Application of Ecological Modeling and Remote Sensing Data Assimilation in Restoration and Conservation of the
Brazilian Atlantic Forest
Ana Paula
Giorgi, UCLA Center for Tropical Research, agiorgi@ucla.edu
(Presenting)
Sassan
Saatchi, UCLA Center for Tropical Research, saatchi@congo.jpl.nasa.gov
Wolfgang
Buermann, UCLA Center for Tropical Research, buermann@ucla.edu
Thomas
Smith, UCLA Center for Tropical Research, tbsmith@ucla.edu
The Brazilian Atlantic Forest is considered a major global biodiversity hotspot and as one of the most endangered ecosystems in the world. Only 7% of the original forest is left intact and majority of the remaining patches are embedded in a mosaic of secondary regrowth, anthropogenic forests, tree plantations, pastures and agricultural crops. Majority of conservation activities in the region are concentrated in preserving forest patches while increasing the restoration efforts and developing larger continuum of forests to sustain the natural habitats and the high biodiversity. These efforts require detailed information on the location and size of forest fragments, their geographical distribution, the type and intensity of anthropogenic threats, and their values in terms of biodiversity and ecosystem services.
We use a combination of ecological niche modeling, a suite of remote sensing and climate data layers, and important endemic species to evaluate the importance of forest fragments in conservation efforts. We have selected occurrence data of two endemic bird species and the Maximum Entropy algorithm (MAXENT) to study their spatial distribution in the region. Remote sensing data from various optical and microwave sensors such as MODIS, QSCAT, SRTM are used to develop direct spectral metrics or derived products such as LAI and NDVI to represent the vegetation and landscape characteristics. Three scenarios are used: remote sensing, climate, and combined remote sensing and climate data to map the geographical range of the bird species. The modeling results indicate that remote sensing data alone can characterize the species range accurately and the combined remote sensing and climate improves the result. We discuss the implication of these results for conservation and restoration plans in the region.