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Remote Sens. 2012, 4(8), 2401-2418;

Use of Landsat and SRTM Data to Detect Broad-Scale Biodiversity Patterns in Northwestern Amazonia

Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
Facultad de Ciencias Biológicas, Universidad Nacional de la Amazonía Peruana, Iquitos, Peru
Department of Biology, University of Turku, FI-20100 Turku, Finland
Center for Conservation Education and Sustainability, Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC 20013, USA
Author to whom correspondence should be addressed.
Received: 20 June 2012 / Revised: 10 August 2012 / Accepted: 10 August 2012 / Published: 15 August 2012
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
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Vegetation maps are the starting point for the design of protected areas and regional conservation plans. Accurate vegetation maps are missing for much of Amazonia, preventing the development of effective and compelling conservation strategies. Here we used a network of 160 inventories across northwestern Amazonia to evaluate the use of Landsat and Shuttle Radar Topography Mission (SRTM) data to identify floristic and edaphic patterns in Amazonian forests. We first calculated the strength of the relationship between these remotely-sensed data, and edaphic and floristic patterns in these forests, and asked how sensitive these results are to image processing and enhancement. We additionally asked if SRTM data can be used to model patterns in plant species composition in our study areas. We find that variations in Landsat and SRTM data are strongly correlated with variations in soils and plant species composition, and that these patterns can be mapped solely on the basis of SRTM data over limited areas. Using these data, we furthermore identified widespread patch-matrix floristic patterns across northwestern Amazonia, with implications for conservation planning and study. Our findings provide further evidence that Landsat and SRTM data can provide a cost-effective means for mapping these forests, and we recommend that maps generated from a combination of remotely-sensed and field data be used as the basis for conservation prioritization and planning in these vast and remote forests. View Full-Text
Keywords: Amazonia; Landsat; SRTM; floristic composition; geology; soils; pteridophytes; vegetation mapping; NDVI Amazonia; Landsat; SRTM; floristic composition; geology; soils; pteridophytes; vegetation mapping; NDVI

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Higgins, M.A.; Asner, G.P.; Perez, E.; Elespuru, N.; Tuomisto, H.; Ruokolainen, K.; Alonso, A. Use of Landsat and SRTM Data to Detect Broad-Scale Biodiversity Patterns in Northwestern Amazonia. Remote Sens. 2012, 4, 2401-2418.

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