A Spatial Decision Support System for Ecotourism Development in Caspian Hyrcanian Mixed Forests Ecoregion

Abstract

Ecotourism, as a form of sustainable nature-based tourism, promotes conservation of ecological and scenic values. In this study, a Spatial Decision Support System, SDSS, was developed based upon Multi Criteria Evaluation, MCE, for ecotourism development in the Caspian Hyrcanian Mixed Forests ecoregion, northern Iran. For this, important criteria and constraints for ecotourism development were shortlisted using the Delphi Method. The criteria were weighted using Analytical Hierarchy Process, AHP. The obtained results indicated that "distance from water resources", "land use", "slope", "soil", "climate", "distance from roads", "land cover density", "erosion", and "distance from residential areas" were the most important criteria, respectively. The findings suggest that GIS-based SDSS is suitable to engage the various criteria affecting the development of ecotourism destinations. This empirical research develops a new method that can significantly facilitate planning forecotourism development with respect to ecological capability of ecotourism destinations.

Evaluation of ecotourism sites: a GIS-based multi-criteria decision analysis

“…Likewise, Suryabhagavan et al (2015) investigated the identification of potential ESs in Hawassa town by using multi-criteria evaluation and concluded that Hawassa town can contribute for the national development through sustainable use of ecotourism potential of this area. Bali et al (2015) proposed a spatial K 47,8 decision support system, based on a multi-criteria evaluation, ecotourism development in the Caspian Hyrcanian mixed forests eco-region northern Iran. Their finding showed that the approach is a suitable tool for ecotourism land evaluation.…”

Elemen-Elemen Kunci Pengelolaan Ekowisata Menghadapi Endemi Dan Pasca Pandemi (Studi Kasus: Bogor Fruit Garden, Gunung Menyan, Desa Pamijahan, Kecamatan Pamijahan Kabupaten Bogor, Pantai Panjang Dan Tahura Di Bengkulu)

“…Namun demikian, terdapat beberapa kendala yang mengganggu dalam manajemen sektor ekowisata antara lain: (i) terjadinya degradasi hutan; (ii) perburuan satwa liar yang menjadi daya tarik; (iii) perusakan hutan untuk tujuan pertanian; (iv) kepunahan keanekaragaman hayati fauna, dan (v) kurangnya partisipasi masyarakat (Frederick & Nguh, 2020). Hasil penelitian di ekoregion Hutan Campuran Hyrcanian Kaspia, Iran utara menunjukkan bahwa: (i) jarak dari sumber daya air; (ii) penggunaan lahan; (iii) kemiringan atau lereng; (iv) lahan dan tanah; (v) iklim; (vi) jarak dari jalan; (vii) kepadatan tutupan lahan; (viii) erosi dan (ix) jarak dari daerah pemukiman adalah kriteria yang paling penting dan dibutuhkan, karena mempengaruhi pengembangan tujuan ekowisata (Bali et al, 2015) Selanjutnya, Kabupaten Bogor dan Bengkulu adalah contoh wilayah di Indonesia yang diberkahi dengan kumpulan sumber daya ekowisata yang sangat besar. Hal ini menjadikannya untuk pengembangan usaha ekowisata berbasis praktik konservasi, dan meningkatkan mata pencaharian masyarakat.…”

Can empirically based model results be fed into mathematical models? MCE for neural network and logistic regression in tourism landscape planning

“…The main attributes that determine the suitability of the landscape for tourism were identified as topography, climate, soil and geology, conservational considerations and attractions. The selected set of criteria considered for the present work was proposed during previous studies including visibility (Bunruamkaew and Murayam 2011;Suryabhagavan et al 2015), vegetation (Chhetri and Arrowsmith 2008;Bali et al 2015;Samanta and Baitalik 2015;Nino et al 2017), biodiversity (Dhami et al 2014;Bunruamkaew and Murayam 2011), elevation (Chhetri and Arrowsmith 2008;Bunruamkaew Fig. 2 Independent layers used in logistic regression and multilayer perceptron analyses and Murayam 2011;Pareta 2013;Samanta and Baitalik 2015;Nahuelhual et al 2013;Aklıbaşında and Bulut 2014;Nino et al 2017), slope (Chhetri and Arrowsmith 2008;Bunruamkaew and Murayam 2011;Suryabhagavan et al 2015;Aklıbaşında and Bulut 2014), distance to cultural heritages (Bunruamkaew and Murayam 2011;Pareta 2013;Suryabhagavan et al 2015), distance to roads (Nahuelhual et al 2013;Samanta and Baitalik 2015;Aklıbaşında and Bulut 2014), distance to rural areas (Nino et al 2017;Mahini et al 2010), soil erosion [25], temperature (Aklıbaşında and Bulut 2014;Suryabhagavan et al 2015), faults (Shojaee et al 2012), distance to water resources (Chhetri and Arrowsmith 2008;Bunruamkaew and Murayam 2011), aspect (Bali et al 2015;…”