Deforestation perspectives of dry temperate forests: main drivers and possible strategies

Frontiers in Environmental Science

Environmental Science

Muhammad Javed¹  Praveen Mittal²  Abu Reza Md. Towfiqul Islam³  Ehsan Ali⁴  Muhammad Farooq Azhar⁴  Zainab Rehman⁴  Sami Ullah⁵  Wajid Zaman⁶  Edris Alam⁷  Aqeel Ahmad⁸ 

[1] Department of Botany, Division of Science and Technology, University of Education, Lahore, Pakistan;Department of Coumputer Engineering and Applications, GLA University, Mathura, India;Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh;Department of Forestry and Range Management, Bahauddin Zakariya University, Multan, Pakistan;Department of Forestry, College of Agriculture, University of Sargodha, Sargodha, Pakistan;Department of Life Sciences, Yeungnam University, Gyeongsan, Republic of Korea;Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates;Department of Geography and Environmental Studies, University of Chittagong, Chittagong, Bangladesh;University of Chinese Academy of Sciences (UCAS), Beijing, China;

关键词: deforestation;    climate change;    causes of deforestation;    binary logistic regression model;    dry temperate forest;    Gilgit Baltistan;   

DOI  :  10.3389/fenvs.2023.1151320

 received in 2023-01-25, accepted in 2023-07-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

Deforestation is the accelerating factor of climate change in developing countries. The German Watch Report 2020 had rated Pakistan number seventh most affected country due to adverse impacts of climate change. The problem of deforestation poses an existential danger to the forest-depleted country. It is of utmost importance to predict the main drivers to control deforestation. This study was conducted from October 2021 to August 2022 in dry temperate forests of the Chilas to investigate the current condition, causes of deforestation, and predicted the main drivers by using a binary regression model. Stratified random sampling techniques and fixed area plot method were used and taken ground measurements during field inventory to access current situation of deforestation. While a non-probability quota sampling technique and semi-structured questionnaire were utilized for the determination of main drivers of deforestation through respondent’s survey. The forest inventory result showed that most trees fall in immature and sub-mature (mainly in 10–20 and 20–30 cm) diameter classes while the binary logistic regression model predicted dominating four primary drivers (unsustainable fuel wood extraction, unsustainable timber extraction and urban crawling and rural expansion/habituation, and free and uncontrolled livestock grazing) and one secondary driver (wood for energy needs). To address the underlying causes of deforestation, the government must supply alternate energy sources, as well as other economic possibilities to reduce dependency on forests.

【 授权许可】

Unknown   
Copyright © 2023 Ali, Azhar, Alam, Rehman, Ullah, Ahmad, Towfiqul Islam, Zaman, Javed and Mittal.