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Mapping Cloud Immersion in Tropical Montane Cloud Forests using Satellite Data

This study aims to map cloud immersion in tropical montane cloud forests using satellite data and assess their biogeography. It explores the hydrological and ecological importance of these forests and the potential applications of cloud immersion frequency data in predicting species distribution and cloud forest hydrology models. The study also discusses the methodology used and future directions for research.

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Mapping Cloud Immersion in Tropical Montane Cloud Forests using Satellite Data

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  1. Udaysankar S. Nair1, Salvi Asefi3, Ron Welch3, Robert O. Lawton2, Deepak Ray4 1 Earth System Science Center, University of Alabama in Huntsville 2 Department of Biological Science, University of Alabama in Huntsville 3 Department of Atmospheric Science, University of Alabama in Huntsville 4 Forestry and Natural Resources, Purdue University Determination of cloud immersion and biogeography of cloud forests  using satellite data

  2. Introduction, cloud forests Methodology MODIS derived cloud immersion frequency Applications Conclusions, future work Outline

  3. Characterized by predictable, frequent and prolonged immersion in orographic clouds. Tropical montane cloud forests

  4. Altitude range 1500 -3500m, coastal areas descends to 1000-300m Tropical montane cloud forests

  5. TMCFs are located within biological hotspots that support about 20% and 16% of plants and vertebrates Retains less than 25% of their original primary vegetation cover Why map TMCFs? Myers et. al., 2000

  6. Why map TMCFs? • TMCFs are water resources with potential to affect agriculture, water distribution and power generation • “Horizontal precipitation” can account for up to 14 – 18 % and 15% - 100% of total precipitation during wet and dry season respectively

  7. Rain: 4310 mm Horizontal: 3560 mm 1500 m 3600 3100 Rain: 6390 mm Horizontal: 350 mm 2600 1320 m Rain: 4590 mm Horizontal: 240 mm 1200 m 2100 1600 1100 600 Caribbean 100 0 Hydrological importance

  8. Mosses and ferns acts as capacitors, modulating runoff Hydrological importance

  9. Characterization of TMCFs are essential for understanding ecological processes in tropical mountains Upscaling of cloud forest hydrology and ecology Why map TMCFs?

  10. Current state of cloud forest mapping • “Version 1”, International TMCF Symposium, 1990, TMCF researchers pointing out locations on a map.

  11. Current state of cloud forest mapping • “Version 2”, Cloud Forest Agenda, based on DEM and “expert testimony” • Review of literature • Information on particular study sites dictated the mapping at the regional scale

  12. 10 30 50 70 90 Current state of cloud forest mapping

  13. Methodology • If cloud is present at a location, estimate cloud base height using satellite derived cloud top height an cloud properties. • If the cloud base height is equal to or less than the surface elevation at that location, then it is flagged as being immersed in cloud. • Cloud immersion frequency is determined as the percentage of observations for which cloud immersion occurs at the particular location.

  14. Cloud top height Cloud top pressure Optical depth Effective radius Temperature profile Assumptions Cloud Thickness Estimation of cloud base height from satellite imagery MODIS

  15. Study Area and Data Sources • Study areas: Monteverde, Costa Rica and the Hawaiian islands. • MODIS Terra data: Cloud optical properties retrieved from level-1B, Level-2 cloud top pressure,and atmospheric thermodynamic profiles • NCEP 1deg x 1 deg atmospheric analysis

  16. Study Area and Data Sources • Photographic observations of cloud base height

  17. Comparison of cloud base heights, estimated using temperature profile from the MODIS and RAMS model

  18. MODIS derived cloud immersion frequency: Monteverde, Costa Rica Potential cloud forests Potential + known cloud forests

  19. MODIS derived cloud immersion frequency: Monteverde, Costa Rica

  20. Potential cloud forests Kona cloud forest (www.hawaiianwalkways.com) MODIS derived cloud immersion frequency: Big Island of Hawaii

  21. Satellite derived cloud immersion frequency may be used as an input in models for predicting species distribution such as GARP (Genetic Algorithm for Rule Set Production). Probability of cloud incidence is a key parameter utilized by paramterized cloud forest hydrology models such as CLINT (Cloud Interception Model, Jarvis, 2000) Potential Applications

  22. RMS errors of 300m is encountered in cloud base height estimations Cloud immersion frequency maps successfully identifies known cloud forest locations in Costa Rica and Hawaii Conclusions

  23. Examine seasonal variability in cloud immersion for Central American region and Hawaii Further evaluate the products by regional experts Future work

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