Inferring Species Richness and Turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery

Abstract

Background: The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.

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Document Details

Document Type
Technical Report
Publication Date
Oct 24, 2012
Accession Number
ADA569976

Entities

People

  • Igor Linkov
  • Matteo Convertino
  • Mukund Desai
  • Nathan C. Lowry
  • Rami S. Mangoubi

Organizations

  • Engineer Research and Development Center

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Artificial Satellites
  • Computational Science
  • Cross Correlation
  • Databases
  • Detection
  • Ecology
  • Ecosystems
  • Engineering
  • Information Science
  • Intensity
  • Maximum Likelihood Estimation
  • Monitoring
  • Probability
  • Probability Density Functions
  • Remote Sensing
  • Satellite Imaging
  • United States

Fields of Study

  • Environmental science

Readers

  • Neural Network Machine Learning.
  • Wetland-Land-Environmental Management.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space