Application of Remote Sensing Data for Slum Identification Using Geography Information System (Case: Former Harbor, Singaraja City)

Janah, Ruhilatul; Nugraha, A Sediyo Adi

doi:10.23887/mkfis.v20i1.30421

Cited by 2 publications

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“…Existing land use in 2021 at the research location in Gunungpati Sub-District was obtained from visually interpretation high-resolution images guided by 9 elements (Martuti et al, 2020;Janah & Nugraha, 2021). Based on the results, 5 classes of land use in the region were obtained, including settlements, agricultural land, mixed gardens/vegetation, service/commercial buildings and open land.…”

Section: Land Use Changementioning

confidence: 99%

“…The map was obtained from the global digital image interpretation results, while the 2016 land use map was obtained from the google earth imagery interpretation. Before interpretation of the two images, geometric corrections were conducted to place the position of the pixels according to the object (Janah & Nugraha, 2021;Prasetyo et al, 2018).…”

Section: Land Use Changementioning

confidence: 99%

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Land Use Change Analysis to Springs Conditions in Gunungpati Sub-District, Semarang City

2022

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Semarang City has the fastest development in Central Java Province, where the activities are no longer centralized but have expanded to the upper part of the region. The ongoing development certainly impacts changes with the increasing area of built-up land that converts to another cover, such as vegetation. The phenomenon impacts the balance of the environment, one of which is the source of springs. Therefore, this study aims to map the spatial distribution of the springs and identify their physical quality. A quantitative approach was used with spatial analysis. Meanwhile, data collection techniques used document research, high-resolution image interpretation and field surveys. Field surveys were conducted to test the accuracy of land use maps and measurements of discharge, temperature, pH and brightness of the springs. The results showed that there is a change in land use from 2016 - 2021 with an increase in settlements of around 77.25 hectares and commercial service buildings by 178.79 hectares. For land use with the largest decrease in area, agricultural land covers and mixed garden/vegetation covers an area of 207.01 and 50.57 hectares. There were 114 springs at the research site, of which 5% of the springs had a relatively large discharge above 10 liters/second, while the other 47% had a small discharge. For pH conditions, there were 6 springs with a pH value of less than 6. Land use change from vegetation to flying land impacts the reduction of the water supply in the soil. The impact can be seen by the non-production of several springs, where 21 springs have not been discharged. In conclusion, there is a change in land use with an increase in built-up from 2016 - 2021 by 256.04 hectares. The increase in built-up is partly in conservation areas, hence damaging several springs. Keywords : Land use change; Springs conditions; Gunungpati Sub-District Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Section: Land Use Changementioning

confidence: 99%

Section: Land Use Changementioning

confidence: 99%

Land Use Change Analysis to Springs Conditions in Gunungpati Sub-District, Semarang City

2022

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Machine Learning-Based Local Knowledge Approach to Mapping Urban Slums in Bandung City, Indonesia

Chulafak,

Khomarudin,

Roswintiarti

et al. 2024

Urban Science

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Rapid urban population growth in Bandung City has led to the development of slums due to inadequate housing facilities and urban planning. However, it remains unclear how these slums are distributed and evolve spatially and temporally. Therefore, it is necessary to map their distribution and trends effectively. This study aimed to classify slum areas in Bandung City using a machine learning-based local knowledge approach; this classification exercise contributes towards Sustainable Development Goal 11 related to sustainable cities and communities. The methods included settlement and commercial/industrial classification from 2021 SPOT-6 satellite data by the Random Forest classifier. A knowledge-based classifier was used to derive slum and non-slum settlements from the settlement and commercial/industrial classification, as well as railway, river, and road buffering. Our findings indicate that these methods achieved an overall accuracy of 82%. The producer’s accuracy for slum areas was 70%, while the associated user’s accuracy was 92%. Meanwhile, the Kappa coefficient was 0.63. These findings suggest that local knowledge could be a potent option in the machine learning algorithm.

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Application of Remote Sensing Data for Slum Identification Using Geography Information System (Case: Former Harbor, Singaraja City)

Cited by 2 publications

References 0 publications

Land Use Change Analysis to Springs Conditions in Gunungpati Sub-District, Semarang City

Land Use Change Analysis to Springs Conditions in Gunungpati Sub-District, Semarang City

Machine Learning-Based Local Knowledge Approach to Mapping Urban Slums in Bandung City, Indonesia

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