2022
DOI: 10.47246/cejgsd.2022.4.2.1
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Spatial Determinants of Forest Landscape Degradation in the Kilimanjaro World Heritage Site, Tanzania

Abstract: Forest degradation occurs in natural World Heritage Sites (WHS) in the Global South despite the implementation of various strategic policies and the World Heritage Convention (WHC) on forest protections of the sites and this poses challenges to improving natural heritage sustainability. The current study aims to investigate spatial determinants of forest degradation in the Kilimanjaro WHS, Tanzania, to support strategic policies for forest landscape protection and natural heritage sustainability. Using remotel… Show more

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Cited by 2 publications
(7 citation statements)
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“…Expert surveys was conducted using questionnaires from 21 February to 8 April 2022. A matrix questionnaire was designed (Babbie, 2013;Secor, 2010), guided by the current study objectives and spatial determinant variables captured by Enoguanbhor et al (2022a). The variables included level of elevation, degrees of slope, distance to tourist routes, distance to campsites, distance to picnics, distance to historical sites, and distance to attraction areas.…”
Section: Methodsmentioning
confidence: 99%
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“…Expert surveys was conducted using questionnaires from 21 February to 8 April 2022. A matrix questionnaire was designed (Babbie, 2013;Secor, 2010), guided by the current study objectives and spatial determinant variables captured by Enoguanbhor et al (2022a). The variables included level of elevation, degrees of slope, distance to tourist routes, distance to campsites, distance to picnics, distance to historical sites, and distance to attraction areas.…”
Section: Methodsmentioning
confidence: 99%
“…The positive association indicates that such identified variables contribute to forest degradation, and the negative association indicates that such variables do not contribute to forest degradation. The GIS outcomes on spatial determinants of forest degradation were analysed by Enoguanbhor et al (2022a) using Euclidean distance measurement and linear regression modelling (Yenisetty & Bahadure, 2021;Fotang et al, 2021;Visser & Jones III, 2010;Fotang et al, 2021;Enoguanbhor et al, 2022b). The GIS data was generated through a supervised classification of remotely sensed satellite images from Landsat 7 for 2020 using the maximum likelihood algorithm (Vijayalakshmi et al, 2021;Campbell & Wynne, 2011;Enoguanbhor et al, 2019;Lu et al, 2011;Tso & Mather, 2009;USGS, 2021;.…”
Section: Methodsmentioning
confidence: 99%
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