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AVHRR data
Adv. Space Res. Vol. 13, No. 5, pp. (5)21 9—(5)222, 1993 Printed inGreat Britain. All rights reserved.
0273—1177~3$24.00 Copynght @ 1993 COSPAR
DEVELOPMENT OF GLOBAL DROUGHT-WATCH SYSTEM USING NOAA/AVHRR DATA F. Kogan and J. Sullivan NOAA/NESDIS/OR,4lSatelljte Research Laboratory, Rm 712, World Weather Building, Washington, DC 20233, U.S.A. ABSTRACT
Recently, NOAA/NESDIS developed the Vegetation Condition Index (VCI) which has proved to be a good indicator of drought. This paper provides a background of VCI development, data processing and outlines a PC—based system designed for early—warning drought diagnostics. There are several examples showing the application of VCI during recent years for detecting and tracking droughts. INTRODUCTION The population of our planet is very vulnerable to the effects of drought. Anong disasters, drought is one of the most adverse and powerful environmental phenomena, since it normally covers very large areas and its impacts can be very often devastating. The consequences of drought include losses of agricultural production, destruction of ecological resources, and water—supply deficit. In developed countries, all of these lead to considerable economic losses. In developing countries, drought leads to famine, human suffering, death, and abandonment of whole geographic regions. Nearly fifty percent of the world agricultural area is susceptible to drought each year. It is difficult to find countries which do not experience the adverse consequences of drought. Unfortunately, 1992 is not exceptional. It will be remembered as a year when a region of 2.6 million square miles in Southern Africa and nearly one million square miles in eastern Africa were devastated by intensive and prolonged drought. These droughts directly affected the lives of nearly 20 million people /1/. Recently, the National Oceanic and Atmospheric Administration (NOAA) developed a new technique for drought monitoring based on analysis of vegetation state and dynamics observed by the Advanced Very High Resolution Radiometer (AVHRR) flown on NOAA operational polar orbiting satellites. This paper describes this technique, shows examples of drought monitoring from satellites, and describes the principles for developing a of drought monitoring system on a global basis. VEGETATION CONDITION INDEX The drought monitoring technique is based on a concept of vegetation index which stems from the relationship between electromagnetic reflectance in the visible and near infrared spectral bands. Presence of chlorophyll pigment and the leaf scattering mechanisms in plants cause low spectral reflectance in the visible and high reflectance in the near infrared respectively /2/. Reflectance values change in the opposite direction if vegetation is under stress /3/. Thus, the normalized difference between the values of these two channels was selected as a measure of the degree of vegetation greenness and was called the Normalized Difference Vegetation Index (NDVI). There are many limits to remote sensing of vegetation using AVHRR data and the NDVI derived from them. The presence of clouds, aerosols, and non—vegetative surfaces, certain effects of sun—target—sensor geometry, data sampling, degradation of satellite orbit and sensor distort the “greenness” signal quite often /4/. Clouds, in particular, can result in large jumps in the data between
(5)219
F. Kogan and J. Sullivan
(5)220
one week and the next. These spurious fluctuations must be removed from data set but correction procedures based on physical theory are not presently available. Therefore, for each pixel in our data set we applied data smoothing techniques (filtering) to the NDVI time series. The technique we used was a “compromise” one: a balance between fitting the original data as closely as possible while at the same time deleting large jumps in value between neighboring points in the time series. If the original NDVI value for week j is (NDVI) 1 and the filtered value is (ndvi)~,then the sum N ~ [(ndvi)~ — (NDVI)~]/N (1) j =1 is an overall measure of how close the original and filtered values are close to each other. Here j=l,2,... N are weeks. Analogously, the sum of N Z [(ndvi)~+~— (ndvi)~] /N (2) J=l
is overall measure of the jump between two consecutive filtered values. Typically, if we change the (ndvi)~ values so that sum (1) decreases, then sum (2) will increase, and vice versa. A “compromise”, or balance, is achieved by minimizing the compound sum: 2/N) (3) N N-i W * {~[(ndvi)1 - (NDVI)1] /N + ~‘[(ndvi)~~,- (ndvi)3] The value of the weight W can be adjusted from 0 to ~ W=0 forcing all filtered values equal to a constant; and W ~ forcing (ndvi)~ = (NDVI).. A filter similar to this is also used to process other kinds of satellite data /5/. We chose W=0.2 by comparing the filter’s performance to a compound median filter /6/ that we had used previously. This filtering was applied to the weekly NDVI time series. A further refinement has been to separate the short—term weather signal in the NDVI data from the long—term ecological signal. This was done by scaling smoothed weekly NDVI values relative to the amplitude of their range at each location during 1985—1991, the period for which archived data is available. The weekly weather signal was amplified by ranking it on a linear scale where the minimum value in the 7—year data set equal to 0 and the maximum to 100. The new Vegetation Condition Index was calculated as: (NDVI~ VCI~
—
NDVI.)*100
(4) NDVI — NDVIm~n where NDVI, NDVIm~X~ NDVImin are values of the smoothed weekly NDVI, its multi-year maximum, and minimum, respectively, and j is a week; low values of VCI indicate —
bad vegetation conditions and possible unfavorable weather impacts, while high values describe the opposite situation /7/, /8/, /9/. APPLICATION OF VCI FOR DROUGHT MONITORING The VCI was calculated Vegetation Index (GVI) and temporal sampling, are the United States
for several areas of the globe from the NOAA/NESDIS Global product /10/. This product was developed through spatial mapping, and calculating the NDVI. The areas of the globe of America, China, and part of the former Soviet Union.
United States of America. In our previous publications we presented the results of satellite—derived droughts during 1988—1990 /7/, /8/, /9/. Here we analyze the most recent 1992 ddrought. Based on VCI values derived from the NOAA—1l polar orbiting satellite, the 1992 drought started very early in the season. By the end of April, vegetation was under severe stress in the whole northeastern quarter of the US (Fig. la). Such
Global Dmught-Watch System
‘11
(5)221
____
a
:‘~:
_
Fig. 1. Vegetation Condition Index (a) at the end of April and (b) in mid—August 1992, United States. If land color is white — MDVI is less than (0.05). conditions resulted from six months of unusually dry weather, especially during May and June /11/. However, since June temperature was below normal and July precipitation considerably above normal, soil moisture conditions improved /11/. The same improved conditions were shown on the image of the end-of-July VCI (Fig. lb). At the same time dryness increased in the western US. This resulted in decreased water content in reservoirs and increased fire activity /11/. ~
~,
.~
...
~
~ ~-
~
~
~
ix,.
~.. ~.
~
~
~.
~
.,.
.
‘.i,.1~
~
.
.
!~fi
Fig. 2. Vegetation Condition Index (a) at the beginning of February and (b) at the end of May 1991, China. Legend in Fiq. 1. China. Unusually severe drought occurred in southeastern China during the growing season of 1991. The first report of dryness in the southeastern China was published in mid-March in the Weather and Crop Bulletin. Meanwhile, satellite data indicated stressed vegetation much earlier, at the beginning of February (Fig 2a). Ground data analysis indicated that stressed vegetation conditions resulted from a persistent shortage of rains during October 1990 through February 1991. Near-normal March 1991 rains brought short relief to stressed vegetation. However, deficit of rains resumed in April and May. As a result of that, drought expanded considerably (Fig. 2b). We should indicate that low values of VCI along the middle Yangtze River are due to flooding. In this sense, VCI analysis in the area of heavy rainfall should not be done considering that low values could be due to standing water. Former USSR and Eastern Europe. Dry weather in early spring 1992 caused stressed vegetation conditions in most of the northern half of the area. VCI values at the beginning of May clearly indicated that. In late spring and early summer vegetation conditions improved in the eastern part of the area. Meanwhile, extremely dry weather in the western part in June and July caused an increasingly worsening vegetation condition. As VCI values indicated, the drought started to
F. Kogan and J. Sullivan
(5)222
form in that area at the end of June and by mid-August the drought reached its highest development (Fig.
Fig. 3.
3).
Vegetation Condition Index in mid-August, 1992, former Soviet Union and
Eastern Europe.
Legend in Fig.
1.
SATELLITE-BASED Almost
every
year
millions of
affected by droughts.
acres
DROUGHT-WATCH SYSTEM of
agricultural
land
in
the world
are
Coincidence of droughts in several principal agricultural
areas has always had very unfavorable effects, quite often leading to devastating economic and social impacts. As mankind faces the inevitable recurrence of drought in the future, development of drought—mitigation measures is an important task. The first step in this endeavor is an efficient drought-watch. A satellite-based drought-watch system will serve this goal. At present the potential for developing such a system based on available satellite data is good. Weekly vegetation index data of 16 km resolution are available from NOAA/NESDIS in real-time for any part of the globe between 75~N and 55~ S. The Vegetation Condition Index, derived from these data, has the ability to detect drought and to measure its intensity, duration, dynamics, and impacts on vegetation. The VCI can easily serve the practical purpose of drought monitoring on continental and regional scales. The described approach can be aplied towards a finer resolution vegetation index data as well. REFERENCES
/1/ UNDRO. Drought Emergency in Southern Africa. UNDRO NEWS, May/June, 1992, p.4-7. /2/ Tucker, C.J., and P.J.Sellers, Satellite remote sensing of primary production, mt. J. Remote Sensing, 7, 1395—1416 (1986). /3/ Gray, T.I., D.G. McCrary, The environmental vegetation index, a tool potentially useful for arid land management, AgRISTARS Report EW—Nl—04076 JSC-ET1 w340 301 m518 30 17132 (1981) /4/ Rao, K.P., S.J.Holnes, R.K.Anderson, J.S.Winston, P.E.Lehr (editors), Weather Satellites: Systems, Data, and Environmental Applications, American Meteorological Society, Boston, 1990. /5/ Twomey, S., Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements, Elsevier Scientific Publishing Co., New York, 1987. /6/ Velleman, P and D.C. Hoaglin, Applications, basics and computing of exploratory data analysis, Duxbury Press, Boston, 1981. /7/ Kogan, F.N., Vegetation index for areal analysis of crop conditions, in: Proceedings of the 18th Conference on Agricultural and Forest Meteorology, ANS, W.Lafayette, 1987, p. 103. /8/ Kogan, F.M., Remote sensing of weather impacts on vegetation in nonhomogeneous areas, Irit. J. Remote Sensing, vol. 11, No. 8, 1405—1419 (1990). /9/ Kogan, F.N., Monitoring the 1988 US Drought from Satellite, in: Proceedings
5th Conference on Satellite Meteorology and Oceanography, London, September 3-7, 1990,
p.
186.
/10/ Tarpley, J.P., Schnieder, S.R., Money, R.L., Global vegetation indices from NOAA-7 Meteorological satellite, J. Climate and Applied Meteorology, 23, 491 (1984) /11/ NOAA, North America Climate Advisory: Growing Season Update Drought Impact Outlook, August 17, 1992.
0273—1177~3$24.00 Copynght @ 1993 COSPAR
DEVELOPMENT OF GLOBAL DROUGHT-WATCH SYSTEM USING NOAA/AVHRR DATA F. Kogan and J. Sullivan NOAA/NESDIS/OR,4lSatelljte Research Laboratory, Rm 712, World Weather Building, Washington, DC 20233, U.S.A. ABSTRACT
Recently, NOAA/NESDIS developed the Vegetation Condition Index (VCI) which has proved to be a good indicator of drought. This paper provides a background of VCI development, data processing and outlines a PC—based system designed for early—warning drought diagnostics. There are several examples showing the application of VCI during recent years for detecting and tracking droughts. INTRODUCTION The population of our planet is very vulnerable to the effects of drought. Anong disasters, drought is one of the most adverse and powerful environmental phenomena, since it normally covers very large areas and its impacts can be very often devastating. The consequences of drought include losses of agricultural production, destruction of ecological resources, and water—supply deficit. In developed countries, all of these lead to considerable economic losses. In developing countries, drought leads to famine, human suffering, death, and abandonment of whole geographic regions. Nearly fifty percent of the world agricultural area is susceptible to drought each year. It is difficult to find countries which do not experience the adverse consequences of drought. Unfortunately, 1992 is not exceptional. It will be remembered as a year when a region of 2.6 million square miles in Southern Africa and nearly one million square miles in eastern Africa were devastated by intensive and prolonged drought. These droughts directly affected the lives of nearly 20 million people /1/. Recently, the National Oceanic and Atmospheric Administration (NOAA) developed a new technique for drought monitoring based on analysis of vegetation state and dynamics observed by the Advanced Very High Resolution Radiometer (AVHRR) flown on NOAA operational polar orbiting satellites. This paper describes this technique, shows examples of drought monitoring from satellites, and describes the principles for developing a of drought monitoring system on a global basis. VEGETATION CONDITION INDEX The drought monitoring technique is based on a concept of vegetation index which stems from the relationship between electromagnetic reflectance in the visible and near infrared spectral bands. Presence of chlorophyll pigment and the leaf scattering mechanisms in plants cause low spectral reflectance in the visible and high reflectance in the near infrared respectively /2/. Reflectance values change in the opposite direction if vegetation is under stress /3/. Thus, the normalized difference between the values of these two channels was selected as a measure of the degree of vegetation greenness and was called the Normalized Difference Vegetation Index (NDVI). There are many limits to remote sensing of vegetation using AVHRR data and the NDVI derived from them. The presence of clouds, aerosols, and non—vegetative surfaces, certain effects of sun—target—sensor geometry, data sampling, degradation of satellite orbit and sensor distort the “greenness” signal quite often /4/. Clouds, in particular, can result in large jumps in the data between
(5)219
F. Kogan and J. Sullivan
(5)220
one week and the next. These spurious fluctuations must be removed from data set but correction procedures based on physical theory are not presently available. Therefore, for each pixel in our data set we applied data smoothing techniques (filtering) to the NDVI time series. The technique we used was a “compromise” one: a balance between fitting the original data as closely as possible while at the same time deleting large jumps in value between neighboring points in the time series. If the original NDVI value for week j is (NDVI) 1 and the filtered value is (ndvi)~,then the sum N ~ [(ndvi)~ — (NDVI)~]/N (1) j =1 is an overall measure of how close the original and filtered values are close to each other. Here j=l,2,... N are weeks. Analogously, the sum of N Z [(ndvi)~+~— (ndvi)~] /N (2) J=l
is overall measure of the jump between two consecutive filtered values. Typically, if we change the (ndvi)~ values so that sum (1) decreases, then sum (2) will increase, and vice versa. A “compromise”, or balance, is achieved by minimizing the compound sum: 2/N) (3) N N-i W * {~[(ndvi)1 - (NDVI)1] /N + ~‘[(ndvi)~~,- (ndvi)3] The value of the weight W can be adjusted from 0 to ~ W=0 forcing all filtered values equal to a constant; and W ~ forcing (ndvi)~ = (NDVI).. A filter similar to this is also used to process other kinds of satellite data /5/. We chose W=0.2 by comparing the filter’s performance to a compound median filter /6/ that we had used previously. This filtering was applied to the weekly NDVI time series. A further refinement has been to separate the short—term weather signal in the NDVI data from the long—term ecological signal. This was done by scaling smoothed weekly NDVI values relative to the amplitude of their range at each location during 1985—1991, the period for which archived data is available. The weekly weather signal was amplified by ranking it on a linear scale where the minimum value in the 7—year data set equal to 0 and the maximum to 100. The new Vegetation Condition Index was calculated as: (NDVI~ VCI~
—
NDVI.)*100
(4) NDVI — NDVIm~n where NDVI, NDVIm~X~ NDVImin are values of the smoothed weekly NDVI, its multi-year maximum, and minimum, respectively, and j is a week; low values of VCI indicate —
bad vegetation conditions and possible unfavorable weather impacts, while high values describe the opposite situation /7/, /8/, /9/. APPLICATION OF VCI FOR DROUGHT MONITORING The VCI was calculated Vegetation Index (GVI) and temporal sampling, are the United States
for several areas of the globe from the NOAA/NESDIS Global product /10/. This product was developed through spatial mapping, and calculating the NDVI. The areas of the globe of America, China, and part of the former Soviet Union.
United States of America. In our previous publications we presented the results of satellite—derived droughts during 1988—1990 /7/, /8/, /9/. Here we analyze the most recent 1992 ddrought. Based on VCI values derived from the NOAA—1l polar orbiting satellite, the 1992 drought started very early in the season. By the end of April, vegetation was under severe stress in the whole northeastern quarter of the US (Fig. la). Such
Global Dmught-Watch System
‘11
(5)221
____
a
:‘~:
_
Fig. 1. Vegetation Condition Index (a) at the end of April and (b) in mid—August 1992, United States. If land color is white — MDVI is less than (0.05). conditions resulted from six months of unusually dry weather, especially during May and June /11/. However, since June temperature was below normal and July precipitation considerably above normal, soil moisture conditions improved /11/. The same improved conditions were shown on the image of the end-of-July VCI (Fig. lb). At the same time dryness increased in the western US. This resulted in decreased water content in reservoirs and increased fire activity /11/. ~
~,
.~
...
~
~ ~-
~
~
~
ix,.
~.. ~.
~
~
~.
~
.,.
.
‘.i,.1~
~
.
.
!~fi
Fig. 2. Vegetation Condition Index (a) at the beginning of February and (b) at the end of May 1991, China. Legend in Fiq. 1. China. Unusually severe drought occurred in southeastern China during the growing season of 1991. The first report of dryness in the southeastern China was published in mid-March in the Weather and Crop Bulletin. Meanwhile, satellite data indicated stressed vegetation much earlier, at the beginning of February (Fig 2a). Ground data analysis indicated that stressed vegetation conditions resulted from a persistent shortage of rains during October 1990 through February 1991. Near-normal March 1991 rains brought short relief to stressed vegetation. However, deficit of rains resumed in April and May. As a result of that, drought expanded considerably (Fig. 2b). We should indicate that low values of VCI along the middle Yangtze River are due to flooding. In this sense, VCI analysis in the area of heavy rainfall should not be done considering that low values could be due to standing water. Former USSR and Eastern Europe. Dry weather in early spring 1992 caused stressed vegetation conditions in most of the northern half of the area. VCI values at the beginning of May clearly indicated that. In late spring and early summer vegetation conditions improved in the eastern part of the area. Meanwhile, extremely dry weather in the western part in June and July caused an increasingly worsening vegetation condition. As VCI values indicated, the drought started to
F. Kogan and J. Sullivan
(5)222
form in that area at the end of June and by mid-August the drought reached its highest development (Fig.
Fig. 3.
3).
Vegetation Condition Index in mid-August, 1992, former Soviet Union and
Eastern Europe.
Legend in Fig.
1.
SATELLITE-BASED Almost
every
year
millions of
affected by droughts.
acres
DROUGHT-WATCH SYSTEM of
agricultural
land
in
the world
are
Coincidence of droughts in several principal agricultural
areas has always had very unfavorable effects, quite often leading to devastating economic and social impacts. As mankind faces the inevitable recurrence of drought in the future, development of drought—mitigation measures is an important task. The first step in this endeavor is an efficient drought-watch. A satellite-based drought-watch system will serve this goal. At present the potential for developing such a system based on available satellite data is good. Weekly vegetation index data of 16 km resolution are available from NOAA/NESDIS in real-time for any part of the globe between 75~N and 55~ S. The Vegetation Condition Index, derived from these data, has the ability to detect drought and to measure its intensity, duration, dynamics, and impacts on vegetation. The VCI can easily serve the practical purpose of drought monitoring on continental and regional scales. The described approach can be aplied towards a finer resolution vegetation index data as well. REFERENCES
/1/ UNDRO. Drought Emergency in Southern Africa. UNDRO NEWS, May/June, 1992, p.4-7. /2/ Tucker, C.J., and P.J.Sellers, Satellite remote sensing of primary production, mt. J. Remote Sensing, 7, 1395—1416 (1986). /3/ Gray, T.I., D.G. McCrary, The environmental vegetation index, a tool potentially useful for arid land management, AgRISTARS Report EW—Nl—04076 JSC-ET1 w340 301 m518 30 17132 (1981) /4/ Rao, K.P., S.J.Holnes, R.K.Anderson, J.S.Winston, P.E.Lehr (editors), Weather Satellites: Systems, Data, and Environmental Applications, American Meteorological Society, Boston, 1990. /5/ Twomey, S., Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements, Elsevier Scientific Publishing Co., New York, 1987. /6/ Velleman, P and D.C. Hoaglin, Applications, basics and computing of exploratory data analysis, Duxbury Press, Boston, 1981. /7/ Kogan, F.N., Vegetation index for areal analysis of crop conditions, in: Proceedings of the 18th Conference on Agricultural and Forest Meteorology, ANS, W.Lafayette, 1987, p. 103. /8/ Kogan, F.M., Remote sensing of weather impacts on vegetation in nonhomogeneous areas, Irit. J. Remote Sensing, vol. 11, No. 8, 1405—1419 (1990). /9/ Kogan, F.N., Monitoring the 1988 US Drought from Satellite, in: Proceedings
5th Conference on Satellite Meteorology and Oceanography, London, September 3-7, 1990,
p.
186.
/10/ Tarpley, J.P., Schnieder, S.R., Money, R.L., Global vegetation indices from NOAA-7 Meteorological satellite, J. Climate and Applied Meteorology, 23, 491 (1984) /11/ NOAA, North America Climate Advisory: Growing Season Update Drought Impact Outlook, August 17, 1992.