Optimal Sensors Positioning to Detect Forest Fire Ignitions

Thadeu Brito

1 a

, Ana I. Pereira

1,2 b

, Jos

e Lima

1,3 c

, Jo

ao P. Castro

4 d

and Ant

onio Valente

3,5 e

Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit

ecnico de Braganc¸a, Braganc¸a, Portugal

Algoritmi Research Centre, University of Minho, Campus de Gualtar, Braga, Portugal

INESC TEC, INESC Technology and Science, Porto, Portugal

CIMO - Centro de Investigac¸

ao da Montanha, Instituto Polit

ecnico de Braganc¸a, Braganc¸a, Portugal

Engineering Department, School of Sciences and Technology, UTAD, Vila Real, Portugal

{brito, apereira, jllima, jpmc}@ipb.pt, avalente@utad.pt

Keywords:

Wildﬁres, Regional Climate, Forest Fire Ignition, Ignition Detection.

Abstract:

Forests have been harassed by ﬁre in recent years. Whether by human action or for other reasons, the burned

area has increased harming fauna and ﬂora. It is fundamental to detect an ignition early in order to ﬁreﬁghters

ﬁght the ﬁre minimizing the ﬁre impacts. The proposed Forest Monitoring System aims to improve the nature

monitoring and to enhance the existing surveillance systems. A set of innovative operations is proposed that

will allow to identify a forest ignition and also will monitor the fauna. For that, a set of sensors are being

developed and placed in the forest to transmit data and identify forest ﬁre ignition. This paper addresses a

methodology that identiﬁes the optimal positions to place the developed sensors in order to minimize the ﬁre

hazard. Some preliminary results are shown by a stochastic algorithm that spread points to position the sensor

modules in areas with a high risk of ﬁre hazard.

1 INTRODUCTION

The project Forest Alert Monitoring System (SAFe)

proposes to create and execute a set of innovative op-

erations to minimize the alert time of forest ﬁres ig-

nitions. Consequently, these actions will contribute

to the existing surveillance systems, helping the civil

protection teams to decide about the development ac-

tions.

In this sense, the SAFe system proposes an intel-

ligent procedure for monitoring situations of poten-

tial forest risk. The developed system, both hardware

and software, combines distributed standalone sensor

modules that will acquire and transmit several rele-

vant data for efﬁcient characterization of existing for-

est conditions. According to (Aslan et al., 2012) this

information, combined with a system based on arti-

ﬁcial intelligence, will allow the efﬁcient and intelli-

gent analysis of the data, promoting the creation of

warnings of dangerous situations by warning several

surveillance agents (for example, ﬁreﬁghters, civil

https://orcid.org/0000-0002-5962-0517

https://orcid.org/0000-0003-3803-2043

https://orcid.org/0000-0001-7902-1207

https://orcid.org/0000-0003-0647-8892

https://orcid.org/0000-0002-5798-1298

protection or town hall). Additionally, all this infor-

mation and analysis will be made available through a

web platform based on a graphical interface.

This proposal arises from the diagnosis of the un-

reliability of observation towers managed by human

operators, which led to the testing of several technolo-

gies to improve detection capability, such as the use

of ﬁxed or mobile surveillance cameras in the visible

and infrared band (Alkhatib, 2014). The accuracy of

these systems is affected by terrain, time of day and

weather conditions such as cloudiness, light reﬂection

and smoke from industrial or social activities.

This work presents a methodology to identify the

ideal positions to place the developed sensor modules

in order to minimize the ﬁre hazard. The data to eval-

uate the ﬁre risk was obtained from national environ-

mental agencies. Based on these data it was proposed

a method to identify possible points in the Study Area

with a high risk of the forest ﬁre.

This paper is organized as follows. After an intro-

duction in Section 1, the system architecture is pre-

sented in Section 2. Section 3 shows the selected area

to address the study and Section 4 describes the sen-

sor modules that will be spread in the forest to collect

the data. Section 5 presents the LoRaWAN architec-

ture while the preliminary results are demonstrated in

Section 6. The conclusions and some future work are

presented in Section 7.

2 SYSTEM ARCHITECTURE

Many applications and tools need to be combined to

develop the forest monitoring system as well as the

entire process involved (Singh and Sharma, 2017).

Developing a strategy for joining all tools and appli-

cations is also of high importance. As a result, Fig-

ure 1 illustrates in a simpliﬁed scheme the main com-

ponents of the entire SAFe project system. The fol-

lowing topics list the points indicated in Figure 1:

• (1) Study Area: Region to be determined for the

implementation of sensor modules.

• (2) Sensor Modules: the set of sensors that make

the acquisition of forest data.

• (3) LoRaWAN Gateway: Device that receives

data from each sensor module scattered within the

study area.

• (4) Internet: Internet communication that will fa-

cilitate real-time viewing.

• (5) LoRa Communication: LoRa-based radio

frequency communication to European Union

standards (Zhang et al., 2009).

• (6) Server: Component that will store all col-

lected data, where the artiﬁcial intelligence sys-

tem will also be developed to correlate data from

sensor modules with external data (local scale

real-time ﬁre hazard indexes, weather data, avail-

ability fuel content and moisture content of the

vegetation).

• (7) Control Center: Alerts for hazardous situa-

tions or forest ﬁre ignitions, alert notiﬁcations are

tailored to each surveillance agent in the region.

• (8) External Data: Provided by national envi-

ronmental forecasting, risk and disaster response

agencies, as well as local entities.

Figure 1: Illustration of the system architecture.

Among all the tools and applications developed by

the SAFe project, this work will focus on the Study

Area. This is outlined in Section 3.

3 STUDY AREA

The application of sensor modules for forest data ac-

quisition will be implemented in the Braganc¸a region,

in the Serra da Nogueira area, as shown in Figure 2.

Due to the characteristics of this forest, spreading the

sensor modules across the region would be chaotic

and hard to understand the data (Lloret et al., 2009).

Therefore, it is necessary to develop a strategy to

place the sensor modules. The chosen points must

consider the data provided by the national environ-

mental agencies.

Figure 2: Geographic localization of Serra da Nogueira.

Data obtained by (Copernicus, 2019).

Some factors are deterministic for the choice of

these points, such as soil occupation, history and es-

timation of areas at ﬂame hazard, areas that have

been burned over the years, terrain elevation and for-

est density, among others. For the analysis of these

factors, QGIS software (QGIS, 2019) is used as a vi-

sualization base and geographic data are provided by

Copernicus (Copernicus, 2019). The coordinate sys-

tem has the ETRS89/PT-TM06 (EPSG:3763) UTM

Zone 29N standard with Mercator Transverse Univer-

sal projection, the unit of measurement used is in me-

ters. Thus, it is possible to develop a methodology

that respects the characteristics of the Study Area ac-

cording to the range of sensor modules, which is de-

scribed in the following section.

4 SENSOR MODULES

There are multitude of ﬂame sensors on the market,

the type of products found can be based on image

analysis (cameras), gas detection (smoke detector),

heat change (thermal cameras), radiation wave spec-

trum among the others. However, some methods have

disadvantages during their application. For exam-

ple, image analysis to detect ﬁre ignitions may con-

tain human failures (Chen et al., 2004), smoke sen-

sors have high energy consumption (Baranov et al.,

2015), and thermal cameras have a high installation

cost (Katayama et al., 2009). Because of this, the

development of sensor modules is based on low-cost

sensors through radiation detection.

Figure 3 shows the prototype design of a sensor

module. Figure 3a describes the sensor module box

whereas Figure 3b describes the components. The

central element is the Arduino Uno (1) which through

LoRa communication (2) sends data from ﬁve in-

frared radiation sensors (3), temperature and humidity

sensors (4), UV index (5), and soil moisture (6). The

box is 100 mm long, 70 mm wide and 72 mm high. It

is made in a 3D printer, so that printing did not waste

ﬁlaments with supports. It is also water-resistant.

(a) Sensor module simulation

(b) Sensor module description

Figure 3: Prototype design of a sensor module.

All sensors described are used in a standard man-

ner, i.e. without any change in the method suggested

by the manufacturers. Therefore, the box can be pro-

duced and assembled for the ﬁrst analysis. The ﬁnal

sensor module box is presented in Figure 4.

Figure 4: Real sensor module.

5 COMMUNICATION SYSTEM

LoRa (Long Range) is the adopted protocol to estab-

lish the connection between sensors and the central

servers. More details of the communication system

can be found at (Adorno et al., 2019). LoRa is a

wide-area network technology that has been driven

by the IoT (Internet of Things) and thus is low-power

consumption devices (it enables long-range transmis-

sions of about 10 km in rural areas). It is uses spread

spectrum modulation techniques derived from chirp

spread spectrum on license-free sub-gigahertz radio

frequency bands. The developed wireless sensors net-

work uses the 868 MHz, one of the standard fre-

quencies for LoRa in Europe. On the upper network

layer of LoRa, LoRaWAN is one of several protocols

that were developed to access cloud-based services.

It manages the communication between LoRaWAN

gateways and end-node devices. In fact, the Indus-

try 4.0 boosted the IoT and IIoT (Industrial Internet

of Things) that makes use of LoRaWAN in several

areas, such as Smart Cities, agriculture and logistics

among the others.

Figure 5 shows the architecture of the LoRaWAN.

The nodes, through LoRa, connect to the gateways

that send the acquired data to the application server

where the processing will be done. At the proposed

system, the ignition detection should be done.

6 PRELIMINARY RESULTS

Before placing the detection prototype in the forest,

some tests are required to perform in a controlled en-

vironment. Lab testing is important to ensure that the

prototype is able to acquire data and send it remotely.

Figure 5: LoraWAN architecture. From (LoRa Alliance

Technical Marketing Workgroup, 2019).

This avoids possible communication failures as well

as some false alarms. In parallel, after some short

analysis made from the data obtained by (Copernicus,

2019), it was possible to establish some parameters

for choosing the locations that will receive the sen-

sor modules. The following subsections describe the

results obtained regarding lab testing and geographic

information.

6.1 The SAFe Box

After all the design, 3D printing and prototype assem-

bly, the ﬁrst evaluation is to determine if the sensors

can function without interruptions. Then, the proto-

type was turned on for 30 days collecting data within

the laboratory with a 2-second collection interval. All

data were recorded without interruption, ie all sen-

sors were able to collect data at any time of the day

and without large peak oscillations. Due to a large

amount of data, Figure 6 shows only data collected in

a single day.

Figure 6: Chart from the collected data in one day. Values

are dimensionless since the output of sensors are presented

after a 10 bit Analog to digital conversion.

In the data acquisition chart presented in Figure 6,

it is possible to establish the minimums and maxi-

mums values of the ﬂame sensors, where values have

ranged from 0 to 1023 (values from Arduino UNO

analog input). When the sensor is close to the ﬂame,

the values tend to be close to 0. On the other hand,

when the sensor moves away from the ﬂame, the val-

ues tend to approach 1023. The sensitivity of the sen-

sor varies with distance, intensity and ﬂame volume.

By simulating the forest ﬁre ignition with a candle,

it is possible to detect the presence of ﬁre at about 5

m. The same process occurs for the soil moisture sen-

sors, that is, when the soil is wet the acquired data are

close to 0 and when the soil is dry the values are close

to 1023. The UV index sensor provided low values

due to the lack of light exposure once the tests were

performed indoors.

With the ﬁrst analysis, it was observed that the

sensors did not suffer interruptions in data collection,

so the use of LoRa communication becomes viable

for the SAFe project approach. Then, the second anal-

ysis evaluates prototype ﬁxation in real situations, that

is, being ﬁxed to a tree trunk in the Study Area region.

Figure 7 shows the mounting and placement of sen-

sors on the stem of the tree. Note that the orientation

of the sensors is free, that means there are no barri-

ers to the capture of infrared spectra. Another factor

is wireless, all data collection was done with LoRa

communication.

(a) Prototype ﬁxation. (b) Placement of sensors.

Figure 7: SAFe box acquiring data from Serra da Nogueira

forest.

As mentioned earlier, installing modules on all

tree trunks in a forest can become a daunting and

costly task. Finding the ignition point of ﬁre can dra-

matically slow down the work of this task. Therefore,

the following subsection 6.2 demonstrates the strat-

egy for ﬁnding the highest ﬁre risk points.

6.2 Fragments of Study Case

The total coverage area of the monitoring system is

expected to be a radial 10 km, so installing the mod-

ules with a distance of 5 m between them along this

circular 10 km area will be chaotic and laborious. To

solve the problem of territorial extension of 10 km

not becoming chaotic for data collection modules and

communication, it was determined that a possible ap-

proach is to separate the whole terrain into small frag-

ments. These fragments will be divided into a maxi-

mum of 1 km of diameter. In this way, it is possible

to understand the critical points of each fragment, to

position the sensors according to local resources and

to prepare the communication infrastructure.

Before considering the total fragmentation to 10

km, it is necessary to study the region according to

some parameters that contribute to the likelihood of

forest ﬁres and then to deﬁne the best central point

to start the study. This central point should contain

an intersection area between the parameters that con-

tribute to the likelihood of forest ignition. The type of

soil occupation contributes to the incidence of wild-

ﬁre (Verde, 2010), as shown in Figure 8 (Braganc¸a

region has a mixed occupation).

Figure 8: Different type of soil occupation activities. Data

obtained by (Copernicus, 2019).

Particularly, it is important to analyse the data

of transitional woodland-shrub, non irrigated arable

land, broad-leaved forest, ﬁre hazard, the history of

the burned area over the last years, the soil relief and

tree density among others. With the analysis made

from Figure 9, it is possible to notice this relationship

between the type of land occupation with the ﬁre haz-

ard estimate and the burned areas over the last years

(comparing Figure 9a with Figure 9b).

Since the relief interferes with soil moisture, the

vegetation is drier in higher altitudes and conse-

quently prone to ignition of ﬂames. Figure 10a and

Figure 10b demonstrate the occurrence of a high for-

est density in the high altitude regions. Therefore, the

center point should also contain a high altitude rela-

tive to the other points within 10 km along with a high

forest density.

By comparing the data displayed in QGIS, shown

in Figures 9 and 10, it is possible to determine some

points that comply with the parameters that increase

the likelihood of forest ﬁre. The central region was

(a) Fire Hazard. Data obtained by (ICNF,

2019).

(b) Burned areas over the last years. Data

obtained by (Copernicus, 2019).

Figure 9: The Study Area with the ﬁre risk estimate data

and the burned areas information. Data obtained by (Coper-

nicus, 2019).

deﬁned with coordinates (105327, 232506) and alti-

tude near 1050 m, shown by Figure 11, in Serra da

Nogueira.

From this central point as a reference, the Fire

Hazard layer is activated and by selecting between the

values 0 and 5 it is possible to choose only the regions

of greatest interest, i.e. the regions with danger values

4 and 5. Thus, some regions are not considered for the

(a) Soil relief. Data obtained by (Coperni-

cus, 2019).

(b) Tree cover density. Data obtained by

(Copernicus, 2019).

Figure 10: The Study Area Figure presents the soil relief

with forest density in the Serra da Nogueira. Data obtained

by (Copernicus, 2019).

selection of place the sensor modules, which makes

the process a little less chaotic. Figure 12 demon-

strates the ﬁre hazard layer at the boundaries of the

10 km radius from the center point.

Figure 12 addresses 1 km region around the de-

ﬁned center point, shown in Figure 13a. The pre-

sented area can be zoomed in and the developed al-

gorithm can be applied to have as base the ﬁre hazard

Figure 11: Central point in Braganc¸a region.

Figure 12: Fire hazard layer along the Study Area. Data

obtained by (ICNF, 2019).

map. The data to feed the algorithm was extracted

from the 1 km radius ﬁre hazard layer from the center

point, presented in Figure 13b. As already mentioned,

values between 0 and 3 do not present great possibil-

ities for forest ignition, so only regions with values 4

and 5 are selected (Figure 13c).

The selected regions are transformed into polygon

formats since QGIS cannot access the data in Raster

format. Then each region generates a set of polygons

that are entered separately in the random point inser-

tion algorithm. For the distribution of random points,

it was set as 10000 maximum points for each poly-

gon and with a distance between each point of 5 m.

The operation of random point distribution is shown

in Algorithm 1.

After the algorithm determines the location of the

random points, a point mask is created over the poly-

gons that have been inserted as a fastening space. Fig-

ure 14 shows the result of this mask over the region

with hazard values 4 and 5. Note that in both Fig-

ure 14a and Figure 14b the entire ﬁre hazard region

has been completely populated, which validates the

approach of the implemented algorithm.

(a) Study Area Fragment. (b) Fire hazard layer along

the Fragment.

Figure 13: Sequence of images demonstrating the selection

of regions with the highest risk of forest ignition within the

Study Area Fragment. Data obtained by (ICNF, 2019).

Algorithm 1: Random insertion points.

Initialize layer with polygons

p ← Set the maximum amount of points

d ← Set the minimum distance between each point

argmaxID ← Length of amount of polygons

f polygon ← Number of full polygon

while f polygon ! = argmaxID do

if polygon selected has < p and free space then

Spread a random points inside polygon for

each d m

else if show a debug message then

Target polygon as full

Get another polygon.

At the end of each presented Algorithm run, the

amount of time is reported in log format. It is also

collected from each generated mask the total random

points entered. This information is shown in the Ta-

ble 1.

Table 1: Results of the algorithm.

Layer Points Time [s]

Region with ﬁre hazard values 4 24 601 460.30

Region with ﬁre hazard values 5 18 482 464.16

By zooming the region a bit more, it can be found

a better distribution of the points, as shown in the set

(a) Points generated for regions with ﬁre hazard

values 4.

(b) Points generated for regions with ﬁre hazard

values 5.

Figure 14: Mask of points generated by the Algorithm 1

according to each region inserted as a ﬁxation space.

of images of Figure 15. When using the QGIS mea-

surement tool, it is noted that the points are 5 m away

to each other and are also limited by the chosen re-

gions. These identiﬁed points will be the candidates

to place a sensor module to acquire the data.

7 CONCLUSIONS AND FUTURE

WORK

Monitoring forests can warn of possible ﬁre ignition

that early detected help combat teams to minimize

ﬁre impacts. The presented Forest Monitoring System

aimed to contribute to support the actual surveillance

systems by implementing a set of innovative opera-

tions that allow to identify a forest ignition based on

a set of sensors positioned in the forest. This work

presented a methodology to propose a set of locations

to install the sensor boxes. The results are promis-

ing since the data gathered from the installed sensors

boxes prototypes are operational. Moreover, a ﬁrst

version of the prototype sensor box was developed,

tested and validated in laboratory and real scenarios

(a) Zoomed view of the region with

ﬁre hazard 4 values over the gener-

ated dot mask.

(b) Zoomed view of the region with

ﬁre hazard 5 values over the gener-

ated dot mask.

Figure 15: Magniﬁed view of each chosen region with dis-

tance measurements between each point made via QGIS.

transmitting the acquired data to the central process-

ing. As future work, the forest data will be collected

and analysed with artiﬁcial intelligent algorithm in or-

der to identify data patterns and alerts to the control

servers, triggering in the case of an ignition detection.

ACKNOWLEDGEMENTS

This work has been supported by Fundac¸

ao La Caixa

and FCT — Fundac¸

ao para a Ci

encia e Tecnologia

within the Project Scope: UIDB/5757/2020.

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