Deforestation modelling using logistic regression and GIS
A methodology has been used by means of which modellers and planners can quantify the certainty in predicting the location of deforestation. Geographic information system and logistic regression analyses were employed to predict the spatial distribution of deforestation and detects factors influenci...
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2015-05-01
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| Series: | Journal of Forest Science |
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| Online Access: | https://jfs.agriculturejournals.cz/artkey/jfs-201505-0002_deforestation-modelling-using-logistic-regression-and-gis.php |
| _version_ | 1828008015970697216 |
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| author | M. Pir Bavaghar |
| author_facet | M. Pir Bavaghar |
| author_sort | M. Pir Bavaghar |
| collection | DOAJ |
| description | A methodology has been used by means of which modellers and planners can quantify the certainty in predicting the location of deforestation. Geographic information system and logistic regression analyses were employed to predict the spatial distribution of deforestation and detects factors influencing forest degradation of Hyrcanian forests of western Gilan, Iran. The logistic regression model proposed that deforestation is a function of slope, distance to roads and residential areas. The coefficients for the explanatory variables indicated that the probability of deforestation is negatively related to slope, distance from roads and residential areas. Although the distance factor was found to be a contributor to deforestation, its effect is lower than that of slope. The correlates of deforestation may change over time, and so the spatial model should be periodically updated to reflect these changes. Like in any model, the quality may be improved by introducing the new variables that may contribute to explaining the spatial distribution of deforestation. |
| first_indexed | 2024-04-10T08:18:28Z |
| format | Article |
| id | doaj.art-710d01e3503c430f81d8ca19e9da3ed7 |
| institution | Directory Open Access Journal |
| issn | 1212-4834 1805-935X |
| language | English |
| last_indexed | 2024-04-10T08:18:28Z |
| publishDate | 2015-05-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Journal of Forest Science |
| spelling | doaj.art-710d01e3503c430f81d8ca19e9da3ed72023-02-23T03:42:38ZengCzech Academy of Agricultural SciencesJournal of Forest Science1212-48341805-935X2015-05-0161519319910.17221/78/2014-JFSjfs-201505-0002Deforestation modelling using logistic regression and GISM. Pir Bavaghar0Faculty of Natural Resources, Center for Research & Development of Northern Zagros Forests, University of Kurdistan, Sanandaj, IranA methodology has been used by means of which modellers and planners can quantify the certainty in predicting the location of deforestation. Geographic information system and logistic regression analyses were employed to predict the spatial distribution of deforestation and detects factors influencing forest degradation of Hyrcanian forests of western Gilan, Iran. The logistic regression model proposed that deforestation is a function of slope, distance to roads and residential areas. The coefficients for the explanatory variables indicated that the probability of deforestation is negatively related to slope, distance from roads and residential areas. Although the distance factor was found to be a contributor to deforestation, its effect is lower than that of slope. The correlates of deforestation may change over time, and so the spatial model should be periodically updated to reflect these changes. Like in any model, the quality may be improved by introducing the new variables that may contribute to explaining the spatial distribution of deforestation.https://jfs.agriculturejournals.cz/artkey/jfs-201505-0002_deforestation-modelling-using-logistic-regression-and-gis.phpmanmade areasphysiographic factorsroadsprobabilityhyrcanian forests |
| spellingShingle | M. Pir Bavaghar Deforestation modelling using logistic regression and GIS Journal of Forest Science manmade areas physiographic factors roads probability hyrcanian forests |
| title | Deforestation modelling using logistic regression and GIS |
| title_full | Deforestation modelling using logistic regression and GIS |
| title_fullStr | Deforestation modelling using logistic regression and GIS |
| title_full_unstemmed | Deforestation modelling using logistic regression and GIS |
| title_short | Deforestation modelling using logistic regression and GIS |
| title_sort | deforestation modelling using logistic regression and gis |
| topic | manmade areas physiographic factors roads probability hyrcanian forests |
| url | https://jfs.agriculturejournals.cz/artkey/jfs-201505-0002_deforestation-modelling-using-logistic-regression-and-gis.php |
| work_keys_str_mv | AT mpirbavaghar deforestationmodellingusinglogisticregressionandgis |