Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia

Wahap, N A; Shafri, Helmi Zulhaidi Mohd

doi:10.1088/1755-1315/540/1/012003

Cited by 25 publications

(10 citation statements)

References 17 publications

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“…The composite algorithm was used to stitch the SR data from January to December of the current year, and the median value was selected to synthesize the image with the smallest annual cloud cover. The QA band was then used to automatically mask clouds, snow and cloud shadows to remove clouds from images (Wahap and Shafri, 2020). The vector data of the study area was uploaded to GEE to crop the three-phase remote sensing images.…”

Section: Data Source and Processingmentioning

confidence: 99%

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Zhang

2022

Front. Environ. Sci.

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Frequent mining activities can bring about problems such as soil erosion and environmental pollution, which are detrimental to the efficient use of land and the sustainable development of cities. Existing studies have paid little attention to mining areas and lack comparative analysis of landscape changes in multiple mining pits. In this paper, the main urban area of Anshan City, where the mining areas are concentrated, was used as the research area, and the Landsat TM/OLI surface reflectance (SR) data of the Google Earth Engine (GEE) platform and the random forest algorithm were used to map the land use in 2008, 2014, and 2020. On this basis, land use dynamics and landscape pattern indices were used to analyze the changes in land use and landscape patterns in the Anshan City area. In addition, a moving window method was combined to further analyze and compare the landscape changes between different pits. The results show that:1. From 2008 to 2020, the construction land in Anshan urban area continued to decline, the forest land continued to expand, and the construction land was shifted to the forest land and cultivated land. Mining land increased before 2014 and remained almost unchanged after 2014, which is in line with the actual situation. 2. During the study period, the landscape fragmentation degree and landscape heterogeneity in the urban area of Anshan kept increasing. The high value areas of landscape fragmentation were the urban-rural combination areas and the mining areas. Among them, the reclamation of Dagushan and Donganshan is better, while the reclamation of Anqian, Yanqianshan and Xiaolingzi mines needs to be strengthened. 3. The random forest algorithm based on GEE shows a high degree of accuracy for land use classification. The overall classification accuracy in 3 years exceeds 90% and the kappa coefficient exceeds 0.85. The study results can be used as an essential reference for optimizing the urban ecological environment and provide technical backing for the urbanization construction and rational use of land in Anshan City.

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Section: Data Source and Processingmentioning

confidence: 99%

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Zhang

2022

Front. Environ. Sci.

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“…Developing geospatial classification models using Google Earth Engine has been the recent research trends [16,[50][51][52] as it allows to process large sets earth imagery data without the need to have high end software or hardware requirements. The availability historical data sets and the ability to preprocess the data in Google Earth Engine allows fast simulation of LULC change analysis therefore developing enhanced application with improved feature is faster using the web interface.…”

Section: Proposed Future Workmentioning

confidence: 99%

Machine Learning Predictors for Sustainable Urban Planning

Nagappan¹,

Daud²

2021

IJACSA

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While essential for economic reasons, rapid urbanization has had many negative impacts on the environment and the social wellbeing of humanity. Heavy traffic, unexpected geohazards are some of the effects of uncontrollable development. This situation points its fingerto urban planning and design; there are numerous automation tools to help urban planners assess and forecast, yet unplanned development still occurs, impeding sustainability. Automation tools use machine learning classification models to analyze spatial data and various trend views before planning a new urban development. Although there are many sophisticated tools and massive datasets, big cities with colossal migration still witness traffic jams, pollution, and environmental degradation affecting urban dwellers' quality. This study will analyze the current predictors in urban planning machine learning models and identify the suitable predictors to support sustainable urban planning. A correct set of predictors could improve the efficiency of the urban development classification models and help urban planners to enhance the quality of life in big cities.

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“…The use of remote sensing techniques used in different domains that range from change detection in urbanization, LULC mapping, crop prdouctions etc. are discussdd in [5][6][7][8][9][10][11][12][13].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and Remote Sensing Technique for Urbanization Change Detection in Tangail District

Talukder

Mim

Ahmed

et al. 2021

Intelligent Sustainable Systems

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Presently, urban growth is a typical marvel globally, and it is obvious in non-industrial countries with current rapidity. Land-use changes are inferable from the fast urbanization in recent decades in non-industrial countries. With the fast growth of the populace, urbanization in Bangladesh has occurred in the same way as other different urban communities of the world. Tangail District has likewise been confronting rapid urban growth and land-use changes for the most recent couple of years. This paper aims to examine land-use changes investigation of Tangail District by utilizing multi-transient Landsat Thematic Mapper information. Remote sensing procedures are being used for analyzing examples of urban development utilizing geographic information systems (GIS). To understand the land-use change and urban development pattern, GIS and remote sensing procedures are cost-effective. In this research, Landsat images are ordered in four land-use classes utilizing different supervised classification including random forest algorithm. The spatial and fleeting changes of Tangail District concerning urban development and land use have been portrayed from Sentinel-2A data. We made an endeavor to explore the progressions from the years 2017, through 2020 for Tangail District. This study could significantly assist the urban planners through fundamental data about the degree and pattern of Tangail District's urbanization to deal with the rapid urban development and give the essential urban drawbacks and benefits.

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Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia

Cited by 25 publications

References 17 publications

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Machine Learning Predictors for Sustainable Urban Planning

Machine Learning and Remote Sensing Technique for Urbanization Change Detection in Tangail District

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