Dalin Liang

On-orbit rapid detection of land surface anomalies is important for ensuring ecological security and human safety. Land surface anomalies (e.g., fire, industrial heat source, and deforestation, etc.) are often accompanied by different degrees of thermal anomalies. Existing methods for detecting thermal anomalies have focused primarily on high-temperature anomalies, without available approach for detecting widespread low-temperature anomalies. Here, a Novel Method based on Constructed Reference land surface temperatures (LST) for on-orbit remote sensing detection of various Thermal Anomalies (NMCRTA) is proposed and further evaluated using Landsat 8 LST product. In this method, we first construct an fitted reference temperature based on LST spatiotemporal trend surface modeling and a real reference temperature based on contextual averaging. Then, the difference (including the step of removing atmospheric effects) between on-orbit observed LST and fitted reference LST, and the difference between on-orbit observed LST and real reference LST are calculated, respectively. Finally, these two temperature differences are utilized to detect thermal anomalies using corresponding thresholds. The results indicate that the NMCRTA can effectively detect deforestation, newly constructed buildings, and river drying, with an overall F1-score of 0.867, in a 100 × 100 km region scale. Meanwhile, the NMCRTA exhibited excellent accuracy in detecting fires, deforestation, and landslides at the 15 × 15 km scene scale, achieving F1-scores of 0.943, 0.857, and 0.791, respectively. Furthermore, the NMCRTA can continuously capture different thermal anomaly events associated with a newly constructed industrial heat source and perform well in nighttime. The NMCRTA is promising for future on-orbit remote sensing detection of various land surface anomalies, as a valuable supplement to optical on-orbit detection method.

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.

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.