Abstract
Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.
Highlights
A challenging problem of the earth’s ecosystem is wildland fires, which affect the balance of greenhouse gases, plant distribution, and inhabitant safety
We propose a novel super-resolution burned-area mapping (SRBAM) method based on space–temperature information, that we name STI
When we compared the reference images with the four experimental results, we found that STI outperformed the other three super-resolution mapping (SRM) methods, and the results from STI were more similar to the reference images
Summary
A challenging problem of the earth’s ecosystem is wildland fires, which affect the balance of greenhouse gases, plant distribution, and inhabitant safety. Burned-area mapping using the classification results of coarse images is usually not ideal [3]. The basic idea is to combine subpixel shifting images in the same scene to produce a resolution-enhanced image These SRM methods have been applied in many areas, including flood inundation mapping [34], water boundary extraction [35], change detection [36], urban development [37]. This important temperature information should be utilized to improve the final mapping’s accuracy To solve these issues, we propose a novel SRBAM method based on space–temperature information, that we name STI. 2019, 11, 2695 information with the temperature information According to this objective function, the particle swarm optimization algorithm (PSOA) is employed to obtain the final burned-area mapping. Landsat-8 Operational Land Imager (OLI) dataset show the superiority of STI over other state-of-the-art methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
