X. L. Li, J. Wang, W. G. Song, J. Ma, L. Telesca and Y. M. Zhang (2014) Automatic Smoke Detection in MODIS Satellite Data based on K-means Clustering and Fisher Linear Discrimination. Journal/Photogrammetric Engineering And Remote Sensing 80 971-982. [In English]

Keywords: ,CIRRUS CLOUD DETECTION, BOREAL FOREST-FIRE, WATER-VAPOR BAND, AVHRR, IMAGERY, IMPROVED ALGORITHM, RADIATION BUDGET, UNITED-STATES, PLUMES, IMPACT, AEROSOLS

Abstract: Satellite-based remote sensing technique provides images to detect and monitor forest fire smoke. Aiming at automatically separating smoke plumes from other cover types, several bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra/Aqua satellites were selected. A smoke identification algorithm that integrates K-means clustering and Fisher Linear Discrimination was developed. It's evaluated that the algorithm can identify more than 98 percent of the smoke pixels by using the k-folds cross-validation technique. Then, the algorithm was validated in: (a) Daxing'anling area (China) on 29 April 2009, (b) Amur Region (Russia) on 29 April 2009, (c) Australia on 30 September 2011, and (d) Canada on 19 June 2013, in which several fires occurred. By comparing the results with the grayscale images, it can be seen that the algorithm has the capability to capture heavy smoke as well as part of dispersed smoke. The results suggest that the proposed algorithm can be used as an innovative tool for detecting forest fire smoke.