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Chinese Journal of Ecology ›› 2023, Vol. 42 ›› Issue (4): 956-965.doi: 10.13292/j.1000-4890.202304.014

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Analysis on topographic effects of commonly used vegetation indices in complex mountain area based on Sentinel-2 data.

CHEN Yixin1,2,3, MO Dengkui1,2,3, YAN Enping1,2,3*   

  1. (1College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China; 2Hunan Provincial Key Laboratory of Forestry Remote Sensing Big Data and Ecological Security, Changsha 410004, China; 3South Key Laboratory of Forest Resources Management and Monitoring, National Forestry and Grassland Administration, Changsha 410004, China).

  • Online:2023-04-03 Published:2023-04-06

Abstract: Using typical topographic correction models to compare the removal effectiveness of topographic effect on different vegetation indices under complex mountain conditions can provide a scientific basis for accurate assessment of vegetation. Taking Sentinel-2 images of Yanling County, Hunan Province as data source, C model, SCS+C model, and Teillet-regression model were used for topographic correction of the band-ratio vegetation indices (NDVI and SR) and non-band-ratio vegetation indices (MNDVI and RSR), aiming to analyze the removal effectiveness of topographic effect on the four vegetation indices from five aspects: visual effects, correlation, slope, aspect, and vegetation coverage. The results showed that: (1) The band-ratio vegetation indices effectively suppressed the noise caused by terrain. When the slope was less than 15°, the topographic effects of vegetation indices were suppressed to different degrees. When the slope was greater than 15°, topographic correction was the most effective on MNDVI, followed by RSR, while NDVI and SR were prone to over-correction. (2) All the three topographic correction models could reduce the topographic effects of the vegetation indices in rugged terrain areas, especially when the slope was greater than 15°. The C model was the most effective, followed by the SCS+C model, and the Teillet model was the least effective. (3) MNDVI after topographic correction effectively restrained the influence of complex mountain topographic effects and improved the estimation accuracy of vegetation coverage. When slope was less than 25°, the Teillet model was the most effective. When slope was greater than 25°, the C model was the most effective.


Key words: vegetation index, topographic effect, topographic correction, Sentinel-2, complex mountain.