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GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment

Received: 13 March 2015     Accepted: 31 March 2015     Published: 9 April 2015
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Abstract

This study applied time series analysis to examine land use/land cover (LULC) change and distribution in Usangu watershed and multinomial logistic regression in the GIS environment to model the influence of the related driving factors. Historical land use/cover data of the watershed were extracted from the 2000, 2006 and 2013 Landsat images using GIS and remote sensing data processing and analysis techniques. Data was analyzed using ArcMap 10.1, ERDAS Imagine, SPSS and IDRISI Selva software. Eight factors likely to influence LULC change and LULC distribution were assessed. These include elevation, slope, distance from roads, distance from rivers networks, population density, Normalized Vegetation Index (NDVI), annual rainfall and soil types. Results show that LULC changes are mainly influenced by variations in annual rainfall, population density and distance from road networks. LULC distribution is determined mainly by terrain and edaphic factors namely elevation, slope and soil types. NDVI does not influence LULC change nor determine the LULC distribution, but can be used to show concentration of LULC types on a landscape. Combination of GIS, remote sensing and statistical analysis capabilities are powerful tools for assessing and model processes of land use change and their underlying causes in terms of time and space. It is concluded that ingeniously integration of remote sensing, GIS application combined with multi-source spatial data analysis give great possibility of quantifying and explaining the temporal and spatial LULC changes and distribution in a given watershed.

Published in American Journal of Remote Sensing (Volume 3, Issue 1)
DOI 10.11648/j.ajrs.20150301.12
Page(s) 6-16
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

LandUse/Cover, Change and Distribution, GIS, Remote Sensing, Multinomial Logit Regression, Usangu

References
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  • APA Style

    Canute Hyandye, Christina Geoffrey Mandara, John Safari. (2015). GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment. American Journal of Remote Sensing, 3(1), 6-16. https://doi.org/10.11648/j.ajrs.20150301.12

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    ACS Style

    Canute Hyandye; Christina Geoffrey Mandara; John Safari. GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment. Am. J. Remote Sens. 2015, 3(1), 6-16. doi: 10.11648/j.ajrs.20150301.12

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    AMA Style

    Canute Hyandye, Christina Geoffrey Mandara, John Safari. GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment. Am J Remote Sens. 2015;3(1):6-16. doi: 10.11648/j.ajrs.20150301.12

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  • @article{10.11648/j.ajrs.20150301.12,
      author = {Canute Hyandye and Christina Geoffrey Mandara and John Safari},
      title = {GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment},
      journal = {American Journal of Remote Sensing},
      volume = {3},
      number = {1},
      pages = {6-16},
      doi = {10.11648/j.ajrs.20150301.12},
      url = {https://doi.org/10.11648/j.ajrs.20150301.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20150301.12},
      abstract = {This study applied time series analysis to examine land use/land cover (LULC) change and distribution in Usangu watershed and multinomial logistic regression in the GIS environment to model the influence of the related driving factors. Historical land use/cover data of the watershed were extracted from the 2000, 2006 and 2013 Landsat images using GIS and remote sensing data processing and analysis techniques. Data was analyzed using ArcMap 10.1, ERDAS Imagine, SPSS and IDRISI Selva software. Eight factors likely to influence LULC change and LULC distribution were assessed. These include elevation, slope, distance from roads, distance from rivers networks, population density, Normalized Vegetation Index (NDVI), annual rainfall and soil types. Results show that LULC changes are mainly influenced by variations in annual rainfall, population density and distance from road networks. LULC distribution is determined mainly by terrain and edaphic factors namely elevation, slope and soil types. NDVI does not influence LULC change nor determine the LULC distribution, but can be used to show concentration of LULC types on a landscape. Combination of GIS, remote sensing and statistical analysis capabilities are powerful tools for assessing and model processes of land use change and their underlying causes in terms of time and space. It is concluded that ingeniously integration of remote sensing, GIS application combined with multi-source spatial data analysis give great possibility of quantifying and explaining the temporal and spatial LULC changes and distribution in a given watershed.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment
    AU  - Canute Hyandye
    AU  - Christina Geoffrey Mandara
    AU  - John Safari
    Y1  - 2015/04/09
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajrs.20150301.12
    DO  - 10.11648/j.ajrs.20150301.12
    T2  - American Journal of Remote Sensing
    JF  - American Journal of Remote Sensing
    JO  - American Journal of Remote Sensing
    SP  - 6
    EP  - 16
    PB  - Science Publishing Group
    SN  - 2328-580X
    UR  - https://doi.org/10.11648/j.ajrs.20150301.12
    AB  - This study applied time series analysis to examine land use/land cover (LULC) change and distribution in Usangu watershed and multinomial logistic regression in the GIS environment to model the influence of the related driving factors. Historical land use/cover data of the watershed were extracted from the 2000, 2006 and 2013 Landsat images using GIS and remote sensing data processing and analysis techniques. Data was analyzed using ArcMap 10.1, ERDAS Imagine, SPSS and IDRISI Selva software. Eight factors likely to influence LULC change and LULC distribution were assessed. These include elevation, slope, distance from roads, distance from rivers networks, population density, Normalized Vegetation Index (NDVI), annual rainfall and soil types. Results show that LULC changes are mainly influenced by variations in annual rainfall, population density and distance from road networks. LULC distribution is determined mainly by terrain and edaphic factors namely elevation, slope and soil types. NDVI does not influence LULC change nor determine the LULC distribution, but can be used to show concentration of LULC types on a landscape. Combination of GIS, remote sensing and statistical analysis capabilities are powerful tools for assessing and model processes of land use change and their underlying causes in terms of time and space. It is concluded that ingeniously integration of remote sensing, GIS application combined with multi-source spatial data analysis give great possibility of quantifying and explaining the temporal and spatial LULC changes and distribution in a given watershed.
    VL  - 3
    IS  - 1
    ER  - 

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Author Information
  • Institute of Rural Development Planning (IRDP), Dodoma, Tanzania

  • Institute of Rural Development Planning (IRDP), Dodoma, Tanzania

  • Institute of Rural Development Planning (IRDP), Dodoma, Tanzania

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