# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 1 Introduction  
Forest fires are natural ecosystem processes that have a significant impact on environment protection, ecosystem equilibria, and climate change [1; 2]. Early detection of forest fires is helpful for monitoring the high rate of fire spread, instantaneous fire radiative energy, smoldering ratio, and consequences of biomass burning for climate and ecosystem functions. Remote sensing has been widely used in forest fire detection. The use of thermal infrared images is a well-known technique to detect actively burning fires. At present, most satellite-based fire detection algorithms are based on the detection of thermal anomalies produced by the high temperatures of burning fuels in midwave infrared (MWIR) and longwave infrared (LWIR) bands. Those algorithms can be divided into single-date and multi-temporal detection methods according to the number of source images. Single-date fire detection methods are commonly classified as either fixed-threshold or contextual algorithms [3]. Fixed-threshold algorithms regard a pixel as an active fire pixel if the brightness temperature (BT) or radiance in the MWIR and LWIR bands exceeds pre-specified thresholds [4; 5; 6; 7]. In contrast, contextual algorithms first label a pixel as a \"potential active fire\" with a similar series offixed thresholds. The tests are then used to verify whether the pixel is a \"true fire pixel\" or a \"false fire pixel\" by comparing the observed value with the background intensity, which is predicted by averaging the intensities over the neighboring pixels [8; 9; 10; 11; 12]. If the observed value is sufficiently higher than the ambient background intensity, the pixel is identified as a fire pixel. For fixed-threshold and contextual algorithms, the optimal thresholds are difficult to determine because of the variability of the natural background in the time and spatial domains. It is impossible to detect all the active burning pixels, especially for small fires. Unlike fixed-threshold or contextual algorithms, multi-temporal fire detection methods focus on exploiting the multi-temporal nature of data in a thermal anomaly detection process. For example, [PERSON] _et al._[13] developed a multi-temporal detection algorithm for Spinning Enhanced Visible and Infrared Imager (SEVIRI) images that detects fire pixels by comparing successive observations. [PERSON] and [PERSON] [14] proposed a non-linear Dynamic Detection Model (DDM) for multi-temporal thermal infrared images. [PERSON] _et al._[15] applied a robust satellite technique (RST) to forest fire detection in near real time. They proposed a new index, which they called the Absolute Local Index of Change of the Environment (ALICE), as a quantitative standard to monitor forest fire pixels. [PERSON] and [PERSON] [16] developed a multi-temporal Kalman filter approach for geostationary active fire detection. Although these methods performed well on test data, potential limitations, including background surface properties, weather influence, and missing pixel values, have hindered further development. In addition, most existing multi-temporal fire detection algorithms do not take full advantage of spatial contextual information. In this paper, we propose a novel spatio-temporal model that uses spatial contextual and temporal information to detect active forest fires. The spatio-temporal model (STM) based forest fire detection method relies on the strong correlation between an inspected pixel and its spatial neighborhood. Spatial correlation is used in STM for predicting the background intensity of the pixel. In addition, to make a more robust fire detection method, the STM method adds temporal information to the anomaly detection by assigning different weights to the inspection image and previous images. A test area from China's Heilongjiang province was used to validate the STM method. A fire broke out in the Yinanhe forest on 27 April 2009, and it was contained on 11 May 2009. Thirteen HJ-1B/IRS images were used for this work.