A comparative study of Kuantan bauxite mineralogy as potential material in civil engineering

Najwa, I N Iqhlima; Awang, Halizah; Ramadhansyah, P. J.; Idris, Ali Mohamad

doi:10.1088/1755-1315/244/1/012002

Cited by 1 publication

(1 citation statement)

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“…While the footprint of industry expansions was not precisely identified in the modelling, it was able to capture likely hotspots for industrial development (Figure 7). In regard to the quality of the simulated mine site locations from inland at Bukit Goh to coastal areas (Hartmann & Moosdorf, 2012), they do overlap with relevant underlying basic volcanic rock which is dominated by basalt lithology (Figure 4), the parent rock for bauxite deposits found in Kuantan (Iqhlima Najwa et al, 2019). One of the proposed mining areas simulated is under the Pahang State Development Corporation (PKNP) Mineral Industries and BG Mining operator surrounding the Bukit Goh area covering 3642 ha is expected to yield 170,000 tonnes of bauxite ore per month for consecutive 3 years (PKNP, 2019).…”

Section: Simulating Future Land Use and Future Researchmentioning

confidence: 99%

Modelling past and future land‐use changes from mining, agriculture, industry and biodiversity in a rapidly developing Southeast Asian region

Nasir

Ang

Nath

et al. 2023

Integrative Conservation

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Rapidly developing regions in Southeast Asia, such as Kuantan, Malaysia, require robust spatial analysis to understand changing landscape patterns and their socioenvironmental impacts to guide sustainable development and conservation planning. This study aims to characterise and evaluate the historic and future projections of land‐use and land‐cover (LULC) change patterns to understand the dynamics of the regional development process and identify potential future land‐use conflicts. We first map coarse‐scale land‐cover classes using Landsat 5 TM and Landsat 8 OLI data and a Random Forest classifier in the Google Earth Engine platform, and then use auxiliary reference data to manually construct fine‐scale LULC for 3 years: 2010, 2015 and 2020. Subsequently, we modelled future LULC change patterns in 2030 using Land Change Modeller, which applies a multilayer perceptron neural network and Markov chain analysis. The study showed that the region's land cover in the last 10 years has been largely altered by human intervention, driven by an increase in oil palm plantations, followed by mining, residential and industrial site expansion, with a consequent decline in forest and vegetation cover. The 2030 land‐use projections revealed a continuation of these land‐use development patterns. The modelling showed that industry, mining and residential LULC are clustered and growing closer in proximity while expanding extensively, likely causing future land‐use conflict and lead to further environmental degradation. Furthermore, our analysis showed extensive decline in forest cover within reserves. Our modelling demonstrated that natural resource management needs to take an integrated approach as the drivers of land‐use changes are complex, competing and dynamic.

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Section: Simulating Future Land Use and Future Researchmentioning

confidence: 99%