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A weighty issue : estimation of fire size with geographically weighted logistic regression

Hjalmarsson, Julia LU (2016) In Master Thesis in Geographical Information Science GISM01 20162
Dept of Physical Geography and Ecosystem Science
Abstract
Size estimations of fires that occurred centuries ago have been the subject of study for many decades. More accurate spatial fire histories from tree rings were possible by either drawing the sample location on detailed topographic maps or using GPS receivers. A popular method of delineating fire sizes is to draw an outline around the fire-scarred samples considering topographic and landscape features. This is a rather subjective method that cannot be replicated. Other more replicable methods have also been suggested to estimate fire size among them methods that use buffers (kernel ranges), grids, or Thiessen polygons. However, even those have a subjective component.

Geographically weighted logistic regression (GWLR), not previously... (More)
Size estimations of fires that occurred centuries ago have been the subject of study for many decades. More accurate spatial fire histories from tree rings were possible by either drawing the sample location on detailed topographic maps or using GPS receivers. A popular method of delineating fire sizes is to draw an outline around the fire-scarred samples considering topographic and landscape features. This is a rather subjective method that cannot be replicated. Other more replicable methods have also been suggested to estimate fire size among them methods that use buffers (kernel ranges), grids, or Thiessen polygons. However, even those have a subjective component.

Geographically weighted logistic regression (GWLR), not previously used to estimate fire sizes, seemed promising since the method allows for the changing relationships between different topographic, landscape, or socioeconomic features to be considered in the analysis. Logistic regression is done with binomial data: fire/no fire. Geographically weighted regression (GWR) is a relatively new and more objective method that considers the geography of the data. Instead of using one regression coefficient over a whole study area, several regression coefficients are calculated for the different sample locations which might help explain the relationships better.

The GWLR analyses consistently found only one variable that explained fire location. This variable changed between the different analyses. The corrected Akaike Information Criterion (AICc) increased in every GWLR analysis when adding more variables (a lower AICc value means a higher quality model) while the R2 value increased (more variables explain more of the variance). The optimal output of such analyses would be that the R2 value increases and the AICc decreases which would mean that the added variables help explain more of the variance AND that the model has higher quality.

A probability analysis of whether close trees burn at the same time shows that trees closer to each other have a higher probability of burning compared to trees that are further apart. This is especially true between the years 1400 - 1700 (before human influences on the landscape). Between 1700 and 2000, this clear pattern partially vanished.

While GWR could be considered the most objective method of fire-size estimations (of the ones studied), it could not be used to estimate fire sizes. Fire size and location seems to depend more on the distance from fire-scarred trees than on different landscape features. Different methods of estimating fire sizes are more reliable before humans have added to the natural fire regimes. After human influence, the uncertainty of the fire sizes increases between the different methods of calculating fires sizes (up to 46% in this study). (Less)
Popular Abstract
Destructive forest and wildland fires are very common today and generate news every year. In the past, however, fires have been a vital source of renewal in many ecosystems. Many species are adapted to and depended on fire. To be able to restore natural fire regimes, the estimation of fire sizes is essential.

Size estimations of fires that occurred centuries ago have been the subject of study for many decades. More accurate spatial fire histories from tree rings were possible by either drawing the sample location on detailed topographic maps or using GPS receivers. A popular method of delineating fire sizes is to draw an outline around the fire-scarred samples considering topographic and landscape features. This is a rather subjective... (More)
Destructive forest and wildland fires are very common today and generate news every year. In the past, however, fires have been a vital source of renewal in many ecosystems. Many species are adapted to and depended on fire. To be able to restore natural fire regimes, the estimation of fire sizes is essential.

Size estimations of fires that occurred centuries ago have been the subject of study for many decades. More accurate spatial fire histories from tree rings were possible by either drawing the sample location on detailed topographic maps or using GPS receivers. A popular method of delineating fire sizes is to draw an outline around the fire-scarred samples considering topographic and landscape features. This is a rather subjective method that cannot be replicated. Other more replicable methods have also been suggested to estimate fire size among them methods that use buffers, grids, or Thiessen polygons. However, even those have a subjective component.

Geographically weighted logistic regression (GWLR), not previously used to estimate fire sizes, seemed promising since the method allows for the changing relationships between different topographic, landscape, or socioeconomic features to be considered in the analysis. Logistic regression is done with binomial data: fire/no fire. Geographically weighted regression (GWR) is a relatively new and more objective method that considers the geography of the data. Instead of using one regression coefficient over a whole study area, several regression coefficients are calculated for the different sample locations which might help explain the relationships better.

Different methods of estimating fire sizes are more reliable before humans have added to the natural fire regimes. After human influence, the uncertainty of the fire sizes increases between the fires (up to 46% in this study). Additionally, while GWR could be considered the most objective method of fire-size estimations (of the ones mentioned here), it could not be used to estimate a fire size. Fire size seems to depend more on the distance from fire scarred trees than on different landscape features. (Less)
Popular Abstract (Swedish)
Destruktiva skogsbränder är vanliga idag och genererar nyheter varje år. Förr i tiden var skogsbränder dock ett viktigt ursprung till förnyelse i många ekosystem. Många arter är anpassade och beroende av skogsbränder. Uppskattningen av skogsbrandstorleken är viktig för att återinsätta bränder i skogen med en naturlig rotation.

Storleksuppskattning av dåtida skogsbränder har undersökts i många årtionden. Mer exakta rumsliga brandhistorier från trädringar var möjligt genom att rita den geografiska platsen av alla prover på detaljerade topografiska kartor eller genom att använda GPS mottagare. En populär metod för att avgränsa brandstorlekar är att rita en kontur runt brandärrade prover genom att avväga topografiska och landskapselement.... (More)
Destruktiva skogsbränder är vanliga idag och genererar nyheter varje år. Förr i tiden var skogsbränder dock ett viktigt ursprung till förnyelse i många ekosystem. Många arter är anpassade och beroende av skogsbränder. Uppskattningen av skogsbrandstorleken är viktig för att återinsätta bränder i skogen med en naturlig rotation.

Storleksuppskattning av dåtida skogsbränder har undersökts i många årtionden. Mer exakta rumsliga brandhistorier från trädringar var möjligt genom att rita den geografiska platsen av alla prover på detaljerade topografiska kartor eller genom att använda GPS mottagare. En populär metod för att avgränsa brandstorlekar är att rita en kontur runt brandärrade prover genom att avväga topografiska och landskapselement. Detta är en ganska subjektiv metod som inte kan replikeras. Andra, mer reproducerbara, metoder har också föreslagits för att uppskatta brandstorlek bland annat metoder som använder buffer, rutnät eller Thiessen polygoner. Även de metoderna har dock en subjektiv komponent.

Geografiskt Viktad Logistik Regression har inte tidigare använts för att uppskatta brandstorlekar. Metoden verkade lovande eftersom den tillåter förändrade relationer mellan olika topografiska, landskaps-, och socioekonomiska element som ska betraktas i analysen. Logistik regression görs med binomiala data: brand/icke brand. Geografiskt Viktad Regression är en relativ ny och mer objektiv metod som avväger geografin i data. Istället för att använda en regressionskoefficient över hela studieområdet beräknas flera regressionskoefficienter för de olika provplatserna. Detta kan hjälpa förklara relationer bättre.

Olika metoder för att uppskatta brandstorlekar är mer tillförlitliga innan människor påverkade den naturliga brandrotationen. Efter den mänskliga påverkan har oöverensstämmelse mellan olika metoder som uppskattar brandstorleken ökat (med upp till 46 % i denna studie). Dessutom har det visats att även om Geografiskt Viktad Regression kan anses vara den mest objektiva metoden för att uppskatta brandstorlekar (bland dem som använts i avhandlingen) kan det faktiskt inte användas för att uppskatta brandstorlekar. Brandstorlek verkar bero mer på avstånd mellan brandärrade träd än på olika landskapselement. (Less)
Please use this url to cite or link to this publication:
author
Hjalmarsson, Julia LU
supervisor
organization
course
GISM01 20162
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Physical Geography and Ecosystem analysis, GIS, geographically weighted logistic regression, fire reconstruction, fire size
publication/series
Master Thesis in Geographical Information Science
report number
58
language
English
id
8892907
date added to LUP
2016-10-04 16:23:03
date last changed
2016-10-07 10:48:00
@misc{8892907,
  abstract     = {Size estimations of fires that occurred centuries ago have been the subject of study for many decades. More accurate spatial fire histories from tree rings were possible by either drawing the sample location on detailed topographic maps or using GPS receivers. A popular method of delineating fire sizes is to draw an outline around the fire-scarred samples considering topographic and landscape features. This is a rather subjective method that cannot be replicated. Other more replicable methods have also been suggested to estimate fire size among them methods that use buffers (kernel ranges), grids, or Thiessen polygons. However, even those have a subjective component.

Geographically weighted logistic regression (GWLR), not previously used to estimate fire sizes, seemed promising since the method allows for the changing relationships between different topographic, landscape, or socioeconomic features to be considered in the analysis. Logistic regression is done with binomial data: fire/no fire. Geographically weighted regression (GWR) is a relatively new and more objective method that considers the geography of the data. Instead of using one regression coefficient over a whole study area, several regression coefficients are calculated for the different sample locations which might help explain the relationships better.

The GWLR analyses consistently found only one variable that explained fire location. This variable changed between the different analyses. The corrected Akaike Information Criterion (AICc) increased in every GWLR analysis when adding more variables (a lower AICc value means a higher quality model) while the R2 value increased (more variables explain more of the variance). The optimal output of such analyses would be that the R2 value increases and the AICc decreases which would mean that the added variables help explain more of the variance AND that the model has higher quality.

A probability analysis of whether close trees burn at the same time shows that trees closer to each other have a higher probability of burning compared to trees that are further apart. This is especially true between the years 1400 - 1700 (before human influences on the landscape). Between 1700 and 2000, this clear pattern partially vanished.

While GWR could be considered the most objective method of fire-size estimations (of the ones studied), it could not be used to estimate fire sizes. Fire size and location seems to depend more on the distance from fire-scarred trees than on different landscape features. Different methods of estimating fire sizes are more reliable before humans have added to the natural fire regimes. After human influence, the uncertainty of the fire sizes increases between the different methods of calculating fires sizes (up to 46% in this study).},
  author       = {Hjalmarsson, Julia},
  keyword      = {Physical Geography and Ecosystem analysis,GIS,geographically weighted logistic regression,fire reconstruction,fire size},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master Thesis in Geographical Information Science},
  title        = {A weighty issue : estimation of fire size with geographically weighted logistic regression},
  year         = {2016},
}