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Capsule network-based approach for estimating grassland coverage using time series data from enhanced vegetation index

The degradation and desertification of grasslands pose a daunting challenge to China's arid and semiarid areas owing to the increasing demand for them in light of the rise of animal husbandry. Monitoring grasslands by using big data has emerged as a popular area of research in recent years. As grassland degradation is a slow and gradual process, the accurate identification of grassland cover is key to monitoring it. Vegetation coverage is currently monitored mainly by combining inversion-based methods with field surveys, which requires significant human effort and other resources and is thus unsuitable for use at a large scale. We proposed to use time series from the enhanced vegetation index (EVI) in capsule network-based methods to identify grasslands. The process classified grassland coverage into four levels, high, medium, low, and other, based on Landsat images from 2019. The accuracy in classifying the grasslands at each level was higher than 90%, with an overall accuracy of 96.32% and a kappa coefficient of 0.9508. The proposed method outperformed the SVM, RF, and LSTM algorithms in terms of classification accuracy.

Availability
253551Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
Artificial Intelligence in Geosciences
Call Number
551
Publisher
Beijing : KeAi Communications Co. Ltd..,
Collation
9 hlm PDF, 3,657 KB
Language
Inggris
ISBN/ISSN
2666-5441
Classification
551
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.2, December 2021
Subject(s)
Deep learning
Remote sensing
Classification
Grassland coverage
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

File Attachment
  • Capsule network-based approach for estimating grassland coverage using time series data from enhanced vegetation index
    The degradation and desertification of grasslands pose a daunting challenge to China's arid and semiarid areas owing to the increasing demand for them in light of the rise of animal husbandry. Monitoring grasslands by using big data has emerged as a popular area of research in recent years. As grassland degradation is a slow and gradual process, the accurate identification of grassland cover is key to monitoring it. Vegetation coverage is currently monitored mainly by combining inversion-based methods with field surveys, which requires significant human effort and other resources and is thus unsuitable for use at a large scale. We proposed to use time series from the enhanced vegetation index (EVI) in capsule network-based methods to identify grasslands. The process classified grassland coverage into four levels, high, medium, low, and other, based on Landsat images from 2019. The accuracy in classifying the grasslands at each level was higher than 90%, with an overall accuracy of 96.32% and a kappa coefficient of 0.9508. The proposed method outperformed the SVM, RF, and LSTM algorithms in terms of classification accuracy.
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