing extension of

the Geomatica 2018 software.

2.2.3 Orthorectification of the WV-2 MSI

Orthorectification is one of the most important steps

of satellite data pre-processing (Aguilar et al., 2013b),

which is used to correct the geometry distortions of

the data created during data acquisition (Belfiore and

Parente, 2016). Orthorectification of the WV-2

imagery was performed using OrthoEngine 2018

software, based on the DSM previously created from

the WV-2 stereo imagery. The process was performed

only for one multispectral and one panchromatic

image, where the representation of anthropogenic

objects was better, e.g. less shadows and lower

building distortions. This is especially the case with

tall buildings such as skyscrapers or industrial plants.

In addition to the DTM, available polygons of

anthropogenic objects were also used for the

orthorectification of multispectral and panchromatic

images. Based on the available polygons, a more

accurate orthorectification (true orthorectification)

was performed.

2.2.4 Pan-Sharpening of WV-2 Imagery

Pan-sharpening of satellite imagery combines high-

resolution panchromatic imagery with lower-

resolution MSI, creating a unique MSI with a

resolution equal to the resolution of the panchromatic

image (Belfiore et al., 2016). There are many

different methods for pan-sharpening of satellite

images, which ultimately affect the appearance and

quality of the sharpened images (Belfiore et al., 2016;

Rajput et al., 2019). It should be noted that each

sharpening method degrades the MSI input to some

degree, primarily by deforming the shape or spectral

values of the original image to improve the spatial

resolution of the final image (Cheng and Chaapel,

2010). Although a special Hyperspherical Color

Space (HCS) method (DigitalGlobe, 2010c) was

developed to sharpen WV images, this method did not

prove to be the best solution in the visual comparison

of different sharpening algorithms. However, the

PANSHARP2 algorithm from the Geomatica 2018

software was used to sharpen the WV-2 images,

which caused significantly less distortion of the

spectral values compared to the HCS algorithm and

other algorithms tested.

2.3 Generating Criteria for the

GIS-MCDA

A total of four criteria (LULC, NDVI, slope and TWI)

generated from the WV-2 MSI and the DTM were

used in the GIS-MCDA process.

The LULC model was created using the GEOBIA

method. First, the WV-2 MSI was segmented using

the Mean Shift method (Comaniciu and Meer, 2002).

The spectral detail parameter was set to 15.5, the

spatial detail was set to 15 and the minimum segment

size was set to 20. The band sequence 8 4 1 (NIR2,

Yellow, Coastal) is used, which allows different types

of land cover to be viewed in different colors. This

band combination can be used to quickly identify land

use and land cover (URL3). Vegetation is in red,

built-up areas are in blue, and changes in vegetation

and structures are shown in different colors. A total

of 1,200 samples were collected in six classes (water,

asphalt/concrete/rock, buildings, low vegetation,

forest, and soil). The segmented WV-2 image was

then classified using the Support Vector Machine

(SVM) algorithm (Aguilar et al., 2014; Lin et al.,

2015; Wu et, al, 2017; Mugiraneza et al., 2019). The

created DSM was used as an additional parameter.

NDVI is a measure of the condition or health of

vegetation in a certain area, determined based on the

reflectance of certain spectral values (Maglione et al.,

2014; Moody et al., 2014). Healthy vegetation

reduces the susceptibility of a certain area to the

process of soil erosion, as plants stabilize soil deposits

with their roots and promote the infiltration of surface

runoff into the soil (Arabameri et al., 2020). For this

reason, high NDVI values, which represent healthy

vegetation, also represent permeable areas. The

NDVI model was created based on the spectral bands

of the WV-2 MSI using the following formula

(Maglione et al., 2014):

𝑁𝐷𝑉𝐼 =





(1)

The slope is a measure of the slope of each cell of

the raster surface, i.e. the terrain surface (Gallant and

Wilson, 2000). The Spatial Analyst extension of

ArcGIS 10.1 software was used to create slope

criteria using the Slope tool based on the Horn

method (Horn, 1981). Slope affects the velocity of

surface runoff, with velocity generally increasing as

slope increases (Morgan, 2009). Therefore, the slope

was used as one of the criteria because as the slope

increases, the possibility of water retention and

infiltration into the underground decreases.

The TWI is a hydrological measure of the

potential wetness of a certain terrain, which defines

Imperviousness Density Mapping Based on GIS-MCDA and High-Resolution Worldview-2 Imagery

225

the tendency of a certain area to accumulate water

(Różycka et al., 2017; Raduła et al., 2018). TWI

enables the detection and analysis of different

waterlogged areas (e.g. swamps, sinkholes, ravines,

river valleys, etc.) characterized by high values of this

index, and drier areas (e.g. steeper slopes and

elevations) with low values of this index (Gallant and

Wilson 2000). In this study, TWI was calculated

using the Topographic Wetness Index tool from the

Saga GIS extension for ArcGIS 10.1. The input data

for the calculation of TWI were the slope and the size

of the catchment area.

2.4 GIS-MCDA

Applied GIS-MCDA covered the following three

steps: (1) standardization of criteria, (2)

determination of weight coefficients (W

) and (3)

aggregation of standard criteria and W

The developed six classes of LULC models were

reclassified regarding the infiltration capacity of the

substrate according to the NRCS methodology

(USDA, 2017; Hong and Adler, 2008). Then,

standardization was performed using the Fuzzy logic

approach in ArcMap software. The Fuzzy

Membership tool standardizes the raster criteria

according to the chosen fuzzification algorithm on a

scale of 0-1, where a value of 1 indicates a maximum

membership strength that gradually decreases

towards 0 (ESRI, 2023). The linear method was used

and impervious classes received higher values.

The NDVI criterion was also linearly standardized

with lower NDVI values representing impervious

surfaces being assigned a higher value.

The slope criterion was also linearly reclassified

so that higher slope values represent lower

throughput.

The criterion TWI was linearly reclassified so that

higher index values mean potentially higher

throughput and lower index values mean lower

throughput potential.

The Analytic Hierarchy Process (AHP) was used

to determine the W

required for the creation of the

final imperviousness density model. The highest W

was assigned to the LULC, which is the most

important criterion in imperviousness density

mapping. The NDVI criterion is the next in terms of

importance, as it allows well distinction between

permeable and impervious surfaces. The smallest W

were assigned to the criteria of slope and TWI

because their influence exists, but is not as significant

as, for example, LULC. (Table 1). The CR was 0.1,

which is 10% and is considered acceptable.

Table 1: AHP preference matrix and assigned W

for the

created criteria.

LULC NDVI SLOPE TWI W

LULC 1 5 8 9 64.79

NDVI 0.2 1 6 7 24.64

SLOPE 0.125 0.167 1 2 6.33

TWI 0.111 0.143 0.5 1 4.24

In the final step, the GIS-MCDA model was created,

based on the aggregation of standardized criteria and

their W

3 RESULTS AND DISCUSSION

Using the methodology GIS-MCDA and the Fuzzy

logic approach to standardize the criteria, a model of

imperviousness density was created. A total of four

criteria were created and used in the further process

(LULC, NDVI, slope and TWI).

The developed LULC model distinguished

impervious surfaces very well compared to the WV-

2 MSI. The most dominant LULC class within the

study area is forest, while impervious classes

(buildings, asphalt/concrete/rock) are mostly

concentrated in urban coastal parts (Figure 3A).

The NDVI model best showed the difference

between permeable surfaces, shown in shades of

green, and less permeable and impervious surfaces,

shown in shades of red (Figure 3B). Numerous

researchers have used the NDVI and other indices

derived from MSI in impervious surface extraction

(Sun et al., 2015; Feng and Fan, 2019), and therefore

this is certainly one of the more important criteria.

The slope criterion created on the DTM serves as

a criterion that increases the degree of imperviousness

on steeper slopes (Ansari et al., 2016). Within the

research area, leveled and slightly sloping terrain

prevails, which is why, if only this criterion is

considered, most areas have a greater possibility of

permeability. Steeper slopes comprise a very small

part of the research area, which is mainly related to

anthropogenic forms (quarry and waste dump) or

natural slopes, and these areas are marked as less

permeable (Figure 3C).

The criterion TWI distinguished areas that tend to

accumulate water from the topographic basin. The

areas that have a lower tendency to accumulate water,

therefore, have a lower probability of water

infiltration. TWI values within the research area are

determined by the morphology of the relief, with

elevated parts recording lower values of this index,

which indicates less water accumulation (Figure 3D).

On the other hand, the lower parts of the relief record

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

226

higher values of TWI, that is, they indicate a greater

possibility of accumulating a significant amount of

water. According to TWI, it is possible to distinguish

two areas of water accumulation within the research

area. The first area includes the coastal part including

the center of the city of Zadar, and the second area the

depression on the outskirts of the city.

Figure 3: A) LULC; B) NDVI; C) Slope; D) TWI.

Figure 4: Imperviousness density based on A) Sentinel 2

MSI; B) WV-2 MSI.

The final imperviousness density model created

from four linear standardized criteria shows that there

are no completely permeable surfaces in Zadar

(Figure 5). The lowest value for imperviousness of

0.23 was achieved by surfaces covered with forest,

low vegetation and bare soil, while those covered

with asphalt, concrete and rock have higher values.

Although green areas and bare soil are permeable

surfaces, they have an infiltration capacity that is

overloaded during extreme rainfall, when the

substrate is oversaturated.

Comparing the imperviousness density model

created with a spatial resolution of 0.5 m (Figure 4B),

it is clear that it is significantly more detailed than the

existing 10 m Copernicus model (Figure 4A). A

quantitative comparison of these two models would

not be relevant considering the large difference in

spatial resolution. In the model created from the WV-

2 imagery, objects are extracted in more detail, unlike

the Copernicus model based on Sentinel 2.

Figure 5: Imperviousness density model.

4 CONCLUSIONS

This paper describes a new approach to

imperviousness density mapping using the GIS-

MCDA method, based on four predisposing criteria.

Depending on the quality of the input data, other

criteria can also be considered, such as the drainage

system or a more detailed LULC model. The existing

Copernicus imperviousness density model is well

suited for less detailed hydrological-hydraulic

analyses. For more detailed analyses, it is necessary

to create an imperviousness density model with

higher spatial resolution. However, a more detailed

Imperviousness Density Mapping Based on GIS-MCDA and High-Resolution Worldview-2 Imagery

227

model requires a longer processing time, so a perfect

balance should be found considering the purpose of

the final model. The created model of imperviousness

density in Zadar with a spatial resolution of 0.5 m is

certainly the most detailed model of imperviousness

of this area, which will serve as one of the spatial

input data for hydrological-hydraulic 2D modeling in

further studies. The same method will also be applied

to create an imperviousness density model from data

collected by a UAV multispectral camera with 10

bands and an aero-LiDAR system (ALS).

ACKNOWLEDGEMENTS

This research was produced as part of the Interreg

STREAM (Strategic development of flood

management) project, funded by the Italy-Croatia

cross-border cooperation program 2014-2020 and the

project UIP-2017-05-2694 financially supported by

the Croatian Science Foundation.

REFERENCES

Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P.

J. (2018). Quantitative remote sensing at ultra-high

resolution with UAV spectroscopy: a review of sensor

technology, measurement procedures, and data

correction workflows. Remote Sensing, 10(7), 1091.

Aguilar, M. A., Bianconi, F., Aguilar, F. J., & Fernández, I.

(2014). Object-based greenhouse classification from

GeoEye-1 and WorldView-2 stereo imagery. Remote

sensing, 6(5), 3554-3582.

Aguilar, M. Á., del Mar Saldaña, M., & Aguilar, F. J.

(2013a). Generation and quality assessment of stereo-

extracted DSM from GeoEye-1 and WorldView-2

imagery. IEEE Transactions on Geoscience and

Remote Sensing, 52(2), 1259-1271.

Aguilar, M. A., del Mar Saldana, M., & Aguilar, F. J.

(2013b). Assessing geometric accuracy of the

orthorectification process from GeoEye-1 and

WorldView-2 panchromatic images. International

Journal of Applied Earth Observation and

Geoinformation, 21, 427-435.

Ansari, T. A., Katpatal, Y. B., & Vasudeo, A. D. (2016).

Spatial evaluation of impacts of increase in impervious

surface area on SCS-CN and runoff in Nagpur urban

watersheds, India. Arabian Journal of Geosciences,

9(18), 1-15.

Arabameri, A., Blaschke, T., Pradhan, B., Pourghasemi, H.

R., Tiefenbacher, J. P., & Bui, D. T. (2020). Evaluation

of recent advanced soft computing techniques for gully

erosion susceptibility mapping: A comparative study.

Sensors, 20(2), 335.

Belfiore, O. R., & Parente, C. (2016). Comparison of

different algorithms to orthorectify WorldView-2

satellite imagery. Algorithms, 9(4), 67.

Belfiore, O. R., Meneghini, C., Parente, C., & Santamaria,

R. (2016). Application of different Pan-sharpening

methods on WorldView-3 images. ARPN J. Eng. Appl.

Sci, 11(7).

Brun, S. E., & Band, L. E. (2000). Simulating runoff

behavior in an urbanizing watershed. Computers,

environment and urban systems, 24(1), 5-22.

Campbell, J. B., & Wynne, R. H. (2011). Introduction to

remote sensing. Guilford Press.

Cheng, P., & Chaapel, C. (2010). Pan-sharpening and

geometric correction: worldview-2 satellite.

GeoInformatics, 13(4), 30.

Comaniciu, D., & Meer, P. (2002). Mean shift: A robust

approach toward feature space analysis. IEEE

Transactions on pattern analysis and machine

intelligence, 24(5), 603-619.

DHMZ - Državni hidrometeorološki zavod (2017).

Available from: https://meteo.hr/objave_najave_nat

jecaji.php?section=onn&param=objave&el=priopcenj

a&daj=pr12092017 (Accessed on 17 November 2022).

DigitalGlobe, Inc. (2010a): The Benefits of the 8 Spectral

Bands of WorldView-2, https://docslib.org/doc/

5291751/the-benefits-of-the-eight-spectral-bands-of-

worldview-2 (Accessed on 19 November 2022).

DigitalGlobe, Inc. (2010b): DigitalGlobe Core Imagery

Products Guide, https://lps16.esa.int/posterfiles/paper1

213/%5bRD12%5d_digitalglobe-core-imagery-produc

ts -guide.pdf (Accessed on 20 November 2022).

DigitalGlobe, Inc. (2010c) - WorldView-2 Imagery Basics

and Erdas Imagine, https://fdocuments.net/document

/worldview2-basics-and-erdas-imagine.html?page=1

(Accessed on 22 November 2022).

Duan, C., Zhang, J., Chen, Y., Lang, Q., Zhang, Y., Wu, C.,

& Zhang, Z. (2022). Comprehensive Risk Assessment

of Urban Waterlogging Disaster Based on MCDA-GIS

Integration: The Case Study of Changchun, China.

Remote Sensing, 14(13), 3101.

ESRI (2023). Fuzzy Membership (Spatial Analyst)—

ArcMap| Documentation

(n.d.). Available from:

https://desktop.arcgis.com/en/arcmap/latest/tools/spati

al-analyst-toolbox/fuzzy-membership.htm (Accessed

on 24 February 2023).

Feng, S., & Fan, F. (2019). A hierarchical extraction

method of impervious surface based on NDVI

thresholding integrated with multispectral and high-

resolution remote sensing imageries. Ieee Journal of

Selected Topics in Applied Earth Observations and

Remote Sensing, 12(5), 1461-1470.

Fu, Y., Liu, K., Shen, Z., Deng, J., Gan, M., ... & Wang, K.

(2019). Mapping impervious surfaces in town–rural

transition belts using China’s GF-2 imagery and object-

based deep CNNs. Remote Sensing, 11(3), 280.

Gallant, J. C., & Wilson, J. P. (2000). Terrain analysis:

principles and applications (Vol. 538). New York: John

Wiley & Sons.

Goldbergs, G., Maier, S. W., Levick, S. R., & Edwards, A.

(2019). Limitations of high resolution satellite stereo

imagery for estimating canopy height in Australian

tropical savannas. International Journal of Applied

Earth Observation and Geoinformation, 75, 83-95.

Graovac, V. (2004). Populacijski razvoj Zadra. Geoadria,

9(1), 51-72.

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

228

Hallegatte, S., Green, C., Nicholls, R. J., & Corfee-Morlot,

J. (2013). Future flood losses in major coastal cities.

Nature climate change, 3(9), 802-806.

Herold, M., Roberts, D. A., Gardner, M. E., & Dennison, P.

E. (2004). Spectrometry for urban area remote

sensing—Development and analysis of a spectral

library from 350 to 2400 nm. Remote sensing of

environment, 91(3-4), 304-319.

Hong, Y., & Adler, R. F. (2008). Estimation of global SCS

curve numbers using satellite remote sensing and

geospatial data. International Journal of Remote

Sensing, 29(2), 471-477.

Horn, B. K. (1981). Hill shading and the reflectance map.

Proceedings of the IEEE, 69(1), 14-47.

Lin, C., Wu, C. C., Tsogt, K., Ouyang, Y. C., & Chang, C. I.

(2015). Effects of atmospheric correction and

pansharpening on LULC classification accuracy using

WorldView-2 imagery. Information Processing in

Agriculture, 2(1), 25-36.

Liu, C., Shao, Z., Chen, M., & Luo, H. (2013). MNDISI: A

multi-source composition index for impervious surface

area estimation at the individual city scale. Remote

sensing letters, 4(8), 803-812.

Magaš, D. (1991). Neke promeje u gradskim naseljima

zadarske regije u razdoblju 1857.-1971. godine. Radovi

Zavoda za povijesne znanosti HAZU u Zadru, (33), 239-

258.

Maglione, P., Parente, C., & Vallario, A. (2014). Coastline

extraction using high-resolution WorldView-2 satellite

imagery. European Journal of Remote Sensing, 47(1),

685-699.

Maxar (2020): Worldview-2. Datasheet. (Accessed on 19

November 2022).

Moody, D. I., Brumby, S. P., Rowland, J. C., Altmann, G. L.,

& Larson, A. E. (2014, October). Change detection and

classification of land cover in multispectral satellite

imagery using clustering of sparse approximations

(CoSA) over learned feature dictionaries. In 2014 IEEE

Applied Imagery Pattern Recognition Workshop (AIPR)

(pp. 1-10). IEEE.

Morgan, R. P. C. (2009). Soil erosion and conservation. John

Wiley & Sons.

Mugiraneza, T., Nascetti, A., & Ban, Y. (2019). WorldView-

2 data for hierarchical object-based urban land cover

classification in Kigali: integrating rule-based approach

with urban density and greenness indices. Remote

Sensing, 11(18), 2128.

Okujeni, A., Canters, F., Cooper, S. D., Degerickx, J.,

Heiden, U., Hostert, P., ... & van der Linden, S. (2018).

Generalizing machine learning regression models using

multi-site spectral libraries for mapping vegetation-

impervious-soil fractions across multiple cities. Remote

sensing of environment, 216, 482-496.

Raduła, M. W., Szymura, T. H., & Szymura, M. (2018).

Topographic wetness index explains soil moisture better

than bioindication with Ellenberg’s indicator values.

Ecological Indicators, 85, 172-179.

Rajput, U. K., Ghosh, S. K., & Kumar, A. (2016). Multi-

sensor satellite pan-sharpening based on IHS and

Window Pseudo Wigner distribution integrated

approach: Application to WorldView-2 imagery. Intern-

ational Journal of Image and Data Fusion,7(2),119-147.

Różycka, M., Migoń, P., & Michniewicz, A. (2017).

Topographic Wetness Index and Terrain Ruggedness

Index in geomorphic characterisation of landslide

terrains, on examples from the Sudetes, SW Poland.

Zeitschrift für geomorphologie, Supplementary issues,

61(2), 61-80.

Šiljeg, A. (2013). Digitalni model reljefa u analizi

geomorfometrijskih parametara–primjer PP Vransko

jezero (Doctoral dissertation, Doktorski rad, PMF,

Sveucilište u Zagrebu).

Su, S., Tian, J., Dong, X., Tian, Q., Wang, N., & Xi, Y.

(2022). An impervious surface spectral index on

multispectral imagery using visible and Near-Infrared

bands. Remote Sensing, 14(14), 3391.

Su, S., Tian, J., Dong, X., Tian, Q., Wang, N., & Xi, Y.

(2022). An impervious surface spectral index on

multispectral imagery using visible and Near-Infrared

bands. Remote Sensing, 14(14), 3391.

Sun, G., Chen, X., Jia, X., Yao, Y., & Wang, Z. (2015).

Combinational build-up index (CBI) for effective

impervious surface mapping in urban areas. IEEE

Journal of selected topics in applied earth observations

and remote sensing, 9(5), 2081-2092.

URL1: Imperviousness density 2018. https://land.coper

nicus.eu/pan-european/high-resolution-layers/impervio

usness/status-maps/imperviousness-density-2018?tab=

metadata (Accessed on 17 November 2022).

URL2: https://catalyst.earth/catalyst-system-files/help/CO

MMON/concepts/DEM_buildfromstereo.html

(Accessed on 3 December 2022).

URL3:https://studylib.net/doc/7954755/worldview--2-band

-combinations---c-agg (Accessed on 20 December2022).

USDA (2017): Part 630 Hydrology: National Engineering

Handbook, Chapter 9: Hydrological Soil-Cover

Complexes.

Weng, Q. (2012). Remote sensing of impervious surfaces in

the urban areas: Requirements, methods, and trends.

Remote Sensing of Environment, 117, 34-49.

Woodruff, J. D., Irish, J. L., & Camargo, S. J. (2013).

Coastal flooding by tropical cyclones and sea-level rise.

Nature, 504(7478), 44-52.

Wu, Q., Zhong, R., Zhao, W., Fu, H., & Song, K. (2017). A

comparison of pixel-based decision tree and object-

based Support Vector Machine methods for land-cover

classification based on aerial images and airborne lidar

data. International Journal of Remote Sensing, 38(23),

7176-7195.

Xu, J., Zhao, Y., Zhong, K., Ruan, H., & Liu, X. (2016).

Coupling modified linear spectral mixture analysis and

soil conservation service curve number models to

simulate surface runoff: application to the main urban

area of Guangzhou, China. Water, 8(12), 550.

Yang, J., & He, Y. (2017). Automated mapping of

impervious surfaces in urban and suburban areas:

Linear spectral unmixing of high spatial resolution

imagery. International Journal of Applied Earth

Observation and Geoinformation, 54, 53-64.

Zhang, L., Zhang, M., & Yao, Y. (2018). Mapping seasonal

impervious surface dynamics in Wuhan urban

agglomeration, China from 2000 to 2016. International

journal of applied earth observation and

geoinformation, 70, 51-61.

Imperviousness Density Mapping Based on GIS-MCDA and High-Resolution Worldview-2 Imagery

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