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Related Experiment Video

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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MODISTools - downloading and processing MODIS remotely sensed data in R.

Sean L Tuck1, Helen Rp Phillips2, Rogier E Hintzen2

  • 1Department of Plant Sciences, University of Oxford Oxford, OX1 3RB, U.K.

Ecology and Evolution
|January 6, 2015
PubMed
Summary
This summary is machine-generated.

MODISTools is an R package that simplifies accessing and processing NASA MODIS satellite data for ecological studies. It automates data handling, reducing errors and effort, and aids in analyzing relationships between species richness and vegetation indices.

Keywords:
Conservation biologyPREDICTSearth observationglobal changeland processesmacroecologyremote-sensingsatellite imagery

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Area of Science:

  • Ecology
  • Remote Sensing
  • Data Science

Background:

  • Remotely sensed data, particularly from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), are crucial for ecological research due to their global coverage and resolution.
  • Existing methods for accessing and processing MODIS data can be labor-intensive and prone to human error, especially for studies involving multiple sites.

Purpose of the Study:

  • To introduce MODISTools, an R package designed to streamline the acquisition, downloading, and processing of MODIS data for ecological applications.
  • To automate data handling for multiple locations, time periods, and MODIS products, thereby reducing researcher effort and potential errors.
  • To facilitate ecological studies requiring extensive spatial and temporal data, such as meta-analyses and global experiments.

Main Methods:

  • Development of the MODISTools R package for automated downloading and processing of MODIS data.
  • Implementation of a reproducible workflow within MODISTools, including data quality checks.
  • Application of MODISTools to analyze the relationship between species richness in temperate forests and MODIS-derived vegetation indices (e.g., productivity measures).

Main Results:

  • MODISTools successfully automates the downloading and processing of MODIS data for ecological analyses.
  • Species richness across multiple higher taxa in temperate forests showed a positive correlation with vegetation index measures (maximum, mean, and variability).
  • Higher taxon identity and vegetation index gradients explained a significant portion of the variation in species richness, with models achieving high R-squared values.

Conclusions:

  • MODISTools provides an efficient and reproducible method for ecologists to utilize MODIS data, particularly for multi-site studies.
  • The package facilitates the analysis of ecological patterns, such as the relationship between biodiversity and vegetation productivity.
  • MODISTools is a valuable tool for advancing ecological research by simplifying complex remote sensing data processing.