Mô hình tự động phân loại dữ liệu lớp phủ bề mặt phục vụ kiểm kê khí nhà kính bằng ảnh viễn thám.

Thi Oanh Nong; Xuan Truong Tran; Hoang Trung Ta; Viet Nga Trinh

doi:10.54491/jgac.2023.57.699

Applied research | No. 57 (2023)

Automatic classification model for greenhouse gas inventory using remote sensing data

DOI: 10.54491/jgac.2023.57.699

10.54491/jgac.2023.57.699

Published 2023-09-25

Thi Oanh Nong⁺⁻
Xuan Truong Tran⁺⁻
Hoang Trung Ta⁺⁻
Viet Nga Trinh⁺⁻

Thi Oanh Nong

Hanoi University of Mining and Geology

Xuan Truong Tran

Hanoi University of Mining and Geology

Hoang Trung Ta

Department of Survey, Mapping and Geographic Information Viet Nam

Viet Nga Trinh

Department of National Remote Sensing

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Keywords

Ảnh viễn thám
Phủ bề mặt
Random Forest
AFOLU Remote sensing
Random Forest
Landcover
AFOLU

Working Languages

How to Cite

Nong, T. O., Tran, X. T., Ta, H. T., & Trinh, V. N. (2023). Automatic classification model for greenhouse gas inventory using remote sensing data. Journal of Geodesy and Cartography, (57), 55–64. https://doi.org/10.54491/jgac.2023.57.699

Abstract

Monitoring greenhouse gas emissions has recently received attention from scientists, policy-makers and agencies. In order to estimate greenhouse gas emissions, an accurate land cover map of the area to be evaluated is essential. This study uses the Random Forest algorithm to classify Landsat 8, Sentinel 1,2 satellite images to build land cover maps according to AFOLU classification schema recommendations, serving greenhouse gas inventories. The remote sensing image classification results in the Northwest and Southwest regions showed an overall accuracy of 80.5%, suitable for building surface coverage maps for greenhouse gas inventory.

https://doi.org/10.54491/jgac.2023.57.699

PDF (Tiếng Việt) | Download: 212

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