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Determination of cloud immersion and biogeography of cloud forests using satellite data Udaysankar S. Nair 1, Salvi Asefi 3, Ron Welch 3, Robert O. Lawton.

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Presentation on theme: "Determination of cloud immersion and biogeography of cloud forests using satellite data Udaysankar S. Nair 1, Salvi Asefi 3, Ron Welch 3, Robert O. Lawton."— Presentation transcript:

1 Determination of cloud immersion and biogeography of cloud forests using satellite data Udaysankar S. Nair 1, Salvi Asefi 3, Ron Welch 3, Robert O. Lawton 2, Deepak Ray 4 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

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

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

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

5 Why map TMCFs? 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 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 Hydrological importance Rain: 6390 mm Horizontal: 350 mm 1320 m Rain: 4310 mm Horizontal: 3560 mm 1500 m Rain: 4590 mm Horizontal: 240 mm 1200 m 3600 3100 2600 2100 1600 1100 600 100 0 Caribbean

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

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

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 Current state of cloud forest mapping 10 30 50 70 90

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

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

21 Potential Applications 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)

22 Conclusions 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

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


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