A New Remote Sensing Index for the Detection of Multi-Type Forest Anomalies Based on Sentinel-2 Imagery

Forest anomalies (e.g., pests, deforestation, and fires) are increasingly frequent phenomena on Earth's surface. Rapid detection of these anomalies is crucial for sustainable forest management and development. On-orbit remote sensing detection of multi-type forest anomalies using single-temporal images is one of the most promising methods for achieving it. Nevertheless, existing forest anomaly detection methods rely on time series image analysis or are designed to detect a single type of forest anomaly. In this study, a Forest Anomaly Comprehensive Index (FACI) is proposed to detect multi-type forest anomalies using single-temporal Sentinel-2 images. First, the spectral characteristics of different forest anomaly events were analyzed to obtain potential band combinations. Then, the formulation of FACI was determined using imagery simulated by the LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes (LESS) model. The thresholds for FACI for different anomalies were determined using the interquartile method and 90 in situ survey samples. The accuracy of FACI was quantitatively assessed using an additional 90 in situ survey samples. Evaluation results indicated that the overall accuracy of FACI in detecting the three forest anomalies was 88.3%, with a Kappa coefficient of 0.84. The overall accuracy of existing indices (NDVI, NDWI, SAVI, BSI, and TAI) is below 80%, with Kappa coefficients less than 0.7. In the end, a case study in Ji'an, Jiangxi Province, confirmed the ability of FACI to detect different stages of pest infection, as well as deforestation and forest fires, using single-temporal satellite images. The FACI provides a promising method for the on-orbit satellite detection of multi-type forest anomalies in the future.