Modeling spatial pattern of deforestation using GIS and logistic regression: A case study of northern Ilam forests, Ilam province, Iran

Arekhi, Saleh

doi:10.5897/ajb11.1122

Cited by 11 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis results showed that the distance from the forest edge effect is negative, it means that the long distance of forest edge causes the deforestation occur is getting lower. Arkehi (2013) states that the distance of the forest edge are factors that influence the occurrence of deforestation, forest closer to the chances of deforestation is increasing.…”

Section: Binary Regression Logisticmentioning

confidence: 99%

“…The spatial model of sociodeforestation can be constructed with a variety of techniques or methods. Logistic regression approach is proven to be able to be u ed in analyzing deforestation (Arkehi 2013). s Park (2013) compared the development of critical model by using frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Model of Deforestation in Kalimantan from 2000 to 2013

Purwanto¹,

Rusolono²,

Prasetyo³

2015

jtfm

View full text Add to dashboard Cite

Forestry sector is the biggest carbon emission contributor in Indonesia which is mainly caused by deforestation. A significant area of forest cover still can be found in Kalimantan Island (one of the largest island in Indonesia) although an alarming rates deforestation is also exist. This study was purposed to established spatial model of deforestation in Kalimantan islands. This information is expected to provide options to develop sustainable forest management in Kalimantan trought optimizing environment and socioeconomic purposes. This study used timeseries land cover data from the Ministry of Environment and Forestry (2000-2013) and is validated by SPOT 5/6 images in 2013. The spatial model of deforestation were developed using binary logistic. The results of logistic regression analysis obtained spatial model of deforestation in Kalimantan = 1.1480714-(0.033262*slope)-(0.002242*elevation)-(0.000413*distance from forest edge) + (0.000045*Gross Regional Domestic Product).

show abstract

Section: Binary Regression Logisticmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Model of Deforestation in Kalimantan from 2000 to 2013

Purwanto¹,

Rusolono²,

Prasetyo³

2015

jtfm

View full text Add to dashboard Cite

show abstract

“…It is recognized as a major driver of the loss of biodiversity and ecosystem services. Globally, it has been occurring at an alarming rate of 13 million hectares per year [1]. It is believed that high population growth coupled with the rapid expansion of agriculture is responsible for the accelerated rates of deforestation, especially in developing countries [2].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Causes of Deforestation in Malawi: A Case of Mwazisi

Ngwira

Watanabe

2019

Land

View full text Add to dashboard Cite

Deforestation is recognized as a major driver of the loss of biodiversity and ecosystem services. It also disturbs natural processes such as biogeochemical, hydrological, and ecological cycles. In Malawi, deforestation is estimated to be responsible for the loss of 33,000 hectares per year, and is mainly attributed to agriculture expansion, tobacco growing, and excessive use of biomass. However, little research has been conducted at either the local level or that of forests located on customary land. This research aimed to identify and analyze the underlying driving factors associated with the proximate factors of agriculture expansion, tobacco growing, and brick burning in Mwazisi. Landsat images for 1991, 2004, and 2017 were downloaded from the United States Geological Survey website and used to analyze changes in forest cover. Interviews with households (n = 399) and Natural Resource Committee members, a focus group discussion with key officers, and observations were conducted during field data collection in 2017. The results of the land cover analysis showed that forest covered 66% of the study area in 1991, and by 2017 it had decreased to 45.8%. Most households depend on wood from customary land forests for tobacco curing (69%) and brick burning (68%). Furthermore, 47.6% of the households have expanded their agriculture land by approximately 0.57 hectares during the past 15 years. The interview survey and the focus group discussion identified that the underlying driving factors towards these anthropogenic activities are: (a) population growth, (b) poverty, (c) expensive alternative building materials, (d) lack of awareness, (e) lack of resources, (f) lack of commitment from the tobacco companies, and (g) market system of the cash crops grown in the area. In conclusion, a set of economic, institutional, social, and demographic factors, which are associated with imbalanced relationship between rural and urban areas, underpin agriculture expansion, tobacco growing, and brick burning, and have thereby contributed to the decline of the forest cover in Mwazisi, Malawi.

show abstract

“…2009;Wyman . 2010;Arekhi 2011;Kumar et al 2014;Ahmad et al 2015). Based on the conditions seen in the field, it was found that there were several accesses within the forest areas in the form of small paths that were often used and passed by the community.…”

Section: Driving Force Of Forest Cover Changesmentioning

confidence: 99%

“…24, (2): 186-195, December 2018EISSN: 2089-2063 were the most influencing factor for the changesbecause it relates to accessibility into forest areas and the availability of forest resources (Wyman . 2010;Arekhi 2011;Wijaya et al et al 2015).…”

Section: Spatial Models Of Forest Cover Changes In T1 and T2mentioning

confidence: 99%

Spatial Modeling of Forest Cover Change in Kubu Raya Regency, West Kalimantan

Ikhsani

Jaya²,

Saleh³

2018

jtfm

View full text Add to dashboard Cite

Forest cover change is one of the environmental issues that continually gotten international attention. This study describes how to develop a spatial model of change in each village-based typology by considering various this biophysical and social-economic factors. The village typologies were investigated by applying the clustering analysis approach. The objective of this study was to develop the spatial model and to identify the driving forces of forest cover change by the village in Kubu Raya Regency of West Kalimantan. Based on the proportion of forest in 2015, the study found that there are two village typologies within the study area with 81% overall accuracy (OA). The -1 Typology 1 (T1) which has change rate of ha year consisted of 56 villages, while the Typology 2 (T2) low 5,001.8 -1 which has change about ha year covered 34 villages. The study also recognized that the most high rate 8,050.6 significant driving forces of change in 1 were the distance from rivers (X ) and settlements (X ), whereas in 2 were T T 2 3 the distance from roads (X ) and (X ). The best spatial model of change are Y = -the edge of the forest in 2015 the 1 9 0.01+0.0001X +0.0004X with OA of % for and Y = 0.02+0.0001X -83 and mean deviation (SR) 10.5% T1 2 3 1 0.0002X with OA % for 2. The study concludes that the proximity from the center of the human 53 and SR 13.3% T 9activities holds a significant influence on the behavior of forest cover changes.

show abstract

Modeling spatial pattern of deforestation using GIS and logistic regression: A case study of northern Ilam forests, Ilam province, Iran

Cited by 11 publications

References 0 publications

Spatial Model of Deforestation in Kalimantan from 2000 to 2013

Spatial Model of Deforestation in Kalimantan from 2000 to 2013

An Analysis of the Causes of Deforestation in Malawi: A Case of Mwazisi

Spatial Modeling of Forest Cover Change in Kubu Raya Regency, West Kalimantan

Contact Info

Product

Resources

About