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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.

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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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Related in: MedlinePlus

Mapcurves GOF scores for each LULC data set when compared to the FAO/IAEA combined distribution map. Data sets are sorted in order from highest to lowest GOF with the FAO/IAEA combined distribution map.
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Figure 10: Mapcurves GOF scores for each LULC data set when compared to the FAO/IAEA combined distribution map. Data sets are sorted in order from highest to lowest GOF with the FAO/IAEA combined distribution map.

Mentions: The comparison of the binary suitability maps to the 1996 fly belts resulted in Africover, CLIPcover, IGBP DISCover, and MODIS 1 km Type 1, 2, and 3 products having significant levels of agreement. The comparison of the binary suitability maps to the FAO/IAEA combined distribution map resulted in Africover, IGBP DISCover, UMd GLCC, and MODIS 1 km type 1, 2, and 3 products having significant levels of agreement (Table 4 and Figure 10).


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

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

Mapcurves GOF scores for each LULC data set when compared to the FAO/IAEA combined distribution map. Data sets are sorted in order from highest to lowest GOF with the FAO/IAEA combined distribution map.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Mapcurves GOF scores for each LULC data set when compared to the FAO/IAEA combined distribution map. Data sets are sorted in order from highest to lowest GOF with the FAO/IAEA combined distribution map.
Mentions: The comparison of the binary suitability maps to the 1996 fly belts resulted in Africover, CLIPcover, IGBP DISCover, and MODIS 1 km Type 1, 2, and 3 products having significant levels of agreement. The comparison of the binary suitability maps to the FAO/IAEA combined distribution map resulted in Africover, IGBP DISCover, UMd GLCC, and MODIS 1 km type 1, 2, and 3 products having significant levels of agreement (Table 4 and Figure 10).

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