Resources and Environmental Carrying Capacity Using RS and GIS

Wang, Shixin; Shang, Ming; Zhou, Yi; Li, Wenliang; Wang, Feng; Wang, Litao

doi:10.15244/pjoes/70927

Cited by 12 publications

(7 citation statements)

References 26 publications

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“…Peng and Deng (2021) 2020) also used the concept of load and carrier (loadcarrier) to estimate a city's environmental carrying capacity with an emphasis on the urban water carrying capacity for Beijing, Shanghai, Tianjin, and Chongqing. In a study conducted by Wang et al (2017), the results showed that urban carrying capacity was higher in the eastern and coastal cities in China than the central and western urban areas. They concluded that this difference was rooted in pollution from coal fuel in cold seasons.…”

Section: Literature Reviewmentioning

confidence: 98%

“…This article includes a comprehensive analysis regarding the carrying capacity of Iranian provinces and uses the mean variance analysis method for weighting to avoid the involvement of isolated bits of knowledge in extracting the weight of the indicators. Reviewing the literature, some researchers (Irankhahi et al, 2017; Lane, 2010; Li et al, 2019b; Liu et al, 2018; Qiu et al, 2012; Wang et al, 2017) conducted the variance–mean method to compare the carrying capacity of thirty cities using 30 repeatable indices (Wei et al, 2016). The model proposed by Irankhahi et al (2017) was built based on the optimal and the overall agreed limits of the chosen measures used to assess hot spots.…”

Section: Introductionmentioning

confidence: 99%

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Spatial analysis of resources and environmental carrying capacity in Iran

Karami

Sayahnia

Mahmoudi

et al. 2023

Natural Resources Forum

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Urbanization and mass movement of the population from rural areas and small cities to megacities have led to environmental, economic, and social problems in Iran. In dealing with these challenges, assessing resource and environmental carrying capacity (RECC) is considered an effective method to leverage space and capital to achieve sustainable development. This study aimed to rank the provincial RECC in Iran. Toward this purpose, environmental indices were generated from remotely sensed and statistical census data. Then, the provinces were scored in terms of environmental, economic, and infrastructural carrying capacities, and RECC using the mean variance analysis method. Results demonstrated that in most areas, there is no relationship between economic and infrastructural capacities and development. Statistically, a correlation coefficient of −0.53 between economic and environmental carrying capacities indicated excessive use of environmental capacities. Moreover, the spatial distribution pattern of environmental, economic, and infrastructural carrying capacity was entirely heterogeneous between the provinces; there was a northeast–southwest pattern in terms of infrastructural capacity and an economic pattern from north to south. The distribution pattern of RECC is most consistent with the environmental capacity, pointing at the high weight of the indicators of the RECC model. In conclusion, this research offers a new vision for policymakers and provides a theoretical and applicable framework for implementing sustainable strategies in land‐use planning. It is recommended that the RECC concept and tools can be used not only for planning but also for measuring the efficiency of spatial development programs and establishing land balances in the region.

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Section: Literature Reviewmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Spatial analysis of resources and environmental carrying capacity in Iran

Karami

Sayahnia

Mahmoudi

et al. 2023

Natural Resources Forum

View full text Add to dashboard Cite

show abstract

“…Different applications, such as those in earth sciences, natural resource management (Wang et al, 2017), environmental protection (Ghodeif et al, 2013) urban and regional planning (Abdelfattah and Kumar, 2014;Dihkan et al, 2018), water resources management (Mouratidis et al, 2010), defense, transportation, tourism, statistics, and education, require geographic data because they need analysis at regional or more national levels (Başaraner and Selçuk, 2004). Spatial data are generally resolutionor 8 scale-dependent and should be analyzed and presented at the resolution or scale best suited for the modeled events and processes (Weibel and Dutton, 1998).…”

Section: Fig 1 Publication Of Geographic Datamentioning

confidence: 99%

Design and use of crowdsourcing geographic data collection system during the monitoring phase of Environmental Impact Assessment reports

2021

Global NEST: The International Journal

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When the whole earth is considered as a geographic place, it is clearly seen that the physical and social processes in the Earth’s crust have a direct or indirect connection with geography. In this century named information era, it is vital to find new solutions to people’s problems by getting different results from gathering, providing, and querying information. Geographic information systems are frequently used in the preparation and implementation stages of environmental impact assessment (EIA) projects. However, the failure to use spatial data effectively during the monitoring phase, which is the final step of the assessment process, causes the inability to determine whether the activities undertaken in the report are implemented. This situation causes EIA reports and activities to be questioned or even not accepted by the public. By inter-organizational data sharing, geographical data needs can be met and duplicate data productions can be avoided.  However, the problem of updating the geographical data provided always comes out as the real problem.  At this point, there are approaches that are defined as Crowdsourcing which refers the way of solving the problems of the institutions by utilizing the knowledge and experience outsourced. The most important institutional concern regarding crowdsourcing and geographical data extraction reveals itself as data consistency and accuracy. With the system developed within the scope of this study, the status of EIA projects in implementation with the development of the project will be effectively controlled with the participation of the administration and the public. A crowdsourcing-based system has been prepared and introduced to ensure the participation of the public. With this study, the development of the project and the problems experienced during the monitoring phase can be controlled. Thus, it will be ensured that any negative situation that could be experienced will be prevented or minimized in a way that will not harm the environment.

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“…Integrasi SIG dan PJ berupa analisis multi kriteria, umum digunakan dalam analisis-analisis keruangan untuk pengambilan keputusan (Gorelick et al, 2017;Haokip et al, 2021;Rahman & Saha, 2008;Reba et al, 2013;Store & Kangas, 2001). Integrasi SIG dan PJ berupa analisis multi kriteria dapat digunakan untuk menganalisis daya dukung lingkungan (Wang et al, 2017), kesesuaian lahan (Baja et al, 2019), dan pemilihan lokasi serta pengambilan keputusan yang kompleks (Zucca et al, 2008). Analisis berdasarkan multi faktor dan multi sudut pandang ini sangat penting sebagai pengetahuan bagi pemangku kepentingan dalam perencanaan wilayah dan untuk evaluasi pengembangan wilayah (Dapueto et al, 2015;Mosadeghi et al, 2015;Shahabi & Hashim, 2015).…”

Section: Pendahuluanunclassified

Klasifikasi Tutupan Lahan Peta Rupabumi Indonesia dalam Identifikasi Kesesuaian Kawasan Permukiman di Kalimantan Tengah

Arimjaya

Wibowo

2021

Idn. J. Earth Sci.

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Berdasarkan Katalog Unsur Geografis Indonesia (KUGI), Peta Topografi Indonesia (RBI) memiliki 6 kategori unsur dengan 152 unsur klasifikasi tutupan lahan. Oleh karena itu, untuk keperluan analisis kesesuaian penggunaan lahan, klasifikasi tutupan lahan RBI perlu disederhanakan. Penelitian ini bertujuan untuk menyederhanakan klasifikasi kategori unsur tutupan lahan RBI dan menganalisis kesesuaian kawasan pemukiman berdasarkan variabel tutupan lahan dan penggunaan lahan. Studi kasus klasifikasi tutupan lahan dan kesesuaian penggunaan lahan untuk pemukiman telah dilakukan di Provinsi Kalimantan Tengah. Hasilnya, klasifikasi tutupan lahan Provinsi Kalimantan Tengah dikelompokkan menjadi 15 kategori. Sebagian besar tutupan lahan di Kalimantan Tengah adalah hutan dengan luas mencapai 65%. Sementara itu, kesesuaian kawasan permukiman berdasarkan variabel tutupan lahan dan penggunaan lahan menyebar mengikuti sebaran spasial semak belukar dan lahan gundul seluas 16% Kalimantan Tengah. Based on th Indonesian Geographical Feature Catalogue (KUGI), the Indonesian Topographic Map (RBI) has 6 feature categories with 152 land cover classification features. Therefore, for land-use suitability analysis purposes, the RBI land cover classification needs to be simplified. This study aims to simplify the classification of RBI's land cover element categories and analyze the suitability of residential areas based on land cover and land-use variables. A case study of land cover classification and land-use suitability for settlements was conducted for the Central Kalimantan province. As a result, the land cover classifications of Central Kalimantan Province are grouped into 15 categories. Most of the land cover in Central Kalimantan is the forest with covering 65% of the area. Meanwhile, the suitability of residential areas based on land cover and land use variables spread following the spatial distribution of shrubland and bare land covering 16% of Central Kalimantan.

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Resources and Environmental Carrying Capacity Using RS and GIS

Cited by 12 publications

References 26 publications

Spatial analysis of resources and environmental carrying capacity in Iran

Spatial analysis of resources and environmental carrying capacity in Iran

Design and use of crowdsourcing geographic data collection system during the monitoring phase of Environmental Impact Assessment reports

Klasifikasi Tutupan Lahan Peta Rupabumi Indonesia dalam Identifikasi Kesesuaian Kawasan Permukiman di Kalimantan Tengah

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