IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2024)

Analysis of the Effectiveness of the Red-Edge Bands of GF-6 Imagery in Forest Health Discrimination

  • Jiahui Chen,
  • Hanqiu Xu

DOI
https://doi.org/10.1109/JSTARS.2024.3367320
Journal volume & issue
Vol. 17
pp. 5621 – 5636

Abstract

Read online

The red-edge band is closely related to biochemical parameters that characterize the growth condition of green plants and is an important factor in monitoring vegetation health. Therefore, red-edge indices based on the red-edge band have been developed to measure vegetation health. However, due to the limited availability of satellites with a red-edge band, most existing red-edge indices were not developed based on satellite data. Fortunately, the launch of the GaoFen-6 (GF-6) satellite provides favorable conditions for monitoring vegetation health using satellite imagery, as it has two red-edge bands with a spatial resolution of 16 m. To investigate the effectiveness of the red-edge bands on the GF-6 satellite in monitoring forest health, this study selected six red-edge indices and conducted tests in Zhangjiajie region in Hunan Province, China and Hetian Basin in Fujian Province, China. The selected indices are the normalized difference red-edge index 1 (NDRE1), the modified chlorophyll absorption ratio index 2, the red-edge chlorophyll (CIred-edge), the inverted red-edge chlorophyll index, the red-edge position, and the Missouri emergency resource information system terrestrial chlorophyll index. The results showed that when applied to NDRE1 and CIred-edge, the red-edge bands of GF-6 can effectively distinguish forest health conditions, with a discrimination accuracy of 92.3% and 92.5%, respectively. However, the performance of the GF-6 red-edge bands with the other four indices yielded accuracy generally lower than 70%. Overall, the two red-edge bands added to the GF-6 satellite contribute to discerning forest health conditions, with NDRE1 and CIred-edge being the preferred red-edge indices.

Keywords