Download - Drought Assessment + Impacts: A Preview
Drought Assessment +
Impacts Preview
Remote Sensing for Global Environmental Change Richard MacLean Jenkins Macedo
November 4, 2013
What is Drought? An Oklahoma Experience
URL: http://www.youtube.com/watch?v=oRSFMLByat0
U.S. DROUGHT MONITOR
Source: URL: http://www.youtube.com/watch?v=XAY4fmPH8sU
“Drought-induced reduction in global terrestrial net primary production from
2000 through 2009.” Zhao & Running, 2010
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?
APPROACH • MODIS Gross Primary Production/NNP Algorithm
o Data frame § 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.
§ 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.
o evaluate environmental water stress combining information from evaporation and precipitation.
“A remotely sensed global terrestrial drought severity index.” Mu et al, 2013
PURPOSE • 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.
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.
“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
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
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
“Drought stress and carbon uptake in an Amazon forest measured with spaceborn
imaging spectroscopy” Asner, G.P., D. Nepstad, G. Cardinot, D. Ray,
2004
Purpose • 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
Approach • 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
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.
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.