Quick Spatial Assessment of Drought Information Derived from MODIS Imagery Using Amplitude Analysis
The normalized difference vegetation index (NDVI)
and normalized difference moisture index (NDMI) derived from the
moderate resolution imaging spectroradiometer (MODIS) have been
widely used to identify spatial information of drought condition. The
relationship between NDVI and NDMI has been analyzed using
Pearson correlation analysis and showed strong positive relationship.
The drought indices have detected drought conditions and identified
spatial extents of drought. A comparison between normal year and
drought year demonstrates that the amplitude analysis considered both
vegetation and moisture condition is an effective method to identify
drought condition. We proposed the amplitude analysis is useful for
quick spatial assessment of drought information at a regional scale.
[1] L.S. Unganai, and F.N. Kogan, "Drought Monitoring and Corn Yield
Estimation in Southern Africa from AVHRR Data", Remote Sensing of
Environment, vol. 63, no. 3, pp. 219-232, 1998.
[2] H.C. Claudio, Y. Cheng, D.A. Fuentes, J.A. Gamon, H. Luo, W. Oechel,
H.L. Qiu, A.F. Rahman, and D.A. Sims, "Monitoring drought effects on
vegetation water content and fluxes in chaparral with the 970 nm water
band index", Remote Sensing of Environment, vol. 103, no. 3, pp.
304-311, 2006.
[3] S.M. Herrmann, A. Anyamba, and C.J. Tucker, "Recent trends in
vegetation dynamics in the African Sahel and their relationship to
climate", Global Environmental Change Part A, vol. 15, no. 4, pp.
394-404, 2005.
[4] A.J. Peters, D.C. Rundquist, and D.A. Wilhite, "Satellite detection of the
geographic core of the 1988 Nebraska drought", Agricultural and Forest
Meteorology, vol. 57, no. 1-3, pp. 35-47, 1991.
[5] M.L. Lin, and C.W. Chen, "Application of fuzzy models for the
monitoring of ecologically sensitive ecosystems in a dynamic semi-arid
landscape from satellite imagery", Engineering Computations:
International Journal for Computer-Aided Engineering and Software, vol.
27, no. 1, pp. 5-19, 2010.
[6] M.L. Lin, C.W. Chen, Q.B. Wang, Y. Cao, J.Y. Shih, Y.T. Lee, C.Y.
Chen, and S. Wang, "Fuzzy model-based assessment and monitoring of
desertification using MODIS satellite imagery", Engineering
Computations: International Journal for Computer-Aided Engineering
and Software, vol. 26, no. 7, pp. 745-760, 2009.
[7] S.M. Quiring, and S. Ganesh, "Evaluating the utility of the Vegetation
Condition Index (VCI) for monitoring meteorological drought in Texas",
Agricultural and Forest Meteorology, vol. 150, no. 3, pp. 330-339, 2010.
[8] Y. Bayarjargal, A. Karnieli, M. Bayasgalan, S. Khudulmur, C. Gandush,
and C.J. Tucker, "A comparative study of NOAA-AVHRR derived
drought indices using change vector analysis", Remote Sensing of
Environment, vol. 105, no. 1, pp. 9-22, 2006.
[9] C. Bhuiyan, R.P. Singh, and F.N. Kogan, "Monitoring drought dynamics
in the Aravalli region (India) using different indices based on ground and
remote sensing data", International Journal of Applied Earth Observation
and Geoinformation, vol. 8, no. 4, pp. 289-302, 2006.
[10] F.N. Kogan, "Global drought and flood-watch from NOAA
polar-orbitting satellites", Advances in Space Research, vol. 21, no. 3, pp.
477-480, 1998.
[11] P.R. Bajgiran, A.A. Darvishsefat, A. Khalili, and M.F. Makhdoum,
"Using AVHRR-based vegetation indices for drought monitoring in the
Northwest of Iran", Journal of Arid Environments, vol. 72, no., pp.
1086-1096, 2008.
[12] S. Jin, and S.A. Sader, "MODIS time-series imagery for forest disturbance
detection and quantification of patch size effects", Remote Sensing of
Environment, vol. 99, no., pp. 462-470, 2005.
[13] B.C. Gao, "NDWI - A Normalized Difference Water Index for remote
sensing of vegetation liquid water from space", Remote Sensing of
Environment, vol. 58, no., pp. 257-266, 1996.
[14] E.H. Wilson, and S.A. Sader, "Detection of forest harvest type using
multiple dates of Landsat TM imagery", Remote Sensing of Environment,
vol. 80, no., pp. 385-396, 2002.
[15] D.J. Hayes, and W.B. Cohen, "Spatial, spectral and temporal patterns of
tropical forest cover change as observed with multiple scales of optical
satellite data", Remote Sensing of Environment, vol. 106, no., pp. 1-16,
2007.
[16] N.R. Goodwin, N.C. Coops, M.A. Wulder, S. Gillanders, T.A. Schroeder,
and T. Nelson, "Estimation of insect infestation dynamics using a
temporal sequence of Landsat data", Remote Sensing of Environment,
vol. 112, no., pp. 3680-3689, 2008.
[17] D.J. Hayes, W.B. Cohen, S.A. Sader, and D.E. Irwin, "Estimating
proportional change in forest cover as a continuous variable from
multi-year MODIS data", Remote Sensing of Environment, vol. 112, no.,
pp. 735-749, 2008.
[18] A. Anyamba, C.J. Tucker, and J.R. Eastman, "NDVI anomaly patterns
over Africa during the 1997/98 ENSO warm event", International Journal
of Remote Sensing, vol. 22, no. 10, pp. 1847-1859, 2001.
[1] L.S. Unganai, and F.N. Kogan, "Drought Monitoring and Corn Yield
Estimation in Southern Africa from AVHRR Data", Remote Sensing of
Environment, vol. 63, no. 3, pp. 219-232, 1998.
[2] H.C. Claudio, Y. Cheng, D.A. Fuentes, J.A. Gamon, H. Luo, W. Oechel,
H.L. Qiu, A.F. Rahman, and D.A. Sims, "Monitoring drought effects on
vegetation water content and fluxes in chaparral with the 970 nm water
band index", Remote Sensing of Environment, vol. 103, no. 3, pp.
304-311, 2006.
[3] S.M. Herrmann, A. Anyamba, and C.J. Tucker, "Recent trends in
vegetation dynamics in the African Sahel and their relationship to
climate", Global Environmental Change Part A, vol. 15, no. 4, pp.
394-404, 2005.
[4] A.J. Peters, D.C. Rundquist, and D.A. Wilhite, "Satellite detection of the
geographic core of the 1988 Nebraska drought", Agricultural and Forest
Meteorology, vol. 57, no. 1-3, pp. 35-47, 1991.
[5] M.L. Lin, and C.W. Chen, "Application of fuzzy models for the
monitoring of ecologically sensitive ecosystems in a dynamic semi-arid
landscape from satellite imagery", Engineering Computations:
International Journal for Computer-Aided Engineering and Software, vol.
27, no. 1, pp. 5-19, 2010.
[6] M.L. Lin, C.W. Chen, Q.B. Wang, Y. Cao, J.Y. Shih, Y.T. Lee, C.Y.
Chen, and S. Wang, "Fuzzy model-based assessment and monitoring of
desertification using MODIS satellite imagery", Engineering
Computations: International Journal for Computer-Aided Engineering
and Software, vol. 26, no. 7, pp. 745-760, 2009.
[7] S.M. Quiring, and S. Ganesh, "Evaluating the utility of the Vegetation
Condition Index (VCI) for monitoring meteorological drought in Texas",
Agricultural and Forest Meteorology, vol. 150, no. 3, pp. 330-339, 2010.
[8] Y. Bayarjargal, A. Karnieli, M. Bayasgalan, S. Khudulmur, C. Gandush,
and C.J. Tucker, "A comparative study of NOAA-AVHRR derived
drought indices using change vector analysis", Remote Sensing of
Environment, vol. 105, no. 1, pp. 9-22, 2006.
[9] C. Bhuiyan, R.P. Singh, and F.N. Kogan, "Monitoring drought dynamics
in the Aravalli region (India) using different indices based on ground and
remote sensing data", International Journal of Applied Earth Observation
and Geoinformation, vol. 8, no. 4, pp. 289-302, 2006.
[10] F.N. Kogan, "Global drought and flood-watch from NOAA
polar-orbitting satellites", Advances in Space Research, vol. 21, no. 3, pp.
477-480, 1998.
[11] P.R. Bajgiran, A.A. Darvishsefat, A. Khalili, and M.F. Makhdoum,
"Using AVHRR-based vegetation indices for drought monitoring in the
Northwest of Iran", Journal of Arid Environments, vol. 72, no., pp.
1086-1096, 2008.
[12] S. Jin, and S.A. Sader, "MODIS time-series imagery for forest disturbance
detection and quantification of patch size effects", Remote Sensing of
Environment, vol. 99, no., pp. 462-470, 2005.
[13] B.C. Gao, "NDWI - A Normalized Difference Water Index for remote
sensing of vegetation liquid water from space", Remote Sensing of
Environment, vol. 58, no., pp. 257-266, 1996.
[14] E.H. Wilson, and S.A. Sader, "Detection of forest harvest type using
multiple dates of Landsat TM imagery", Remote Sensing of Environment,
vol. 80, no., pp. 385-396, 2002.
[15] D.J. Hayes, and W.B. Cohen, "Spatial, spectral and temporal patterns of
tropical forest cover change as observed with multiple scales of optical
satellite data", Remote Sensing of Environment, vol. 106, no., pp. 1-16,
2007.
[16] N.R. Goodwin, N.C. Coops, M.A. Wulder, S. Gillanders, T.A. Schroeder,
and T. Nelson, "Estimation of insect infestation dynamics using a
temporal sequence of Landsat data", Remote Sensing of Environment,
vol. 112, no., pp. 3680-3689, 2008.
[17] D.J. Hayes, W.B. Cohen, S.A. Sader, and D.E. Irwin, "Estimating
proportional change in forest cover as a continuous variable from
multi-year MODIS data", Remote Sensing of Environment, vol. 112, no.,
pp. 735-749, 2008.
[18] A. Anyamba, C.J. Tucker, and J.R. Eastman, "NDVI anomaly patterns
over Africa during the 1997/98 ENSO warm event", International Journal
of Remote Sensing, vol. 22, no. 10, pp. 1847-1859, 2001.
@article{"International Journal of Earth, Energy and Environmental Sciences:56798", author = "Meng-Lung Lin and Qiubing Wang and Fujun Sun and Tzu-How Chu and Yi-Shiang Shiu", title = "Quick Spatial Assessment of Drought Information Derived from MODIS Imagery Using Amplitude Analysis", abstract = "The normalized difference vegetation index (NDVI)
and normalized difference moisture index (NDMI) derived from the
moderate resolution imaging spectroradiometer (MODIS) have been
widely used to identify spatial information of drought condition. The
relationship between NDVI and NDMI has been analyzed using
Pearson correlation analysis and showed strong positive relationship.
The drought indices have detected drought conditions and identified
spatial extents of drought. A comparison between normal year and
drought year demonstrates that the amplitude analysis considered both
vegetation and moisture condition is an effective method to identify
drought condition. We proposed the amplitude analysis is useful for
quick spatial assessment of drought information at a regional scale.", keywords = "NDVI, NDMI, Drought, remote sensing, spatialassessment.", volume = "4", number = "7", pages = "295-5", }
