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Remote Sensing and GIS Research Agenda
Remote sensing of tropical forests in many cases has focused on tropical moist and rainforests with little emphasis on tropical dry forests. Past work in tropical dry forests (Tdfs) has involved the extraction of ecosystem age/successional stage based on a) spectral reflectance and b) thermal properties. In both cases work has been conducted using the Landsat Thematic Mapper Sensor. The use of hyperspectral remote sensing satellites such as Hyperion (122 spectral bands and 30 m spatial resolution or multispectral such as ASTER (3 spectral bands with 15 m spatial resolution) has not been explored yet in Tdf research.
TROPI-DRY works involves a multi-scale multi temporal study using Landsat TM, ASTER and MODIS remote sensing data. In addition, TROPI-DRY works on the integration of NASA MODIS remote sensing derived products such as Leaf Area Index (LAI) and net primary productivity (NPP).
The fundamental goal of TROPI-DRY’s remote sensing agenda is to contribute to improve to our limited knowledge of how Tdfs recover over time and how different successional stages manifest themselves when observed through remote sensing platforms and derived data products.
Three questions are been be addressed in this research area using different spatial and temporal data sets:
- How changes in ecosystem complexity manifest themselves at the regional level when observed across different temperature/precipitation regimes using standardized measurements of ecosystem structure, composition and dynamics;
- How this complexity manifests itself through multi- and hyperspectral earth observation sensors; and
- How we can model those linkages (ground and orbit) using Bayesian network approaches.
In addition to landscape level questions, TROPI-DRY conducts a significant amount of research on exploring the linkages between phenology, hyperspectral signatures and leaf traits in all our research sites.
Further, TROPI-DRY is conductive intensive research on time series analysis of AVHRR and MODIS remote sensing data to better understand linkages between ecosystem phenology, biophysical drives and climate change using TimeSat as a main research tool.