Classification of Vegetation to Estimate Forest Fire …downloads.hindawi.com/journals/mpe/2019/6296417.pdfMathematicalProblemsinEngineering includethetraditionalclassicationwithandwithouttrain-ing[,],the

Research ArticleClassification of Vegetation to Estimate Forest Fire DangerUsing Landsat 8 Images Case Study

Ksenia S Yankovich 1 Elena P Yankovich 2 and Nikolay V Baranovskiy 3

1UFR Geographie Histoire Economie et Societes University of Paris 7 (Paris Diderot University) Paris 75013 France2School of Earth Sciences amp Engineering National Research Tomsk Polytechnic University Tomsk 634050 Russia3School of Energy amp Power Engineering National Research Tomsk Polytechnic University Tomsk 634050 Russia

Correspondence should be addressed to Elena P Yankovich yankovichtpuru

Received 9 December 2018 Revised 30 January 2019 Accepted 21 February 2019 Published 7 March 2019

Academic Editor Ana C Teodoro

Copyright copy 2019 Ksenia S Yankovich et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The vegetation cover of the Earth plays an important role in the life of mankind whether it is natural forest or agricultural cropThestudy of the variability of the vegetation cover as well as observation of its condition allows timely actions to make a forecast andmonitor and estimate the forest fire conditionThe objectives of the researchwere (i) to process the satellite image of theGilbirinskiyforestry located in the basin of Lake Baikal (ii) to select homogeneous areas of forest vegetation on the basis of their spectralcharacteristics (iii) to estimate the level of forest fire danger of the area by vegetation types The paper presents an approach forestimation of forest fire danger depending on vegetation type and radiant heat flux influence using geographic information systems(GIS) and remote sensing data The Environment for Visualizing Images (ENVI) and the Geographic Resources Analysis SupportSystem (GRASS) software were used to process satellite images The arearsquos forest fire danger estimation and visual presentationof the results were carried out in ArcGIS Desktop software Information on the vegetation was obtained using the analysis of theLandsat 8 Operational Land Imager (OLI) images for a typical forestry of the Lake Baikal natural area The maps (schemes) of theGilbirinskiy forestry were also used in the present articleThe unsupervised k-means classification was used Principal componentanalysis (PCA) was applied to increase the accuracy of decoding The classification of forest areas according to the level of firedanger caused by the typical ignition source was carried out using the developed method The final information product was themap displaying vector polygonal feature class containing the type of vegetation and the level of fire danger for each forest quarterin the attribute tableThe fire danger estimationmethod developed by the authors was applied to each separate quarter and showedrealistic results The method used may be applicable for other areas with prevailing forest vegetation

1 Introduction

Forest fires pose a global threat to natural systems andhumans Every year the number of forest fires in the worldis growing The problem of forest fire danger estimation inthe Lake Baikal basin is of particular relevance [1ndash3] Theanthropogenic impact on the forest is constantly increasingOn the other hand the number of natural disasters isincreasing The forest fire danger classes of the territory weredetermined in the process of the basic forestmanagement andare based on forest types rock groups age of forest standsand proximity to local infrastructure (roads settlementsand industrial objects) The use of satellite imagery data to

determine or specify the probability of forest fires is oneof the best possible solutions Satellite remote sensing hasbecome the main source of data for predicting fire riskmapping of forest fuel detecting and monitoring forest firesand estimating the damage done to vegetation after a forestfire [4ndash10]

The ability to determine the place where a fire is likelyto occur is a prerequisite for the fire management Thevegetation cover is the most important factor in forest firesbecause it reflects the presence of forest fuels [11] The use ofremote sensing data in the classification and mapping of veg-etation becomes the main method of fuel assessment Exist-ing methodologies for determining the type of vegetation

HindawiMathematical Problems in EngineeringVolume 2019 Article ID 6296417 14 pageshttpsdoiorg10115520196296417

2 Mathematical Problems in Engineering

include the traditional classification with and without train-ing [12 13] the method based on phenology [14] and theobject classification [15]

The vegetation type maps are necessary for spatial cal-culation of fire danger and for estimation of fire risk byusing them in models that simulate growth and intensity ofa fire in a landscape Forest fuel maps are used in variouswidespread systems for the forest fire danger forecastingFor example such models are used in the North Americanmodels National Fire Danger Rating System (NFDRS) Firebehavior (BEHAVE) Fire Area Simulator (FARSITE) andNational Fire Management Analysis System (NFMAS) [17ndash20] The McArthur Forest Fire Danger Rating System andthe McArthur Grassland Fire Danger Rating System [21] arewidely used in Australia The Canadian Forest Fire DangerRating System is used in Canada which consists of twomain subsystems the Fire Weather Index (FWI) and FireBehavior Prediction System [20 22] The Forest Fire SatelliteMonitoring Information System of Russian Federal ForestryAgency (SMIS-Rosleshoz) used in the Russian Federation isbased on the Nesterov index Ground-based observationsaerial photography modelling and remote sensing data[20 23ndash27] are used for mapping In general data on thegreenness of vegetation meteorological data data on wettingthe surface and moisture content of forest fuel are necessaryto forecast and to monitor forest fires [28]

In addition numerous studies showed the feasibility ofgeographic information system (GIS) and remote sensingdata [29 30] The GIS is a widely used tool for processingspatial data and displaying results A model of a potentialforest fire using satellite data and the GIS was developed inChina to identify areas with a high probability of forest fire[31] In Sheriza et al [32] five fire danger classifications wereidentified in developed forest fuel maps and the degree of firedanger in peat-bog forests was assessed The vegetation typesin the study areawere analyzed using digital classification sys-tems namely two vegetation indices the extended vegetationindex (AVI) and the Tasseled Cap (TC) conversion

The objectives of the research were

(i) to process the satellite image of the Gilbirinskiyforestry located in the basin of Lake Baikal

(ii) to select homogeneous areas of forest vegetation onthe basis of their spectral characteristics

(iii) to estimate the level of forest fire danger of the area byvegetation types

The paper presents an approach for estimation of forestfire danger depending on vegetation type and radiant heatflux influence using GIS and remote sensing data The ENVIand the GRASS software were used to process the satelliteimages The arearsquos forest fire danger estimation and visualpresentation of the results was carried out in the ArcGISDesktop software The final information product was themap displaying vector polygonal feature class containing thetype of vegetation and the level of fire danger for each forestquarter in the attribute table

The structure of the article is following Section 2 containsdescriptions of methods Section 3 describes the results andcontains discussion Section 4 contains conclusions

2 Materials and Methods

21 Study Area andData Resources Thestudy was conductedin the Gilbirinskiy forestry (Ivolginskiy district of the Repub-lic of Buryatia) in a protected natural area The study areais located between latitudes 51∘3510158405010158401015840N and 51∘4610158404010158401015840N andlongitudes 106∘4210158400010158401015840E and 107∘0210158400010158401015840E [33] This area is55 km southeast of Lake Baikal and belongs to the northerntip of the Selenga middle mountains (Figure 1) The area ofthe forestry is about 270 km2 [34]The forestry is divided into126 forest quarters The forest quarter is the main accountingunit of the forest fund in the Russian Federation and it haspermanent boundaries

The region of the study like the whole of Buryatiahas a sharply continental climate with cold winters andhot summers The average temperature in summer is about+ 185∘C and in winter is about ndash22∘C and the averageannual temperature is about ndash16∘C The average annualrainfall is 244mm A significant feature of the climate is thelong duration of sunshine for 1900-2200 hours [35] Naturalplantations occupy about 90 of the territory The rest of theterritory is filled with glades slopes pebbles forest culturesarable land pastures etcThemain forest-forming species arelarch pine cedar birch and aspen [36]

The land use map of the study area was created on thebasis of the Landsat 8OLI medium spatial resolution imageobtained on August 21 2015 (pathrows 7891 times 7791) [37]The image had 11 bands with different wavelength rangesSix spectral bands were used for analysis (Table 1) Since thisimage was taken under the cloudless conditions it was anexcellent source for classifying the types of vegetation of theGilbirinskiy forestry and the surrounding area The map offorest quarters was used as background data

22 Image Processing Technique The radiometric calibrationwas the first important step in Landsat 8 data processingThe Landsat 8 data set consists of normalized values calleda digital number (DN) that represents multispectral imagedata These numbers have no physical meaning They mayalso be completely incompatible in each independent data setand even in different bands of the same set DN values fordigital processing of Landsat 8 image are usually convertedinto one of two physical parameters reflectance or spectralradiance [38]

It was chosen to convert DN pixel values into Top ofAtmosphere (TOA) reflectance for data processing Theseadjustments were made based on the coefficients presentedin the metadata file (MTLtxt) that comes with the imageset [39] The conversion of one unit to another was carriedout using the radiometric calibration tool ilandsattoar of theGRASS software [40 41]

The atmospheric correction was the next step after theradiometric calibration The data on the underlying surfaceand the objects upon it obtained by the sensor system were

Mathematical Problems in Engineering 3

51deg4

6

51deg4

651

deg45

51deg4

451

deg43

51deg4

251

deg41

51deg3

651

deg37

51deg3

851

deg39

51deg4

551

deg44

51deg4

351

deg42

51deg4

151

deg40

51deg3

651

deg37

51deg3

851

deg39

2 6

879

3

13

15

71

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

123

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

65 66 67 69

111 120 121

46

4

18 19 20

74 75 76 77 78

40

70

6160

68 72

33 3436

35

112 113 114 115 116

124 125

86 87 88 89 90 91 92 93 94

118 119

122

99 100 102 103 104 105 106 107 108 109 110

1

101

Gilbirinskiy forestry

106deg42

106deg43

106deg44

106deg45

106deg46

106deg47

106deg48

106deg49

106deg52

106deg53

106deg54

106deg55

106deg56

106deg57

106deg58

106deg59

106deg50

106deg51

107deg0

107deg1

41forest quarter boundariesand its number

km

0 2 4

RUSSIA

Tomsk

NovosibirskUlan-Udeh

Ulan-Udeh

Krasnoyarsk

Irkutsk

Irkutsk

Moskva

Bury

atia

BaikalN

5149 48

51deg4

0

Figure 1 The study area (Gilbirinskiy forestry Republic of Buryatia Russia)

Table 1 Landsat 8 OLI spectral bands

Spectral band Wavelength (120583m) Resolution (m)2 ndash Blue 0450 ndash 0515 303 ndash Green 0525 ndash 0600 304 ndash Red 0630 ndash 0680 305 ndash Near Infrared NIR 0845 ndash 0885 306 ndash Short Wavelength Infrared SWIR 2 1560 ndash 1660 307 ndash Short Wavelength Infrared SWIR 3 2100 ndash 2300 30


Second Simulation of a Satellite Signal in the Solar

Spectrum (6S)

Input data aer radiometric

rescaling

e altitude of the target

Date and time of the survey

Id number of the satellite

Atmospheric model

Aerosol model

e spectral conditions

Atmospheric correction

Figure 2 Scheme for the atmospheric correction (satellite module in the Solar Spectrum)

distorted due to the influence of many factors among whichthe main one is the atmosphere [42]

There are various algorithms to perform the atmosphericcorrection such as standard absolute correction standardrelative correction and corrections based on specifiedmodels[43] This research was focused on the method that used theatmospheric model [44ndash47] The main atmospheric effectstaken into account included absorption by water vapouroxygen ozone and carbon dioxide and scattering on aerosolsand molecules The input parameters in this model werethe geometry of the sun and sensor location atmosphericmodel for gaseous components aerosol model (type andconcentration) optical thickness of the atmosphere surfacereflection coefficient and spectral bands The parameters forthe model were received from the metafile supplied with theraster image Peculiarities of each image shooting were takeninto account

Popular models for the atmospheric correction are theFast Line of Spectral Hypercubes (FLAASH) implemented inthe ENVI software and Second Simulation of a Satellite Signalin the Solar Spectrum (6S) (Figure 2) This model is availablein the GRASS software [40] Model 6S is located on the freeaccess online platform that allows designing the model anddownloading it as a file [48]

The atmospheric correction was performed with theiatcorr tool available in the GRASS software [49] The toolinput data consisted of the configuration file of a specificband and the image of this band Each spectral band wasprocessed separately The central coordinates of the imagewere latitude - 51∘411015840349410158401015840N longitude - 107∘051015840354110158401015840EThe exposure time as well as the location was indicatedon the source site [50] and in the metadata The time wasgiven on the source site as a set of numbers ldquo2015 233 0345 269418490rdquo in the format ldquoyear day in year hoursminutes decimal secondsrdquo while the time alone was givenin the SCENE CENTER TIME column of the metadata file

Since the image shooting was performed in August theldquoatmospheric modelrdquo was chosen as the parameter averagelatitudes (summer) The aerosol model depends on thelatitude of the study area In this case the continental modelwas usedThe average height above sea level was obtained onthe basis of the Shuttle Radar Topography Mission (SRTM)file [51] for the study area using the runivar module of theGRASS software Values of parameters for atmospheric andaerosol models were selected from standard options Sincean optical visibility index obtained on the ground was notavailable for the area the value for the model of aerosolconcentration was set to 0 The optical thickness parameterwas taken from the site [52] The sensor was on board thesatellite so the code indicating the position of the sensor is-1000 The last parameter was the number of the spectralband selected for processing and was set for Landsat 8OLIin following manner B2 ndash 116 B3 ndash 117 B4 ndash 118 B5 ndash 120 B6ndash 122 B7 ndash 123

23 Image Classification The image was classified afterpreprocessing stage Classification is the process of sorting(distributing by classes) of image elements (pixels) into afinite number of classes based on the values of their attributesIf a pixel satisfies a certain classification condition it belongsto a certain class that corresponds to this condition

During the classification informational and spectralclasses were distinguished Information classes are thoseobjects that need to be recognized in the picture various typesof vegetation water surface land types etcThe spectral classis a group of pixels that have approximately the same bright-ness in a certain spectral range The task of classification isto compare the spectral classes with the information ones[53 54]

There are two main approaches to the classificationsupervised (with training) and unsupervised (without train-ing) Classification with training is the process by which the


Choice of class centers

Separation of objects by classes

Calculation of new centers



Is a difference between centers smaller than a

parameter

Is a maximum number of iterations reached

No

Yes

Yes

Classification is completed

Input data

Input parameters numbers of classes

numbers of iterations

Figure 3 K-means algorithm applied to classification

brightness value of each pixel is compared with the standardsEach pixel belongs to the most appropriate class of objectsClassification without training is a process characterized bythe automatic distribution of image pixels This method isbased on an analysis of the statistical brightness distributionof pixels A combination of different classification methodsis also used which gives optimal results especially for largedata sets [55]

In this work the unsupervised classification methodwas used The two most common classification algorithmswithout training were used (namely k-means and ISODATA[3]) The method of classification without training IterativeSelf-Organizing Data Analysis Technique (ISODATA) is aprocess that is based on the cluster analysis One classincludes pixels which brightness values are closest in thespace of spectral features [55 56] The idea of the k-meansalgorithm is to minimize the distances between objects inclusters based on their distance to the centre of mass [55 56]During classifying of satellite images such objects are pixelswith their values in n-dimensional space (n is the numberof spectral bands) It is necessary to specify two requiredparameters to start the algorithm number of classes (clusters)and condition to stop the algorithmThe number of iterations(repetitions of the procedure that distributes objects intoclasses) or the parameter of changing the barycenter positioncan be used as a condition to stop the algorithm (Figure 3)

The unsupervised classification of image pixels by the k-means method was used in accordance with the reflectanceof each of them in the spectrum ranges All clusteringoperations were performed in the ENVI software

Two types of territories (areas with or without vegetationcover) were identified after the initial classification of theimage Then reclassification was carried out using a maskcreated on the basis of the territory with vegetation in theENVI softwareThe level of forest fire danger was determined

by the type of vegetation Therefore a new classification wasrequired

It has been shown that the most informative componentscan be used [57] The number of main components is equalto the number of channels processed A colour image of thefirst three main components can be created for visualizationUsually the first two or three components are able to describealmost the entire variability of the spectral characteristicsThe remaining components are most often subject to noiseBy discarding these components (in our case 4 5 and 6)the amount of data can be reduced without noticeable lossof information

24Methods to Assess Forest Fire DangerCaused by VegetationTypes Previously it was proposed to estimate the forest firedanger using reference ignition source [16] Focused solarradiation was used as the source in that paper Howeverit is possible to generalize the method to the case of forestfuels exposure to a radiant heat flux including heat flux fromthe core of the tree trunk to the surface during lightningactivity heat flux from a heated mass or particles duringanthropogenic load and heat flux from the fire front duringthe propagation of burning over the forested territory or theoccurrence ofmassive forest fires)The forest fire danger fromexposure to radiant heat flux can be estimated using analysisof the vegetation type over particular forest area This allowsclassifying the forest according to the level of fire danger offorest fuel

It is necessary to describe the procedure for the gradualelimination of small areas that do not represent a significantfire danger Initially it is required to exclude sites that belongto the road network of the quarter As there are no forest fuelsin these sites ignition is impossible in them Other areas ofthe forested area are considered subject to fire danger The


1B

2B

3W

4W

5W

6D

7W

8D

9D

10D

11CO

12CY

13CO

14L

15R

16M

17M

18CO

19R

20L

(a)

1B

6D

11CO

2B

7W

12CY

3W

8D

13CO

4W

9D

14L

5W

10D

15R

16M

17M

18CO

19R

20L

(b)

M

4W

9D

14L

19R

5W

10D

15R

20L

1 2 3B B W

6 7 8D W D

11 12 13CO CY CO

16 17 18M CO

(c)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(d)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(e)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(f)

Figure 4 Diagram of a study area for selecting the most fire-prone sites with results from three initial iterations ((a)ndash(c)) and three finaliterations ((d)ndash(f)) [16]

next stage is the elimination of areas with water bodies andwater-saturated marshes since either there are no forest fuelsor themoisture content is highThen territories located in thelowlands should be excluded from further consideration Fur-thermore sites with deciduous forest are excluded from con-sideration because it is known that the probability of forestfire occurrence is low in deciduous forests Then areas withmixed forest can similarly be ignored In such forest standslayers of forest fuel are formed mainly from leaves in theupper layer and needles are separated from each other andat some distance from the surface Individual needles do notburn with open flame they only smoulder Afterwards rela-tively young coniferous forest stands are regarded as nonpos-sessing notable fire danger and can be excluded from consid-eration After a series of consecutive iterations amapwith thehighest fire danger areas (old coniferous forest) is received

There are several types of forest and land-covers (1) forestclear-cuts near road (label ldquoRrdquo) (2) water land cover (labelldquoWrdquo) (3) wetland (label ldquoBrdquo) (4) valley bottom (label ldquoLrdquo)(5) deciduous forest (label ldquoDrdquo) (6) mixed forest (label ldquoMrdquo)(7) young-growth conifer forest (label ldquoCYrdquo) and (8) old-growth conifer forest (label ldquoCOrdquo) There is an amount ofsmall and moderate probabilities of forest fire (Figure 4)After each iteration the amount of screened sites with smallandmoderate probabilities of wildfire (gray colour) increases

The probability of forest fire occurrence in vegetationconditionswhen forest fuel is exposed to radiant heat fluxwasdetermined as follows for a particular forest quarter

119875119891119891 asympNFSNTS

or 119875119891119891 asymp119878119865119878119878119879119878

(1)

where 119875119891119891 is the probability of forest fire occurrence causedby forest conditions 119873119865119878 (FS ndash fire sites) is the number offire danger sites in the quarter 119873119879119878 (TS ndash total number ofsites) is the total number of sites in the quarter 119878119865119878 is the areaoccupied by coniferous vegetation in the quarter and S TS isthe total area of the forest quarter

The level of forest fire danger was determined by thecalculated value of the probability of a fire caused by theinfluence of the radiant flux A total of five categories wereused to determine the level of forest fire danger fromemergency to low Elimination of forest areas with low firedanger assessment of probability of the forest fires causedby the radiant heat flux and determination of the forestfire danger level were implemented in the ArcGIS Desktopsoftware [58] using the forest taxation data and the vegetationtype The data on the composition of vegetation are eitherterrestrial taxation descriptions or the results of a satelliteimage processing Operations were performed using the builtmodel in which the geoprocessing tools were connected toeach other in sequence The script was written in Python onthe basis of standard processing tools [59 60]The algorithmis shown in Figure 5 The result of all operations was athematicmap showing the fire danger levels of forest quarters

The general scheme has been developed to estimate forestfire danger based on a comprehensive data analysis accord-ing to forest conditions using satellite image interpretation(Figure 6)

3 Results and Discussion

The first stage was the satellite image processing usingfree software GRASS GIS The correction parameters weredefined and set and the radiometric calibration and the


Add FieldFireDanger

Table-1

Table toTable

Output TableGeodatabase

P

Table DataExcell

P

Add Field Stat

Table-2

CalculateField

FireDanger

Table-3

CalculateField Stat

ProcessedTable

SummaryStatistics

Table1

Add FieldP_FireDanger

Table2Add Field

ADRESS (1)

Table 6

CalculateField Level

Table5

CalculateField

P_FireDanger

Table4

Add FieldLevel

Table3

CalculateField ADRESS

(1)


(2)

Table 2

TableFireDange Joining Table GeoTable 1

Add FieldADRESS (2)

Table 1AtributTable

(Forest

P

ApplySymbologyFrom Layer

ematicMap

SymbologyLayer

P

Figure 5 Model to classify forest according to forest fire danger level

Input data from Landsat 8 OLI

Radiometric rescaling


Unsupervisedclassification

Creating a thematic map

Metadata

Atmospheric model

Parameters of classification

Study area

GRASS GIS

Assessment of forest fire danger

GRASS GIS ENVI ArcGIS

Figure 6 Forest fire danger assessment scheme based on satellite image interpretation

atmospheric correction were implemented The dimension-less DN values characterizing the pixels of the image wereconverted into reflectance corrected for the local solar zenithangle [39]

Further atmospheric corrections were made for eachspectral band Model 6S was used Corrected spectral bandfiles were obtained as a result of processing (Figure 7)

A multiband image was formed from corrected rasters(from 2 to 7 bands) The image was used for the classificationof spectral characteristics into separate groups The area ofinterest on the multiband raster was selected (which includesthe study area) using the Region of interest (ROI) tool of theENVI software and cropping by area of interest (Figure 8)

The results of the classification are shown in Figure 9Classification parameters the number of clusters the number

of iterations and the threshold for changing the position ofthe centre of mass were determined on the basis of visualinterpretation of the image and the literature data [2 3]In order to make it easier to separate surfaces with similarreflectance a sufficiently large number of classes were chosen15 The maximum possible number of iterations was set to25 The threshold for changing the position of the centre ofmasswas taken 5 For all classes in each channel the averagereflectivity values were calculated and spectral signatureswere constructed (Table 2 Figure 9) Figure 11 shows theresult of an unsupervised classification that includes allclasses of the study area In our case these are water surfacesvarious types of vegetation and soil The type of surfacefor a particular class is determined by spectral responsesie by the shape (extremes) of the spectral signature For


Table 2 Average values of reflectance

BasicStats Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 9 Class 10 Class 11 Class 12 Class 13 Class 14 Class 15

Band 2 003 002 002 003 002 002 003 002 002 002 003 004 004 004 006Band 3 002 002 002 003 002 003 004 003 003 003 004 006 005 005 007Band 4 003 002 002 003 002 003 005 002 002 003 004 007 005 007 010Band 5 007 016 019 017 022 021 019 025 028 034 028 027 022 017 020Band 6 007 008 009 012 010 013 016 012 013 015 018 025 020 022 029Band 7 006 004 005 007 005 007 010 006 006 007 009 015 012 016 022

Before Aer

Figure 7 Images in natural colours before and after the atmospheric correction

Figure 8 Multiband raster in colour composition 5-4-3

example water surfaces have lower reflectivity in the classes4ndash6 than other surfaces The characteristic features of thevegetation spectral curves are high values of reflectivity in thenear infrared range and lower values in red and in shortwaveinfrared (Short Wavelength Infrared SWIR) [61] The classes2 to 6 and 8 to 11 with a pronounced peak in the near infrared

wavelength range (Band5) correspond to this criterion Forother classes that have a high reflectivity value in Band 5(7 12-15) the value in Band 6 is also quite large (Figure 9Table 2) Spectral signatures with such characteristic peakscorrespond to other types of terrestrial surface

The principal components of the spectral responseswere calculated using the created vegetation mask whichincreased the information amount Six components wereselected (Figure 10) The first two components are fairlyrepresentative as they contain most of the variability ofthe data Their values were used as the input in the newclassification

Figure 11 shows the spatial change in the values of themain components in the territory of the Gilbirinskiy forestryand the adjacent territory

The classification of the first two components of thepixel reflectance values allowed a more detailed separationof the surface with vegetation This made it possible notonly to separate vegetation from other surfaces but also todifferentiate the vegetation by type The division into classeswas based on the values of spectral signatures (Figure 12)and visual interpretation (Figures 7 and 8) During the activeprocess of photosynthesis vegetation absorbs radiation in thevisible spectrumand significantly reflects in the near infraredFor visual interpretation classical colour compositions fromspectral bands 4 3 2 (natural colours) 5 4 and 3 (vegetationred) were used The colour composition in natural colours


25 0 25 5 75 10 km

Class 1 Class 7 Class 13Class 2 Class 8 Class 14Class 3 Class 9 Class 15Class 4 Class 10 Forestry borderClass 5 Class 11Class 6 Class 12

000

005

010

015

020

025

030

035

2 3 4 5 6 7Class 1 Class 2 Class 3 Class 4 Class 5Class 6 Class 7 Class 8 Class 9 Class 10Class 11 Class 12 Class 13 Class 14 Class 15

Figure 9The result of the unsupervised classification of the Gilbirinskiy forestry and spectral class signatures (y-axis is the reflectance valueaverage for the class x-axis is the spectral band)

0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE

NUMBER OF THE COMPONENT

Number Eigenvalue1 00031902 00004393 00000374 00000055 00000026 0000001

Figure 10 The information content of the main components of the image

allowed visually identifying various types of surfaces aswell as verifying the correctness of the classification Thesecond colour composition gives an idea of the vegetationtype The brighter the red colour is the more active theprocess of photosynthesis takes place in the area Thusfor example coniferous vegetation has darker shades andcultural vegetation is brighter

The following division into classes was obtained afterreclassification 1 2 4 coniferous forest 5 7 deciduous forest6 lands partially covered with vegetation 3 deadwood 8cultural vegetation and grass Then the rest of the lands

were classified The mask was created and the analysis of themain components was carried out Then classification wasimplemented All actions were similar to the previous onesso another classified part of the image can be obtained

Reclassification was done to combine the same classesinto one in order to obtain the final imageThus the followingcategories of land (Figure 13) were identified on the territoryof the Gilbirinskiy forestry as a result of the interpretationof the space image water bodies plantless ground partiallygreen soils and deadwood cultivated vegetation deciduousforest coniferous forest


25 0 25 5 75 10 km

Figure 11 Colour composition of the main components 3 2 and 1

0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER

Class 1Class 2Class 3Class 4Class 5Class 6Class 7Class 8

Figure 12 Spectral signatures of forest vegetation

Classification of Forests by the Level of Fire DangerThe decod-ing results were converted into a vector view (Figure 14) andcombined with taxation units (quarters) using the ArcGIStools The areas occupied by various land categories weredefined for the territory of the forest stand

The classification of forest areas according to the levelof fire danger caused by thermal radiation was carried out(Figure 15) The level of fire danger was determined by thecalculated value of the probability of a fire caused by theaction of the radiant flux A total of five categories were usedto determine the level of forest fire danger extreme highmean moderate and low levels [16]

25 0 25 5 75 10 km

Forestry borderWater basinsPlantless groundPartially green soilsDead woodCultivated vegetationDeciduous forestConiferous forest

Figure 13 Categories of land in the forestry and adjacent area

The possibilities to estimate fire danger from the dataon the vegetation and land use were demonstrated using thesatellite image The method was applied to the Gilbirinskiyforestry of the Republic of Buryatia

The paper discusses the method of pretreatment andcorrection of Landsat 8 satellite images which can be appliedalmost without changes to images of the area obtained atanother time Based on the corrected data classes wereidentified and compared with the corresponding type ofterritory by spectral responses Fire danger estimation wascarried out according to the vectorised classified data Thefire danger estimation approach developed by the authorswasapplied to each separate quarter and showed realistic resultsThe method used may be applicable for other areas in whichforest vegetation prevails

The obtained land use maps and the fire danger mapcan serve to prevent fires and their consequences in theterritory of the Gilbirinskiy forestry Decision making basedon map data is a tool used in various industries Given thecharacteristics and speed of change of forest areas it can benoted that the composed maps will be relevant for a longperiod of time This is due to the low rate of change in thearea of forest plantations the growth of coniferous trees andcutting down in a specially protected natural zone


21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101

Vegetation cover Area (km2)Partially green soil 213Deciduous forest 582Coniferous forest 1117Dead wood 598Cultivated vegetation 27Plantless ground 157Water basins 02

Categories of landin the forestry

Partially green soilPlantless groundWater basins

Coniferous forestDeciduous forestCultivated vegetationDead wood

N

Figure 14 Areas of different types of vegetation within the Gilbirinskiy forestry

2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km

e level of fire danger

Extreme

High

Average

Moderate

Low

N

Number ofquarters

Forest firedanger level

Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571

Figure 15 Map with forest fire danger level


4 Conclusion

Summing up the approach has been developed to estimatethe forest fire danger by vegetation type with a radiant heatflux as the reference source of ignition It should be notedthat the developed method can be used to estimate forest firedanger both from thunderstorm activity and from anthro-pogenic factors In the first case the heat flux from the cloud-to-ground lightning discharge channel which is in contactwith deciduous or coniferous trees should be estimated Ina number of situations direct contact of a lightning channelwith a layer of ground forest fuel is possible when the electricdischarge current spreads in the ground cover and upper soillayer In the case of an anthropogenic ignition situationsmustbe distinguished when it is necessary to estimate the radiantheat flux from a massive heated body or a local heatingsource (particles heated to high temperatures or firebrands)In addition situation is possible when concentrated solarradiation focused by glass containers or their fragments isaffected to a forest fuel layer [62]

In general the types of vegetation in the Gilbirinskiyforestry were obtained by the computerized classification andthe satellite image interpretation using the ENVI GRASSand ArcGIS softwareThe proposed approach allows estimat-ing the localization and composition of vegetation as well asidentifying the areas with highest fire danger Thus for themarked areas with a high level of risk it is recommendedto conduct further detailed studies which should includestudy of the territory on the ground and creation of referencezones It is also necessary to develop recommendations forreforestation and minimizing forest fire risks The techniquewas used for typical forestry of the Republic of Buryatia andcan be applied to a large part of the territory belonging to thenatural zone of Lake Baikal

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

This work was supported by Russian Foundation for BasicResearches Scientific Project no 17-29-05093

References

[1] P Migon ldquoChapter 13 - geoheritage and world heritage sitesrdquoin Geoheritage E Reynard and J Brilha Eds pp 237ndash249Elsevier 2018

[2] B L Shcherbov V D Strakhovenko and F V SukhorukovldquoThe ecogeochemical role of forest fires in the Baikal regionrdquoGeography and Natural Resources vol 29 no 2 pp 150ndash1552008

[3] Ts ZDorzhiev BaoYuhai EN Badmaeva V Batsaihan Ch BUrbazaev and Yushan ldquoForest fires in the Republic of Buryatiafor 2002ndash2016rdquo Nature of Inner Asia vol 3 no 4 2017

[4] S Frolking M W Palace D B Clark J Q Chambers H HShugart and G C Hurtt ldquoForest disturbance and recoverya general review in the context of spaceborne remote sensingof impacts on aboveground biomass and canopy structurerdquoJournal of Geophysical Research Biogeosciences vol 114 no G22009

[5] E Santi S Paloscia S Pettinato et al ldquoThe potential ofmultifrequency SAR images for estimating forest biomass inMediterranean areasrdquo Remote Sensing of Environment vol 200pp 63ndash73 2017

[6] R J Frazier N C Coops M AWulder T Hermosilla and J CWhite ldquoAnalyzing spatial and temporal variability in short-termrates of post-fire vegetation return from Landsat time seriesrdquoRemote Sensing of Environment vol 205 pp 32ndash45 2018

[7] T R McCarley C A Kolden N M Vaillant et al ldquoMulti-temporal LiDAR and Landsat quantification of fire-inducedchanges to forest structurerdquoRemote Sensing of Environment vol191 pp 419ndash432 2017

[8] A Verger I Filella F Baret and J Penuelas ldquoVegetation base-line phenology from kilometric global LAI satellite productsrdquoRemote Sensing of Environment vol 178 pp 1ndash14 2016

[9] R Meng J Wu K L Schwager et al ldquoUsing high spatialresolution satellite imagery to map forest burn severity acrossspatial scales in a Pine Barrens ecosystemrdquo Remote Sensing ofEnvironment vol 191 pp 95ndash109 2017

[10] J M N Silva A C L Sa and J M C Pereira ldquoComparison ofburned area estimates derived from SPOT-VEGETATION andLandsat ETM+ data in Africa Influence of spatial pattern andvegetation typerdquo Remote Sensing of Environment vol 96 no 2pp 188ndash201 2005

[11] P S Roy ldquoForest fire and degradation assessment using satelliteremote sensing and geographic information systemrdquo 2003httpwwwwamisorgagmpubsagm8Paper-18pdf

[12] M C Hansen R S Defries J R G Townshend and RSohlberg ldquoGlobal land cover classification at 1 km spatialresolution using a classification tree approachrdquo InternationalJournal of Remote Sensing vol 21 no 6-7 pp 1331ndash1364 2000

[13] C E Churches P J Wampler W Sun and A J SmithldquoEvaluation of forest cover estimates for Haiti using supervisedclassification of landsat datardquo International Journal of AppliedEarth Observation and Geoinformation vol 30 no 1 pp 203ndash216 2014

[14] E Yan G Wang H Lin C Xia and H Sun ldquoPhenology-basedclassification of vegetation cover types inNortheast China usingMODIS NDVI and EVI time seriesrdquo International Journal ofRemote Sensing vol 36 no 2 pp 489ndash512 2015

[15] M Mitchell R R Wilson D J Twedt A E Mini and J DJames ldquoObject-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valleyrdquo RemoteSensing Applications Society and Environment vol 4 pp 55ndash602016

[16] G V KuznetsovN V Baranovskiy A Comeron et al ldquoFocusedsunrsquos rays and forest fire danger new conceptrdquo in Proceedingsof the Remote Sensing of Clouds and the Atmosphere XVIII andOptics in Atmospheric Propagation and Adaptive Systems XVI2013

[17] J E Deeming R E Burgan and J D Cohen ldquoThe national fire-danger rating systemmdash 1978rdquo General technical report INT-39


Intermountain Forest and Range Experiment Station OgdenUtah USDA Forest Service 1978

[18] M A Finney ldquoFARSITE fire area simulator mdash model devel-opment and evaluationrdquo Research Paper RMRS-RP-4 (FortCollins CO 51 pp) Rocky Mountain Research Station 1998

[19] S Lundgren W Mitchell and M Wallace ldquostatus report onNFMASmdashan interagency system update projectrdquo Fire Manage-ment Notes vol 55 pp 11-12 1995

[20] L A Arroyo C Pascual and J AManzanera ldquoFiremodels andmethods to map fuel types The role of remote sensingrdquo ForestEcology and Management vol 256 no 6 pp 1239ndash1252 2008

[21] A GMcArthurFire behaviour in eucalypt forests vol 25 Com-monwealth of Australia Forest and Timber Bureau CanberraAustralian Capital Territory 1967

[22] C E vanWagner ldquoDevelopment and structure of the Canadianforest fire weather index systemrdquo Forest Technology Report 35Ottawa Canada Canadian Forestry Service 1987

[23] R Lasaponara and A Lanorte ldquoOn the capability of satelliteVHR QuickBird data for fuel type characterization in frag-mented landscaperdquo Ecological Modelling vol 204 no 1-2 pp79ndash84 2007

[24] R E Keane R Burgan and J van Wagtendonk ldquoMappingwildland fuels for fire management across multiple scalesintegrating remote sensing GIS and biophysical modelingrdquoInternational Journal of Wildland Fire vol 10 no 3-4 pp 301ndash319 2001

[25] L Wang W P Sousa P Gong and G S Biging ldquoComparisonof IKONOS and QuickBird images for mapping mangrovespecies on the Caribbean coast of Panamardquo Remote Sensing ofEnvironment vol 91 no 3-4 pp 432ndash440 2004

[26] S L Ustin D A Roberts J A Gamon G P Asner and RO Green ldquoUsing imaging spectroscopy to study ecosystemprocesses and propertiesrdquo Bioscience vol 54 no 6 pp 523ndash5342004

[27] A A Kononov andM-H Ka ldquoModel-associated forest param-eter retrieval using VHF SAR data at the individual tree levelrdquoIEEE Transactions on Geoscience and Remote Sensing vol 46no 1 pp 69ndash84 2008

[28] E H Chowdhury and Q K Hassan ldquoOperational perspectiveof remote sensing-based forest fire danger forecasting systemsrdquoISPRS Journal of Photogrammetry and Remote Sensing vol 104pp 224ndash236 2015

[29] P A Hernandez-Leal A Gonzalez-Calvo M Arbelo A Bar-reto andAAlonso-Benito ldquoSynergy ofGIS and remote sensingdata in forest fire danger modelingrdquo IEEE Journal of SelectedTopics in Applied Earth Observations and Remote Sensing vol1 no 4 pp 240ndash247 2008

[30] A Butt R Shabbir S S Ahmad and N Aziz ldquoLand usechange mapping and analysis using remote sensing and GIS acase study of simly watershed Islamabad Pakistanrdquo EgyptianJournal of Remote Sensing and Space Science vol 18 no 2 pp251ndash259 2015

[31] J-H Zhang F-M Yao C Liu L-M Yang and V K BokenldquoDetection emission estimation and risk prediction of forestfires in China using satellite sensors and simulation modelsin the past three decades-an overviewrdquo International Journalof Environmental Research and Public Health vol 8 no 8 pp3156ndash3178 2011

[32] S M Razali A A Nuruddin I A Malek and N A PatahldquoForest fire hazard rating assessment in peat swamp forest usingLandsat thematic mapper imagerdquo Journal of Applied RemoteSensing vol 4 no 1 p 043531 2010

[33] GEOPORTAL FGBU (ROSLESINFORG) httpgeoportalros-lesinforgru8080

[34] Lesohozyaystvennyie reglamentyi httpegov-buryatiaruralhactivitiesdocumentslesokhozyaystvennye-reglamenty

[35] Official site MO ldquoIvolginskiy rayonrdquo httpegov-buryatiaruivolgao-munitsipalnom-obrazovaniiob-organizatsii

[36] Forest resourceshttpegov-buryatiaruabout republicnature-resourceslesnye-resursy-

[37] Landsat Science httpslandsatgsfcnasagov[38] Landsat 8 Data Users Handbook mdash Landsat Missions https

landsatusgsgovlandsat-8-data-users-handbook[39] Using the USGS Landsat Level-1 Data Product mdash Landsat Mis-

sions httpslandsatusgsgovusing-usgs-landsat-8-product[40] GRASS GIS - Home httpsgrassosgeoorg[41] GRASS GIS manual ilandsattoar httpsgrassosgeoorg

grass77manualsilandsattoarhtml[42] H Kusetogullari and A Yavariabdi ldquoUnsupervised change

detection in landsat images with atmospheric artifacts a fuzzymultiobjective approachrdquo Mathematical Problems in Engineer-ing vol 2018 Article ID 7274141 16 pages 2018

[43] D Lu P Mausel E Brondizio and E Moran ldquoAssessment ofatmospheric correction methods for landsat tm data applicableto amazon basin lba researchrdquo International Journal of RemoteSensing vol 23 no 13 pp 2651ndash2671 2002

[44] P S Chavez Image-Based Atmospheric Corrections-Revisitedand Improved 2006

[45] Y Zhang X Wang and Y Chen ldquoAn improved 6s codefor atmospheric correction based on water vapor contentrdquoAdvances in Remote Sensing vol 01 no 01 pp 14ndash18 2012

[46] E F Vermote D Tanre J L Deuze M Herman and J-JMorcrette ldquoSecond simulation of the satellite signal in the solarspectrum 6s an overviewrdquo IEEE Transactions on Geoscienceand Remote Sensing vol 35 no 3 pp 675ndash686 1997

[47] E Vermote C JusticeM Claverie and B Franch ldquoPreliminaryanalysis of the performance of the landsat 8OLI land surfacereflectance productrdquo Remote Sensing of Environment vol 185pp 46ndash56 2016

[48] 6SV - Run 6SV http6sltdriorgpagesrun6SVhtml[49] GRASS GIS manual iatcorr httpsgrassosgeoorggrass76

manualsiatcorrhtml[50] EarthExplorer - Home httpsearthexplorerusgsgov[51] SRTM Data Search httpsrtmcsicgiarorgSELECTIONin-

putCoordasp[52] AERONET Data Display Interface - WWW DEMONSTRAT

httpsaeronetgsfcnasagovcgi-bintype piece of map operav2 new

[53] J Pushparaj and A V Hegde ldquoA comparative study on extrac-tion of buildings from Quickbird-2 satellite imagery with ampwithout fusionrdquo Cogent Engineering vol 4 no 1 2017

[54] H A L Kiers J-P Rasson P J F Groenen and MSchader Eds Data Analysis Classification and Related Meth-ods Springer 2000

[55] S Bandyopadhyay and U Maulik ldquoAn evolutionary techniquebased on K-means algorithm for optimal clustering in R119873rdquoInformation Sciences vol 146 no 1ndash4 pp 221ndash237 2002

[56] L Galluccio O Michel P Comon and A O Hero ldquoGraphbased k-means clusteringrdquo Signal Processing vol 92 no 9 pp1970ndash1984 2012

[57] J A Richards ldquoThematic mapping from multitemporal imagedata using the principal components transformationrdquo RemoteSensing of Environment vol 16 no 1 pp 35ndash46 1984


[58] ArcGIS Desktop httpsdesktoparcgiscom[59] N V Baranovskiy and E P Yankovich ldquoGeoinformation mon-

itoring of forest fire danger on the basis of remote sensing dataof surface by the artificial earth satelliterdquo Journal of Automationand Information Sciences vol 47 no 8 pp 11ndash23 2015

[60] E P Yankovich and N V Baranovskiy ldquoForest taxation datageoprocessing for assessment of forest fire danger causedby focused sunlightrdquo in Proceedings of the 14th InternationalMultidisciplinary Scientific Geoconference and EXPO SGEM2014 pp 607ndash612 June 2014

[61] J R Jensen Remote Sensing of the Environment An EarthResource Perspective Prentice Hall Upper Saddle River NJUSA 2000

[62] V Babrauskas Ignition Handbook Principles and Applicationsto Fire Safety Engineering Fire Investigation Risk Managementand Forensic Science Fire Science Publishers Issaquah WashDC USA 2003

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of


Mathematical Problems in Engineering

Applied MathematicsJournal of


Probability and StatisticsHindawiwwwhindawicom Volume 2018

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Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences


Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

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Dierential EquationsInternational Journal of

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Decision SciencesAdvances in


AnalysisInternational Journal of


Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom


include the traditional classification with and without train-ing [12 13] the method based on phenology [14] and theobject classification [15]

The vegetation type maps are necessary for spatial cal-culation of fire danger and for estimation of fire risk byusing them in models that simulate growth and intensity ofa fire in a landscape Forest fuel maps are used in variouswidespread systems for the forest fire danger forecastingFor example such models are used in the North Americanmodels National Fire Danger Rating System (NFDRS) Firebehavior (BEHAVE) Fire Area Simulator (FARSITE) andNational Fire Management Analysis System (NFMAS) [17ndash20] The McArthur Forest Fire Danger Rating System andthe McArthur Grassland Fire Danger Rating System [21] arewidely used in Australia The Canadian Forest Fire DangerRating System is used in Canada which consists of twomain subsystems the Fire Weather Index (FWI) and FireBehavior Prediction System [20 22] The Forest Fire SatelliteMonitoring Information System of Russian Federal ForestryAgency (SMIS-Rosleshoz) used in the Russian Federation isbased on the Nesterov index Ground-based observationsaerial photography modelling and remote sensing data[20 23ndash27] are used for mapping In general data on thegreenness of vegetation meteorological data data on wettingthe surface and moisture content of forest fuel are necessaryto forecast and to monitor forest fires [28]

In addition numerous studies showed the feasibility ofgeographic information system (GIS) and remote sensingdata [29 30] The GIS is a widely used tool for processingspatial data and displaying results A model of a potentialforest fire using satellite data and the GIS was developed inChina to identify areas with a high probability of forest fire[31] In Sheriza et al [32] five fire danger classifications wereidentified in developed forest fuel maps and the degree of firedanger in peat-bog forests was assessed The vegetation typesin the study areawere analyzed using digital classification sys-tems namely two vegetation indices the extended vegetationindex (AVI) and the Tasseled Cap (TC) conversion

The objectives of the research were

(i) to process the satellite image of the Gilbirinskiyforestry located in the basin of Lake Baikal

(ii) to select homogeneous areas of forest vegetation onthe basis of their spectral characteristics

(iii) to estimate the level of forest fire danger of the area byvegetation types

The paper presents an approach for estimation of forestfire danger depending on vegetation type and radiant heatflux influence using GIS and remote sensing data The ENVIand the GRASS software were used to process the satelliteimages The arearsquos forest fire danger estimation and visualpresentation of the results was carried out in the ArcGISDesktop software The final information product was themap displaying vector polygonal feature class containing thetype of vegetation and the level of fire danger for each forestquarter in the attribute table

The structure of the article is following Section 2 containsdescriptions of methods Section 3 describes the results andcontains discussion Section 4 contains conclusions

2 Materials and Methods

21 Study Area andData Resources Thestudy was conductedin the Gilbirinskiy forestry (Ivolginskiy district of the Repub-lic of Buryatia) in a protected natural area The study areais located between latitudes 51∘3510158405010158401015840N and 51∘4610158404010158401015840N andlongitudes 106∘4210158400010158401015840E and 107∘0210158400010158401015840E [33] This area is55 km southeast of Lake Baikal and belongs to the northerntip of the Selenga middle mountains (Figure 1) The area ofthe forestry is about 270 km2 [34]The forestry is divided into126 forest quarters The forest quarter is the main accountingunit of the forest fund in the Russian Federation and it haspermanent boundaries

The region of the study like the whole of Buryatiahas a sharply continental climate with cold winters andhot summers The average temperature in summer is about+ 185∘C and in winter is about ndash22∘C and the averageannual temperature is about ndash16∘C The average annualrainfall is 244mm A significant feature of the climate is thelong duration of sunshine for 1900-2200 hours [35] Naturalplantations occupy about 90 of the territory The rest of theterritory is filled with glades slopes pebbles forest culturesarable land pastures etcThemain forest-forming species arelarch pine cedar birch and aspen [36]

The land use map of the study area was created on thebasis of the Landsat 8OLI medium spatial resolution imageobtained on August 21 2015 (pathrows 7891 times 7791) [37]The image had 11 bands with different wavelength rangesSix spectral bands were used for analysis (Table 1) Since thisimage was taken under the cloudless conditions it was anexcellent source for classifying the types of vegetation of theGilbirinskiy forestry and the surrounding area The map offorest quarters was used as background data

22 Image Processing Technique The radiometric calibrationwas the first important step in Landsat 8 data processingThe Landsat 8 data set consists of normalized values calleda digital number (DN) that represents multispectral imagedata These numbers have no physical meaning They mayalso be completely incompatible in each independent data setand even in different bands of the same set DN values fordigital processing of Landsat 8 image are usually convertedinto one of two physical parameters reflectance or spectralradiance [38]

It was chosen to convert DN pixel values into Top ofAtmosphere (TOA) reflectance for data processing Theseadjustments were made based on the coefficients presentedin the metadata file (MTLtxt) that comes with the imageset [39] The conversion of one unit to another was carriedout using the radiometric calibration tool ilandsattoar of theGRASS software [40 41]

The atmospheric correction was the next step after theradiometric calibration The data on the underlying surfaceand the objects upon it obtained by the sensor system were


51deg4

6

51deg4

651

deg45

51deg4

451

deg43

51deg4

251

deg41

51deg3

651

deg37

51deg3

851

deg39

51deg4

551

deg44

51deg4

351

deg42

51deg4

151

deg40

51deg3

651

deg37

51deg3

851

deg39

2 6

879

3

13

15

71

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

123

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

65 66 67 69

111 120 121

46

4

18 19 20

74 75 76 77 78

40

70

6160

68 72

33 3436

35

112 113 114 115 116

124 125

86 87 88 89 90 91 92 93 94

118 119

122

99 100 102 103 104 105 106 107 108 109 110

1

101


106deg42

106deg43

106deg44

106deg45

106deg46

106deg47

106deg48

106deg49

106deg52

106deg53

106deg54

106deg55

106deg56

106deg57

106deg58

106deg59

106deg50

106deg51

107deg0

107deg1


km

0 2 4

RUSSIA

Tomsk


Ulan-Udeh

Krasnoyarsk

Irkutsk

Irkutsk

Moskva

Bury

atia

BaikalN

5149 48

51deg4

0






Spectrum (6S)


rescaling




Atmospheric model

Aerosol model



















parameter


No

Yes

Yes


Input data













1B

2B

3W

4W

5W

6D

7W

8D

9D

10D

11CO

12CY

13CO

14L

15R

16M

17M

18CO

19R

20L

(a)

1B

6D

11CO

2B

7W

12CY

3W

8D

13CO

4W

9D

14L

5W

10D

15R

16M

17M

18CO

19R

20L

(b)

M

4W

9D

14L

19R

5W

10D

15R

20L

1 2 3B B W

6 7 8D W D

11 12 13CO CY CO

16 17 18M CO

(c)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(d)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(e)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(f)





119875119891119891 asympNFSNTS

or 119875119891119891 asymp119878119865119878119878119879119878

(1)







Add FieldFireDanger

Table-1

Table toTable


P

Table DataExcell

P

Add Field Stat

Table-2

CalculateField

FireDanger

Table-3

CalculateField Stat

ProcessedTable

SummaryStatistics

Table1


Table2Add Field

ADRESS (1)

Table 6


Table5

CalculateField

P_FireDanger

Table4

Add FieldLevel

Table3


(1)


(2)

Table 2


Add FieldADRESS (2)

Table 1AtributTable

(Forest

P


ematicMap

SymbologyLayer

P







Metadata

Atmospheric model


Study area

GRASS GIS













Before Aer









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018









51deg4

6

51deg4

651

deg45

51deg4

451

deg43

51deg4

251

deg41

51deg3

651

deg37

51deg3

851

deg39

51deg4

551

deg44

51deg4

351

deg42

51deg4

151

deg40

51deg3

651

deg37

51deg3

851

deg39

2 6

879

3

13

15

71

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

123

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

65 66 67 69

111 120 121

46

4

18 19 20

74 75 76 77 78

40

70

6160

68 72

33 3436

35

112 113 114 115 116

124 125

86 87 88 89 90 91 92 93 94

118 119

122

99 100 102 103 104 105 106 107 108 109 110

1

101


106deg42

106deg43

106deg44

106deg45

106deg46

106deg47

106deg48

106deg49

106deg52

106deg53

106deg54

106deg55

106deg56

106deg57

106deg58

106deg59

106deg50

106deg51

107deg0

107deg1


km

0 2 4

RUSSIA

Tomsk


Ulan-Udeh

Krasnoyarsk

Irkutsk

Irkutsk

Moskva

Bury

atia

BaikalN

5149 48

51deg4

0






Spectrum (6S)


rescaling




Atmospheric model

Aerosol model



















parameter


No

Yes

Yes


Input data













1B

2B

3W

4W

5W

6D

7W

8D

9D

10D

11CO

12CY

13CO

14L

15R

16M

17M

18CO

19R

20L

(a)

1B

6D

11CO

2B

7W

12CY

3W

8D

13CO

4W

9D

14L

5W

10D

15R

16M

17M

18CO

19R

20L

(b)

M

4W

9D

14L

19R

5W

10D

15R

20L

1 2 3B B W

6 7 8D W D

11 12 13CO CY CO

16 17 18M CO

(c)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(d)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(e)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(f)





119875119891119891 asympNFSNTS

or 119875119891119891 asymp119878119865119878119878119879119878

(1)







Add FieldFireDanger

Table-1

Table toTable


P

Table DataExcell

P

Add Field Stat

Table-2

CalculateField

FireDanger

Table-3

CalculateField Stat

ProcessedTable

SummaryStatistics

Table1


Table2Add Field

ADRESS (1)

Table 6


Table5

CalculateField

P_FireDanger

Table4

Add FieldLevel

Table3


(1)


(2)

Table 2


Add FieldADRESS (2)

Table 1AtributTable

(Forest

P


ematicMap

SymbologyLayer

P







Metadata

Atmospheric model


Study area

GRASS GIS













Before Aer









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018










Spectrum (6S)


rescaling




Atmospheric model

Aerosol model



















parameter


No

Yes

Yes


Input data













1B

2B

3W

4W

5W

6D

7W

8D

9D

10D

11CO

12CY

13CO

14L

15R

16M

17M

18CO

19R

20L

(a)

1B

6D

11CO

2B

7W

12CY

3W

8D

13CO

4W

9D

14L

5W

10D

15R

16M

17M

18CO

19R

20L

(b)

M

4W

9D

14L

19R

5W

10D

15R

20L

1 2 3B B W

6 7 8D W D

11 12 13CO CY CO

16 17 18M CO

(c)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(d)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(e)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(f)





119875119891119891 asympNFSNTS

or 119875119891119891 asymp119878119865119878119878119879119878

(1)







Add FieldFireDanger

Table-1

Table toTable


P

Table DataExcell

P

Add Field Stat

Table-2

CalculateField

FireDanger

Table-3

CalculateField Stat

ProcessedTable

SummaryStatistics

Table1


Table2Add Field

ADRESS (1)

Table 6


Table5

CalculateField

P_FireDanger

Table4

Add FieldLevel

Table3


(1)


(2)

Table 2


Add FieldADRESS (2)

Table 1AtributTable

(Forest

P


ematicMap

SymbologyLayer

P







Metadata

Atmospheric model


Study area

GRASS GIS













Before Aer









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018















parameter


No

Yes

Yes


Input data













1B

2B

3W

4W

5W

6D

7W

8D

9D

10D

11CO

12CY

13CO

14L

15R

16M

17M

18CO

19R

20L

(a)

1B

6D

11CO

2B

7W

12CY

3W

8D

13CO

4W

9D

14L

5W

10D

15R

16M

17M

18CO

19R

20L

(b)

M

4W

9D

14L

19R

5W

10D

15R

20L

1 2 3B B W

6 7 8D W D

11 12 13CO CY CO

16 17 18M CO

(c)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(d)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(e)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(f)





119875119891119891 asympNFSNTS

or 119875119891119891 asymp119878119865119878119878119879119878

(1)







Add FieldFireDanger

Table-1

Table toTable


P

Table DataExcell

P

Add Field Stat

Table-2

CalculateField

FireDanger

Table-3

CalculateField Stat

ProcessedTable

SummaryStatistics

Table1


Table2Add Field

ADRESS (1)

Table 6


Table5

CalculateField

P_FireDanger

Table4

Add FieldLevel

Table3


(1)


(2)

Table 2


Add FieldADRESS (2)

Table 1AtributTable

(Forest

P


ematicMap

SymbologyLayer

P







Metadata

Atmospheric model


Study area

GRASS GIS













Before Aer









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018









1B

2B

3W

4W

5W

6D

7W

8D

9D

10D

11CO

12CY

13CO

14L

15R

16M

17M

18CO

19R

20L

(a)

1B

6D

11CO

2B

7W

12CY

3W

8D

13CO

4W

9D

14L

5W

10D

15R

16M

17M

18CO

19R

20L

(b)

M

4W

9D

14L

19R

5W

10D

15R

20L

1 2 3B B W

6 7 8D W D

11 12 13CO CY CO

16 17 18M CO

(c)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(d)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(e)

M

1 2 3 4 5B B W W W

6 7 8 9 10D W D D D

11 12 13 14 15CO CY CO L R

16 17 18 19 20M CO R L

(f)





119875119891119891 asympNFSNTS

or 119875119891119891 asymp119878119865119878119878119879119878

(1)







Add FieldFireDanger

Table-1

Table toTable


P

Table DataExcell

P

Add Field Stat

Table-2

CalculateField

FireDanger

Table-3

CalculateField Stat

ProcessedTable

SummaryStatistics

Table1


Table2Add Field

ADRESS (1)

Table 6


Table5

CalculateField

P_FireDanger

Table4

Add FieldLevel

Table3


(1)


(2)

Table 2


Add FieldADRESS (2)

Table 1AtributTable

(Forest

P


ematicMap

SymbologyLayer

P







Metadata

Atmospheric model


Study area

GRASS GIS













Before Aer









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018









Add FieldFireDanger

Table-1

Table toTable


P

Table DataExcell

P

Add Field Stat

Table-2

CalculateField

FireDanger

Table-3

CalculateField Stat

ProcessedTable

SummaryStatistics

Table1


Table2Add Field

ADRESS (1)

Table 6


Table5

CalculateField

P_FireDanger

Table4

Add FieldLevel

Table3


(1)


(2)

Table 2


Add FieldADRESS (2)

Table 1AtributTable

(Forest

P


ematicMap

SymbologyLayer

P







Metadata

Atmospheric model


Study area

GRASS GIS













Before Aer









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018












Before Aer









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018









25 0 25 5 75 10 km


000

005

010

015

020

025

030

035



0

00005

0001

00015

0002

00025

0003

1 2 3 4 5 6

EIG

ENVA

LUE









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018









25 0 25 5 75 10 km


0

005

01

015

02

025

03

035

2 3 4 5 6 7

REFL

ECTA

NE

BAND NUMBER





25 0 25 5 75 10 km







21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018









21

6

879

3

13

15

58

73

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

59

31 3225

47 50 52

4127 28 29

117

43 45

42

44

37

126

111

46

4

18 19 20

74 75 76 77 78

40

7069

124 125

116 118

6160

7271

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

120 121119

122123

99 100 102 103 104 105 106 107 108 109 110

0 2 4 6

km

514948

101





N


2

16

879

3

13

15

7173

38

11

26

96

5

12

17

95

53

79

30

14

39

54

63 64

56

10

62

22

67 6865 66

57

97 98

80

23

16

55

83 84 85

24

21

81 82

31 3225

61

47 48 49 50 51 52

4127 28 29

117

43 45

42

44

37

126

69

111 120 121

46

4

18 19 20

74 75 76 77 7870

124 125

116 118

58 59 60

72

33 3436

35

112 113 114 115

86 87 88 89 90 91 92 93 94

119

122123

99 100 101 102 103 104 105 106 107 108 109 110

40

0 2 4 6

km


Extreme

High

Average

Moderate

Low

N

Number ofquarters


Area(km2)

16 Extreme 324

21 High 457

35 Mean 821

26 Moderate 522

28 Low 571



4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018









4 Conclusion



Data Availability




Acknowledgments


References







































































Journal of












Journal of







Volume 2018






Volume 2018





























































Journal of












Journal of







Volume 2018






Volume 2018





















Journal of












Journal of







Volume 2018






Volume 2018















Journal of












Journal of







Volume 2018






Volume 2018








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