The utilisation of sentinel-2A images and google earth engine for monitoring tropical Savannah grassland

Abstract

A fast, precise and efficient method of savannah grassland mapping and monitoring is essential to support sustainable livestock feed management. This study aims to utilise Sentinel-2A Level-1C imagery to map and monitor tropical savannah grasslands on Sabu Island, Indonesia. Normalized Difference Vegetation Index (NDVI) images were generated to identify vegetation objects from 50 image scenes covering each month from 2016 to 2020 through the Google Earth Engine (GEE). Principal Component Analysis (PCA) was applied to the 50 NDVI data to produce monthly images (12 months). The grassland objects were classified from Sentinel-2A images using the parallelepiped algorithm and resulted in an overall accuracy of 82.86%. Results showed a range of the average monthly NDVI between 0.127 and 0.449, which falls within the grassland class. NDVI combined with GEE can quickly and accurately identify grasslands, creating highly recommended tools for monitoring tropical savannah grasslands.

Keywords:

• Tropical savannah grassland
• NDVI
• GEE
• PCA
• parallelepiped

Acknowledgements

The authors would like to thank (1) the Agriculture and Animal Husbandry Department of Sabu Raijua Regency for providing field data, (2) the Meteorology, Climatology and Geophysics Council of Sabu Raijua Regency for providing climate data, (3) the Master of Science Programme in Remote Sensing, Faculty of Geography, Universitas Gadjah Mada, Indonesia, for providing research facilities and (4) all staff and officials at the National Land Agency of Sabu Raijua Regency for supporting this research. This paper publication is part of MRP’s master thesis; no funding agency is associated with this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the first author, MRP, upon reasonable request.