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

Canute Hyandye; Christina Geoffrey Mandara; John Safari

doi:doi:10.11648/j.ajrs.20150301.12

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

Canute Hyandye, Christina Geoffrey Mandara, John Safari

Published in American Journal of Remote Sensing (Volume 3, Issue 1)

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), 2024. 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

Canute Hyandye

Institute of Rural Development Planning (IRDP), Dodoma, Tanzania
Christina Geoffrey Mandara

Institute of Rural Development Planning (IRDP), Dodoma, Tanzania
John Safari

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

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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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