Using remote sensing for modeling and monitoring species distributions

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Interpolated climate surfaces have been widely used to predict species distributions and develop environmental niche models. However, the spatial coverage and density of meteorological sites used to develop these surfaces vary among countries and regions, such that the most biodiverse regions often have the most sparsely sampled climatic data. We explore the potential of satellite remote sensing (S-RS) products-which have consistently high spatial and temporal resolution and nearly global coverage-to quantify species-environment relationships that predict species distributions. We propose several new environmental metrics that take advantage of high temporal resolution in S-RS data and compare these approaches to classic climate-only approaches using the live oaks (Quercus section Virentes) as a case study. We show that models perform similarly but for some species, particularly in understudied regions, show less precision in predicting spatial distribution. These results provide evidence supporting efforts to enhance environmental niche models and species distribution models (ENMs/SDMs) with S-RS data and, when combined with other approaches for species detection, will likely enhance our ability to monitor biodiversity globally.

Original languageEnglish (US)
Title of host publicationRemote Sensing of Plant Biodiversity
PublisherSpringer International Publishing
Pages199-223
Number of pages25
ISBN (Electronic)9783030331573
ISBN (Print)9783030331566
DOIs
StatePublished - Jan 1 2020

Bibliographical note

Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s) 2020.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Abiotic niche
  • Biotic niche
  • Climate variables
  • Environmental niche models
  • Phenology
  • Remotely sensed environmental data
  • Species distribution models

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