Abstract
Reducing forest covered areas and changing it to pasture, agricultural, urban and rural areas is performed every year and this causes great damages in natural resources in a wide range. In order to identify the effective factors on reducing the forest cover area, multiple regression was used from 1995 to 2015 in Mazandaran forests. A Multiple regressions can link the decline in forest cover (dependent variable) and its effective factors (independent variable) are well explained. In this study, Landsat TM data of 1995 and Landsat ETM+ data of 2015 were analyzed and classified in order to investigate the changes in the forest area. The images were classified in two classes of forest and non-forest areas and also forest map with spatial variables of physiography and human were analyzed by regression equation. Detection satellite images showed that during the studied period there was found a reduction of forest areas up to approximately 257331 ha. The results of regression analysis indicated that the linear combination of income per capita, rain and temperature with determined coefficient 0.4 as independent variables were capable of estimating the reduction of forest area. The results of this study can be used as an efficient tool to manage and improve forests regarding physiographical and human characteristics. Keywords: Land change Modeler, Multiple linear regression, remote sensing, Mazandaran forests
Highlights
Land cover refers to the habitat or present vegetation type, such as forest and agriculture area
Analysis is performed by a remote sensing based Land Change Modeler (LCM) method
It should be noted that according to the information obtained from the Natural Resources and Watershed Management Office of Mazandaran province during the studied period, no afforestation has taken place in the region
Summary
Land cover refers to the habitat or present vegetation type, such as forest and agriculture area. It is widely accepted that LULC has an important effect on both the functioning of the Earth’s systems as a whole (Lambin et al, 2001) and the majority of ecosystems (Hansen et al, 2001; Millennium, 2005; Fischlin et al, 2007) This change is based on the purposes of need, which is not necessarily only making the change in land cover and change in intensity and management (Verburg et al, 2000). The capabilities of analysis of geographical information can provide analysis of type, condition and extent in destruction, and detecting forest land cover changes by remote sensing data in GIS medium can present suitable recognition of how to change forest land cover and recommend suitable strategies in its management (Bakr et al, 2010). Using remote sensing data and linear multiple regression equation in GIS medium provide suitable recognition of how to change forest land cover and determine the effective factors on it. Analysis is performed by a remote sensing based Land Change Modeler (LCM) method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Applied Sciences and Environmental Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
