NORMALIZATION OF MODIFIED PERPENDICULAR DROUGHT INDEX USING LTDR AND GIMMS DATASET FOR DROUGHT ASSESSMENT IN THE UNITED STATES Abduwasit Ghulam a,*, Alimujiang Kasimub and Tim Kusky a a
b
Center for Environmental Sciences, Saint Louis University, St. Louis, MO 63103, USA Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba 263-8522, Japan
*Presenting author: Email:
[email protected]; TeL: +1-314-977-7062, Fax: +1-314-977-3568
ABSTRACT Recently, simple methods for real-time surface dryness estimation – perpendicular drought indices based on red and near-infrared wavelengths of satellite data has been developed. In this paper, the normalized form of the method is proposed to assess droughts over U.S. great plains using long term records of the Advanced Very High Resolution Radiometer (AVHRR) from both Land Long Term Data Record (LTDR) and the Global Inventory Modeling and Mapping Studies (GIMMS) dataset. Index Terms— Droughts, perpendicular drought indices, drought estimation 1. INTRODUCTION Drought is of crucial importance not only to species distributions in natural environments but to farmers and people throughout the world who rely on food and other materials related to water supply. Associated with drought, normalized difference vegetation index (NDVI) decreases while the LST increases slightly earlier than plant cover decreases [1]. Decrease in NDVI may not be observed until after the drought has caused significant variations in canopy structure and leaf colour. The combined response of NDVI and LST may deliver more reliable information on droughts. In the early stages of water stress, increased LST resulting from reduced surface evapotranspiration can be used as a measurement variable while NDVI serves as a reference for LST and evapotranspiration corresponding normal and stressed conditions, albeit there is no change in NDVI. Therefore, except for a simple ratio (the slope) of the LST versus the NDVI, empirical drought monitoring approaches based on integrating remotely sensed LST and NDVI products have been used widely in regional drought mapping and assessing soil surface moisture status in recent years [2]. These methods are simplifications of LST–NDVI
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space with a triangle in contrast to the trapezoidal space introduced by Moran et al. (1994) [3]. However, they are dependent on detailed ancillary data such as vapor pressure deficit, wind speed etc. Surface types may have different LST/NDVI slope and intercept for equal atmospheric and surface moisture conditions since the location of a pixel in the LST–NDVI space is influenced by many elements. There has been a large drying trend over the Earth during the last three decades [4] and many parts of the world have been suffering water crisis. If the trend continues as expected, the consequences may be severe in only a couple of decades and could pose significant water resource challenges to large segments of the global population [5]. Therefore, satellite remote sensing of surface moisture status and drought conditions is of great interest for sustainable development of eco-environments. A long term record of satellite data available to the global community from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) have shown excellent potential for monitoring surface dryness anomalies and droughts over an extended spatial scale. The objective of this paper as normalizing the perpendicular drought indices (PDIs) to assess recent droughts over the U.S. great plains is a part of the attempt has been made to develop operational drought estimation methods. 2. DATA AND METHODS 2.1 Data In this study, daily reflectance and normalized difference vegetation index (NDVI) data from Land Long Term Data Record (LTDR) of NASA’s REASoN project are used. Spatial resolution of both daily reflectance and NDVI data is 0.05 degrees. LTDR project reprocessed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA satellites from 1981 - present by
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applying the preprocessing improvements identified in AVHRR Pathfinder II project and the atmospheric and BRDF corrections used in MODIS preprocessing. In addition, the Global Inventory Modeling and Mapping Studies (GIMMS) dataset is also collected for comparison analysis of two datasets. NDVI product is available for a 25 year period spanning from 1981 to 2006 with the GIMMS database. The dataset is derived from imagery obtained from AVHRR instrument onboard the NOAA satellite series 7, 9, 11, 14, 16 and 17. This is an NDVI dataset that has been corrected for calibration, view geometry, volcanic aerosols, and other effects not related to vegetation change [6-7]. 2.2 Methodology Recently, a simple method for the estimation of surface dryness, namely, perpendicular drought index (PDI) has been developed [8] and further proved to be effective in large scale applications using MODerate Resolution Imaging Spectroradiometer (MODIS) data [9]. Regarding inherent constraints and limitations of PDI, Ghulam et al. (2007) [10] developed a modified perpendicular drought index (MPDI). The MPDI is based on the combination of two important indicators of drought, soil moisture and fraction of green vegetation (Eq. 1).
MPDI
RRed MRNIR f v ( Rv, Red MRv, NIR ) (1 f v ) M 2 1
(1)
Where, Rv, Red and ,Rv, NIR are vegetation reflectances in the Red and NIR bands, respectively. For a specific vegetation growth, Rv, Red and Rv, NIR can be regarded as a coefficient. In our study, Rv, Red and Rv, NIR are determined as 0.05 and 0.5 by field measurements, respectively. fv is the fraction of the vegetation, and may be calculated using [11] equation. The method demonstrated potential advantages for estimating surface dryness and soil moisture. The index may provide quantitative information of droughts like Palmer Drought Severity Index (PDSI) [12] when the MPDI based drought critic values are developed or observed. Such an objective can also be reached by normalizing of the index using long term time series of satellite observations over droughty and healthy years. Because the Red and NIR bands are also affected by soil color and fertilization conditions to certain extent, a normalized MPDI may eliminates these perturbances, and consequently, make the index comparable over continental scales. In this paper, the modified perpendicular drought index (MPDI) is normalized to provide drought information using AVHRR data over the U.S. great plains. The new method called normalized perpendicular drought index (NPDI) can be expressed as Eq. 2.
NPDI
MPDI i , j MPDI min MPDI max MPDI min
(2)
Where MPDIi,j, MPDImin, MPDImax are the smoothed MPDI for a certain time periods (weekly or monthly), namely multi-year maximum and multi-year minimum, respectively, for each grid cell. Formula (2) reflects different responses of vegetation and soil moisture to surface drought. MPDImin, MPDImax are calculated by statistical analysis of multi-year MPDI from stressed years and healthy years, respectively. The original daily reflectance data were spoiled by clouds and weather fluctuations. To tackle this problem, as much as possible short-time syntheses are generated. The hazy pixels still presented on the reflectance data were removed by a cloud masking based on simple thresholding in the visible, near infrared (NIR) channels [9]. Then, MPDIi,j, MPDImin, MPDImax are calculated for each grid. To eliminate high frequency noise and separate medium and low frequency variations, the data were smoothed using a linear regression over a year of scale. The average value of the linear function was then taken for the processing cell. Therefore, the resulting curve was comparable among years. 3. RESULTS The years of droughts were determined by comparison of annual time series data covered healthy and droughty seasons over each processing cell. The long term smoothed mean curve served as a boundary guidelines for healthy or droughty, and the years much under the average were pointed out as the years of droughts. NDVImax and NDVImin used to calculate fv were set as -1 and +1 according to the scaled range of the provided dataset. M § 1 was determined using statistical analysis of multi-year data. The 1988-1989 drought was one of the most disastrous droughts in the history of the U.S. with very severe losses to agriculture and related industries. The impact of the drought (1988) on the U.S. economy has been estimated at $40 billion [13]. The driest and hottest periods in over 90 years were recorded in parts of the Plains, the Midwest, and the lower Mississippi Valley, and the severity of the Midwest drought peaked in June [14]. Therefore, NPDI maps of June 28, 1988, May 08, 1989 (drought affected years) and June 28, 1998 (a year of less drought or healthier vegetation status) were selected to evaluate the index. As shown in Fig. 1, droughts over the U.S. Great Plains, the Midwest and western U.S. were observed. The 1998-map showed no drought effect over the whole U.S. except some high values appeared on the north-western corner of Missouri, southern Texas and Louisiana, eastern U.S. and over the U.S.Canadian border regions in the north. Spatial distributions of droughts over the study area are in agreement with previous studies. However, further
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validation of the results by using synchronous rainfall, soil moisture and by comparison with other drought indices including satellite based and field-measurement based (e.g.
PDSI) are expected in the future. But, these topics are beyond of the scope of this paper due to the limited page amount restrictions as a conference paper.
Fig. 1 NPDI based drought monitoring maps over the U.S. Great Plains, Midwest and
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[7]
4. CONCLUSIONS This paper is a part of an ongoing research on developing simple, effective and operational drought estimation methods. We focused on the normalization of the proposed method and preliminary analysis of the results. It is concluded from the results that spatial distributions of droughts over the U.S. are in agreement with previous publications. Further validation and comparison of results with current drought estimation methods is beyond the scope of this paper. Future work include: 1) comparison of NPDI derived results with the PDSI [12] obtained using historical weather data including temperature, precipitation, and Vegetation Condition Index (VCI) and Temperature Condition Index [13]; 2) testing the potential of the method in applications to monitor vegetation dynamics and health, and its relations with drought, and water conditions over the global scale.
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