A new remote sensing index for multi-type forest anomalies detection based on Sentinel-2 imagery

Forest anomalies (e.g., pests, deforestation, and fires) are common phenomena of the earth’s surface. Rapid detection of these anomalies is important 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 and are designed for a single type of forest anomaly. Here, a Forest Anomaly Comprehensive Index (FACI) was proposed to rapidly detect multi-type forest anomalies (i.e., pests, deforestation, and fires) using different thresholds and single-temporal Sentinel-2 images. First, the spectral characteristics of different forest anomaly events were analyzed to obtain potential band combinations for comprehensive anomalies detection. Then, the FACI form based on the potential bands was determined using images simulated by the LESS model. The threshold separability of FACI was compared to that of existing indices (NDVI, NDWI, SAVI, BSI, and TAI). In the evaluation, the thresholds for FACI and existing indices were determined using the interquartile method and 90 field survey samples, while their accuracy was quantitatively assessed with an additional 90 field survey samples and Sentinel-2 images. Finally, the evaluation results indicated that the overall accuracy of FACI in detecting the three forest anomalies was 88.3%, with the corresponding Kappa coefficient of 0.84. While all the overall accuracy of existing indices are below 80%, with Kappa coefficient less than 0.7. Meanwhile, a case study in Ji'an, Jiangxi Province confirmed the ability of FACI to detect different stages of pest infection, as well as the deforestation and forest fires using single-temporal satellite images. Overall, FACI represents a promising method for detecting multi-type forest anomalies in future real-time on-orbit satellite applications.