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Drought Assessment + Impacts: A Preview


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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.

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Drought Assessment + Impacts: A Preview

  1. 1. Drought Assessment + Impacts Preview Remote Sensing for Global Environmental Change Richard MacLean Jenkins Macedo November 4, 2013
  2. 2. What is Drought? An Oklahoma Experience URL:
  3. 3. U.S. DROUGHT MONITOR Source: URL:
  4. 4. “Drought-induced reduction in global terrestrial net primary production from 2000 through 2009.” Zhao & Running, 2010
  5. 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. 6. 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. •  • 
  7. 7. “A remotely sensed global terrestrial drought severity index.” Mu et al, 2013
  8. 8. 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.
  9. 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. •  •  •  •  • 
  10. 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. 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. 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. 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. 14. 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
  15. 15. 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
  16. 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. 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.