This preview presents a summary of four selected research on remote sensing drought assessment and impacts at both the regional and global levels as part of the course requirement for remote sensing for global environmental change. The papers are presented by Richard MacLean, graduate student in Geographic Information Systems for Development and Environment and Jenkins Macedo, graduate student in Environmental Science and Policy.

1. Drought Assessment +
Impacts Preview
Remote Sensing for Global Environmental Change
Richard MacLean
Jenkins Macedo
November 4, 2013

2. What is Drought?
An Oklahoma Experience
URL: http://www.youtube.com/watch?v=oRSFMLByat0

3. U.S. DROUGHT MONITOR
Source: URL: http://www.youtube.com/watch?v=XAY4fmPH8sU

4. “Drought-induced reduction in global
terrestrial net primary production from
2000 through 2009.” Zhao & Running, 2010

5. PURPOSE
• to test the hypothesis whether warming climate of the
past decade continued to increase Net Primary
Production (NPP), or if different climate constraints were
more important?

6. APPROACH
•
MODIS Gross Primary Production/NNP Algorithm
o Data frame
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Remote sensing datasets
calculate global 1-km MODIS NPP from 2000 through 2009.
used collection 5 (C5) 8-day composite 1-km fraction of photosynthetically active
radiation (FPAR) and Leaf Area Index (LAI) from the MODIS sensor as remotely
sensed vegetation property dynamics to the algorithm.
collection 4 (C4) MODIS 1-km land cover (MOD12Q1)
collection 5 (C5) MODIS Climate Model Grid (CMG) 0.5 degree 8-day snow cover
(MOD10C2)
Collection 5 (C5) MODIS 16-day 1-km NDVI/EVI (MOD12A2.
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Meteorological Datasets
reanalysis dataset from the National Center for Environmental Prediction (NCEP)
a Palmer Drought Severity Index (PDSI) ta 0.5 degree resolution was used.
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7. “A remotely sensed global terrestrial
drought severity index.” Mu et al, 2013

8. PURPOSE
•
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the authors first discussed the various strengths models and concepts of
drought indices and noted that most of those models rely heavily on both
reanalysis meteorological and remotely sensed data, which contains
substantial uncertainties.
Mu et al., 2007, 2009, 2011b developed a MODIS ET model to estimate
ET and PET using MODIS data.
o using the MODIS ET/PET model and NDVI (Huete et al. 2002) data
products they calculated remotely sensed drought severity index (DSI)
globally.

9. APPROACH
•
MODIS ET/PET
o Data frame
§
Remotely sensed inputs data
MOD16 ET & PET primary inputs to calculate DSI globally.
o for all terrestrial ecosystems at continuous 8-day, monthly, and annual steps
at 1-km spatial resolution.
Daily meteorological reanalysis data and 8-day remotely sensed vegetation
property dynamics from MODIS as inputs.
used the Penman-Monteith equation (P-M) to calculate global remotely sensed ET,
and integrates both P-M and Priestley-Taylor (1972) methods to estimate PET.
ET algorithm account for several parameters such as surface energy partitioning,
environmental constraints on ET, wet and moist soil surfaces, and transpiration
from canopy stomata.
Atmospheric relative humidity to quantify proportion of wet soil and wet canopy
components.
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10. “Regional aboveground live carbon losses
due to drought-induced tree dieback in
piñon-juniper ecosystems”
Huang, C., G.P. Asner, N.N. Barger, J.C. Neff,
M.L. Floyd, 2010

11. PURPOSE
• Monitor landscape level
•
changes in C storage
associated with large
scale mortality events.
Quantify the change in
piñon-juniper
aboveground biomass
(AGB) with remote
sensing techniques.
source: wikimedia commons

12. APPROACH
• Multi year Landsat (ETM
•
+) time series of dry
season Photosynthetic
Veg (PV) cover.
Paired with field
measurements of
standing live and dead
biomass.
source: Huang et al., 2010

13. “Drought stress and carbon uptake in an
Amazon forest measured with spaceborn
imaging spectroscopy”
Asner, G.P., D. Nepstad, G. Cardinot, D. Ray,
2004

14. Purpose
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Potential for significant
decrease in Amazonian
carbon accumulation driven
by El Niño/Southern
Oscillation
Standard remotely sensed
greenness may miss small
changes in leaf area during
droughts.
source: NASA Earth Observatory

15. Approach
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Image spectroscopy with EO-1
Hyperion data
“[Q]uantify relative differences in
canopy water content and carbon
uptake resulting from drought stress”
Precipitation exclusion ground study
used to correlate spectroscopy with
water stress
Related spectroscopy estimates of PAR
and soil water to model of NPP

16. Drought in the United States
The data cutoff for Drought Monitor maps is Tuesday at 7 a.m. Eastern Time. The maps, which are based on analysis of the
data, are released each Thursday at 8:30 a.m. Eastern Time.

17. Bibliography
Asner, G.P., Nepstad, D., Cardinot, G., and Ray, D. (2004). Drought Stress and Carbon Uptake in
an Amazon Forest Measured with Spaceborne Imaging Spectroscopy. PNAS, Vol. 101, No. 16,
pg. 6039-6044.
Huang, C., Anser, G.P., Barger, N.N., Neff, J.C., and Floyd, M.L. (2010). Regional Aboveground
Live Carbon Losses due to Drought-Induced Tree Dieback in Pinon-Juniper Ecosystems. Remote
Sensing of Environment, Vol. 114, pg. 1471-1479.
Mu, Q., Zhao, M., Kimball, J.S., McDowell, N.G., and Running, S.W. (2013). A Remotely Sensed
Global Terrestrial Drought Severity Index. American Meteorological Society, pg. 83-98.
Zhao, M. & Running, S.W. (2010). Drought-Induced Reduction in Global Terrestrial Net Primary
Production from 2000 through 2009. Science, Vol. 329, pg. 940-943.