An active fire detection algorithm based on multi

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An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data

T

⁎

Zhengyang Lina,b, Fang Chena,b,c,d, , Zheng Niud, Bin Lia, Bo Yua, Huicong Jiaa, Meimei Zhanga a Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China b University of Chinese Academy of Sciences, Beijing 100049, China c Hainan Key Laboratory of Earth Observation, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Sanya 572029, China d The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

A R T I C LE I N FO

A B S T R A C T

Keywords: FengYun-3C VIRR Active fire detection Multi-temporal

The Visible and Infra-Red Radiometer (VIRR) is an improved third-generation sensor used for Earth observation with channels ranging from visible to thermal bands and carried on board the Chinese FengYun-3C satellite. An active fire detection algorithm based on VIRR data has already been designed and tested in different study areas worldwide. Most of the previously related algorithms were developed merely focusing on the spatial and spectral features of pixels while the temporal attributes of these observed active fires were ignored. In this research, multi-temporal VIRR data were used to construct time series of pixels. The core content of the algorithm consists of the changes in the time-series profiles together with the observed data. By calculating the predicted midinfrared (MIR) value and the stable MIR value of the target area, fire pixels can be easily distinguished. To assess the performance of this algorithm, a total of eight target areas distributed across the world were used for testing. Two stages of validation were carried out with data of different spatial resolutions. A rough comparison was carried out first. During this step, results from Collection 6 of MODIS Fire and Thermal Anomalies products (MOD14A1) and results generated from the previously used algorithm were used for comparison. The detailed validation work was conducted with the support of Landsat series (including ETM+ and OLI sensors) data even though the different imaging time may affect the actual validation results.

1. Introduction

Moderate Resolution Imaging Spectroradiometer (MODIS) on the EOSTERRA/EOS-AQUA satellites (Kaufman et al., 1998; Giglio et al., 2003; Giglio et al., 2016), the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel-3 satellite(Wooster et al., 2012; Calle and Salvador, 2013), the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (S-NPP) satellite (Csiszar et al., 2014; Schroeder et al., 2014; Polivka et al., 2016; Waigl et al., 2017) and the Infrared Sensor (IRS) on the Chinese HJ satellite (He et al., 2011; Wang et al., 2012). These different satellite datasets have been used in the study of and to support a variety of active fire detection algorithms and products. Besides, many geostationary satellites have also been used in the research of active fire detection. The Geostationary Operational Environmental Satellites (GOES) (Xu et al., 2010; Koltunov et al., 2012; Koltunov et al., 2016), the Meteosat Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) (Calle et al., 2006; Roberts and Wooster, 2008; Amraoui et al., 2010; Sifakis et al., 2011) are typical data sources

Wildfires have a high frequency of occurrence around the globe. Active wildland fires cause great loss of human life and damage to property annually (Schultz et al., 2008; Bowman et al., 2009). In addition, wildfires also have serious environmental, ecological and social effects. Biomass burning caused by wildfires is a significant factor in land cover change and also an important source of aerosols as well as atmospheric pollution (Drever et al., 2009; Margolis and Balmat, 2009; Liu et al., 2014). Therefore, researchers have been studying effective methods to detect active fires based on satellite data because of their effectiveness and timeliness and because of the wide spatial range to which they can be applied. Advanced sensors have been utilized in active fire detection. The sensors used include the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administration (NOAA) satellites series (Dozier, 1981; Cheng et al., 2013; Kalpoma and Kudoh, 2006; Abuelgasim et al., 2002), the

⁎ Corresponding author at: Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China. E-mail address: [email protected] (F. Chen).

https://doi.org/10.1016/j.rse.2018.04.027 Received 24 April 2017; Received in revised form 10 April 2018; Accepted 11 April 2018 0034-4257/ © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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which have been studied by researchers. Compared with geostationary satellites, one of the advantages is that polar-orbiting satellites could provide fine spatial resolution. Therefore, many of the fire detection algorithms based on polar-orbiting satellites focus merely on the spatial analysis, including using the ‘fixed threshold method’ and ‘spatial contextual method’. Temporal information was usually ignored in early algorithms based on polar-orbiting satellite data. In order to explore and enhance the ability to detect fires, researchers have begun to consider the possibility of detecting fires by introducing the time factor (Sivathanu and Tseng, 1997; Lasaponara et al., 2003; Roy et al., 2005; Kushida, 2010; Panuju et al., 2010; Lin et al., 2016). The FengYun-3C (FY-3C) satellite is the latest Chinese polar-orbiting meteorological satellite and provides data with global coverage daily. FengYun-3A (FY-3A) and FengYun-3B (FY-3B) were launched on 27th May 2008 and 5th November 2010, respectively. The first two satellites were designed mostly for experimental purposes and were considered successful. The third satellite (the second batch satellite), named FengYun-3C was launched on 23rd September 2013. FY-3C was designed to serve for at least 5 years and has been the primary satellite for relevant research since its launch. A total of 12 different instruments are carried on FengYun-3C, namely the Visible Infrared Radiometer, Microwave Scanning Radiometer, Microwave Temperature Sounder, Microwave Humidity Sounder, Microwave Imager, Medium Resolution Spectral Imager, UV-ozone sounder, Total Ozone UV Detector, Solar Radiation and Earth Radiation Detector, Space Environmental Monitoring Suites, and GNSS Occultation Detectors. The MODIS instruments have been in service for almost two decades and FengYun-3 satellites series can provide substitute data for research and other applications. On 15th November 2017, the 4th flight unit of the FY-3 series, FengYun-3D, was launched (World Meteorological Organization, 2017) and consecutive satellites up to FengYun-3G are planned. Upgraded, more stable instruments will be carried on board future FengYun-3 satellites series, which will benefit various communities by providing continuous observations in the coming decades (National Satellite Meteorological Center, 2010). Therefore, for fire-related research, an effective active fire detection algorithm based on FengYun-3C VIRR data should be developed. The first version of the algorithm inherited the ‘contextual’ calculation component from the MODIS Collection 4 active fire detection algorithm and introduced a ‘dynamic threshold’ as well as an ‘infrared gradient’ to enhance the performance of the algorithm and make it suitable for the VIRR instrument (Lin et al., 2017). In general, the first version of the algorithm has the advantages of the MODIS algorithm and adapted to VIRR data quite well. However, there are still problems with the algorithm, including false alarms in areas of high reflection and omission errors where there are water bodies. The latter problem has already been solved in the MODIS Collection 6 active fire detection algorithm (Giglio et al., 2016). To solve these problems, the core component of the new algorithm will be constructed around the analysis of multitemporal data from the FengYun-3C VIRR. The new algorithm will detect active fires by utilizing the notable changes visible in time series and will avoid the typical errors described previously. This algorithm has been designed for VIRR data; however, the design could also be transmitted to other similar sensors to take full advantage of the data and reduce errors to a minimum.

Table 1 Fengyun-3C VIRR spectral features. Channel no.

Band range (μm)

Noise equivalent temperature difference (ρ OR 300 K)

Dynamic range (ρ OR K)

3 4

3.55–3.93 10.3–11.3

0.3 K 0.2 K

180-350 K 180-330 K

data are publicly available and can be downloaded through the website of National Satellite Meteorological Center (http://satellite.nsmc.org. cn/PortalSite/Default.aspx). The geometric and radiometric corrections are necessary. The first one is carried out using the method of geolocation look-up table. The radiometric correction contains nonlinear corrections for radiance and linear corrections for reflectance from parameters as well as the correction for the solar altitude. The detail of the parameters for radiometric corrections can be found in the L1 files. In this study, the major data source was the FengYun-3C VIRR instrument which is a development of the Multichannel Visible and Infrared Scanning Radiometer (MVISR; 10 channels) instrument on the FengYun-1C/D satellites. The VIRR channels provide a spectral range extending from the visible (VIS) to the thermal infrared (TIR) and a spatial resolution of 1 km. VIRR data provide global coverage twice a day in the infrared channels or once a day in the visible channels. When detecting active fire pixels, the VIRR algorithm will mainly use the midinfrared (MIR) channel and the TIR bands of the VIRR. Details of the spectral and spatial features of the VIRR channels that were used in this study are shown in Table 1. The third channel of VIRR provides MIR brightness temperature values which can be used as the main parameter in active fire detection. This channel, centered at 3.74 μm, has a spectral range of 3.44 to 3.93 μm, which has a peak spectral radiance for blackbodies emitting at temperatures between 737 and 842 K. However, the saturation temperature of the MIR channel is 350 K which can result in excessive errors. To produce satisfactory results, the active fire detection algorithm also uses one of the TIR channels. The 4th VIRR channel, centered at 10.80 μm, has a spectral range of 10.3 to 11.3 μm and a pixel saturation temperature of 330 K.

3. Algorithm description Over the years, different levels of refinements of the MODIS active fire detection algorithm and products have been made based on validation analyses (Csiszar et al., 2006; Wang et al., 2007; Xie et al., 2007; Schroeder et al., 2008; Maier et al., 2013; Cheng et al., 2013; Giglio et al., 2016). Many of these algorithms are focused on the detection of pixels with high MIR values in a single scene. Anomalous values in the time series are not considered because, for the most part, active wildland fires fade away within a short period, which means that the time factor also has a significant impact on the final results. Modeling the diurnal temperature cycle (DTC) for target areas is an effective method of detecting rapid changes such as wildfires (Roberts and Wooster, 2014), whereas the FengYun-3C satellite flies in a polar orbit and can only provide data for a single scene twice a day. Given that the data quality, viewing angle and cloud cover percentage etc. may affect the availability of the data, the amount of valid data available for analysis will be even less. Therefore, a series of data covering a certain length of time – set to about 20 days before the target day in this research – is used to construct the time series for each pixel in the study area. For each pixel, the time series produces a clear profile of changes, which can provide the algorithm with enough information to calculate the stable values and make predictions. A better understanding of the general idea behind the algorithm can be obtained from the flowchart of the detection algorithm shown in Fig. 1. The output from this algorithm will be a binary image with pixels classified as either ‘fire’ or ‘non-fire’. Cloud, smoke, water bodies and other types of pixels are not

2. Input data characteristics The FengYun-3 satellites series fly in sun-synchronous orbits with the FengYun-3C satellite having a local equator crossing time of 10:15 in the descending node and 22:15 in ascending node (https://www. wmo-sat.info/oscar/satellites/view/115). FY-3C flies at an altitude of 836 km and provides data with a spatial resolution of 1.1 km at nadir on a swath of 2800 km (FOV = ± 55.4°). All the FengYun series L1 and L2 data are stored in HDF5 format. A registered account is required and there is a limit on the daily downloading amount of data. The FengYun 377

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FengYun-3C VIRR Data

1.Data Preparaon

Check Input Data 2.Time analysis

Construct Time Series Data

Calculate the value of stable situaon in target areas

MIR

Observed

Calculate the predicon value of the target areas

ā MIR Predicted

MIR

Filter based on observed MIR and TIR values

3.Result

Predicted

ā MIR Stable

Filter based on me series analysis

Fire Pixels Classificaon

Fire pixels

Non-fire pixels

Fig. 1. Flowchart of the multitemporal fire-detection algorithm based on FengYun-3C VIRR data. The algorithm consists of three parts: data preparation, time-series analysis and results. The time-series analysis is contained within the dashed rectangle. The core parts of this analysis have been colored in grey and the main parameters used in the algorithm are in red type. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

affected data from the sequence. The resulting time series will only include the data which meet the conditions given in Eqs. (1) and (2). An example of time series construction and the removal of invalid data is demonstrated in Fig. 3. Day 21 is the target day for detecting fires and the previous 20 days' data are used to construct the time series. The vacant data and outlying data are removed according to the above two equations. The moving average lines of the original data sequence fluctuate a lot and the oscillation is usually uncontrollable whereas the sequence without the outlying data fluctuates within what can be considered an acceptable range. Therefore, the latter version of the time series is more credible and can be used in subsequent calculations.

distinguished specifically. 3.1. Construction of time-series data To construct a suitable and effective time series for subsequent use, several preprocessing steps are necessary. First, the differences between daytime and nighttime temperatures are significant and cannot be ignored. An average difference of > 5 K exists, as Fig. 2 shows. Hence, daytime and nighttime data are used separately according to the observation time of the target data. Second, as the time series is constructed for predicting and calculating stable values, outlying values should be eliminated from the time series. In this algorithm, data that are affected by cloud, are vacant or are obvious anomalous with extremely high/low digital numbers are removed as follows:

Ti ∈ [270, Ti, max ] (i = MIR, TIR; TMIR, max = 350; TTIR, max = 330)

(1)

∆T = TMIR, TS − TTIR, TS , ∆T < 20

(2)

3.2. Calculation of time-series After the construction of the time series without invalid data, predicted and stable values can be calculated for each pixel within a certain time interval before the target day. The length of the time span is denoted by D in this research and is usually empirically set to 20 days based on considerations of data quality, cloud cover and avoidance of periodic seasonal variations. An important part of the theory behind the algorithm is that the MIR and TIR values have a linear relationship in non-fire areas, as shown in Fig. 4. In the previous algorithm description, Lin et al. (2017) have

The first condition is used to eliminate the ‘NaN’ (not a number) pixels or anomalous raw data generated by the instrument. The upper limit is determined by the instrument design demonstrated in Table 1 while the lower limit is set so as to remove the data with obvious low values. The difference between observed MIR and TIR channel values is limited to 20 K (Eq. (2)) in order to remove the cloud-affected or fire378

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Brightness temperature of MIR channel (Unt: K)

Differences between daytime and nighttime pixels 370 Values of daytime pixels Values of nighttime pixels

350

Fire pixel Moving average of daytime pixels

330

Moving average of nighttime pixels

310 290 270 250 11/27/2014

12/17/2014

1/6/2015

1/26/2015

Observation date of the time series

Fig. 2. The time series for a pixel at a single location. Active fires are marked as red crosses. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Brightness tmeperature of MIR channels (Unit:K)

350 335.189

330 315.297 313.423 312.299

311.325 306.508

309.979

310

306.04

299.296

298.606 293.622

Outlying data

290 284.679

Time series of valid data 270.056

270 260.341

Moving average line of data with outlying data Moving average line of data without outlying data

250.423

250 0

2

4

6 8 10 12 14 Day index of the time series

16

18

20

Fig. 3. Results of data selection for a single pixel in a time series. An obvious difference can be seen after the removal of outlying values. The last day is the target day in this figure.

A = [TMIR, TS , TTIR, TS ] = UΣV T

already shown this as well. Fig. 4 shows a scatterplot of the MIR and TIR bands for 5.5 million non-fire pixels from the whole-time series. The dashed line demonstrates the assumed linear relationship between MIR and TIR values. However, the linear relationship between the MIR and TIR channels can be affected by data outliers, including cloud pixels and active fire pixels. Therefore, to obtain the correct relationship between these two sets of values, in this research we made use of the SVD method. SVD is short for Singular Value Decomposition and is a commonly used method for decomposing matrixes. We employed this method here in order to extract and analyze the principal components of the time series. For each pixel, the time series composed of MIR and TIR brightness temperature values will form a 2D vector in the matrix A (D × 2 ). This matrix can be decomposed into a set of values that aim to characterize the key parameters, as Eq. 3 demonstrates:

(3)

U U (D × 2) is a matrix that gives the principle component directions in the pixel training dataset. Σ is a diagonal matrix containing singular values σ1 and σ2 sorted in decreasing order. VT (2 × 2) contains the coefficients used in expanding each column of A in terms of the principle component directions between the MIR and TIR time series for each pixel. This represents the core inner correlation as calculated by the following equations:

[TMIR, After SVD , TTIR, After SVD] = u1 × σ1 × v1T

(4)

TMIR, After SVD = a × TTIR, After SVD + b

(5)

TMIR = aTTIR + b, s. t . min In Eq. (4), u1, σ1, v1 379

D

∑i =1 (aTTIR,i + b − TMIR,i )2 T

(6)

are the first major components of the

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hypothesis while the second illustrates the method of solving for the parameters. The predicted and stable values are calculated using the TIR values for the target day together with the mean values of the TIR series (Eq. (7)). The results for TMIR, stable and TMIR, predict are shown together with observed MIR and TIR values in Fig. 5.

Tsituation = aTTIR, situation + b situation 1 Tsituation = Tpredict ; TTIR, situation = TTIR, aim day situation 2 Tsituation = Tstable; TTIR, situation = TTIR, TS

(7)

3.3. Time-series classification 3.3.1. Algorithm design concepts The classification of fire pixels proceeds according to the predicted and stable values generated by the method described in Chapter 3.2 (Eq. (7)). Predicted and stable value images are generated based on the hypothesis that there is a linear relationship between the TIR and MIR values where the predicted value is based on the TIR values on the target day and the stable value uses the mean values of the TIR series. To identify active fire pixels while avoiding misclassification, a combination of MIR, TIR, predicted and stable values are used. To better demonstrate the advantages of this algorithm, four distinct types of pixels from Fig. 5 are selected. The types ‘active fire’, ‘cloud’ and ‘nonfire’ are generated by the previous algorithm (Lin et al., 2017). A further selection of ‘high reflectance’ pixels is made from the non-fire pixels. Fig. 6 and Fig. 7 show the differences between these different types of pixels. They also demonstrate the internal logic behind the construction of this multi-temporal algorithm. Fig. 6 shows an analysis of the average of different values according to the four different types of pixel and Fig. 7 shows three differences between values which can also help to distinguish between pixel types. In detail, we can analyze both Fig. 6 and Fig. 7 in two different ways: changes in the different types of pixels and variances in the different types of values. The active fire pixels clearly have high MIR values (TMIR) and a large difference between this and the TIR value (large TMIR − TTIR). The difference between the MIR and stable values

Fig. 4. Scatterplot of TIR against MIR pixel values for the complete time series for one pixel.

decomposed outcomes and the matrix multiplication results in terms of the principal components of the sequences of MIR and TIR values. The method of ordinary least square (OLS) is used to solve the hypothesized linear relationship between the MIR and TIR values using parameters a and b (Eq. (5) and (6)). The first of these two equations illustrate the

Fig. 5. A and B show the MIR and TIR channel values for part of Australia on 4th Feb 2015. C shows the predicted values. The predicted values are calculated based on the linear relationship between MIR and TIR values described in the text. These values can be used to derive cloud and fire pixels. D shows the estimated stable values. 380

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Bightness temperature (Unit:K)

330 320

MIR values

TIR values

Prediction values

Stable values

310 300 290 280 Active fire

Cloud

Non-fire

High-reflective

Fig. 6. Histogram showing the average values of the four fundamental data types for all the pixels in the scene shown in Fig. 5.

specify the criteria used for detecting fires.

(TMIR − Tstable) is high as well because of the rapid increase in TMIR for fires compared to normal conditions. The cloud pixels usually have a low TIR (TTIR) value and the TMIR − TTIR value is even higher than for the active fire pixels. The difference between predicted and stable values (Tpredict − Tstable) for these pixels is lower than zero due to the low TIR values. As the high-reflectance pixels are selected from among the non-fire pixels, it should be understood that these two types of pixel share many similarities: the profiles of the MIR, TIR, predicted and stable values are similar. The observed MIR, predicted and stable values are close because there are no changes in these pixels on the target day as compared with the test series. Normal conditions are, therefore, maintained. Based on the features of the different types of the pixel described above, we can further compare the differences in the values and generate a strategy for detecting fires. From Fig. 6, it can be seen that fire pixels have the highest average TMIR among the four types. Even if it is under normal conditions, the high-reflectance pixels still have TMIR higher than the non-fire pixels because of the differences in ground objects. The values of Tstable for active fires, cloud and non-fire pixels lie within similar ranges and are lower than those of the high-reflectance pixels, which indicates that the normal states of the pixels are generally the same. In Fig. 7, the fire pixels have the highest values of TMIR − Tstable and Tpredict − Tstable. Active fire, cloud and high-reflectance pixels all have higher TMIR − TTIR than non-fire pixels, which may result in false alarms although TMIR − TTIR is often considered as an effective parameter for detecting fires. Finally, the combination of the high Tpredict − Tstable for active fire pixels, the low value for cloud pixels and the lack of notable change for the other types could greatly help the algorithm to detect fires. Table 2 summarizes the information in these two figures as well. The equations in Chapter 3.3.2 below

3.3.2. Details of the algorithm From the previous discussion, it can be seen that, in terms of the temperature differences described above, there is an obvious difference between fire pixels and the other pixel types. The equations below give a quantitative description of the situation. The first part, equations Eqs. (8)–(10), describe the step of data pre-processing:

Ti′ = Ti ×

TMIR, target day Ti

, i = predict , stable

′ ΔT1 = TMIR − Tstable

(9)

′ ′ ΔT2 = Tpredict − Tstable

(10)

In Eq. (8), Tstable and Tpredict are the stable and predicted values from Chapter 3.2 (Eq. (7)), respectively. However, the differences used directly to visualize the divergence between observed values and calculated values may not be credible because of random disturbances. In order to minimize the random diurnal disturbances, Tstable and Tpredict are equalized to have the same mean value as TMIR on the target day: these temperatures are denoted by Tstable′ and Tpredict′, respectively. The inherent disturbances or perturbations generated by time series should also be taken into consideration and differentiated from random disturbances. Therefore, for each pixel, the standard deviation of the MIR time series is calculated as the inherent disturbance and denoted by δMIR, TS in subsequent calculations. Next, the pixels are separated into two classes to analyze the fire pixels. The pixels which satisfy either Condition 1 or Condition 2 will be classified as active fires while the others will be classified as non-fire:

Bightness temperature (Unit:K)

25 MIR-Stable 20 MIR-TIR

15

Predict-Stable

10 5

0 Active fire

(8)

Cloud

Non-fire

-5

Highreflective

-10 Fig. 7. Histogram showing the average values of three parameters for all the pixels in the scene shown in Fig. 5. 381

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Table 2 Summary of the features of the four different pixel types. Types

Features (most cases)

Reasons to select

Changes

Active fire

i. Normal MIR values in the series ii. Extremely high MIR values on the target day. iii. High MIR-Stable/MIR-TIR/Predict-Stable values

Main class

In the target day

Cloud

i. Normal MIR values in the series; ii. Low TIR values on the target day. iii. High MIR-TIR and low Predict-Stable values

Strong interfere factor

In the target day

Non-fire

i. Normal MIR values in the series; ii. Normal MIR values on the target day. iii. MIR-Stable and Predict-Stable close to 0

Main class

No changes

High-reflective

i. Relative high MIR values in the series; ii. Relative high MIR values in the target day. iii. MIR-Stable and Predict-Stable close to 0

Strong interfere factor

No changes

ΔT1 > ΔT2 + δMIR, TS + 3 (11) ⎧ ⎪ TTIR > 280K and TTIR > TTIR, threshold, TTIR, threshold = TTIR (12) ⎨ TMIR > TMIR, threshold, TMIR, threshold = TMIR + δMIR ⎪ ΔT2 > 0 (14) ⎩

(13)

the operator. Active fires have profiles that are very different from those of the other types of pixel.

Condition 1

4. Results

Differences in BT values (Unit: K)

(11) ⎧ ΔT1 > ΔT2 + δMIR, TS + 3 Condition 2 ⎨ T > 330 (15) MIR ⎩ Based on the information obtained from Fig. 6 and Fig. 7, fire pixels have high MIR BT values as well as high ΔT1(TMIR − Tstable′) and high ΔT2(Tpredict′ − Tstable′). The first condition is designed for detecting fires in all situations. Among the equations, Eq. (12) and (13) are common standards in which dynamic thresholds are set to eliminate pixels with low TIR BT and insufficiently high MIR BT. Eq. (14) is derived directly from Fig. 7 and is used to eliminate more cloud pixels. Eq. (11) is the core of the algorithm where ΔT1 should be larger than ΔT2. In non-fire regions, TMIR, Tpredict′ and Tstable′ are nearly the same because there are no changes compared with the time series. Fire and cloud pixels should have ΔT1 higher than ΔT2 while ΔT2 for cloud pixels is always less than zero, thus allowing fire pixels to be detected. Condition 2 is similar to the ‘Absolute Threshold test’ that is part of the MODIS V4/V6 fire detection algorithms and the earlier VIRR algorithm (Giglio et al., 2003; Giglio et al., 2016; Lin et al., 2017). The introduction of Eq. (11) helps to remove the high-temperature pixels that have relatively high MIR values in the series because of sunglint or high-reflectance surfaces. To improve visualization of the effect of the fire detection algorithm, Fig. 8 is drawn based on Condition 1 after moving all the items to the left of

To quantify the accuracy of the time-series algorithm and test its effectiveness in various situations, a total of eight different target areas were selected. Fig. 9 illustrates the approximate locations and imaging times. Examples of the results are shown in the thumbnails. These fire events can be found on NASA Earth Observatory website (NASA, 2017). Six of the eight target areas have also been studied in previous research (although the detailed spatial range may differ). The validation work carried out in this study aimed to test the accuracy of the multi-temporal algorithm and to demonstrate its superiority to the previous VIRR algorithm. To achieve these two goals, MODIS active fire products (MOD14A1-C6) and results obtained using the previous VIRR algorithm were used. These two products can only provide 1-km images whereas detailed assessment of the algorithm requires higher spatial resolution scenes. Sudden instrumental failure and the missing spectral coverage in the MIR and SWIR prevents users from utilizing Medium Resolution Spectral Imager (MERSI) data as reference data although it can provide data with a spatial resolution of up to 250 m and the same imaging time as the VIRR. Landsat data, including ETM+ and OLI data, was, therefore, used for assessing the detailed results. There was a possibility that the difference in imaging times could affect the results, but these data were still able to provide sufficient information about fire events. Landsat images captured after

15 10 5 0 -5 -10 -15 Active fire Eq. (11)

Cloud

Non-fire

Eq. (12)

Eq. (13)

Highreflective

Eq. (14)

Fig. 8. Histogram of results calculated from Eq. (1) for different types of pixel showing how profiles of fire pixels differ from those of other pixel types. 382

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Fig. 9. Regional subsets used for testing the VIRR algorithms. The dates covered by the study are shown in the boxes.

rate. Any MODIS fire pixels without corresponding VIRR detection will be marked as omission error. The first and second columns in the table give information about the study areas and imaging times for the respective VIRR scenes. ‘True detection’ indicates the number of VIRR pixels that have MODIS fire pixels within a 1-pixel buffer; the buffer is introduced to take account of the differences between the instruments. As Table 3 indicates, in most cases, the algorithm achieves over 80% accuracy. The new algorithm gives results that are an improvement on the existing one. Besides the accuracy, the errors should also be taken into consideration. The

active fire events and that contain obvious burn scars could also be utilized for assessing the accumulated VIRR detection results. The inherent differences between the MODIS and VIRR instruments should be taken into consideration. MYD14A1 products are not concluded as reference data because the divergence in imaging time between Aqua and FengYun-3C is more obvious than that between Terra and FengYun-3C. The detailed information regarding both the correct and wrong detections is shown in Table 3. The scenes listed lie within the time range shown in Fig. 9. True and false detected VIRR active fire pixels are computed respectively for the accuracy and commission error Table 3 Summary of statistics derived from the detection results. Target area

VIRR imaging timea

Accuracyc

Section 1 Canada

2014/07/20 2014/07/22 2014/07/27 2015/04/08

18:05 19:05 20:55 16:35

85.52% 92.12% 76.67% 100%

82.86% 84.00% 81.12% 0

10.17% 10.96% 9.94% 0

20.33% 12.28% 11.70% 100%

14.43% 7.85% 23.44% 0

17.18% 15.99% 18.88% 0

2015/01/12 2015/01/13 2015/01/23 2016/08/07 2016/08/09 2016/08/10 2015/09/07 2015/09/20 2014/05/05 2014/05/05 2014/05/10 2014/05/16 2014/05/16 2014/05/17 2014/05/18 2016/04/24 2016/04/27 2016/04/28 2015/02/02 2015/02/04 2015/02/06

15:20 15:00 15:15 10:30 11:35 11:15 07:20 08:15 03:10 04:55 05:00 03:05 04:45 04:30 04:10 05:05 05:50 05:30 01:55 01:15 02:20

100% 100% 100% 75.41% 88.54% 87.25% 100% 100% 94.55% 96.77% 79.37% 82.61% 83.60% 96.43% 92.54% 89.47% 70.24% 71.56% 94.73% 99.71% 82.95%

95.00% 88.37% 85.29% 98.51% 83.33% 100% 33.33% 74.80% 68.77% 100% 92.06% 40.58% 40.22% 96.69% 98.35% 100.00% 62.07% 64.55% 89.81% 100.00% 96.89%

0 12.96% 5.88% 7.14% 6.45% 8.96% 60.61% 25.18% 11.22% 4.39% 10.00% 9.05% 12.38% 1.98% 13.38% 71.70% 63.84% 51.21% 9.29% 11.52% 9.52%

2.88% 14.81% 8.88% 7.14% 18.13% 79.58% 93.93% 24.47% 17.07% 35.12% 4.73% 10.00% 19.05% 23.61% 1.57% 84.91% 88.93% 84.66% 3.57% 28.92% 1.59%

0 0 0 24.60% 11.46% 12.75% 0 0 5.46% 3.23% 20.64% 16.77% 16.40% 3.90% 12.18% 10.53% 29.76% 28.00% 5.71% 2.94% 17.04%

4.80% 12.50% 14.71% 1.49% 16.67% 0 66.67% 25.53% 31.10% 0 7.94% 59.42% 59.78% 3.31% 1.63% 0 37.93% 35.45% 10.19% 0 3.17%

Section 2 Mexico Section 3 Chile Section 4 Portugal Section 5 Tanzania Section 6 Russia

Section 7 Nepal Section 8 Australia

Omission errorb

a

Commission errors

This column indicates the observing time (UTC) of the listed scenes. The first column inside ‘Accuracy’, ‘Omission Error’ and ‘Commission Error’ contains results from the current algorithm (in italic font). The second column inside these columns contains results from the previous VIRR algorithm (in normal font). c ‘Accuracy’ indicates the true detection rates of VIRR pixels which are considered as truly detected or within the 1-pixel buffer compared with MODIS products. b

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commission errors made by this algorithm are due mainly to the loose constraint in the multi-temporal thresholds set for classifying fire pixels, which leads to excessive detections. Some of the commission errors come from clouds in the MODIS scene that are not in the same location in the corresponding VIRR scene. Compared with these, the omission errors are much more serious. Although this new algorithm performs better than the previous one, the fire detection results achieved using both are poor: an average of 10% of fire pixels are omitted by the new algorithm and around 20% by the old one. Among the target areas, some serious errors occur in Tanzania in central Africa and in Nepal in South Asia, where the performances of both algorithms are very unsatisfactory. Both algorithms failed to detect at least 60% of the fire pixels in these areas. In Tanzania, the number of fires is much lower than that detected by MODIS and the number omitted was even greater when the study area was enlarged. The high temperatures in these nonfire areas may be an important factor behind these errors. In Nepal, the total number of detected fire pixels in the MODIS products is huge although the size of the individual fire events is small. Although the multi-temporal method modifies and improves the results compared with the previous algorithm, the performance is still not as good as the MODIS products. Both of these places were affected by sustained wildfires before the dates listed. The multi-temporal method can cope with occasional thermal anomaly events but, as the cases of Nepal and Tanzania show, the method seems unsuitable for application to longlasting, continuous fires. Overall, although the error rates are still unsatisfactory, the multitemporal method produces improved detection results compared with the previous algorithm, both in terms of increased accuracy and reduced errors.

Fig. 10. Comparison with MODIS products. The background consists of Landsat ETM+ images. Results produced by the VIRR method and MOD14A1 are marked in distinct colors. The fires were near the Lake Baikal, Russia on 18th May 2014 as the location map indicates.

algorithm. The problem of omitted fire pixels is quite serious in many cases, as was discussed above. One obvious type of mistake occurs in areas where there are water bodies and fires are omitted automatically by the water mask and MODIS active fire detection V6 had already been improved to avoid the omission in waterbodies (Giglio et al., 2016). In this research, the ‘Land-and-sea mask’ data were not used. All the detected fires were identified by comparing the changes in the time series with those in the observed data. Therefore, the changes in water areas reflected in the time series were captured and analyzed. The superiority of the new algorithm compared to the previous VIRR algorithm can be seen from Fig. 12, which shows detections generated in the Gulf of Mexico near the site of an explosion and subsequent fire on an offshore drilling platform. The background image is generated from a 30 m Landsat-8 OLI image and shows the precise location of the platforms before the explosion. The detection results are superimposed on the Landsat images. These fires would have been omitted by the previous algorithm automatically whereas the VIRR multi-temporal method and the MODIS products succeed in detecting the sudden fire events. Fig. 13 illustrates another typical example of an attempt to correct errors made by the previous algorithm due to the misclassification caused by the ‘Land-and-Sea’ mask. These wildfires occurred near the Great Slave Lake, Canada on 22nd July 2014. Fig. 13(B) shows the landand-sea classification results obtained from VIRR L1 data. The results of the fire detection are superimposed on Landsat-8 OLI data in Fig. 13(A), which covers the geographical area marked by the red box in Fig. 13(B). As the Landsat image shows, some of the land pixels are misclassified as water pixels, which leads to omission errors when using the previous FengYun-3C algorithm. The multi-temporal method introduced in this article overcomes the limitation of the Land and Sea Mask in L1 data and successfully detects active fires.

4.1. Comparison with MODIS products The MODIS Fire and Thermal Anomalies products have been utilized in various scientific research projects and practical operations for decades. In this research, MOD14A1 collection 6 products were used to improve fire detection in areas containing water bodies. Fig. 10 illustrates one of the fire locations near Lake Baikal Lake, Russia on 18th May 2014. The fire detection locations found using the multi-temporal method generally match the results obtained using the MODIS products. Another example is shown in Fig. 11, where the burn scars of fires in Portugal in August 2016 are profiled by the VIRR multitemporal method. The comparison of the perimeters of the burned areas with those found using MODIS products illustrates the feasibility of using the multi-temporal method for another purpose. In general, the multi-temporal method generates similar detections as the MODIS products but when the pixel-level results are counted, the number detected using the VIRR algorithm is usually less than that found using MODIS. The ability of the algorithm to cope with a few occasional thermal anomalies and common high-temperature false alarms helps the algorithm to narrow down the results and enhance the accuracy. However, this aspect of the design of the time-series construction may also cause significant problems in some areas, as in the case of the omission errors made in Tanzania and in Nepal. 4.2. Comparison with previous VIRR algorithm The VIRR instrument has 10 1-km resolution spectral channels covering the visible to TIR regions. The VIRR is designed for global monitoring, including identification of anomalously high temperatures. The Global Fire Spot Monitoring (GFR) product is one of the official products provided by the National Satellite Meteorological Center of the Chinese Meteorological Agency. However, GFR files contain many errors and obvious geolocation shifts, which makes them unsuitable for use as reference data. The previous algorithm was designed to improve the accuracy of VIRR data and produced satisfactory results. Nonetheless, errors still exist because of the limitations of the previous

4.3. Comparison with Landsat series data For validating fire detection by MODIS and the Geostationary Operational Environmental Satellite (GOES), Advanced Spaceborne 384

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Fig. 11. Comparison with MODIS products. A) and B) are the results produced by the VIRR method and the MODIS products, respectively. The vectors drawn in black illustrate the basic perimeters of the burn scars derived from the dNBR values in the Landsat-8 OLI images.

have seriously affected the quantitative analysis. However, data from Landsat sensors, including the Enhanced Thematic Mapper Plus (ETM +) and Operational Land Imager (OLI) onboard Landsat-7 and 8, respectively, can still be used as one type of reference data, mainly for detailed assessment. Besides, the multi-temporal method analyzes more data and can be utilized for monitoring the complete cycle of events during a fire. With the support of color composites constructed from R (7)-G (5)-B (2) and dNBR (differenced Normalized Burn Ratio) values, the burn scars are clearly visible in the Landsat images, even by visual inspection. The profiles of the burned area are compared with the results from two VIRR algorithms for validation. Fig. 14 illustrates comparisons between the VIRR multi-temporal method, the MODIS product and the previous VIRR algorithm and shows the perimeters of burn scars drawn on Landsat 8 OLI imagery. These fire events occurred at the beginning of February 2015 in Australia and were completely over by 10th February. A background Landsat 8 OLI image was captured at 02:05 UTC on 14th February 2015. Together, these three sets of results can be used to profile the fire perimeters. Among these, the existing VIRR algorithm omits some fire pixels within the fire area during the fire event. The multi-temporal VIRR algorithm misses some fires on the lower edge but detects fire pixels on the upper edge that are omitted by the MODIS products. Fig. 12. Active fire detection results for part of the Gulf of Mexico where an explosion occurred in early April 2015. The background consists of Landsat OLI images and shows the precise locations of the fires. The results obtained using the VIRR multi-temporal method and MOD14A1 are also marked.

5. Conclusions In this paper, we introduced a new active fire detection algorithm based on FengYun-3C VIRR multi-temporal data. FengYun-3C is the second Chinese polar-orbiting meteorological satellite in the FengYun-3 series. VIRR is one of the sensors onboard FengYun-3C and provides data in a spectral range from the visible to the thermal infrared. An algorithm that has a basic ‘contextual’ structure similar to that of the MODIS fire detection algorithms is already in existence. Aiming to improve upon the existing algorithm, the new algorithm makes use of multi-temporal data and the time-series analysis method is also introduced. By introducing time-series data for analysis, commonly occurring high-temperature objects can be discarded. Fires that occur over or near water bodies can be detected using this algorithm,

Thermal Emission and Reflection Radiometer (ASTER) and Landsat series data have previously been used for quantitative assessment (Morisette et al., 2005; Schroeder et al., 2008; Tekeli et al., 2009). To further enhance the detection ability of the SWIR channels of these sensors, related Landsat active fire detection algorithms have also been developed (Schroeder et al., 2016; Schroeder et al., 2008). In this research, the difference in acquisition times between images acquired on the same day prevented the use of traditional methods of assessing the results because the short-term variations in the fire conditions might 385

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Fig. 13. Active fire detection results for an area near the Great Slave Lake, Canada on 22nd July 2014. The background images consist of Landsat OLI data. The detection results as well as typical errors can be seen in the figures. The background images are from Landsat OLI (RBG composite: band 7-band 5-band 4).

20 days of data before the target date significantly increased the requirements data storage and calculation of the time series. In addition, multi-temporal methods of active fire detection require high-quality data as too many invalid data or abnormal observation values will make it impossible to construct the time series and predict values. The analysis suffers due to the lack of validation data with sufficient temporal

something that the previous VIRR detection algorithm was not capable of. Although there are obvious advantages to using multi-temporal data, there is some insufficiency existing in the whole procedure. To construct the time series for analysis, both the quantity and the quality of the data involved should be taken into consideration. The use of

Fig. 14. Detection results for active fires with fire perimeters drawn based on the dNBR derived from Landsat 8 OLI data. The background is a Landsat 8 OLI image captured at 02:05 on 14th February 2015 where the burned scars have been clear to profile. The fire detection results produced by the different algorithms are superimposed on the Landsat scene, with the imaging times also shown. 386

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and spatial resolution. However, the validation of the current work shows that the new algorithm may not correctly detect fire pixels, particularly under certain conditions, including but not limited to cloudy area, regions dominated by small fire incidents, etc. As demonstrated in the results table, omission error rate is still high even if it has been apparently improved. In the study areas, Tanzania and Nepal, an average of over 60% MODIS fire pixels are not detected by the VIRR algorithm. In detail, the disturbances of numerous small fire incidents or cloud pixels are considered as critical causes to the omission errors. Hence, to achieve better results, further research in future is required from both the refinements in data source and deep research in the combination of temporal and spatial analysis. Subsequent research into the use of FengYun-3C data for active fire detection will still focus on improving the accuracy of the algorithm. Fire radiative power (FRP) could help quantify the scale of fire incidents, which will also be studied and introduced into the following research. Typical study areas including central Africa, South Asia (including Southeast Asia, where a large number of agricultural fires occur and cloud can heavily interfere with the algorithm) and North America will be taken into considered in future.

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