Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

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Multivolume work

Persistent identifier:: 1663674213

Title:: Proceedings, XXth congress

Sub title:: Istanbul, 12 - 23 July 2004

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1663674213

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.; Auch bezeichnet als XXth International Congress for Photogrammetry and Remote Sensing

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Document type:: Multivolume work

Volume

Persistent identifier:: 166368779X

Title:: Proceedings, XXth congress

Scope:: IV, 226 Seiten

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 166368779X

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(35,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: FOREST FIRE RISK ZONE MAPPING FROM SATELLITE IMAGERY AND GIS A CASE STUDY Esra Erten, Vedat Kurgun, Nebiye Musaoglu

Document type:: Multivolume work

Structure type:: Chapter

at ly of he nd on ed he €, ge ge International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 The satellite images were corrected for the influence of atmosphere and topographic relief. These data were geo-coded by the help of rectified SPOT data according to the European Datum (ED50) and a UTM projection to combine and analyse with the other data. In the end, the atmospherically, radio metrically and geometrically corrected / geo-coded images were supplied by ERDAS software. The purpose of digital land cover classification is to link the spectral characteristics of the image to a meaningful information class value, which can be displayed as a map so that resource managers or scientists can evaluate the landscape in an accurate and cost effective manner (Weber and Dunno, 2001). In this study, the Maximum Likehood supervised classification algorithm was used (Lillesand Kieffer, 2000). The best way to compare images from different dates is to classify the two images separately and to compare the statistical results. The classification of satellite images were utilised in land cover analyses and in determining change between the land use before the fire and that after the fire. In order to obtain effective and more accurate conclusions, mathematical operations in the GIS analysis were formed. The input information on forest fire influencing factors indicates the weights in the fire risk in an area. The factors were analysed in the following order of importance: vegetation type, slope, aspect, distance from roads and settlements. First classes represent high risk places and last classes represent the minor risk place. Each class has different weights. The vegetation types were classified according to the moisture context that has an influence on breaking out forest fire. For example, the vegetation type that is very dry is the most flammable whereas the fresh type is inflammable. Slope influence behaviour of fire was evaluated the second highest weight. Fire travels most rapidly up-slopes and least rapidly down-slopes. Slope classes were created according to this rule. Aspect was assigned equal weight with slope. Since the sunlight is much more reflected on the slopes in the south, fire breaks out fast and spreads in the south sides. Distance from roads and settlements were evaluated the third highest weight. The risk factor decreases farther from these places. It means that a zone close to these places were evaluated a higher rating. Water bodies areas do not affect the forest fire risk. These zones have no weights in determination of fire rating class. The equation used in GIS to determine forest fire risk places is shown in equation I. . RC=T*Vr+ 5%(S+4) + 3%(Dp+Dy) (1) In this equation, RC is the numerical index of forest fire risk zones where Vr indicates vegetation type with 5 classes, S the slope factor with 5 classes, A the aspect variable with 4 classes, D, and Ds indicate distance factor from road and settlement. Finally, based on these analysis carried out, a fire risk zone map was produced. 35 3.3 GIS Geographic Information System (GIS) has also developed functions such as analyzing available information and using them as a decision and a support system as well as it compiles the information as a whole and stores it. In that way, GIS analyses show the spatial distribution of the observed forest condition and also may help to find cause parameters for the observed — and classified — forest decline phenomena. The link between remote sensing results and an efficient forestry GIS can therefore work as a tool for an operational and practically oriented monitoring system for forest damage assessment and management. It may play an important role as a planning tool for forest and land-use affairs in a broader sense (Faber et al., 1994). Parameters Weight Classes Factors Fire Rating classes Vegetation 7 Very dry 5 Very High Dry 4 High Moist 3 Medium Fresh-like 2 Low Fresh 1 Very Low Slope 5 >% 35 5 Very High % 35-25 4 High % 25-10 3 Medium % 10-5 2 Low <%5 1 Very Low Aspect 5 South 5 Very High West 4 High East 3 Medium North 2 Low Distance from 3 < 100m 5 Very High roads 100-200m 4 High 200-300m 3 Medium 300-400m 2 Low > 400m l Very Low Distance from 3 « 1000m 3 Very High settlements 1000- 4 High 2000m 2000- 3 Medium 3000m > 3000m 2 Low Table ! The weight of parameters in determination of fire risk areas.

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