Vegetation change detection based on time series analysis by Apache Spark and RasterFrame

  • Affiliations:

    Hanoi University of Mining and Geology, Vietnam

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  • Received: 18th-Sept-2020
  • Revised: 9th-Jan-2021
  • Accepted: 2nd-Feb-2021
  • Online: 28th-Feb-2021
Pages: 42 - 52
Views: 27295
Downloads: 8063
Rating: 5.0, Total rating: 805
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Spatial big data has a large scale and complex, therefore, it cannot be collected, managed, and analyzed by traditional data analytic software shortly. These platforms in many situations are restricted to vectors data. However, the raster data generated by the sensors on the enormous number of satellites now needs to be processed in parallel on the cluster environment. The article introduces the satellite image data analyzing method using the RasterFrames library on the Apache Spark platform. The RasterFrames library examines raster data for Python, Scala, and SQL, bringing the power of Spark DataFrames to access to Earth Observation, cloud computing, and data science. In the experimental part, the NDVI and the change in the average value of NDVI in the time series are calculated to demonstrate the vegetation mantle changes in Phu Tho province. These results are the reference data source in the assessment of weather, climate, and environmental changes in the study area during that time.

How to Cite
Nguyen, D.Mai Thi and Vu, T.Hoai Thi 2021. Vegetation change detection based on time series analysis by Apache Spark and RasterFrame (in Vietnamese). Journal of Mining and Earth Sciences. 62, 1 (Feb, 2021), 42-52. DOI:

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