Real-time Live Fuel Moisture Retrieval with MODIS Measurements

Transcription

1 Real-time Live Fuel Moisture Retrieval with MODIS Measurements Xianjun Hao, John J. Qu 1 {xhao1, jqu}@gmu.edu School of Computational Science, George Mason University 4400 University Drive, Fairfax, VA 22030, USA 1 NASA/GSFC/614, NPP/PSG, Greenbelt, MD 20771, USA ABSTRACT Live fuel moisture is one of the most important fuel properties and a critical parameter for fire danger rate forecasting and fire behavior analysis. Many approaches have been proposed to retrieve live fuel moisture with satellite remote sensing measurements. In this paper, we presented live fuel moisture retrieval with near real-time MODIS measurements, discussed the applications of real-time fuel moisture data products and the challenge problems in real-time fuel moisture retrieval. Index Terms: Live Fuel Moisture, MODIS, remote sensing, real-time I. Introduction Live fuel moisture is one of the most important fuel properties and a critical parameter for fire danger rate forecasting, and fire behavior estimation and analysis. Many research works have been conducted to retrieve live fuel moisture with satellite remote sensing, most approaches rely on the empirical or statistical relations between vegetation indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference ater Index (NDI). Some researchers also take surface temperature into account for live fuel moisture retrieval based the NDVI-LST tri-angle or regressive analysis. In this paper, we analyzed the sensitivity of MODIS solar reflectance bands to live fuel moisture change, compared the approaches of live fuel moisture retrieval, and proposed improvements of fuel moisture retrieval with MODIS measurements. The validation of live fuel moisture is usually quite difficult, burned scar can be used to analyze the reliability of retrieved live fuel moisture in some extent because of the dependence of fire behavior on live fuel moisture, vegetations with low moisture content are usually burned out during fire occurrence. e used southern-eastern regions for case study and analysis to demonstrate and validate our approaches. In addition, many current approaches use composite data products for fuel moisture retrieval, which have limited temporal resolution because of the long composite periods. Real-time fuel moisture is quite helpful for fire danger rate estimation and analysis of fire behaviors. Especially, in eastern states, prescribed burning is common and very important. For the planning of prescribed burning, real-time live fuel moisture is extremely important. MODIS instrument aboard NASA satellites Terra and Aqua provides opportunity to retrieve live fuel moisture at high temporal resolution with two day-time overpasses a day. Based on MODIS Direct Broadcast, by migrating current algorithms to real-time live fuel moisture retrieval and integrating them to a real-time data processing system we developed, live fuel moisture data can be generated twice a day, this can improve fire danger rate forecasting, and enhance the capabilities of current fire behavior models and decision support systems. e also discussed the key issues of real-time live fuel moisture retrieval. II. Fuel Moisture Retrieval and Analysis Fuel moisture content (FMC) is the ratio of 1

2 water content to dry weight, i.e., fresh dry FMC = *100 ( 1) here dry fresh is the fresh weight of vegetation, dry is weight of dry content in vegetation. For live fuel moisture, because of the internal bio-physical and bio-chemical mechanism of live fuels and the variation of fuel types, it s very complex to estimate fuel moisture. Field work has limitations in practice. Remote sensing technology can be applied to retrieve FMC indirectly. Many algorithms have been developed for live fuel moisture retrieval. The key idea is to relate the fuel moisture content as a function of remote sensing variables, i.e. FMC = F v, v,..., v ) ( 2) ( 1 2 n explicitly through regressive analysis or implicitly through numerical inversion, where { v k } are remote sensing variables such as reflectance, vegetation index, surface temperature, etc.. In practical applications, the primary difficulty is the calibration of FMC equation, i. e. 1) hich remote sensing variables are critical for fuel moisture retrieval? 2) How to describe FMC dependency as an equation and how to determine the coefficients in equation? Because of the complexity of canopy structure, it s quite difficult to describe the relationship between FMC and remote sensing measurements mathematically. Most current algorithms are based on the regressive or empirical analysis. Burgan etc. proposed an approach to estimate FMC with Normalized Difference Vegetation Index (NDVI) (Burgan, R. E., Hartford, R. A., 1997). Their approach has been used for operational use (Burgan, R. E., etc., 1997). Burgan s approach is to set FMC as a linear function of relative greenness (RG), which can be calculated from NDVI. NDVI is defined as NDVI here = ( 3) + and are reflectance at near infrared band and red band respectively. Although NDVI is the most popular vegetation index, it has limitations in fuel moisture retrieval. Physically, NDVI is sensitive to the chlorophyll content change, it is not sensitive to fuel moisture change directly. In addition, NDVI has saturation problem at high biomass regions. MODIS EVI (Enhanced Vegetation Index) is an improvement of NDVI to overcome the saturation problem and reduce the effects of atmosphere and background soil. EVI is defined as EVI = * G ( 4) L + + C C2* here 1 *, Blue, Blue are reflectance at near infrared band, red band, and blue band respectively, L, C 1, C 2, and G are constants. e used MODIS EVI for fuel moisture retrieval currently with similar methodology as Burgan. Case studies show that EVI can reflect the fuel moisture content better than NDVI. Indeed, physically, short-wave infrared bands are more sensitive to fuel moisture change than near infra-red band. The sensitivity of spectral bands to fuel moisture change can be analyzed with PROSPECT (Jacquemoud, S., 1990), a radiative transfer model for analyzing vegetation spectral characteristics at leaf level. Gao (Gao, B. C., 1996) proposed NDI (Normalized Difference ater Index) using short-wave infra-red (SIR) band at 1.24µm, i.e. NDI SIR = ( 5) + SIR 2

3 In practice, the relationship between NDI and FMC are dependant on many factors, especially fuel types. And for MODIS, band 7 and band 6 are also very sensitive to fuel moisture. From model simulation with PROSPECT, we found that MODIS band 7 (2.13µm) is usually more sensitive to FMC then other bands. Chuvieco, E. etc. used multiple variables for fuel moisture content retrieval, it may retrieval fuel moisture more accurately, but for practical use, more coefficients have to be calibrated by comparing satellite remote sensing data and field measurements of fuel moisture. For different geological regions and different fuel types, the coefficients may be quite different. In Burgan s approach, fuel moisture content is calculated from the following formula: MC = RG / 100*( MC + MC max_ MCmin_ ) min_ here MC fuel moisture content, RG is relative greenness index, we selected the Blackjack Bay Complex fire during May 2002 in Georgia. From figure 1, MODIS true color image, we can see heavy smoke from wildland fires. Figure 2 is a composite false color image with MODIS short-wave infra-red bands 7,6, and 5. Short-wave infra-red bands are less sensitive to smoke, we can see the burned area very clearly in the false color image 2. Figure 3 is fuel moisture retrieved from NDVI with Burgan s approach, for regions around fires, the fuel moisture contents are relatively low because of burning. From figure 3, we can see that the spatial pattern of fuel moisture matches the burning area quite good. Figure 4 is fuel moisture content we retrieved with EVI, it shows more detailed information comparing with the fuel moisture retrieved with NDVI, the smaller burned regions are shown more clearly. In eastern states, fires are usually smaller in size, so EVI has advantages in retrieving fuel moisture content than NDVI. RG = ( VI obs _ VI min_ ) /( VI max_ VI min_ ) *100 For a given el, MC max_ and MC min_ are maximum and minimum potential live vegetation moisture contents respectively, VI obs_ is the observed NDVI, VI max_ is the maximum NDVI value observed historically, VI min_ is the minimum NDVI value observed historically. MC max_ and MC min_ are determined with fuel model empirically. Apparently, in Burgan s approach, fuel moisture is set to linearly dependant on relative greenness, which is calculated based on observed and historical NDVI. e used EVI instead of NDVI for the calculation of relative greenness. It s quite difficult to validate fuel moisture content because of the scaling problem of fuel moisture and the complexity of fuel properties. During fire event, dry fuels usually are burned more severely. So burned area can interpret fuel moisture content in some extent. For analysis, Figure 1: MODIS true color image of Blackjack Bay Complex fire, May 8, Figure 2: MODIS false color image (band 3

4 7,band 6, band 5) of Blackjack Bay Complex fire, May 8, frequent prescribed burning. For near real-time retrieval, there are more challenge problems because of the requirement of rapid response, especially how to perform atmospheric correction and angular correction efficiently. For future improvement, we will focus on algorithm refinement, BRDF correction, and validation. Figure 3: Fuel moisture retrieved with NDVI IV. Conclusion In this paper, we discussed live fuel moisture retrieval algorithms with MODIS measurements, proposed near real-time retrieval of live fuel moisture with MODIS EVI, and analyzed its advantages. e also talked about the challenge problems in near real-time retrieval of live fuel moisture and future improvements. Figure 4: Fuel moisture retrieved with EVI Acknowledgement e would like to thank Dr. Allen Riebau and Dr. Yong Qiang Liu from USDA Forest Service for their helps, and thank Mr. Patrick Coronado from NASA GSFC for data support. III. Real-time Live Fuel Moisture Retrieval ith MODIS Measurements MODIS DB technology provides opportunity to retrieval near real-time live fuel moisture. ith near real-time FMC data, it s quite helpful in forest fire danger rate estimation, planning of prescribed burning, and fire behavior analysis. For near real-time application, currently, we used near real-time datasets from NASA GSFC MODIS Direct Readout Portal. In most cases, live fuel moisture content can be retrieved with 30 minutes after satellite overpass. ith near-real time data instead of composite data, fire managers and air quality managers can get more accurate information of current vegetation status, and provides better forecasting of fire danger rate. Near real-time fuel moisture is more important in eastern states because of the REFERENCE [1] Burgan, R.E., Andrews, P.L., Bradshaw, L.S., Chase, C.H., Hartford, R.A., Latham, D.J FAS: wildland fire assessment system, Fire Management Notes, 57(2): 14-17; [2] Burgan, R. E., Hartford, R. A., Live vegetation moisture calculated from NDVI and used in fire danger rating, 13th Conf. on Fire and For. Met., Lorne, Australia, pp , Oct [3] Ceccato P., Flasse S., Tarantola S., Jacquemoud S. and Grégoire J. M., Detecting vegetation leaf water content using reflectance in the optical domain, Remote Sensing of Environment, Vol. 77, pp , [4] Chuvieco, E., Cocero, D., Riaño, P. Martin, Martínez-Vega, J., Riva, D. L., and Pérez, F., 2004, Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire 4

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