Use of satellite remote sensing for determining cloud immersion and biogeography of cloud forests
Abstract
Tropical montane cloud forests (TMCFs) are ecosystems characterized by frequent and prolonged immersion in orographic clouds. TMCFs are biologically rich and diverse and they lie at the core of several of the global biological hotspots identified for conservation purposes. Recent studies show that TMCFs are sensitive to global and regional scale climate changes. Vegetation in TMCFs directly harvest water from clouds, which is usually termed horizontal precipitation, and is an important input to local hydrological cycle. Mosses and ferns present within the TMCFs absorbs moisture during rainfall and releases slowly over time thereby providing another important hydrological function, namely modulation of runoff. In spite of the ecological and hydrological importance of TMCFs, there is scant information regarding the geographical distribution of the TMCFs. One source of information that is currently available is the atlas of the potential cloud forest distribution published by the United Nations Environmental Program. However, this compilation does not directly consider the defining characteristics of cloud forests, namely frequency of immersion in cloud forests, in their classisification scheme. This talk will present the use of NASA MODIS satellite data to determine cloud immersion frequency and thus the biogeography of cloud forests. The MODIS derived cloud top heights and cloud thickness estimated from MODIS retrieval of cloud microphysical properties is used to estimate cloud base height. If the estimate cloud base height at a location is less than or equal to the surface elevation at that point, then that location is defined as experiencing cloud immersion. This classification procedure was applied to determine cloud immersion frequency at two study sites, namely Hawaii and Monteverde, Costa Rica. The cloud immersion frequency maps identifies some of the know cloud forest locations in these study areas. Comparison against a blended product created using numerical modeling and geostationary satellite data also show good agreement over Monteverde, Costa Rica.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2006
- Bibcode:
- 2006AGUFM.B14A..01A
- Keywords:
-
- 1218 Mass balance (0762;
- 1223;
- 1631;
- 1836;
- 1843;
- 3010;
- 3322;
- 4532);
- 1631 Land/atmosphere interactions (1218;
- 1843;
- 3322);
- 1843 Land/atmosphere interactions (1218;
- 1631;
- 3322);
- 3322 Land/atmosphere interactions (1218;
- 1631;
- 1843)