Accurate and efficient burned area mapping and monitoring are fundamental for environmental applications. Studies using Landsat time series for burned area mapping are increasing and popular. However, the performance of burned area mapping with different spectral indices and Landsat time series has not been evaluated and compared. This study compares eleven spectral indices for burned area detection in the savanna area of southern Burkina Faso using Landsat data ranging from October 2000 to April 2016. The same reference data are adopted to assess the performance of different spectral indices. The results indicate that Burned Area Index (BAI) is the most accurate index in burned area detection using our method based on harmonic model fitting and breakpoint identification. Among those tested, fire-related indices are more accurate than vegetation indices, and Char Soil Index (CSI) performed worst. Furthermore, we evaluate whether combining several different spectral indices can improve the accuracy of burned area detection. According to the results, only minor improvements in accuracy can be attained in the studied environment, and the performance depended on the number of selected spectral indices.

Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.

Gas detection is critically important in both industrial production and environmental monitoring. Due to the absorption characteristics of gases in the infrared wavelength domain, thermal infrared multispectral video imaging provides a convenient data sensing method for rapid, large-scale gas detection. In the process of gas detection, the shape of the detected gas leakage region is often distorted by the irregular motion patterns of gases. Although some studies have considered temporal motion information, these spatial-temporal feature extractors were designed originally for regular and salient objects such as vehicles and pedestrians, but not for gas. Moreover, the problem of weak gas signal and lack of a large well-labeled dataset also hinders the development of gas detection with deep learning. Regarding the above issues, a novel gas detection network, Computational Fluid Dynamics neural Network (CFDNet), was proposed for infrared multispectral gas detection. Firstly, to better fit the motion patterns of gas and keep the shape of the detected leakage region, a spatial-temporal fluid motion feature extractor, Computational Fluid Dynamics (CFD) Basic Block, was proposed. CFD Basic Block diffuses and displaces high-dimensional features through a convolution based on the Navier-Stokes equations in computational fluid dynamics. Secondly, to enhance the signal of gaseous targets, a Local Entropy and combination Difference (LED) data feature enhancement module was designed based on the instrument characteristics and local entropy information. Finally, a simulated dataset and a transfer learning framework were built to train a deep learning gas detection model with great generalizability. Experiments show that the proposed method, CFDNet, achieves a better performance than existing approaches. On the real-world dataset used for testing, it reaches an IoU of 50.128%, a Kappa of 59.902%, and an F1 Score of 44.885%. CFDNet demonstrates an excellent performance on keeping the shape of the detected gas leakage region under irregular motion patterns, especially for gas plumes with small area-ratio and low signal-noise-ratio.