Development of a new indicator for identifying vegetation destruction events using remote sensing data

• The slope vegetation index was developed using bands sensitive to vegetation foliage, canopy, and water content. • The proposed index outperforms existing indices, especially in complex scenarios. • The new index is applicable to sensors with green, NIR and SWIR bands, such as Landsat 8 and Landsat 9. • This study contributes to the achievement of sustainable development goals (SDG 15). Frequent climate change and intense anthropogenic activity increase the risk of vegetation destruction. Remote sensing technology, known for its timely observations and wide coverage, is a crucial tool for monitoring vegetation growth. However, accurately detecting vegetation destruction events remains challenging due to their spectral diversity, particularly in complex environments. Existing spectral indices (VIs) have limitations in effectively capturing vegetation dynamics as they are only sensitive to specific physiological parameters of vegetation, such as foliage, canopy, or water content, and are prone to background interference. To address this issue, we proposed the Slope Vegetation Index (SVI) based on Sentinel-2 imagery and PROSAIL model simulation data. Five representative VIs were selected for comprehensive comparison. The results showed that, compared with other VIs, SVI had the highest sensitivity to vegetation physiological parameters, with a correlation coefficient (R 2 ) greater than 0.98. SVI performed best across all vegetation change scenes, with producer accuracy (PA), user accuracy (UA), and F1 score all exceeding 0.90. SVI proved effective in detecting various vegetation destruction events, including logging, insect infestation, landslides, and wildfires. Moreover, SVI was suitable for Landsat-8/9 imagery, achieving an F1 score of over 0.89. Overall, SVI is an effective and robust vegetation monitoring index, offering valuable insights for vegetation resource management and post-disaster ecological restoration.