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.