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Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya.

DeVisser MH, Messina JP - Int J Health Geogr (2009)

Bottom Line: Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas.For single date applications, Africover was determined to be the best land use land cover (LULC) product for tsetse modeling.The method can be used to differentiate between various LULC products and be applied to any such research when there is a known relationship between a species and land cover.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography and Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA. devisse6@msu.edu

ABSTRACT

Background: Tsetse flies are the primary vector for African trypanosomiasis, a disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse flies were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas. Given changing spatial and demographic factors, a model that can predict suitable tsetse fly habitat based on land cover and climate change is critical to efforts aimed at controlling the disease. In this paper we present a generalizable method, using a modified Mapcurves goodness of fit test, to evaluate the existing publicly available land cover products to determine which products perform the best at identifying suitable tsetse fly land cover.

Results: For single date applications, Africover was determined to be the best land use land cover (LULC) product for tsetse modeling. However, for changing habitats, whether climatically or anthropogenically forced, the IGBP DISCover and MODIS type 1 products where determined to be most practical.

Conclusion: The method can be used to differentiate between various LULC products and be applied to any such research when there is a known relationship between a species and land cover.

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An example of a weighted ratio comparison matrix for the calculation of a Mapcurves GOF score.
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Figure 8: An example of a weighted ratio comparison matrix for the calculation of a Mapcurves GOF score.

Mentions: The resulting cross tabulation matrix table is used to create a weighted ratio comparison matrix. The weighted ratio comparison matrix is constructed by taking the area of two intersecting categories divided by the total area of the Map 1 category, which is then multiplied and weighted by the intersecting area divided by the total area of the Map 2 category. By weighting the proportion of spatial overlap for Map 1 by the proportion of spatial overlap of Map 2, distortion caused by the presence of large, but minimally intersecting categories, is prevented [64]. Each cell within the matrix displays the GOF ratio for the intersecting Map 1 and 2 categories in the associated rows and columns, this information can later be used to determine the best reclassification scheme depending on which map is identified as the reference map. The summing of the rows and columns of the weighted ratio comparison matrix will yield the GOF score of each class category contained in both Map 1 and Map 2 (Figure 8). This information can be used to determine the degree of concordance between categories of the two maps, and is used to create a cumulative ratio frequency distribution.


Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya.

DeVisser MH, Messina JP - Int J Health Geogr (2009)

An example of a weighted ratio comparison matrix for the calculation of a Mapcurves GOF score.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2710327&req=5

Figure 8: An example of a weighted ratio comparison matrix for the calculation of a Mapcurves GOF score.
Mentions: The resulting cross tabulation matrix table is used to create a weighted ratio comparison matrix. The weighted ratio comparison matrix is constructed by taking the area of two intersecting categories divided by the total area of the Map 1 category, which is then multiplied and weighted by the intersecting area divided by the total area of the Map 2 category. By weighting the proportion of spatial overlap for Map 1 by the proportion of spatial overlap of Map 2, distortion caused by the presence of large, but minimally intersecting categories, is prevented [64]. Each cell within the matrix displays the GOF ratio for the intersecting Map 1 and 2 categories in the associated rows and columns, this information can later be used to determine the best reclassification scheme depending on which map is identified as the reference map. The summing of the rows and columns of the weighted ratio comparison matrix will yield the GOF score of each class category contained in both Map 1 and Map 2 (Figure 8). This information can be used to determine the degree of concordance between categories of the two maps, and is used to create a cumulative ratio frequency distribution.

Bottom Line: Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas.For single date applications, Africover was determined to be the best land use land cover (LULC) product for tsetse modeling.The method can be used to differentiate between various LULC products and be applied to any such research when there is a known relationship between a species and land cover.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography and Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA. devisse6@msu.edu

ABSTRACT

Background: Tsetse flies are the primary vector for African trypanosomiasis, a disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse flies were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas. Given changing spatial and demographic factors, a model that can predict suitable tsetse fly habitat based on land cover and climate change is critical to efforts aimed at controlling the disease. In this paper we present a generalizable method, using a modified Mapcurves goodness of fit test, to evaluate the existing publicly available land cover products to determine which products perform the best at identifying suitable tsetse fly land cover.

Results: For single date applications, Africover was determined to be the best land use land cover (LULC) product for tsetse modeling. However, for changing habitats, whether climatically or anthropogenically forced, the IGBP DISCover and MODIS type 1 products where determined to be most practical.

Conclusion: The method can be used to differentiate between various LULC products and be applied to any such research when there is a known relationship between a species and land cover.

Show MeSH
Related in: MedlinePlus