Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, >10%) and omission error (OE, >6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management.
Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.
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