A1 Refereed original research article in a scientific journal
Mapping environmental variation in lowland Amazonian rainforests using remote sensing and floristic data
Authors: Anders Sirén, Hanna Tuomisto, Hugo Navarrete
Publisher: TAYLOR & FRANCIS LTD
Publication year: 2013
Journal: International Journal of Remote Sensing
Journal name in source: INTERNATIONAL JOURNAL OF REMOTE SENSING
Journal acronym: INT J REMOTE SENS
Number in series: 5
Volume: 34
Issue: 5
First page : 1561
Last page: 1575
Number of pages: 15
ISSN: 0143-1161
DOI: https://doi.org/10.1080/01431161.2012.723148
Abstract
This article describes a method for detailed mapping of ecological variation in a tropical rainforest based on field inventory of pteridophytes (ferns and lycophytes) and remote sensing using Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. Previously known soil cation optima of the pteridophyte species were first used in calibration, i.e. to infer soil cation concentrations for sites on the basis of their pteridophyte species composition. Multiple linear regression based on spectral reflectance values in the Landsat image was then used to derive an equation that allowed the prediction of these calibrated soil values for unvisited sites in the study area. The predictive accuracy turned out to be high: the mean absolute error, as estimated by leave-one-out cross-validation, was just 7% of the total range of calibrated soil values. This method for detailed mapping of natural environmental variability in lowland tropical rainforest has applications for land-use planning, such as wildlife management, forestry, biodiversity conservation, and payments for carbon sequestration.
This article describes a method for detailed mapping of ecological variation in a tropical rainforest based on field inventory of pteridophytes (ferns and lycophytes) and remote sensing using Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. Previously known soil cation optima of the pteridophyte species were first used in calibration, i.e. to infer soil cation concentrations for sites on the basis of their pteridophyte species composition. Multiple linear regression based on spectral reflectance values in the Landsat image was then used to derive an equation that allowed the prediction of these calibrated soil values for unvisited sites in the study area. The predictive accuracy turned out to be high: the mean absolute error, as estimated by leave-one-out cross-validation, was just 7% of the total range of calibrated soil values. This method for detailed mapping of natural environmental variability in lowland tropical rainforest has applications for land-use planning, such as wildlife management, forestry, biodiversity conservation, and payments for carbon sequestration.
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