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Predicting and quantifying the structure of tropical dry forests in South Florida and the Neotropics using spaceborne imagery
Author(s) -
Gillespie Thomas W.,
Zutta Brian R.,
Early Michael K.,
Saatchi Sassan
Publication year - 2006
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/j.1466-8238.2005.00203.x
Subject(s) - tropical and subtropical dry broadleaf forests , dry forest , geography , ecology , tropics , tropical forest , environmental science , remote sensing , forestry , biology
Aim This research examines environmental theories and remote sensing methods that have been hypothesized to be associated with tropical dry forest structure. Location Tropical dry forests of South Florida and the Neotropics. Methods Field measurements of stand density, basal area and tree height were collected from 22 stands in South Florida and 30 stands in the Neotropics. In South Florida, field measurements were compared to climatic (temperature, precipitation, hurricane disturbance) and edaphic (rockiness, soil depth) variables, spectral indices (NDVI, IRI, MIRI) from Landsat 7 ETM+, and estimates of tree height from the Shuttle Radar Topography Mission (SRTM) and the National Elevation Dataset (NED). Environmental variables associated with tropical dry forest structure in South Florida were compared to tropical dry forest in other Neotropical sites. Results There were significant correlations among temperature and precipitation, and stand density and tree height in South Florida. There were significant correlations between (i) stand density and mean NDVI and standard deviation of NDVI, (ii) MIRI and stand density, basal area and mean tree height, and (iii) estimates of tree height from SRTM with maximum tree height. In the Neotropics, there were no relationships between temperature or precipitation and tropical dry forest structure, however, Neotropical sites that experience hurricane disturbance had significantly shorter tree heights and higher stand densities. Main conclusions It is possible to predict and quantify the forest structure characteristics of tropical dry forests using climatic data, Landsat 7 ETM+ imagery and SRTM data in South Florida. However, results based on climatic data are region‐specific and not necessarily transferable between tropical dry forests at a continental spatial scale. Spectral indices from Landsat 7 ETM+ can be used to quantify forest structure characteristics, but SRTM data are currently not transferable to other regions. Hurricane disturbance has a significant impact on forest structure in the Neotropics.