Detecting and Mapping Invasive Plant Species Using Hyperspectral Data

Authored by: Ruiliang Pu

Biophysical and Biochemical Characterization and Plant Species Studies

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138364714
eBook ISBN: 9780429431180
Adobe ISBN:

10.1201/9780429431180-9

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Abstract

Invasions of plant species have caused significant changes in structure and function of ecosystems at local and regional scales. During the last two decades, researchers have had an increasing interest in studies on the hyperspectral detection and mapping of different invasive plant species (IPS). Based on the existing literature review, this chapter provides an overview on hyperspectral remote sensing (HRS) techniques for detecting and mapping IPS. In this review, the main objectives of include (1) reviewing suitable techniques/methods and their applications in detecting and mapping IPS with hyperspectral data; (2) addressing relevant considerations for detecting and mapping IPS; and (3) discussing challenges and pointing out future directions for identifying and mapping invasive species using hyperspectral remote sensing techniques. In this chapter, the potential of detecting and mapping IPS with HRS techniques is first reviewed. Then seven suitable techniques/methods and their applications in detecting and mapping IPS with hyperspectral data are introduced, including derivative analysis, spectral matching, vegetation index analysis, absorption features analysis, hyperspectral transformation, spectral mixture analysis, and hyperspectral image classification. Next, efficient detection and mapping of IPS with HRS, full radiometric correction, different vegetation types comprised by individual invasive species, and species life history stages or phenological change are addressed. Finally, major challenges including spectral similarity between IPS and background plant species and biological heterogeneity of IPS and future directions of detecting and mapping IPS are discussed.

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