Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data

Abstract

Grand Lake, Colorado has experienced a severe mountain pine beetle outbreak over the past twenty years. The aim of this study was to map lodgepole pine mortality and health decline due to mountain pine beetle. Multispectral data spanning a five-year period from 2006 to 2011 were used to assess the progression from live, green trees to dead, gray-brown trees. IKONOS data from 2011 were corrected to reflectance and validated against an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 2011. These data were used along with additional reflectance-corrected multispectral datasets (IKONOS from 2007 and QuickBird from 2006 and 2009) to create vegetation classification maps using both library spectra and regions of interest. Two sets of classification maps were produced using Mixture-Tuned Matched Filtering. The results were assessed visually and mathematically. Through visual inspection of the classification maps, increasing lodgepole pine mortality over time was observed. The results were quantified using confusion matrices comparing the classification results of the AVIRIS classified data and the IKONOS and QuickBird classified data. The comparison showed that change could be seen over time, but due to the short time period of the data the change was not as significant as expected.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2012
Accession Number
ADA567388

Entities

People

  • Spencer A. Wahrman

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Artificial Satellites
  • Detection
  • Detectors
  • Electromagnetic Spectra
  • Filtration
  • Fungi
  • Geography
  • Hyperspectral Imagery
  • Image Processing
  • Information Science
  • Jet Propulsion
  • North America
  • Remote Sensing
  • Spacecraft
  • Spectra
  • United States
  • Vegetation

Readers

  • Computer Vision.
  • Forest Ecology
  • Mathematics or Statistics