An Integrated Environmental Monitoring Approach through the

Development of Coal Mine, a GIS Open Source Application

L. Duarte

1,2 a

, A. C. Teodoro

1,2 b

, J. Fernandes

1,2

, P. Santos

1,2

and D. Flores

1,2 c

Institute of Earth Sciences, FCUP pole, Rua do Campo Alegre, Porto, Portugal

Department of Geosciences, Environment and Spatial Planning, FCUP, Porto, Portugal

Keywords: Coal Mine, Water Quality, Soils Analysis, Relational Database.

Abstract: Coal related fires may occur in un-mined outcrops, during coal mining, in abandoned mines, during storage

and transportation and in coal waste deposits. The self-burning of coal mobilizes large amounts of pollutants,

for instance, particulate matter, organic compounds and toxic trace elements that can be emitted, released or

leached to soils, waters and air of the surrounding environment. The S. Pedro da Cova (Porto, Portugal) coal

mine was exploited between 1795 and 1972 and had an important role on the economic development of the

region. Nowadays a waste pile of about 28,000 m2 is still deposited in the mine, suffering from self-

combustion since 2005. Geographical Information System (GIS) and spatial databases are frequently used for

monitoring this type of processes. The main objective of this work was to integrate, manipulate and combine

the spatial information obtained in the field with other datasets (geospatial and alphanumerical) in a GIS open

source application connected to a relational database (PostGIS), in order to monitor and assess environmental

conditions in the S. Pedro da Cova coal mine. This is an ongoing project where some campaigns were

conducted and some spatial information was obtained (thermal images, Digital Elevation Model) and also

water and soil samples.

1 INTRODUCTION

The S. Pedro da Cova coal mine was exploited

between 1795 and 1972 and had an important role on

the economic development of the region. Nowadays

a waste pile of about 28,000 m

is still deposited in

the mine, suffering from self-combustion since 2005.

These coal waste piles can be responsible for the

dissemination of pollutants through particulate matter

and gases by air and water. It is thereby crucial to

monitor the geochemical elements mobilization into

soils and water, as well as the combustion process

associated with the coal fires.

In this context, Geographical Information

Systems (GIS) and relational databases are frequently

used when the issue involves several data. For

instance, Chen and Li (2008) developed a WebGIS-

based decision support system that integrates spatial

information techniques and field survey data of a coal

mine waste. Also, Guo et al. (2016) created a

WebGIS system to perform the information

management of a coal mine. The Global Positioning

https://orcid.org/0000-0002-7537-6606

https://orcid.org/0000-0002-8043-6431

https://orcid.org/0000-0003-4631-7831

System (GPS) combining with GIS techniques have

also been used in this context. For instance, Mert and

Dag (2018) used GPS and GIS technologies focusing

in real-time monitoring of excavated coal quality.

Tama et al. (2018) used web-based GIS solutions

as cloud-based WebGIS application to track changes

in land surface of Miedzianka caused by historical

mining of metal ores. Lee and Park (2013) also

analysed the hazard to ground subsidence through a

decision trees approach in a GIS environment.

Several studies use the PostgreSQL-PostGIS

spatial database to manage different layers of

information based on an analytical hierarchy process

(Kumar et al., 2016; Kostecki, 2017; Díaz-Cuevas et

al., 2018; Liu et al., 2019; Obeidavi et al., 2019).

The QGIS, an open source GIS software, has been

already used in geology studies. For instance,

Choudhury and Arutchelvan (2016) used QGIS to

know the implementation of technology intended for

the development of Neyveli mine closure planning

system. Also, Tama and Malinowska (2018) used

QGIS and SAGA to analyse the water hazard caused

286

Duarte, L., Teodoro, A., Fernandes, J., Santos, P. and Flores, D.

An Integrated Environmental Monitoring Approach through the Development of Coal Mine, a GIS Open Source Application.

DOI: 10.5220/0009578402860293

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 286-293

ISBN: 978-989-758-425-1

by ground deformations in the mining area of coal

mine Kopalnia Węgla Kamienneg (KWK) Morcine.

The main objective of this work was to integrate,

manipulate and combine the spatial information

obtained in the field with different datasets in a GIS

open source application connected to a relational

database, in order to monitor and assess

environmental conditions of the S. Pedro da Cova

coal mine. The development of this application

present several advantages in coal mining context,

automatizing the procedures to integrate and analyse

all the data acquired in the field, incorporating several

methodologies and updating the data acquired in the

field through a database created for that purpose.

These possibilities implemented in the GIS

application will improve time-efficiency and

automatize the procedures to study the coal mine.

Under this scope, a GIS open source application

(Coal Mine) and a PostGIS database were created and

connected in order to access the data.

2 STUDY AREA

The study area is located approximately at 10 km east

of Porto (in S. Pedro da Cova, Gondomar), in

northern Portugal, and it is a part of the Douro

Coalfield that represents the most important coal-

bearing deposit in Portugal (Upper Pennsylvanian). It

has a NW–SE alignment, a variable width (30–250 m)

and approximately 53 km in length (Pinto de Jesus,

2001).

The study area is located along the border of the

Valongo Anticline western flank. Stratigraphically in

the study area, it is possible to identify different

metasedimentary formations with ages between the

Precambrian and / or Cambrian, Ordovician, Silurian,

Devonian and Carboniferous (Medeiros, 1980).

Figure 1 presents the study area.

Figure 1: Study area location.

Anthracite was mined in S. Pedro da Cova for

over 177 years, and, as result, a significant waste pile

emerges along the landscape, with near 450 meters

long, deposited along slope.

In 2001, wastes from the national steel industry

were deposited along the mine waste pile northern

border. These have been considered highly enriched

in Lead (Pb), Zinc (Zn) e Chromium (Cr), and for this

reason in 2014 the Portuguese Government started the

removal process of these wastes, nowadays is

estimated that 125.000 tonnes remain on site.

In 2005, after ignition caused by forest fires, a part

of the coal rich waste pile started to combust, and has

been burning, self-feeding, until the present days.

Presently the self-combustion seems confined to one

active focus, in the centre of the waste pile.

The study area is located along the western border

of a sensitive natural area, the Natura 2000 protection

area, and is contiguous to the population, which

enhances the environmental concerns.

2.1 Coal Mine Project

The CoalMine Project financed by the Portuguese

foundation for science and technology (FCT in

Portuguese) aims to characterize and quantify of the

impacts on surrounding environment ecosystems and

health of population living nearby S. Pedro da Cova

mine waste pile. The investigation of the impacts on

soil and water allows to identify organic and

inorganic elements that can potentially damage the

ecosystems and have negative impacts on human

health. This project comprises multiple datasets

referring to different periodic campaigns, that aims

characterize the geochemical composition of water

and soils in the vicinity of the old coal mine, and also

the surface temperature. The project also monitors

eventual landslide mass movements occurring in the

waste piles. The spatial distribution of contamination

and its extension are verified considering spatial

analyses and geo-statistical algorithms.

2.2 Data Acquisition

Two soil sampling campaigns were already

performed. The first was conducted in February 2019,

and the second was conducted in October 2019. The

soil samples of the sampling made in October are still

being processed, so will not be included in this work.

A total of 50 surface (0-20cm) soil samples were

collected over a regular mesh with approximately 100

m spacing between samples. The samples covered an

area of about 480 000 m

, exceeding the areas covered

by the coal mine waste pile as these had been subject

An Integrated Environmental Monitoring Approach through the Development of Coal Mine, a GIS Open Source Application

287

of previous studies (Ribeiro et al. 2010, 2011, 2012,

2015). The sampling was preferentially oriented NE-

SW according to the development of the main

drainage basin, southwest of the waste pile.

The geographic coordinates (in WGS84

coordinate system) of the sampling locations were

identified using a GPS receptor.

Regarding the water quality monitoring, a total of

5 sampling points were selected, 2 in Ribeira de

Silveirinhos (one upstream – A1 – and other

downstream – A4 – from the mine effluents

discharge), two points in mine drainage galleries (A2

and A3) and control point (A5) collected in a spring

without any influence of mine drainage. The

sampling plan defined for this work comprised 5

sampling campaigns at the 5 points mentioned,

collected on a quarterly periodicity in November

2018, February, May, September and December

2019. Figure 2 presents the location of the soil and

water quality monitoring points.

Figure 2: Location of soil and water quality monitoring

points.

For the acquisition of the imagery data, 2

campaigns of flights considering an unmanned aerial

vehicle (UAV) were done. The first campaign

occurred on the 23 July 2019 and the second occurred

on the 30 December 2019. Each flight acquired data

from three different sensors, namely, a thermal

infrared (TIR) sensor, an RGB camera and a

multispectral sensor (blue, green, red, red edge and

near infrared bands).

3 METHODOLOGY

3.1 PostGIS Database

The data acquired was stored in PostGIS 3.0

relational database, which is an extension of

PostgreSQL 12.1 relational database manager. The

database was stored locally (PostGIS, 2020).

The vector data, in shapefile format, was imported

to the database using the PostGIS Shapefile

Import/Export Manager 3.0.0 functionality from

PostGIS 3.0 software (PostGIS, 2020). Consequently,

a connection was established between the database

and the QGIS 3.10 software. The Database Manager

tool from QGIS 3.10 software was used to insert in

the database the alphanumeric data, already existent

(in excel format). The raster data was imported to the

database through the raster2pgsql algorithm,

included in the PostGIS 3.0 software.

As a relational database, connections between

common elements must be assured, in order to reduce

the computational effort of the database. For tabular

data, this connection is established by the definition

of one or more columns, with unique values, as the

primary key and the definition, in a different table, of

the identical of columns with the same data as the

foreign key. The primary key uniquely identifies a

line within the table, while the foreign key constraints

a table to only the records that match the primary key

of another table, in the process referencing and

connecting the two tables (PostGIS, 2020). Figure 3

shows part of the different tabular data found in the

public schema of the database and the identification

of the columns and primary keys that compose it. This

includes data from water and soil analysis, metadata

of the raster files and the punctual temperature

measurements. The aforementioned process of

linking different tabular data is still ongoing, as the

data acquisitions and database optimization.

Figure 3: Tabular data of public schema of the database.

In the created database, every record is identified

with the number of the sample and the date in which

the sample was collected. A connection is, then,

established between the table where these records are

kept and the table where the location of each sample

number is stored, giving not only a temporal, but also

a spatial dimension to the stored data.

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3.2 GIS Application

3.2.1 Coal Mine Implementation

The figure 4 presents a diagram containing all the

procedures performed under this project, since the

PostGIS database and the Coal Mine application

development.

Figure 4: Diagram of Coal Mine application development.

The Coal Mine application was developed under

QGIS 3.10 software, using Python programming

language (QGIS, 2020). The graphic interface was

created based on the composition of Qt Designer

framework widgets (QGIS, 2020). Qt Designer is a

Qt tool which allows to design and build graphical

user interfaces (GUI) through QWidgets. The

windows and dialogs can be personalized in this

framework. The application was created as a toolbar

button. This button opens a main window built as a

QMainWindow class (Figure 5; Qt API, 2020).

Figure 5: Coal Mine application main window.

The main window is composed by 7 menus: File,

Water, Soil, Temperature Points, Temperature Maps,

Digital Elevation Model (DEM) and Land Use Land

Cover (LULC); basic and standard tools to

manipulate the maps such as Zoom in, Zoom out and

Pan; a table of contents named Layers and the canvas

where the layers are spatially presented (Figure 5).

The menu File allows to open vector or raster files

in the application canvas.

The Water and Soil menus connects to the

database where the information of water and soil are

saved and present options to open a specific campaign

(by date) chosen by the user.

The Temperature Points menu also connects to

the database and presents the temperature information

measured in the field) and provides a functionality

that incorporates Kriging algorithm and Inverse

Distance Weighting (IDW) in order to automatize the

procedure to create a continuous temperature surface

(Figure 6). This implementation was based on a

previous GIS application which allows to create

temperature interpolation maps using Kriging

algorithm (Duarte et al., 2017).

The IDW method was implemented based on

v.surf.idw algorithm from GRASS library (GRASS,

2020) and the kriging method was implemented based

on Ordinary Kriging algorithm from SAGA library

(SAGA, 2020).

In this GUI, 5 fields were created based on the

parameters of v.surf.idw algorithm: the field to input

the points, the field to choose the attribute with the

values to interpolate, the cell size of the final raster

surface, the interpolation method (IDW or Kriging),

and finally the field to the output surface (Figure 7).

Figure 6: GUI of Temperature Points menu.

The Temperature Maps menu allows to open, in

the canvas, the temperature maps already created.

These maps where created combining several

methods. The thermal imagery, acquired by the TIR

sensor, was aligned and juxtaposed using the Agisoft

Metashape 1.5.5 software (Agisoft, 2019). The

images were orthorectified, based on the DEM,

creating an orthomosaic of the surface temperatures.

Finally, the orthomosaic was clipped to the extent of

the coal mines’ waste pile using Clip raster with

polygon from SAGA library, (SAGA, 2020). This

menu is connected to the PostGIS database and opens

the temperature maps in raster format.

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The DEM menu also provides two options: the

DEM generated in a specific campaign (generated

from UAV imagery) and the Terrain Analysis

functionality which allows to create slope and aspect

maps from DEM. The algorithm implemented,

r.slope.aspect belongs to GRASS library (GRASS,

2020). This GUI is composed by 3 fields: an input

field to DEM, and 2 output fields to slope and aspect,

respectively.

The LULC menu also connects to the database to

access the LULC map already created. The LULC

map was obtained based on K-Means Cluster

Analysis Operator 1.0 algorithm from SNAP 7.0

software (SNAP, 2019). Also, this menu provides a

functionality to derive the LULC using the K-Means

unsupervised classification method algorithm from

SAGA (SAGA, 2020). The GUI that allows to create

the classification is composed by 3 fields: an input

field, a spin box widget to define the number of

clusters and an output field.

All the data provided in this application is hosted

in the PostGIS database. The application connects

automatically to the database, filters the request and

allowed to access to the information.

The table of contents also provides some

functionalities when a layer is opened, such as: Show

extent which provides the extent of the layer; Remove

layer which allows to remove the layer from canvas;

Zoom to Layer and access to the Attribute Table

(Figure 7).

Figure 7: Basic standard tools of table of contents.

In order to create the application canvas to

visualize the data, a QWidget was built in Qt Designer

and it was promoted to QgsMapCanvas.

3.2.2 PostGIS Connection

As explained in the previous section, the data

acquired in the field campaigns are available to

visualize and analyse in Coal Mine application. This

visualization is only possible through the connection

to a local PostGIS database where the data is hosted

(see section 3.1). The connection to the PostGIS

database under the application and using Python

functions were performed with the following code

lines:

uri = QgsDataSourceUri()

uri.setConnection("localhost", "5432",

"Database", "postgres", "postgres")

uri.setDataSource("public",

"distritos2", "geom", '', "gid")

db = QSqlDatabase.addDatabase("QPSQL");

db.setDatabaseName(uri.database())

db.setPort(int(uri.port()))

db.setUserName(uri.username())

db.setPassword(uri.password())

db.open()

4 RESULTS AND DISCUSSION

The application was tested with the data connected to

PostGIS database. This possibility allows to evaluate

and analyse multiple layers which are opened and can

be overlapped. Also, the new functionalities were

tested, and some results are showed, such as slope and

aspect maps (Figure 8), the interpolation of

temperature values using kriging algorithm (Figure 9)

and the LULC using K-Means algorithm (Figure 10).

Figure 8: Slope map (left) and aspect map (right).

Figure 9: LULC map.

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Figure 10: Unsupervised classification with 2 clusters

defined.

The developed application allowed to create

several maps such as: slope and aspect maps, LULC

map and temperature map. An analysis was

performed considering the referred variables. The

temperature values are independent of the slope

values. However, based on the aspect map, the waste

pile is facing a southerly direction, receiving direct

sunlight for most of the daylight. Despite this, the heat

dispersed on the surface by the sun cannot explain the

presence of the verified high temperatures. This

conclusion stems from the fact that the results

attained, so far, were very similar, despite the

significantly different conditions of both flights. One

campaign was conducted during summer and the

second during winter, under substantially different air

temperature conditions. The winter flight recorded a

maximum surface temperature of 50,9 ⁰C, while,

during the flight that took place in the summer the

highest surface temperature detected was 57,8 ⁰C. It

was also observed that high temperatures seem to be

a deterrent for vegetation growth, regarding the

LULC map and the temperature map. The areas with

the higher temperatures featured little or no

vegetation.

The development of Coal Mine application

present several advantages in coal mine

environmental monitoring context: i) it automatize

several procedures that allows to integrate and

analyse all the data acquired in the field; ii) it is

composed by several methodologies such as the

creation of interpolation surfaces for punctual data,

the creation of slope and aspect maps from DEM and

the automatic land cover classification through

unsupervised classification and; iii) to update the data

acquired in the field through the database. These

possibilities implemented in the GIS application will

improve time-efficiency and automatize the

procedures to study the coal mine environmental

legacy.

Given the periodic nature of these monitoring

campaigns, the Coal Mine application should help to

minimize the potential input errors, automatizing the

data procedures. The results will contribute to support

decision making.

5 CONCLUSIONS

The Coal Mine Project aims the characterization and

quantification of the impacts on surrounding

environment ecosystems and have negative impacts

on human health of population living nearby S. Pedro

da Cova mine waste pile. Under this project, the Coal

Mine application was created in a GIS open source

software in order storage all the data obtained in the

field and generates useful information. The

application allows to visualize, manipulate and create

relevant data. The Coal Mine application can be used

as a decision support tool to help to mitigate and

minimize the severe impacts in the environment

nearby S. Pedro da Cova. The main advantage of the

Coal Mine application is to improve and optimize the

manipulation of the data obtained in situ and using

geo-processing algorithms which will help in the

future to perform the management of the coal mine,

to improve the safety and the automatization of

several processes.

This is an ongoing project where some campaigns

were conducted and different spatial information was

obtained, such as water and soil samples in the field.

Through the UAV flights, several maps were also

obtained such as LULC maps and thermal images. A

lot of information has been collected until now and

with future campaigns this volume tends to grow, so

a spatial and open source database was crucial to

integrate all the data.

In the future, this application will incorporate

other functionalities to provide other maps such as

fire risk maps, groundwater vulnerability to pollution

maps and soil erosion maps. Also, it will provide the

possibility to perform statistical analysis providing

results in the format of histograms, plots and

variograms. The results will also allow the analysis of

the dynamics of the combustion process in the coal

waste pile as well as predict evolution scenarios. The

GIS application is free and open and available to any

user.

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291

ACKNOWLEDGEMENTS

This work was funded through the Foundation for

Science and Technology, through the CoalMine

project with the ref. POCI-01-0145-FEDER-030138,

02-SAICT-2017 and by FEDER funding through the

COMPETE 2020 programme and framed within the

activities of the UIDB/04683/2020. ICT financed

through the European Regional Development Fund

(COMPETE 2020), with ref. POCI-01-0145-ERDF-

007690.

REFERENCES

Agisoft. Agisoft. 2020. https://www.agisoft.com/.

Accessed January 2020.

Chen, Y., Li, D., 2008. A Web-GIS based Decision Support

System for Revegetation in Coal Mine Waste Land. In

WSEAS Int. Conf. on Applied Computer & Applied

Computational Science (ACACOS '08), Hangzhou,

China, April 6-8.

Díaz-Cuevas, P., Camarillo-Naranjo, J.M., Pérez-

Alcántara, J.P., 2018. Relational spatial database and

multi‑criteria decision methods for selecting optimum

locations for photovoltaic power plants in the province

of Seville (southern Spain). Clean Technologies and

Environmental Policy, 20:1889–1902.

Duarte, L., Teodoro, A.C., Gonçalves, J.A., Ribeiro, J.,

Flores, D., Lopez-Gil, A., Dominguez-Lopez, A.,

Angulo-Vinuesa, X., Martin-Lopez, S., Gonzalez-

Herraez, M., 2017. Distributed Temperature

Measurement in a Self-Burning Coal Waste Pile

Through a GIS Open Source Desktop Application.

ISPRS Int. J. Geo-Inf., vol. 6, pp. 87, 2017.

GRASS. The world’s leading Free GIS software. 2020.

http://grass.osgeo.org/. Accessed January 2020.

Guo, X., Wang, R., Wu, Z., 2016. Research and Application

of WebGIS in Coal Mine Information Management

System. In 6th International Conference on Advanced

Design and Manufacturing Engineering (ICADME

2016).

Kostecki, R., 2018. Application of the Spatial Database for

Shoreline Change Analysis and Visualization: Example

from the Western Polish Coast, Southern Baltic Sea.

Quaestiones Geographicae 37(3).

Kumar, K., Ledoux, H., Stoter, J., 2016. Comparative

Analysis of Data Structures for Storing Massive Tins in

a DBMS. In The International Archives of the

Photogrammetry, Remote Sensing and Spatial

Information Sciences, Volume XLI-B2, 2016 XXIII

ISPRS Congress, 12–19 July 2016, Prague, Czech

Republic.

Lee, S., Park, I., 2013. Application of decision tree model

for the ground subsidence hazard mapping near

abandoned underground coal mines. Journal of

Environmental Management 127, 166-176.

Liu, X., Hao, L., Yang, W., 2019. BiGeo: A Foundational

PaaS Framework for Efficient Storage, Visualization,

Management, Analysis, Service, and Migration of

Geospatial Big Data—A Case Study of Sichuan

Province, China. ISPRS Int. J. Geo-Inf., 8, 449.

Medeiros, A., Pereira, E., Moreira, A., 1980. In Notícia

explicativa da folha 9-D (Penafiel) da Carta Geológica

de Portugal à escala 1:50 000. Serviços Geológicos de

Portugal, Lisboa, 47 pp.

Mert, B.A., Dag, A., 2018. Development of GPS and GIS-

Based Monitoring System for the Quality of Excavated

Coal. Acta Montanistica Slovaca Volume 23, number 1,

62-71.

Obeidavi, Z., Rangzan, K., Kabolizade, M., Mirzaei, R.,

2019. A web-based GIS system for wildlife species: a

case study from Khouzestan Province, Iran.

Environmental Science and Pollution Research (2019)

26:16026–16039.

Pinto de Jesus, A., 2001. A Génese e evolução da Bacia

Carbonífera do Douro (Estefaniano C inferior, NW de

Portugal); um modelo. Faculdade de Ciências da

Universidade do Porto, 232 pp.

PostGIS. Spatial and Geographic objects for PostgreSQL.

2020. http://postgis.net/. Accessed January 2020.

QGIS API. QGIS API Documentation. 2020.

http://www.qgis.org/api/. Accessed January 2020.

QGIS. QGIS Project. 2020. http://www.qgis.org/. Accessed

January 2020.

Qt API. Qt Documentation. 2020. https://doc.qt.io/qt-

5/reference-overview.html. Accessed January 2020.

Ribeiro, J., Ferreira da Silva, E., Flores D., 2010. Burning

of coal waste piles from Douro Coalfield (Portugal):

Petrological, geochemical and mineralogical

characterization. International Journal of Coal

Geology 81, 359-372.

Ribeiro, J., Ferreira da Silva, E., Pinto de Jesus, A., Flores,

D., 2011. Petrographic and geochemical

characterization of coal waste piles from Douro

Coalfield. International Journal of Coal Geology 87,

226-236.

Ribeiro, J., Silva, T.F., Mendonça Filho, J.G., Flores, D.,

2012. Polycyclic aromatic hydrocarbons (PAHs) in

burning and non-burning coal waste material. Journal

of Hazardous Materials 199-200, 105-110.

Ribeiro, J., Sant’Ovaia, H., Gomes, C., Ward, C., Flores,

D., 2015. Mineralogy and magnetic parameters of

materials resulting from mining and consumption of

coal from Douro Coalfield (NW Portugal). In: Stracher,

G. B., Prakash, A., Sokol E.V. (Eds.), Coal and Peat

Fires: A Global Perspective. Elsevier. Volume 3: Case

Studies - Coal Fires, pp. 493-507.

Roychoudhury, A., 2018. Mine Closure Planning Issues

and Strategies in Neyveli Mines by using Open Source

Software. International Journal of Engineering

Research & Technology (IJERT), Vol. 5 Issue 11.

SAGA. System for Automated Geoscientific Analyses.

2020. http://www.saga-gis.org/. Accessed January

2020.

SNAP. Sentinel Application Platform. 2020. https://step.

esa.int/main/toolboxes/snap/. Accessed January 2020.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

292

Tama, A., Adamek, K., Pargiela, K., Ochalek, A.,

Krawczyk, A., Lupa, M., 2018. Monitoring of historical

land use changes caused by underground mining in

Miedzianka town, based on a WebGIS tool and inSAR

observations. JCEEA, t. XXXV, z. 65 (1/18), styczeń-

marzec 2018, s. 127-140, DOI:10.7862/rb.2018.14.

Tama, A., Malinowska, A., 2018. The Possibilities of Water

Hazard Managements in transformed Area with Open

Geographical Information System (QGIS). In: Baltic

Geodetic Congress (BGC-Geomatics 2018).

An Integrated Environmental Monitoring Approach through the Development of Coal Mine, a GIS Open Source Application

293