Analysis of Deforestation with Logistic Regression Usinz Remote Sensing and Geographical Information System

Document Type : Research Paper

Authors

1 Academic Member, University of Tabriz

2 Associate Professor, Industrial University of Khaje NasireddingToosi

Abstract

The population growth, industrial development, bio-climate changes and scarcity of land resources are the main reasons and causes of forest degradation in developing countries. To control and decrease forest degradation, the governments need to know where, when, how fast, and why (with what causes) such degradations happen. On the basis of such knowledge, a general and sustainable management of these resources will be possible.
The science and technologies of GIS and remote sensing could be a perfect tool for answering the above questions. Remote sensing can be the basis of fast and inexpensive data collection and the analytical capabilities of a GIS can be used for analyzing the types, location and rates of changes.
In this research, the Landsat TM and ETM+ images of years 1987 and 2001 are used for land use classification and analysis of changes at the forest area of Arasbaran in north-west of Iran. The classification is mainly aimed at the separation of forest from non-forest areas.
A few methods have been studied to calculate and show the occurred changes. These include methods that only describe the change areas (such as subtraction and division methods) and those that describe the area, amount and type of the changes (such as comparison after classification).
By classifying the forest and non-forest areas of years 1987 and 2001 and overlaying them, a map was extracted representing the stable forest area and deforested area. From the topographic data of the study area, some other raster maps were created showing elevation, slope, aspect and distance from population areas.
Information of these maps were entered into a statistical model (a logistic regression model) having the above-mentioned classified map as the dependent parameter and all other maps as the independent parameters. It was resulted that the parameters of distance from populated areas, elevation and aspect have a meaningful relation with the deforestation phenomenon.  From such an analysis, the importance of each factor in the phenomenon was defined and the areas that are in higher risk of deforestation and need an urgent protection were defined.

Keywords

References

منابع
1- Aldrich, J.H., Nelson F.E., (1984), "Linear Probability, Logit and Probit Models", Sage University Paper Series on Quantitative Applications in the Social Sciences, Beverly Hills, California.
2- Armenteras D. Rudas G, Rodriguez N., Sua S. and Romero M. (2006), "Patterns and Causes of Deforestation in the Colombian Amazon", Ecological Indicators 6 pp353-368.
3- Brown S., Hall M., Andrasko K. Anf Ruiz F. (2007):  "Baselines for Land-use Change in the Tropics: Application to Avoided Deforestation Projects", Mitig Adapt Strat Glob Change, no. 12, pp.1001-1026.
4- Coppin R. (1996), "Digital Change Detection in Forest Ecosystems with Remotely Sensed Imagery", Remote Sensing, No. 13, pp. 207-234.
5- Demaris, A., (1992), "Logit Modeling: Practical Applications", Sage University Paper Series on Quantitative Applications in the Social Sciences, Newbury park, California.
6- Desclee B., Bogaert P. and Defourny P. (2006), "Forest Change Detection by Statistical Object-basedmethod", Remote Sensing of Environment, 102, pp. 1-11.
7- Geoghegan J., Schneider L. and Vance C. (2004), "Temporal Dynamics and Spatial Scales: Modeling Deforestation in the Southern Yucata’s Peninsular Region", Geo Journal, No. 61, pp. 353-363.
8- Hayes J. (1999), "Remote Sensing for Monitoring Land Cover and Landuse Change in the Maya Biosphere Reserve", Thesis, University of Maine, Orono.
9- Linkie M., Smith R. and Willams N. (2004), "Mapping and Predicting Deforestation Patterns in the Lowlands of Sumatra", Biodiversity and Conservation, No. 13, pp. 1809-1818.
10- Liu D, Song K, Townshend J and Gong P. (2008), "Using Local Transition Probability Models in Markov Random Fields for Forest Change Detection", Remote Sensing of Environment, 112, pp. 2222-2231.
11- Macleod D. and Congalton G. (1998), "A Quantitative Comparison of Change Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data", PE & RS, Vol. 64, No. 3, pp. 207-216.
12- Mahapatra K. and Kant S. (2005), "Tropical Deforestation: A Multinomial Logistic Model and Some Country-specific policy Prescriptions", Forest Policy and Economics, 7 pp.1-24.
13- Muchoney M. and Haack N. (1994), "Change Detection for Monitoring Defoliation", Photogrammetric Engineering and Remote Sensing, No. 60, pp. 1243-1251.
14- Musaoglu N. (2000), "Multitemporal Forest Change Analysis, A Case Study: SARIYER Region", ISPRS, XXXIII, Part C7, pp. 939-945.
15- Pearce J. and Ferrier S. (2000), "Evaluating the Predictive Performance of Habitat Models Developed Using Logistic Regression", Ecological Modelling, No. 133, pp. 225-245.
16- Puyravaud J. (2003), "Standardizing the calculation of the annual rate of deforestation", Forest Ecology and Management, No. 177, pp.593-596.
17- Reusing M. (2000), "Change Detection of NaturalHighForest in ETHIOPIA Using Remote Sensing and GIS Techniques", ISPRS, XXXIII, Part C7, pp. 1253-1258.
18- Sader A. (1989), "Multispectral and Seasonal Characteristics of Northern Hardwood and Boreal Forest Types in Maine", ASPRS, pp. 109-116.
19- Sader A. and Winne C. (1992), "RGB-NDVI Colour Composites for Visualizing Forest Change Dynamics", International Journal of Remote Sensing, No. 13, pp. 3055-3067.
20- Songer M., Aung M., Senior B. and DeFries R. (2009), "Spatial and Temporal Deforestation Dynamics in Protected and Unprotected dry Forests: A Case Study from Myanmar (Burma)", Biodivers Conserv, No.18, pp.1001-1018.
21- Sremongkontip S, Ali Hussin Y. and Groenindijk L. (2000), "Detecting Changes in the MANGROVE Forests of Southern THAILAND Using Remotely Sensed Data and GIS", ISPRS, XXXIII, Part B7, pp. 567-574.
22- Tabachnick, B.G., and Fidell, L.S. (1996), "Using Multivariate Statistics, New York: Harper and Row".
 

Files

Share

How to cite

Statistics